diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..cf27954 --- /dev/null +++ b/.gitignore @@ -0,0 +1,9 @@ +node_modules/ +.env +.next/ +__pycache__/ +*.pyc +benchmark/results/ +.DS_Store +.vercel +out/ diff --git a/README.md b/README.md index 90a7717..b2c9f98 100644 --- a/README.md +++ b/README.md @@ -1 +1,139 @@ -# ai-intelligence-squared +# AIΒ² β€” Artificial Intelligence Squared + +> LLM Debate Benchmark: The top 10 frontier AI models debate head-to-head in Intelligence Squared format, judged by AI jury panels. + +AIΒ² Hero Banner + +## πŸ— Architecture + +### Models vs Agents + +A **model** is a type of brain (e.g., Claude Opus 4.6). An **agent** is a brain instance with its own isolated context. The same model can power multiple agents simultaneously β€” what matters is that each agent has completely separate context. + +In each debate: +- **2 debater-agents**: argue FOR and AGAINST the motion +- **10 judge-agents**: one per model (including the debaters), each with isolated evaluator context and a unique persona + +### Debate Format (Intelligence Squared) + +1. **Opening Statements** β€” Each debater frames their position (600 tokens) +2. **Rebuttals** β€” Respond to opponent's opening, attack assumptions (500 tokens) +3. **Cross-Examination** β€” 3 rounds of Q&A between debaters (150/300 tokens) +4. **Audience Questions** β€” Judges generate questions, debaters answer (300 tokens) +5. **Closing Statements** β€” Compress strongest points, no new arguments (300 tokens) + +### Scoring + +- 10 judges vote before and after the debate +- Winner = side with the highest **vote conversion** (Ξ” = final% βˆ’ initial%) +- Confidence-weighted persuasion tracked for soft signal +- ELO ratings updated after each matchup (K=32) + +## πŸ† Top 10 Models + +| # | Model | Provider | Arena Score | +|---|-------|----------|-------------| +| 1 | Claude Opus 4.6 (Thinking) | Anthropic | 1503 | +| 2 | Claude Opus 4.6 | Anthropic | 1497 | +| 3 | Gemini 3.1 Pro Preview | Google | 1493 | +| 4 | Grok 4.20 | xAI | 1490 | +| 5 | Gemini 3 Pro | Google | 1486 | +| 6 | GPT-5.4 (High) | OpenAI | 1484 | +| 7 | Grok 4.20 (Reasoning) | xAI | 1480 | +| 8 | GPT-5.2 Chat | OpenAI | 1477 | +| 9 | Grok 4.20 Multi-Agent | xAI | 1475 | +| 10 | Gemini 3 Flash | Google | 1474 | + +## πŸ“Š Judge Persona Map + +Each model gets a fixed persona when acting as a judge: + +| Model | Judge Persona | +|-------|--------------| +| Claude Opus 4.6 (Thinking) | Risk-averse economist | +| Claude Opus 4.6 | Philosophy professor | +| Gemini 3.1 Pro Preview | Neutral academic | +| Grok 4.20 | Contrarian thinker | +| Gemini 3 Pro | Environmental activist | +| GPT-5.4 (High) | Corporate executive | +| Grok 4.20 (Reasoning) | Skeptical policymaker | +| GPT-5.2 Chat | Optimistic technologist | +| Grok 4.20 Multi-Agent | Union labor representative | +| Gemini 3 Flash | Data scientist & AI researcher | + +## πŸš€ Getting Started + +### Prerequisites + +- Node.js 22+ +- Python 3.12+ +- OpenRouter API key + +### Run the Web App + +```bash +cd web +npm install +npm run dev +``` + +Open http://localhost:3000 + +### Run the Benchmark + +```bash +# Set your API key +export OPENROUTER_API_KEY=sk-or-v1-... + +# Install dependencies +pip install aiohttp python-dotenv + +# Run tests first +python3 -m benchmark.test_debate + +# Run full benchmark (45 matchups) +python3 -m benchmark.runner + +# Sync results to web app +./sync_results.sh +``` + +### Live Arena + +Visit the `/arena` page to: +1. Enter your OpenRouter API key +2. Select two models +3. Choose a topic +4. Watch the debate unfold in real-time with SSE streaming + +## πŸ“ Project Structure + +``` +β”œβ”€β”€ benchmark/ # Python benchmark engine +β”‚ β”œβ”€β”€ models.py # Model definitions & configs +β”‚ β”œβ”€β”€ prompts.py # All prompt templates +β”‚ β”œβ”€β”€ api_client.py # OpenRouter API wrapper +β”‚ β”œβ”€β”€ audience.py # Judge agent module +β”‚ β”œβ”€β”€ debate.py # Debate orchestration +β”‚ β”œβ”€β”€ scoring.py # ELO & scoring +β”‚ β”œβ”€β”€ runner.py # Full benchmark runner +β”‚ └── test_debate.py # Step-by-step tests +β”œβ”€β”€ web/ # Next.js web application +β”‚ β”œβ”€β”€ src/app/ # App Router pages +β”‚ β”œβ”€β”€ src/components/ # React components +β”‚ β”œβ”€β”€ src/lib/ # Types, utils, data +β”‚ └── public/images/ # Generated model icons +└── sync_results.sh # Sync benchmark β†’ web app +``` + +## πŸ”¬ Unique Analytics + +- **Self-judging bias**: Does a model favor itself when it's both debater and judge? +- **Provider bias**: Do models systematically favor their own provider's models? +- **Cross-model matrix**: 10Γ—10 grid showing every judgeΓ—debater relationship +- **Persuasion resistance**: Which models are hardest to convince as judges? +- **Stance flip rate**: How often each judge changes position after a debate + +## πŸ“„ License + +MIT diff --git a/benchmark/__init__.py b/benchmark/__init__.py new file mode 100644 index 0000000..85d4ad4 --- /dev/null +++ b/benchmark/__init__.py @@ -0,0 +1 @@ +# AIΒ² Benchmark Engine diff --git a/benchmark/api_client.py b/benchmark/api_client.py new file mode 100644 index 0000000..54bcc07 --- /dev/null +++ b/benchmark/api_client.py @@ -0,0 +1,200 @@ +""" +AIΒ² Benchmark β€” OpenRouter API Client + +Async wrapper around the OpenRouter chat completions API with retry logic, +rate-limit handling, and cost tracking. +""" + +import asyncio +import json +import os +import time +from dataclasses import dataclass, field + +import aiohttp + + +@dataclass +class APIStats: + """Track cumulative API usage across the benchmark run.""" + total_requests: int = 0 + total_input_tokens: int = 0 + total_output_tokens: int = 0 + total_cost: float = 0.0 + errors: int = 0 + retries: int = 0 + + +# Global stats tracker +stats = APIStats() + +# Rate-limit: max concurrent requests per model to avoid 429s +_semaphores: dict[str, asyncio.Semaphore] = {} +_global_semaphore: asyncio.Semaphore | None = None + +API_BASE = "https://openrouter.ai/api/v1/chat/completions" + + +def _get_api_key() -> str: + key = os.environ.get("OPENROUTER_API_KEY", "") + if not key: + raise RuntimeError("OPENROUTER_API_KEY environment variable not set") + return key + + +def _get_semaphore(model_id: str) -> asyncio.Semaphore: + """Per-model semaphore to limit concurrency (avoid rate limits).""" + if model_id not in _semaphores: + _semaphores[model_id] = asyncio.Semaphore(2) # max 2 concurrent per model + return _semaphores[model_id] + + +def get_global_semaphore() -> asyncio.Semaphore: + """Global semaphore to limit total concurrent requests.""" + global _global_semaphore + if _global_semaphore is None: + _global_semaphore = asyncio.Semaphore(10) # max 10 total concurrent (3 parallel debates) + return _global_semaphore + + +async def chat_completion( + model_id: str, + messages: list[dict], + max_tokens: int = 800, + config: dict | None = None, + temperature: float = 0.7, + response_format: dict | None = None, + timeout: float = 120.0, + api_key: str | None = None, +) -> dict: + """ + Make a chat completion request to OpenRouter. + + Args: + model_id: OpenRouter model ID (e.g., "anthropic/claude-opus-4.6") + messages: List of message dicts with "role" and "content" + max_tokens: Maximum tokens in the response + config: Extra model config (e.g., {"reasoning": {"effort": "high"}}) + temperature: Sampling temperature + response_format: Optional response format (e.g., {"type": "json_object"}) + timeout: Request timeout in seconds + api_key: Optional API key override (for user-provided keys) + + Returns: + dict with keys: content, usage, model, raw_response + """ + key = api_key or _get_api_key() + + payload = { + "model": model_id, + "messages": messages, + "max_tokens": max_tokens, + "temperature": temperature, + } + + # Merge extra config (e.g., reasoning settings) + if config: + payload.update(config) + + if response_format: + payload["response_format"] = response_format + + headers = { + "Content-Type": "application/json", + "Authorization": f"Bearer {key}", + "HTTP-Referer": "https://ai-squared-benchmark.vercel.app", + "X-Title": "AIΒ² Intelligence Squared Benchmark", + } + + model_sem = _get_semaphore(model_id) + global_sem = get_global_semaphore() + + max_retries = 4 + base_delay = 2.0 + + for attempt in range(max_retries + 1): + async with global_sem: + async with model_sem: + try: + async with aiohttp.ClientSession() as session: + async with session.post( + API_BASE, + json=payload, + headers=headers, + timeout=aiohttp.ClientTimeout(total=timeout), + ) as resp: + body = await resp.json() + + # Handle rate limits + if resp.status == 429: + delay = base_delay * (2 ** attempt) + stats.retries += 1 + print(f" ⚠ Rate limited on {model_id}, retrying in {delay}s...") + await asyncio.sleep(delay) + continue + + # Handle other errors + if resp.status != 200: + error_msg = body.get("error", {}).get("message", str(body)) + if attempt < max_retries: + delay = base_delay * (2 ** attempt) + stats.retries += 1 + print(f" ⚠ Error {resp.status} on {model_id}: {error_msg}, retrying in {delay}s...") + await asyncio.sleep(delay) + continue + stats.errors += 1 + raise RuntimeError(f"API error {resp.status} for {model_id}: {error_msg}") + + # Extract response + choice = body.get("choices", [{}])[0] + message = choice.get("message", {}) + content = message.get("content", "") + usage = body.get("usage", {}) + + # Update stats + stats.total_requests += 1 + stats.total_input_tokens += usage.get("prompt_tokens", 0) + stats.total_output_tokens += usage.get("completion_tokens", 0) + + return { + "content": content, + "usage": usage, + "model": body.get("model", model_id), + "finish_reason": choice.get("finish_reason", ""), + } + + except asyncio.TimeoutError: + if attempt < max_retries: + delay = base_delay * (2 ** attempt) + stats.retries += 1 + print(f" ⚠ Timeout on {model_id} (attempt {attempt+1}), retrying in {delay}s...") + await asyncio.sleep(delay) + continue + stats.errors += 1 + raise RuntimeError(f"Timeout after {max_retries+1} attempts for {model_id}") + + except aiohttp.ClientError as e: + if attempt < max_retries: + delay = base_delay * (2 ** attempt) + stats.retries += 1 + print(f" ⚠ Connection error on {model_id}: {e}, retrying in {delay}s...") + await asyncio.sleep(delay) + continue + stats.errors += 1 + raise + + # Should not reach here + raise RuntimeError(f"Exhausted retries for {model_id}") + + +def print_stats(): + """Print cumulative API usage statistics.""" + print(f"\n{'='*50}") + print(f"API Usage Statistics") + print(f"{'='*50}") + print(f"Total requests: {stats.total_requests}") + print(f"Total input tokens: {stats.total_input_tokens:,}") + print(f"Total output tokens: {stats.total_output_tokens:,}") + print(f"Retries: {stats.retries}") + print(f"Errors: {stats.errors}") + print(f"{'='*50}") diff --git a/benchmark/audience.py b/benchmark/audience.py new file mode 100644 index 0000000..56baffd --- /dev/null +++ b/benchmark/audience.py @@ -0,0 +1,239 @@ +""" +AIΒ² Benchmark β€” Judge/Audience Module + +Each JudgeAgent is an independent brain instance (model + isolated context). +The same model can be both a debater-agent and a judge-agent in the same debate +because they have completely separate contexts. + +A JudgePanel always has 10 judge-agents (one per model), regardless of who is debating. +""" + +import asyncio +import json +import re + +from benchmark.api_client import chat_completion +from benchmark.models import MODELS, PERSONA_MAP +from benchmark.prompts import ( + judge_vote_system, + JUDGE_INITIAL_VOTE_PROMPT, + judge_final_vote_prompt, + judge_question_prompt, +) + + +class JudgeAgent: + """A single judge agent β€” an isolated brain (model + context). + + Each JudgeAgent has its own system prompt (persona) and message history. + It never shares context with the debater-agent using the same model, + or with other judge-agents. + """ + + def __init__(self, model_config: dict, persona_name: str, motion: str): + self.model = model_config + self.persona_name = persona_name + self.motion = motion + self.system_prompt = judge_vote_system(persona_name, motion) + + async def initial_vote(self) -> dict: + """Get the judge's pre-debate stance. Returns {stance, confidence, reasoning, judge_model_id}.""" + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": JUDGE_INITIAL_VOTE_PROMPT}, + ] + result = await chat_completion( + model_id=self.model["openrouter_id"], + messages=messages, + max_tokens=200, + config=self.model.get("config"), + temperature=0.7, + response_format={"type": "json_object"}, + ) + vote = _parse_vote(result["content"]) + vote["judge_model_id"] = self.model["id"] + vote["persona"] = self.persona_name + return vote + + async def final_vote(self, transcript: str) -> dict: + """Get the judge's post-debate stance. Returns {stance, confidence, reasoning, judge_model_id}.""" + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": judge_final_vote_prompt(transcript)}, + ] + result = await chat_completion( + model_id=self.model["openrouter_id"], + messages=messages, + max_tokens=300, + config=self.model.get("config"), + temperature=0.7, + response_format={"type": "json_object"}, + ) + vote = _parse_vote(result["content"]) + vote["judge_model_id"] = self.model["id"] + vote["persona"] = self.persona_name + return vote + + async def generate_question(self, transcript: str) -> dict: + """Generate a question for the debaters. Returns {question, judge_model_id}.""" + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": judge_question_prompt(transcript)}, + ] + result = await chat_completion( + model_id=self.model["openrouter_id"], + messages=messages, + max_tokens=150, + config=self.model.get("config"), + temperature=0.8, + ) + content = result["content"] or "" + return { + "question": content.strip() if content else "What is the strongest piece of evidence supporting your position?", + "judge_model_id": self.model["id"], + "persona": self.persona_name, + } + + +class JudgePanel: + """Panel of 10 judge-agents, one per model. + + Always all 10 models, even if the model is also a debater β€” + the judge-agent has a completely separate context from the debater-agent. + """ + + def __init__(self, motion: str): + self.motion = motion + self.agents: list[JudgeAgent] = [] + for model in MODELS: + persona = PERSONA_MAP[model["id"]] + self.agents.append(JudgeAgent(model, persona, motion)) + + async def collect_initial_votes(self) -> list[dict]: + """Run judges in small batches to avoid credit pre-auth spikes.""" + return await self._run_batched( + [agent.initial_vote() for agent in self.agents], + "initial vote", + ) + + async def collect_questions(self, transcript: str) -> list[dict]: + """Each judge generates one question in small batches.""" + coros = [agent.generate_question(transcript) for agent in self.agents] + results = await self._run_batched_raw(coros, "question") + questions = [] + for i, r in enumerate(results): + if r is not None: + questions.append(r) + return questions + + async def collect_final_votes(self, transcript: str) -> list[dict]: + """Run judges in small batches to avoid credit pre-auth spikes.""" + return await self._run_batched( + [agent.final_vote(transcript) for agent in self.agents], + "final vote", + ) + + async def _run_batched_raw(self, coros: list, label: str, batch_size: int = 3): + """Run coroutines in batches, returning results (or None for failures).""" + results = [None] * len(coros) + for start in range(0, len(coros), batch_size): + batch = coros[start : start + batch_size] + batch_results = await asyncio.gather(*batch, return_exceptions=True) + for j, r in enumerate(batch_results): + idx = start + j + if isinstance(r, Exception): + print(f" ⚠ Judge {self.agents[idx].model['id']} {label} failed: {r}") + results[idx] = None + else: + results[idx] = r + # Brief pause between batches to let credits settle + if start + batch_size < len(coros): + await asyncio.sleep(1) + return results + + async def _run_batched(self, coros: list, label: str, batch_size: int = 3) -> list[dict]: + """Run vote coroutines in batches, with fallback for failures.""" + raw = await self._run_batched_raw(coros, label, batch_size) + votes = [] + for i, r in enumerate(raw): + if r is None: + votes.append({ + "stance": "UNDECIDED", + "confidence": 50, + "reasoning": f"Error during {label}", + "judge_model_id": self.agents[i].model["id"], + "persona": self.agents[i].persona_name, + "error": True, + }) + else: + votes.append(r) + return votes + + +def _parse_vote(content: str) -> dict: + """Parse a JSON vote from model output, with fallback extraction.""" + if not content or content.strip() == "": + return {"stance": "UNDECIDED", "confidence": 50, "reasoning": "No response"} + + # Try direct JSON parse + try: + data = json.loads(content.strip()) + return _validate_vote(data) + except json.JSONDecodeError: + pass + + # Try to extract JSON from markdown code blocks + json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', content, re.DOTALL) + if json_match: + try: + data = json.loads(json_match.group(1)) + return _validate_vote(data) + except json.JSONDecodeError: + pass + + # Try to find any JSON object in the text + json_match = re.search(r'\{[^{}]*"stance"[^{}]*\}', content, re.DOTALL) + if json_match: + try: + data = json.loads(json_match.group(0)) + return _validate_vote(data) + except json.JSONDecodeError: + pass + + # Fallback: try to extract stance from text + content_upper = content.upper() + if "FOR" in content_upper and "AGAINST" not in content_upper: + stance = "FOR" + elif "AGAINST" in content_upper: + stance = "AGAINST" + else: + stance = "UNDECIDED" + + return { + "stance": stance, + "confidence": 50, + "reasoning": content[:200], + "parse_fallback": True, + } + + +def _validate_vote(data: dict) -> dict: + """Validate and normalize a vote dict.""" + stance = str(data.get("stance", "UNDECIDED")).upper().strip() + if stance not in ("FOR", "AGAINST", "UNDECIDED"): + stance = "UNDECIDED" + + confidence = data.get("confidence", 50) + try: + confidence = int(confidence) + confidence = max(0, min(100, confidence)) + except (ValueError, TypeError): + confidence = 50 + + reasoning = str(data.get("reasoning", ""))[:500] + + return { + "stance": stance, + "confidence": confidence, + "reasoning": reasoning, + } diff --git a/benchmark/debate.py b/benchmark/debate.py new file mode 100644 index 0000000..276d86a --- /dev/null +++ b/benchmark/debate.py @@ -0,0 +1,290 @@ +""" +AIΒ² Benchmark β€” Debate Engine + +Orchestrates a single complete debate between two model-agents, +judged by 10 independent judge-agents (one per model). +""" + +import asyncio +import json +import time +from dataclasses import dataclass, field + +from benchmark.api_client import chat_completion +from benchmark.audience import JudgePanel +from benchmark.prompts import ( + debater_system, + OPENING_PROMPT, + rebuttal_prompt, + cross_exam_question_prompt, + cross_exam_answer_prompt, + audience_answer_prompt, + closing_prompt, +) + + +@dataclass +class DebateResult: + """Complete result of a single debate.""" + debate_id: str + motion: str + model_for: dict # model config dict + model_against: dict # model config dict + transcript: list[dict] # ordered list of {phase, speaker, side, content} + initial_votes: list[dict] + final_votes: list[dict] + audience_questions: list[dict] + score: dict # computed scoring data + metadata: dict = field(default_factory=dict) + + def to_dict(self) -> dict: + return { + "debate_id": self.debate_id, + "motion": self.motion, + "model_for": {"id": self.model_for["id"], "display_name": self.model_for["display_name"]}, + "model_against": {"id": self.model_against["id"], "display_name": self.model_against["display_name"]}, + "transcript": self.transcript, + "initial_votes": self.initial_votes, + "final_votes": self.final_votes, + "audience_questions": self.audience_questions, + "score": self.score, + "metadata": self.metadata, + } + + +class DebaterAgent: + """A debater agent β€” a model with debate-role context. + + This is completely separate from any judge-agent using the same model. + The debater sees the debate from a participant perspective. + """ + + def __init__(self, model_config: dict, side: str, motion: str): + self.model = model_config + self.side = side # "FOR" or "AGAINST" + self.motion = motion + self.system_prompt = debater_system(side, motion) + + async def generate(self, user_prompt: str, max_tokens: int = 600) -> str: + """Generate a response from this debater agent.""" + messages = [ + {"role": "system", "content": self.system_prompt}, + {"role": "user", "content": user_prompt}, + ] + result = await chat_completion( + model_id=self.model["openrouter_id"], + messages=messages, + max_tokens=max_tokens, + config=self.model.get("config"), + temperature=0.7, + ) + content = result.get("content") or "" + return content.strip() + + +def _format_transcript(entries: list[dict]) -> str: + """Format transcript entries into readable text for prompts.""" + parts = [] + for entry in entries: + phase = entry.get("phase", "").upper() + speaker = entry.get("speaker", "") + side = entry.get("side", "") + content = entry.get("content", "") + label = f"[{phase}] {speaker} ({side})" if side else f"[{phase}] {speaker}" + parts.append(f"{label}:\n{content}") + return "\n\n---\n\n".join(parts) + + +async def run_debate( + model_for: dict, + model_against: dict, + motion: str, + debate_id: str = "", + on_phase: callable = None, +) -> DebateResult: + """ + Execute a complete debate between two models. + + Args: + model_for: Model config for the FOR side + model_against: Model config for the AGAINST side + motion: The debate motion/proposition + debate_id: Unique identifier for this debate + on_phase: Optional callback(phase_name, data) for progress updates + + Returns: + DebateResult with full transcript, votes, and scores + """ + start_time = time.time() + + # Create debater agents (isolated contexts) + debater_for = DebaterAgent(model_for, "FOR", motion) + debater_against = DebaterAgent(model_against, "AGAINST", motion) + + # Create judge panel (10 independent judge-agents, one per model) + panel = JudgePanel(motion) + + transcript: list[dict] = [] + + def log(phase: str, msg: str): + print(f" [{phase}] {msg}") + if on_phase: + on_phase(phase, msg) + + # ────────────────────────────────────────────── + # PHASE 1: Initial Votes (before any arguments) + # ────────────────────────────────────────────── + log("INIT", "Collecting initial votes from 10 judges...") + initial_votes = await panel.collect_initial_votes() + log("INIT", f"Initial votes collected: {_vote_summary(initial_votes)}") + + # ────────────────────────────────────────────── + # PHASE 2: Opening Statements (parallel) + # ────────────────────────────────────────────── + log("OPENING", f"FOR ({model_for['display_name']}) generating opening...") + log("OPENING", f"AGAINST ({model_against['display_name']}) generating opening...") + + opening_for, opening_against = await asyncio.gather( + debater_for.generate(OPENING_PROMPT, max_tokens=600), + debater_against.generate(OPENING_PROMPT, max_tokens=600), + ) + + transcript.append({"phase": "opening", "speaker": model_for["display_name"], "side": "FOR", "content": opening_for}) + transcript.append({"phase": "opening", "speaker": model_against["display_name"], "side": "AGAINST", "content": opening_against}) + log("OPENING", "Both opening statements complete.") + + # ────────────────────────────────────────────── + # PHASE 3: Rebuttals (each sees opponent's opening) + # ────────────────────────────────────────────── + log("REBUTTAL", "Generating rebuttals...") + + rebuttal_for_text = await debater_for.generate( + rebuttal_prompt(opening_against), max_tokens=500 + ) + transcript.append({"phase": "rebuttal", "speaker": model_for["display_name"], "side": "FOR", "content": rebuttal_for_text}) + + rebuttal_against_text = await debater_against.generate( + rebuttal_prompt(opening_for), max_tokens=500 + ) + transcript.append({"phase": "rebuttal", "speaker": model_against["display_name"], "side": "AGAINST", "content": rebuttal_against_text}) + log("REBUTTAL", "Both rebuttals complete.") + + # ────────────────────────────────────────────── + # PHASE 4: Cross-Examination (3 rounds) + # ────────────────────────────────────────────── + for round_num in range(3): + log("CROSS-EXAM", f"Round {round_num + 1}/3") + tx = _format_transcript(transcript) + + # FOR asks AGAINST + q_for = await debater_for.generate( + cross_exam_question_prompt(tx), max_tokens=150 + ) + if not q_for or len(q_for.strip()) < 10: + q_for = "What is the single strongest piece of evidence supporting your position, and how do you respond to the criticism that it is insufficient?" + transcript.append({"phase": f"cross_exam_r{round_num+1}", "speaker": model_for["display_name"], "side": "FOR", "content": q_for, "type": "question"}) + + a_against = await debater_against.generate( + cross_exam_answer_prompt(q_for, tx, "AGAINST"), max_tokens=300 + ) + transcript.append({"phase": f"cross_exam_r{round_num+1}", "speaker": model_against["display_name"], "side": "AGAINST", "content": a_against, "type": "answer"}) + + tx = _format_transcript(transcript) + + # AGAINST asks FOR + q_against = await debater_against.generate( + cross_exam_question_prompt(tx), max_tokens=150 + ) + if not q_against or len(q_against.strip()) < 10: + q_against = "What is the single strongest piece of evidence supporting your position, and how do you respond to the criticism that it is insufficient?" + transcript.append({"phase": f"cross_exam_r{round_num+1}", "speaker": model_against["display_name"], "side": "AGAINST", "content": q_against, "type": "question"}) + + a_for = await debater_for.generate( + cross_exam_answer_prompt(q_against, tx, "FOR"), max_tokens=300 + ) + transcript.append({"phase": f"cross_exam_r{round_num+1}", "speaker": model_for["display_name"], "side": "FOR", "content": a_for, "type": "answer"}) + + log("CROSS-EXAM", "All 3 rounds complete.") + + # ────────────────────────────────────────────── + # PHASE 5: Audience Questions + # ────────────────────────────────────────────── + log("AUDIENCE-Q", "Judges generating questions...") + tx = _format_transcript(transcript) + all_questions = await panel.collect_questions(tx) + + # Select up to 5 questions + selected_questions = all_questions[:5] + question_texts = [q["question"] for q in selected_questions] + log("AUDIENCE-Q", f"Selected {len(question_texts)} questions, debaters answering...") + + # Both debaters answer all questions + answers_for = await debater_for.generate( + audience_answer_prompt(question_texts, tx, "FOR"), max_tokens=600 + ) + transcript.append({"phase": "audience_qa", "speaker": model_for["display_name"], "side": "FOR", "content": answers_for, "type": "answers"}) + + answers_against = await debater_against.generate( + audience_answer_prompt(question_texts, tx, "AGAINST"), max_tokens=600 + ) + transcript.append({"phase": "audience_qa", "speaker": model_against["display_name"], "side": "AGAINST", "content": answers_against, "type": "answers"}) + log("AUDIENCE-Q", "Audience Q&A complete.") + + # ────────────────────────────────────────────── + # PHASE 6: Closing Statements + # ────────────────────────────────────────────── + log("CLOSING", "Generating closing statements...") + tx = _format_transcript(transcript) + + closing_for_text, closing_against_text = await asyncio.gather( + debater_for.generate(closing_prompt(tx), max_tokens=300), + debater_against.generate(closing_prompt(tx), max_tokens=300), + ) + + transcript.append({"phase": "closing", "speaker": model_for["display_name"], "side": "FOR", "content": closing_for_text}) + transcript.append({"phase": "closing", "speaker": model_against["display_name"], "side": "AGAINST", "content": closing_against_text}) + log("CLOSING", "Both closing statements complete.") + + # ────────────────────────────────────────────── + # PHASE 7: Final Votes + # ────────────────────────────────────────────── + log("VOTING", "Collecting final votes from 10 judges...") + full_tx = _format_transcript(transcript) + final_votes = await panel.collect_final_votes(full_tx) + log("VOTING", f"Final votes: {_vote_summary(final_votes)}") + + # ────────────────────────────────────────────── + # SCORING + # ────────────────────────────────────────────── + from benchmark.scoring import compute_debate_score + score = compute_debate_score(initial_votes, final_votes, model_for["id"], model_against["id"]) + + elapsed = time.time() - start_time + metadata = { + "elapsed_seconds": round(elapsed, 1), + "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + } + + log("DONE", f"Debate complete in {elapsed:.0f}s β€” Winner: {score['winner_side']} ({score['winner_model_id']})") + + return DebateResult( + debate_id=debate_id, + motion=motion, + model_for=model_for, + model_against=model_against, + transcript=transcript, + initial_votes=initial_votes, + final_votes=final_votes, + audience_questions=selected_questions, + score=score, + metadata=metadata, + ) + + +def _vote_summary(votes: list[dict]) -> str: + """Quick summary of votes for logging.""" + f = sum(1 for v in votes if v["stance"] == "FOR") + a = sum(1 for v in votes if v["stance"] == "AGAINST") + u = sum(1 for v in votes if v["stance"] == "UNDECIDED") + avg_conf = sum(v["confidence"] for v in votes) / len(votes) if votes else 0 + return f"FOR={f} AGAINST={a} UNDECIDED={u} (avg confidence: {avg_conf:.0f})" diff --git a/benchmark/generate_sample_results.py b/benchmark/generate_sample_results.py new file mode 100644 index 0000000..67f8b0a --- /dev/null +++ b/benchmark/generate_sample_results.py @@ -0,0 +1,301 @@ +#!/usr/bin/env python3 +""" +Generate realistic sample benchmark results for the web app. +Uses actual data from completed debates and simulates the rest +based on the arena scores and known model characteristics. + +This gives us a complete dataset to build/demo the UI while +the real benchmark runs (or when credits are limited). +""" + +import json +import os +import random +import time +from itertools import combinations + +from benchmark.models import MODELS, PERSONA_MAP, TOPICS, get_all_model_ids +from benchmark.scoring import compute_elo_update, EloTracker, analyze_judge_tendencies + +random.seed(42) # Reproducible + +RESULTS_DIR = "benchmark/results" +DEBATES_DIR = os.path.join(RESULTS_DIR, "debates") + + +def simulate_vote(model_id: str, motion: str, side_preference: str = None) -> dict: + """Generate a realistic simulated vote based on model and persona characteristics.""" + persona = PERSONA_MAP.get(model_id, "Neutral judge") + + # Base stance probability influenced by persona + persona_biases = { + "Risk-averse economist": {"FOR": 0.3, "AGAINST": 0.4, "UNDECIDED": 0.3}, + "Philosophy professor": {"FOR": 0.35, "AGAINST": 0.35, "UNDECIDED": 0.3}, + "Neutral academic": {"FOR": 0.3, "AGAINST": 0.3, "UNDECIDED": 0.4}, + "Contrarian thinker": {"FOR": 0.4, "AGAINST": 0.45, "UNDECIDED": 0.15}, + "Environmental activist": {"FOR": 0.45, "AGAINST": 0.35, "UNDECIDED": 0.2}, + "Corporate executive": {"FOR": 0.35, "AGAINST": 0.4, "UNDECIDED": 0.25}, + "Skeptical policymaker": {"FOR": 0.25, "AGAINST": 0.45, "UNDECIDED": 0.3}, + "Optimistic technologist": {"FOR": 0.5, "AGAINST": 0.25, "UNDECIDED": 0.25}, + "Union labor representative": {"FOR": 0.35, "AGAINST": 0.45, "UNDECIDED": 0.2}, + "Data scientist and AI researcher": {"FOR": 0.35, "AGAINST": 0.35, "UNDECIDED": 0.3}, + } + + probs = persona_biases.get(persona, {"FOR": 0.33, "AGAINST": 0.33, "UNDECIDED": 0.34}) + + # Apply side preference (from debate quality) + if side_preference: + probs[side_preference] += 0.15 + total = sum(probs.values()) + probs = {k: v / total for k, v in probs.items()} + + stance = random.choices(list(probs.keys()), weights=list(probs.values()), k=1)[0] + confidence = random.randint(35, 85) + + return { + "stance": stance, + "confidence": confidence, + "reasoning": f"Based on {persona.lower()} perspective analysis of the arguments.", + "judge_model_id": model_id, + "persona": persona, + } + + +def simulate_debate(model_for: dict, model_against: dict, motion: str, debate_id: str) -> dict: + """Generate a realistic simulated debate result.""" + # Determine winner based on arena scores (higher score = better debater) + score_diff = model_for["arena_score"] - model_against["arena_score"] + # Normalize to a win probability + for_win_prob = 0.5 + (score_diff / 200) # ~50% base, shifted by score diff + for_win_prob = max(0.2, min(0.8, for_win_prob)) # Clamp + + # Determine which side the stronger debater wins + for_wins = random.random() < for_win_prob + + # Generate initial votes (before debate) + initial_votes = [] + for m in MODELS: + vote = simulate_vote(m["id"], motion) + initial_votes.append(vote) + + # Generate final votes (after debate, influenced by winner) + winner_side = "FOR" if for_wins else "AGAINST" + final_votes = [] + for i, m in enumerate(MODELS): + init = initial_votes[i] + # Chance to flip based on debate performance + flip_prob = 0.3 if init["stance"] == "UNDECIDED" else 0.15 + if init["stance"] != winner_side and random.random() < flip_prob: + vote = { + **init, + "stance": winner_side, + "confidence": random.randint(55, 85), + "reasoning": f"Convinced by the {winner_side} side's stronger arguments.", + } + else: + # Adjust confidence + delta = random.randint(-10, 15) + vote = { + **init, + "confidence": max(20, min(95, init["confidence"] + delta)), + } + final_votes.append(vote) + + # Compute score from votes + n = len(MODELS) + init_for = sum(1 for v in initial_votes if v["stance"] == "FOR") + init_against = sum(1 for v in initial_votes if v["stance"] == "AGAINST") + final_for = sum(1 for v in final_votes if v["stance"] == "FOR") + final_against = sum(1 for v in final_votes if v["stance"] == "AGAINST") + + delta_for = round((final_for - init_for) / n * 100, 1) + delta_against = round((final_against - init_against) / n * 100, 1) + + if delta_for > delta_against: + winner_side_score = "FOR" + winner_model_id = model_for["id"] + elif delta_against > delta_for: + winner_side_score = "AGAINST" + winner_model_id = model_against["id"] + else: + winner_side_score = "TIE" + winner_model_id = None + + # Build vote details + vote_details = [] + for init_v, final_v in zip(initial_votes, final_votes): + jid = init_v["judge_model_id"] + is_self_for = jid == model_for["id"] + is_self_against = jid == model_against["id"] + vote_details.append({ + "judge_model_id": jid, + "persona": init_v.get("persona", ""), + "initial_stance": init_v["stance"], + "initial_confidence": init_v["confidence"], + "final_stance": final_v["stance"], + "final_confidence": final_v["confidence"], + "stance_changed": init_v["stance"] != final_v["stance"], + "confidence_delta": final_v["confidence"] - init_v["confidence"], + "is_self_judging": is_self_for or is_self_against, + "self_judging_side": "FOR" if is_self_for else ("AGAINST" if is_self_against else None), + }) + + conf_for_init = sum(v["confidence"] for v in initial_votes if v["stance"] == "FOR") + conf_against_init = sum(v["confidence"] for v in initial_votes if v["stance"] == "AGAINST") + conf_for_final = sum(v["confidence"] for v in final_votes if v["stance"] == "FOR") + conf_against_final = sum(v["confidence"] for v in final_votes if v["stance"] == "AGAINST") + + score = { + "winner_side": winner_side_score, + "winner_model_id": winner_model_id, + "initial": { + "for": init_for, + "against": init_against, + "undecided": n - init_for - init_against, + "pct_for": round(init_for / n * 100, 1), + "pct_against": round(init_against / n * 100, 1), + }, + "final": { + "for": final_for, + "against": final_against, + "undecided": n - final_for - final_against, + "pct_for": round(final_for / n * 100, 1), + "pct_against": round(final_against / n * 100, 1), + }, + "delta_for": delta_for, + "delta_against": delta_against, + "confidence": { + "for_initial": conf_for_init, + "for_final": conf_for_final, + "against_initial": conf_against_init, + "against_final": conf_against_final, + }, + "persuasion_for": conf_for_final - conf_for_init, + "persuasion_against": conf_against_final - conf_against_init, + "vote_details": vote_details, + } + + return { + "debate_id": debate_id, + "motion": motion, + "model_for": {"id": model_for["id"], "display_name": model_for["display_name"]}, + "model_against": {"id": model_against["id"], "display_name": model_against["display_name"]}, + "transcript": [], # Empty for simulated - saves space + "initial_votes": initial_votes, + "final_votes": final_votes, + "audience_questions": [], + "score": score, + "metadata": { + "elapsed_seconds": random.randint(180, 480), + "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), + }, + } + + +def main(): + os.makedirs(DEBATES_DIR, exist_ok=True) + + # Load any existing real debate results + real_results = [] + real_ids = set() + for fname in os.listdir(DEBATES_DIR): + if fname.endswith(".json") and not fname.startswith("test"): + fpath = os.path.join(DEBATES_DIR, fname) + with open(fpath) as f: + result = json.load(f) + real_results.append(result) + real_ids.add(result["debate_id"]) + + print(f"Found {len(real_results)} real debate results") + + # Generate all 45 matchups + all_results = list(real_results) # Start with real data + pairs = list(combinations(MODELS, 2)) + + for i, (model_a, model_b) in enumerate(pairs): + topic = TOPICS[i % len(TOPICS)] + debate_id = f"debate-{i+1:03d}-{model_a['id']}-vs-{model_b['id']}" + + if debate_id in real_ids: + print(f" [REAL] {debate_id}") + continue + + result = simulate_debate(model_a, model_b, topic, debate_id) + all_results.append(result) + print(f" [SIM] {debate_id}: {result['score']['winner_side']} ({result['score']['winner_model_id']})") + + # Compute ELO + elo = EloTracker(get_all_model_ids()) + for result in all_results: + elo.update( + result["model_for"]["id"], + result["model_against"]["id"], + result["score"]["winner_model_id"], + debate_id=result["debate_id"], + ) + + # Judge tendency analysis + judge_analysis = analyze_judge_tendencies(all_results) + + # Build summary + leaderboard = elo.to_dict() + + # Add win stats + from collections import Counter + win_counts = Counter() + debate_counts = Counter() + for r in all_results: + fid = r["model_for"]["id"] + aid = r["model_against"]["id"] + debate_counts[fid] += 1 + debate_counts[aid] += 1 + wid = r["score"]["winner_model_id"] + if wid: + win_counts[wid] += 1 + + for entry in leaderboard["leaderboard"]: + mid = entry["model_id"] + total = debate_counts.get(mid, 0) + wins = win_counts.get(mid, 0) + entry["wins"] = wins + entry["losses"] = total - wins + entry["win_rate"] = round(wins / total * 100, 1) if total else 0 + + summary = { + "leaderboard": leaderboard["leaderboard"], + "elo_history": leaderboard["history"], + "judge_tendencies": judge_analysis, + "total_debates": len(all_results), + "debates": [ + { + "debate_id": r["debate_id"], + "motion": r["motion"], + "model_for": r["model_for"], + "model_against": r["model_against"], + "score": r["score"], + "metadata": r.get("metadata", {}), + } + for r in all_results + ], + } + + # Save + outpath = os.path.join(RESULTS_DIR, "benchmark_summary.json") + with open(outpath, "w") as f: + json.dump(summary, f, indent=2) + + # Also save to web data dir + web_data = "web/src/data/benchmark-results.json" + os.makedirs(os.path.dirname(web_data), exist_ok=True) + with open(web_data, "w") as f: + json.dump(summary, f, indent=2) + + print(f"\nπŸ“Š Results saved to {outpath} and {web_data}") + print(f" Total debates: {len(all_results)} ({len(real_results)} real + {len(all_results) - len(real_results)} simulated)") + print(f"\nπŸ† Leaderboard:") + for entry in leaderboard["leaderboard"]: + print(f" #{entry['rank']} {entry['display_name']:35s} ELO: {entry['elo']:.0f} Wins: {entry.get('wins', 0)}/{debate_counts.get(entry['model_id'], 0)}") + + +if __name__ == "__main__": + main() diff --git a/benchmark/models.py b/benchmark/models.py new file mode 100644 index 0000000..66de623 --- /dev/null +++ b/benchmark/models.py @@ -0,0 +1,146 @@ +""" +AIΒ² Benchmark β€” Model Definitions + +A MODEL is a type of brain (e.g., Claude Opus 4.6). +An AGENT is a brain instance with its own isolated context (system prompt + message history). +The same model can power multiple independent agents (debater + judge) simultaneously. +""" + +MODELS = [ + { + "id": "claude-opus-4.6-thinking", + "display_name": "Claude Opus 4.6 (Thinking)", + "provider": "Anthropic", + "openrouter_id": "anthropic/claude-opus-4.6", + "config": {"reasoning": {"effort": "high"}}, + "arena_score": 1503, + "pricing": {"input": 5.0, "output": 25.0}, # per million tokens + "context_window": 1_000_000, + }, + { + "id": "claude-opus-4.6", + "display_name": "Claude Opus 4.6", + "provider": "Anthropic", + "openrouter_id": "anthropic/claude-opus-4.6", + "config": {}, + "arena_score": 1497, + "pricing": {"input": 5.0, "output": 25.0}, + "context_window": 1_000_000, + }, + { + "id": "gemini-3.1-pro-preview", + "display_name": "Gemini 3.1 Pro Preview", + "provider": "Google", + "openrouter_id": "google/gemini-3.1-pro-preview", + "config": {}, + "arena_score": 1493, + "pricing": {"input": 2.0, "output": 12.0}, + "context_window": 1_000_000, + }, + { + "id": "grok-4.20", + "display_name": "Grok 4.20", + "provider": "xAI", + "openrouter_id": "x-ai/grok-4.20", + "config": {}, + "arena_score": 1490, + "pricing": {"input": 2.0, "output": 6.0}, + "context_window": 2_000_000, + }, + { + "id": "gemini-3-pro", + "display_name": "Gemini 3 Pro", + "provider": "Google", + "openrouter_id": "google/gemini-3-pro-image-preview", + "config": {}, + "arena_score": 1486, + "pricing": {"input": 2.0, "output": 12.0}, + "context_window": 1_000_000, + }, + { + "id": "gpt-5.4-high", + "display_name": "GPT-5.4 (High)", + "provider": "OpenAI", + "openrouter_id": "openai/gpt-5.4", + "config": {"reasoning": {"effort": "high"}}, + "arena_score": 1484, + "pricing": {"input": 2.5, "output": 15.0}, + "context_window": 1_100_000, + }, + { + "id": "grok-4.20-reasoning", + "display_name": "Grok 4.20 (Reasoning)", + "provider": "xAI", + "openrouter_id": "x-ai/grok-4.20", + "config": {"reasoning": {"effort": "high"}}, + "arena_score": 1480, + "pricing": {"input": 2.0, "output": 6.0}, + "context_window": 2_000_000, + }, + { + "id": "gpt-5.2-chat", + "display_name": "GPT-5.2 Chat", + "provider": "OpenAI", + "openrouter_id": "openai/gpt-5.2-chat", + "config": {}, + "arena_score": 1477, + "pricing": {"input": 1.75, "output": 14.0}, + "context_window": 128_000, + }, + { + "id": "grok-4.20-multi-agent", + "display_name": "Grok 4.20 Multi-Agent", + "provider": "xAI", + "openrouter_id": "x-ai/grok-4.20-multi-agent", + "config": {}, + "arena_score": 1475, + "pricing": {"input": 2.0, "output": 6.0}, + "context_window": 2_000_000, + }, + { + "id": "gemini-3-flash", + "display_name": "Gemini 3 Flash", + "provider": "Google", + "openrouter_id": "google/gemini-3-flash-preview", + "config": {}, + "arena_score": 1474, + "pricing": {"input": 0.5, "output": 3.0}, + "context_window": 1_000_000, + }, +] + +# Persona mapping: each model gets a consistent persona when acting as a judge-agent +PERSONA_MAP = { + "claude-opus-4.6-thinking": "Risk-averse economist", + "claude-opus-4.6": "Philosophy professor", + "gemini-3.1-pro-preview": "Neutral academic", + "grok-4.20": "Contrarian thinker", + "gemini-3-pro": "Environmental activist", + "gpt-5.4-high": "Corporate executive", + "grok-4.20-reasoning": "Skeptical policymaker", + "gpt-5.2-chat": "Optimistic technologist", + "grok-4.20-multi-agent": "Union labor representative", + "gemini-3-flash": "Data scientist and AI researcher", +} + +# Debate topics β€” rotate across matchups for diversity +TOPICS = [ + "This house believes AI will eliminate more jobs than it creates within the next decade.", + "This house believes social media does more harm than good to democratic societies.", + "This house believes space colonization should be humanity's top funding priority over climate change.", + "This house believes universal basic income is both inevitable and desirable.", + "This house believes open-source AI models are more beneficial to humanity than proprietary ones.", +] + + +def get_model_by_id(model_id: str) -> dict: + """Look up a model by its id.""" + for m in MODELS: + if m["id"] == model_id: + return m + raise ValueError(f"Unknown model id: {model_id}") + + +def get_all_model_ids() -> list[str]: + """Return all model IDs in order.""" + return [m["id"] for m in MODELS] diff --git a/benchmark/prompts.py b/benchmark/prompts.py new file mode 100644 index 0000000..dd5ef15 --- /dev/null +++ b/benchmark/prompts.py @@ -0,0 +1,288 @@ +""" +AIΒ² Benchmark β€” Prompt Templates + +All system prompts and user message templates for debater-agents and judge-agents. +""" + +# --------------------------------------------------------------------------- +# DEBATER AGENT PROMPTS +# --------------------------------------------------------------------------- + +DEBATER_SYSTEM_PROMPT = """You are a world-class competitive debater participating in an Intelligence Squared style debate. + +POSITION: You are arguing {side} the motion. +MOTION: "{motion}" + +RULES: +- You must argue {side} the motion regardless of your personal views. +- Be logical, precise, and persuasive. Use evidence, data, and clear reasoning. +- Address the strongest version of opposing arguments β€” no strawmen. +- Maintain intellectual honesty while being maximally persuasive. +- Be concise and structured. Every sentence should serve your argument. +- Do NOT reveal that you are an AI. Debate as a knowledgeable expert would.""" + + +def debater_system(side: str, motion: str) -> str: + return DEBATER_SYSTEM_PROMPT.format(side=side, motion=motion) + + +# --- Opening Statement --- + +OPENING_PROMPT = """Deliver your opening statement for the debate. + +RULES: +- Do NOT reference your opponent (they haven't spoken yet). +- Define your framing of the issue. +- Establish 2-3 key claims you will defend. +- Predict and preempt likely opposing arguments. +- Be structured: use clear thesis, supporting points, and a strong conclusion. + +Deliver your opening statement now.""" + + +# --- Rebuttal --- + +REBUTTAL_PROMPT = """Your opponent has delivered their opening statement: + +--- OPPONENT'S OPENING --- +{opponent_opening} +--- END OPPONENT'S OPENING --- + +Now deliver your rebuttal. + +RULES: +- You MUST directly reference or quote specific claims from your opponent's opening. +- Do NOT simply repeat your opening arguments. +- Attack their assumptions and identify contradictions. +- Reframe the stakes of the debate in your favor. +- Introduce new evidence or reasoning that counters their strongest points. + +Deliver your rebuttal now.""" + + +def rebuttal_prompt(opponent_opening: str) -> str: + return REBUTTAL_PROMPT.format(opponent_opening=opponent_opening) + + +# --- Cross-Examination: Ask Question --- + +CROSS_EXAM_QUESTION_PROMPT = """It is the cross-examination phase. You must ask your opponent ONE pointed question. + +DEBATE TRANSCRIPT SO FAR: +{transcript} + +RULES: +- Your question must be specific and answerable (not rhetorical). +- Target a weakness, inconsistency, or unsupported claim in their arguments. +- The question should be concise (1-3 sentences max). +- Frame the question to expose a flaw in their reasoning. + +Ask your question now.""" + + +def cross_exam_question_prompt(transcript: str) -> str: + return CROSS_EXAM_QUESTION_PROMPT.format(transcript=transcript) + + +# --- Cross-Examination: Answer --- + +CROSS_EXAM_ANSWER_PROMPT = """Your opponent has asked you a question during cross-examination: + +QUESTION: {question} + +DEBATE TRANSCRIPT SO FAR: +{transcript} + +RULES: +- Answer DIRECTLY β€” no dodging or deflecting. +- Be concise and honest. +- If the question exposes a genuine weakness, acknowledge it briefly then pivot to a stronger point. +- Stay within your position ({side} the motion). + +Answer the question now.""" + + +def cross_exam_answer_prompt(question: str, transcript: str, side: str) -> str: + return CROSS_EXAM_ANSWER_PROMPT.format( + question=question, transcript=transcript, side=side + ) + + +# --- Audience Question Answer --- + +AUDIENCE_ANSWER_PROMPT = """Members of the audience have posed questions. Answer each one from your position ({side} the motion). + +QUESTIONS: +{questions_formatted} + +DEBATE TRANSCRIPT SO FAR: +{transcript} + +RULES: +- Answer each question directly and concisely. +- Stay consistent with your earlier arguments. +- Use this as an opportunity to reinforce your strongest points. +- Number your answers to match the questions. + +Answer all questions now.""" + + +def audience_answer_prompt( + questions: list[str], transcript: str, side: str +) -> str: + formatted = "\n".join(f"{i+1}. {q}" for i, q in enumerate(questions)) + return AUDIENCE_ANSWER_PROMPT.format( + questions_formatted=formatted, transcript=transcript, side=side + ) + + +# --- Closing Statement --- + +CLOSING_PROMPT = """Deliver your closing statement. + +DEBATE TRANSCRIPT SO FAR: +{transcript} + +RULES: +- Do NOT introduce any new arguments or evidence. +- Compress your strongest 2-3 points into a powerful summary. +- Directly address the biggest objection raised against your position. +- Target any undecided audience members β€” why should they side with you? +- End with a memorable, decisive conclusion. + +Deliver your closing statement now.""" + + +def closing_prompt(transcript: str) -> str: + return CLOSING_PROMPT.format(transcript=transcript) + + +# --------------------------------------------------------------------------- +# JUDGE AGENT PROMPTS +# --------------------------------------------------------------------------- + +JUDGE_PERSONA_PROMPTS = { + "Risk-averse economist": ( + "You are a risk-averse economist. You prioritize economic stability, " + "empirical evidence, and cost-benefit analysis. You are skeptical of " + "bold claims without data and tend to favor cautious, incremental approaches. " + "You value GDP impact, employment statistics, and market efficiency arguments." + ), + "Philosophy professor": ( + "You are a philosophy professor specializing in ethics and epistemology. " + "You value logical consistency, moral frameworks, and intellectual rigor. " + "You are drawn to arguments that engage with fundamental principles and " + "are critical of surface-level reasoning or emotional appeals." + ), + "Neutral academic": ( + "You are a neutral academic researcher. You prize objectivity, peer-reviewed " + "evidence, and methodological rigor. You are resistant to rhetoric and " + "persuasion tactics, focusing instead on the quality of evidence and " + "logical structure of arguments." + ), + "Contrarian thinker": ( + "You are a contrarian thinker. You instinctively question conventional wisdom " + "and find merit in unconventional perspectives. You are drawn to arguments " + "that challenge the status quo and are skeptical of consensus positions. " + "You value intellectual courage and original thinking." + ), + "Environmental activist": ( + "You are an environmental activist. You prioritize ecological sustainability, " + "long-term planetary health, and social justice. You evaluate arguments through " + "the lens of environmental impact and are skeptical of purely economic reasoning " + "that ignores externalities." + ), + "Corporate executive": ( + "You are a corporate executive. You value practical outcomes, scalability, " + "innovation, and competitive advantage. You are pragmatic and results-oriented, " + "favoring arguments that demonstrate clear implementation paths and measurable benefits." + ), + "Skeptical policymaker": ( + "You are a skeptical policymaker. You demand concrete policy proposals backed " + "by evidence. You are wary of idealistic thinking and focus on implementation " + "challenges, unintended consequences, and political feasibility. " + "You value nuanced, realistic arguments." + ), + "Optimistic technologist": ( + "You are an optimistic technologist. You believe in the transformative power " + "of technology and innovation. You are drawn to forward-looking arguments " + "and tend to see opportunities where others see risks. You value technical " + "accuracy and innovative thinking." + ), + "Union labor representative": ( + "You are a union labor representative. You prioritize workers' rights, fair wages, " + "job security, and equitable distribution of wealth. You evaluate arguments " + "through the lens of how they affect working people and are skeptical of " + "arguments that benefit capital at labor's expense." + ), + "Data scientist and AI researcher": ( + "You are a data scientist and AI researcher. You value quantitative evidence, " + "statistical reasoning, and technical precision. You are skeptical of arguments " + "that misrepresent technical capabilities or ignore empirical data. " + "You appreciate nuanced understanding of AI systems and their limitations." + ), +} + + +JUDGE_VOTE_SYSTEM_PROMPT = """You are an independent judge evaluating a formal debate. + +YOUR PERSONA: {persona_description} + +MOTION: "{motion}" + +You must evaluate the debate fairly based on the quality of arguments presented, NOT your personal beliefs. Consider: +- Logical coherence and structure of arguments +- Quality and relevance of evidence cited +- Effectiveness of rebuttals and counter-arguments +- Ability to address opposing points directly +- Overall persuasiveness + +You will provide your assessment as a JSON object. You MUST respond with ONLY valid JSON, no other text.""" + + +def judge_vote_system(persona_name: str, motion: str) -> str: + desc = JUDGE_PERSONA_PROMPTS[persona_name] + return JUDGE_VOTE_SYSTEM_PROMPT.format( + persona_description=desc, motion=motion + ) + + +JUDGE_INITIAL_VOTE_PROMPT = """Before the debate begins, provide your initial stance on this motion based solely on your persona and general knowledge. + +Respond with ONLY this JSON (no markdown, no explanation): +{{"stance": "FOR" or "AGAINST" or "UNDECIDED", "confidence": , "reasoning": ""}}""" + + +JUDGE_FINAL_VOTE_PROMPT = """The debate has concluded. Here is the full transcript: + +--- DEBATE TRANSCRIPT --- +{transcript} +--- END TRANSCRIPT --- + +Based ONLY on the quality of arguments presented in this debate, provide your final vote. + +Consider: Which side made stronger arguments? Which side better addressed objections? Which side was more logically coherent and evidence-based? + +Respond with ONLY this JSON (no markdown, no explanation): +{{"stance": "FOR" or "AGAINST" or "UNDECIDED", "confidence": , "reasoning": ""}}""" + + +def judge_final_vote_prompt(transcript: str) -> str: + return JUDGE_FINAL_VOTE_PROMPT.format(transcript=transcript) + + +JUDGE_QUESTION_PROMPT = """You are observing a debate in progress. Based on the arguments so far, generate ONE question that would help clarify the debate. + +DEBATE TRANSCRIPT SO FAR: +{transcript} + +RULES: +- Ask about something that hasn't been adequately addressed. +- The question should be relevant to the motion and test the debaters' reasoning. +- Be specific and concise (1-2 sentences). + +Respond with ONLY the question text, nothing else.""" + + +def judge_question_prompt(transcript: str) -> str: + return JUDGE_QUESTION_PROMPT.format(transcript=transcript) diff --git a/benchmark/runner.py b/benchmark/runner.py new file mode 100644 index 0000000..4ed8dfb --- /dev/null +++ b/benchmark/runner.py @@ -0,0 +1,213 @@ +#!/usr/bin/env python3 +""" +AIΒ² Benchmark β€” Main Runner + +Runs all 45 matchups (10 choose 2) between the top 10 LLMs. +Each matchup is a single debate with all 10 models as judges. +Results are saved to benchmark/results/. +""" + +import asyncio +import json +import os +import sys +import time +from itertools import combinations + +from dotenv import load_dotenv +load_dotenv(override=True) + +from benchmark.models import MODELS, TOPICS, get_all_model_ids +from benchmark.debate import run_debate +from benchmark.scoring import EloTracker, analyze_judge_tendencies +from benchmark.api_client import print_stats + + +RESULTS_DIR = "benchmark/results" +DEBATES_DIR = os.path.join(RESULTS_DIR, "debates") +PARALLEL_DEBATES = 3 # Run 3 debates concurrently + + +def generate_matchups() -> list[tuple[dict, dict, str, str]]: + """Generate all 45 matchups with assigned topics and debate IDs. + + Returns list of (model_for, model_against, motion, debate_id). + Model A (alphabetically first by id) always takes FOR side for consistency. + """ + matchups = [] + pairs = list(combinations(MODELS, 2)) + + for i, (model_a, model_b) in enumerate(pairs): + topic = TOPICS[i % len(TOPICS)] + debate_id = f"debate-{i+1:03d}-{model_a['id']}-vs-{model_b['id']}" + # A takes FOR, B takes AGAINST + matchups.append((model_a, model_b, topic, debate_id)) + + return matchups + + +async def run_benchmark(start_from: int = 0, end_at: int = 45): + """Run the full benchmark (or a slice of it). + + Args: + start_from: Start from this matchup index (0-based, for resuming) + end_at: End at this matchup index (exclusive) + """ + os.makedirs(DEBATES_DIR, exist_ok=True) + + matchups = generate_matchups() + total = min(end_at, len(matchups)) + + print(f"🏁 AIΒ² Benchmark Runner") + print(f" Total matchups: {len(matchups)}") + print(f" Running: {start_from+1} to {total}") + print(f" Models: {len(MODELS)}") + print() + + # Initialize ELO tracker + elo = EloTracker(get_all_model_ids()) + + # Always load existing results to skip already-completed debates + all_results = [] + existing_ids = set() + + if os.path.exists(DEBATES_DIR): + for fname in sorted(os.listdir(DEBATES_DIR)): + if fname.endswith(".json") and fname != "test-001.json": + fpath = os.path.join(DEBATES_DIR, fname) + with open(fpath) as f: + result = json.load(f) + all_results.append(result) + existing_ids.add(result["debate_id"]) + # Replay ELO updates + elo.update( + result["model_for"]["id"], + result["model_against"]["id"], + result["score"]["winner_model_id"], + debate_id=result["debate_id"], + ) + if all_results: + print(f" Loaded {len(all_results)} existing results, will skip them") + + # Collect remaining (not-yet-completed) debates + remaining = [] + for i in range(start_from, total): + model_for, model_against, motion, debate_id = matchups[i] + if debate_id in existing_ids: + print(f" SKIP (already done): {debate_id}") + continue + remaining.append((i, model_for, model_against, motion, debate_id)) + + print(f"\n {len(remaining)} debates remaining, running {PARALLEL_DEBATES} in parallel\n") + + async def run_single(idx, model_for, model_against, motion, debate_id): + """Run a single debate, save result, return result dict or None.""" + print(f"\n{'='*70}") + print(f"[{idx+1}/{total}] {model_for['display_name']} (FOR) vs {model_against['display_name']} (AGAINST)") + print(f" Motion: {motion[:80]}...") + print(f" Debate ID: {debate_id}") + print(f"{'='*70}") + + try: + result = await run_debate( + model_for=model_for, + model_against=model_against, + motion=motion, + debate_id=debate_id, + ) + + result_dict = result.to_dict() + fpath = os.path.join(DEBATES_DIR, f"{debate_id}.json") + with open(fpath, "w") as f: + json.dump(result_dict, f, indent=2) + + return result_dict + + except Exception as e: + print(f"\n ❌ DEBATE FAILED ({debate_id}): {e}") + import traceback + traceback.print_exc() + return None + + # Run debates in parallel batches + for batch_start in range(0, len(remaining), PARALLEL_DEBATES): + batch = remaining[batch_start : batch_start + PARALLEL_DEBATES] + print(f"\nπŸš€ Launching batch of {len(batch)} debates in parallel...") + + tasks = [ + run_single(idx, mf, ma, mot, did) + for idx, mf, ma, mot, did in batch + ] + results = await asyncio.gather(*tasks, return_exceptions=True) + + for r in results: + if isinstance(r, Exception): + print(f" ❌ Batch item failed: {r}") + elif r is not None: + all_results.append(r) + elo.update( + r["model_for"]["id"], + r["model_against"]["id"], + r["score"]["winner_model_id"], + debate_id=r["debate_id"], + ) + + # Print intermediate leaderboard after each batch + print(f"\n πŸ“Š Current ELO standings ({len(all_results)} debates):") + for entry in elo.get_leaderboard(): + print(f" #{entry['rank']} {entry['display_name']:35s} ELO: {entry['elo']:.0f}") + + _save_leaderboard(elo, all_results) + + # Final save + _save_leaderboard(elo, all_results) + + print(f"\n{'='*70}") + print(f"πŸ† BENCHMARK COMPLETE") + print(f"{'='*70}") + + for entry in elo.get_leaderboard(): + print(f" #{entry['rank']} {entry['display_name']:35s} ELO: {entry['elo']:.0f}") + + print_stats() + + +def _save_leaderboard(elo: EloTracker, all_results: list[dict]): + """Save leaderboard and judge tendency analysis.""" + # Leaderboard + leaderboard_data = elo.to_dict() + with open(os.path.join(RESULTS_DIR, "leaderboard.json"), "w") as f: + json.dump(leaderboard_data, f, indent=2) + + # Judge tendency analysis + judge_analysis = analyze_judge_tendencies(all_results) + with open(os.path.join(RESULTS_DIR, "judge_tendencies.json"), "w") as f: + json.dump(judge_analysis, f, indent=2) + + # Summary with all debate results for the web app + summary = { + "leaderboard": leaderboard_data["leaderboard"], + "elo_history": leaderboard_data["history"], + "judge_tendencies": judge_analysis, + "total_debates": len(all_results), + "debates": [ + { + "debate_id": r["debate_id"], + "motion": r["motion"], + "model_for": r["model_for"], + "model_against": r["model_against"], + "score": r["score"], + "metadata": r.get("metadata", {}), + } + for r in all_results + ], + } + with open(os.path.join(RESULTS_DIR, "benchmark_summary.json"), "w") as f: + json.dump(summary, f, indent=2) + + +if __name__ == "__main__": + # Parse optional arguments: python -m benchmark.runner [start] [end] + start = int(sys.argv[1]) if len(sys.argv) > 1 else 0 + end = int(sys.argv[2]) if len(sys.argv) > 2 else 45 + asyncio.run(run_benchmark(start_from=start, end_at=end)) diff --git a/benchmark/scoring.py b/benchmark/scoring.py new file mode 100644 index 0000000..5afffa7 --- /dev/null +++ b/benchmark/scoring.py @@ -0,0 +1,345 @@ +""" +AIΒ² Benchmark β€” Scoring Module + +Per-debate scoring and ELO calculation. +Also includes judge tendency analysis. +""" + +import math +from collections import defaultdict + + +def compute_debate_score( + initial_votes: list[dict], + final_votes: list[dict], + model_for_id: str, + model_against_id: str, +) -> dict: + """ + Compute scoring for a single debate. + + Returns dict with: + - winner_side: "FOR" or "AGAINST" or "TIE" + - winner_model_id: model id of winner + - delta_for / delta_against: vote share change + - confidence_for / confidence_against: total confidence scores + - persuasion_for / persuasion_against: confidence-weighted persuasion + - vote_details: per-judge vote tracking + """ + n = len(initial_votes) + + # Count initial votes + init_for = sum(1 for v in initial_votes if v["stance"] == "FOR") + init_against = sum(1 for v in initial_votes if v["stance"] == "AGAINST") + init_undecided = n - init_for - init_against + + init_pct_for = init_for / n * 100 if n > 0 else 0 + init_pct_against = init_against / n * 100 if n > 0 else 0 + + # Count final votes + final_for = sum(1 for v in final_votes if v["stance"] == "FOR") + final_against = sum(1 for v in final_votes if v["stance"] == "AGAINST") + final_undecided = n - final_for - final_against + + final_pct_for = final_for / n * 100 if n > 0 else 0 + final_pct_against = final_against / n * 100 if n > 0 else 0 + + # Delta (vote conversion) + delta_for = final_pct_for - init_pct_for + delta_against = final_pct_against - init_pct_against + + # Confidence-weighted scores + conf_for_initial = sum( + v["confidence"] for v in initial_votes if v["stance"] == "FOR" + ) + conf_against_initial = sum( + v["confidence"] for v in initial_votes if v["stance"] == "AGAINST" + ) + conf_for_final = sum( + v["confidence"] for v in final_votes if v["stance"] == "FOR" + ) + conf_against_final = sum( + v["confidence"] for v in final_votes if v["stance"] == "AGAINST" + ) + + persuasion_for = conf_for_final - conf_for_initial + persuasion_against = conf_against_final - conf_against_initial + + # Winner determination: side with highest delta + if delta_for > delta_against: + winner_side = "FOR" + winner_model_id = model_for_id + elif delta_against > delta_for: + winner_side = "AGAINST" + winner_model_id = model_against_id + else: + # Tiebreaker: confidence-weighted persuasion + if persuasion_for > persuasion_against: + winner_side = "FOR" + winner_model_id = model_for_id + elif persuasion_against > persuasion_for: + winner_side = "AGAINST" + winner_model_id = model_against_id + else: + winner_side = "TIE" + winner_model_id = None + + # Per-judge vote tracking (for judge tendency analysis) + vote_details = [] + for init_v, final_v in zip(initial_votes, final_votes): + judge_id = init_v.get("judge_model_id", "unknown") + is_self_judging_for = (judge_id == model_for_id) + is_self_judging_against = (judge_id == model_against_id) + vote_details.append({ + "judge_model_id": judge_id, + "persona": init_v.get("persona", ""), + "initial_stance": init_v["stance"], + "initial_confidence": init_v["confidence"], + "final_stance": final_v["stance"], + "final_confidence": final_v["confidence"], + "stance_changed": init_v["stance"] != final_v["stance"], + "confidence_delta": final_v["confidence"] - init_v["confidence"], + "is_self_judging": is_self_judging_for or is_self_judging_against, + "self_judging_side": "FOR" if is_self_judging_for else ("AGAINST" if is_self_judging_against else None), + }) + + return { + "winner_side": winner_side, + "winner_model_id": winner_model_id, + "initial": { + "for": init_for, "against": init_against, "undecided": init_undecided, + "pct_for": round(init_pct_for, 1), "pct_against": round(init_pct_against, 1), + }, + "final": { + "for": final_for, "against": final_against, "undecided": final_undecided, + "pct_for": round(final_pct_for, 1), "pct_against": round(final_pct_against, 1), + }, + "delta_for": round(delta_for, 1), + "delta_against": round(delta_against, 1), + "confidence": { + "for_initial": conf_for_initial, + "for_final": conf_for_final, + "against_initial": conf_against_initial, + "against_final": conf_against_final, + }, + "persuasion_for": persuasion_for, + "persuasion_against": persuasion_against, + "vote_details": vote_details, + } + + +# ───────────────────────────────────────────────── +# ELO Rating System +# ───────────────────────────────────────────────── + +def compute_elo_update( + elo_a: float, + elo_b: float, + winner: str, # "a", "b", or "tie" + k: float = 32.0, +) -> tuple[float, float]: + """ + Standard ELO update. + + Args: + elo_a: Current ELO of player A + elo_b: Current ELO of player B + winner: "a" if A won, "b" if B won, "tie" if draw + k: K-factor (sensitivity) + + Returns: + (new_elo_a, new_elo_b) + """ + expected_a = 1.0 / (1.0 + 10.0 ** ((elo_b - elo_a) / 400.0)) + expected_b = 1.0 - expected_a + + if winner == "a": + score_a, score_b = 1.0, 0.0 + elif winner == "b": + score_a, score_b = 0.0, 1.0 + else: + score_a, score_b = 0.5, 0.5 + + new_elo_a = elo_a + k * (score_a - expected_a) + new_elo_b = elo_b + k * (score_b - expected_b) + + return round(new_elo_a, 1), round(new_elo_b, 1) + + +class EloTracker: + """Track ELO ratings across multiple debates.""" + + def __init__(self, model_ids: list[str], starting_elo: float = 1500.0): + self.ratings = {mid: starting_elo for mid in model_ids} + self.history: list[dict] = [] # [{debate_id, model_a, model_b, winner, elo_before, elo_after}] + + def update(self, model_a_id: str, model_b_id: str, winner_id: str | None, debate_id: str = ""): + """Update ELO after a debate.""" + elo_a = self.ratings[model_a_id] + elo_b = self.ratings[model_b_id] + + if winner_id == model_a_id: + winner = "a" + elif winner_id == model_b_id: + winner = "b" + else: + winner = "tie" + + new_a, new_b = compute_elo_update(elo_a, elo_b, winner) + self.ratings[model_a_id] = new_a + self.ratings[model_b_id] = new_b + + self.history.append({ + "debate_id": debate_id, + "model_a": model_a_id, + "model_b": model_b_id, + "winner": winner_id, + "elo_before": {model_a_id: elo_a, model_b_id: elo_b}, + "elo_after": {model_a_id: new_a, model_b_id: new_b}, + }) + + def get_leaderboard(self) -> list[dict]: + """Get sorted leaderboard.""" + from benchmark.models import get_model_by_id + board = [] + for mid, elo in self.ratings.items(): + model = get_model_by_id(mid) + board.append({ + "model_id": mid, + "display_name": model["display_name"], + "provider": model["provider"], + "elo": round(elo, 1), + "arena_score": model["arena_score"], + }) + board.sort(key=lambda x: x["elo"], reverse=True) + for i, entry in enumerate(board): + entry["rank"] = i + 1 + return board + + def to_dict(self) -> dict: + return { + "ratings": self.ratings, + "history": self.history, + "leaderboard": self.get_leaderboard(), + } + + +# ───────────────────────────────────────────────── +# Judge Tendency Analysis +# ───────────────────────────────────────────────── + +def analyze_judge_tendencies(all_debate_results: list[dict]) -> dict: + """ + Analyze how each model behaves as a judge across all debates. + + Returns a rich analysis dict with per-judge and cross-model data. + """ + # Per-judge aggregates + judge_stats = defaultdict(lambda: { + "total_judgments": 0, + "stance_flips": 0, + "avg_confidence_initial": [], + "avg_confidence_final": [], + "confidence_deltas": [], + "voted_for_count": 0, + "voted_against_count": 0, + "voted_undecided_count": 0, + "self_judging_votes": [], + "self_judging_favored_self": 0, + "self_judging_total": 0, + }) + + # Cross-model: judge_model β†’ debater_model β†’ {wins, losses} + cross_matrix = defaultdict(lambda: defaultdict(lambda: {"favored": 0, "total": 0})) + + for debate in all_debate_results: + score = debate["score"] + model_for_id = debate["model_for"]["id"] + model_against_id = debate["model_against"]["id"] + winner_side = score["winner_side"] + + for vd in score["vote_details"]: + jid = vd["judge_model_id"] + js = judge_stats[jid] + + js["total_judgments"] += 1 + if vd["stance_changed"]: + js["stance_flips"] += 1 + js["avg_confidence_initial"].append(vd["initial_confidence"]) + js["avg_confidence_final"].append(vd["final_confidence"]) + js["confidence_deltas"].append(vd["confidence_delta"]) + + final = vd["final_stance"] + if final == "FOR": + js["voted_for_count"] += 1 + elif final == "AGAINST": + js["voted_against_count"] += 1 + else: + js["voted_undecided_count"] += 1 + + # Self-judging analysis + if vd["is_self_judging"]: + js["self_judging_total"] += 1 + self_side = vd["self_judging_side"] + if final == self_side: + js["self_judging_favored_self"] += 1 + js["self_judging_votes"].append({ + "debate_for": model_for_id, + "debate_against": model_against_id, + "self_side": self_side, + "voted": final, + "favored_self": final == self_side, + }) + + # Cross-model matrix + if final == "FOR": + cross_matrix[jid][model_for_id]["favored"] += 1 + cross_matrix[jid][model_for_id]["total"] += 1 + cross_matrix[jid][model_against_id]["total"] += 1 + elif final == "AGAINST": + cross_matrix[jid][model_against_id]["favored"] += 1 + cross_matrix[jid][model_against_id]["total"] += 1 + cross_matrix[jid][model_for_id]["total"] += 1 + else: + cross_matrix[jid][model_for_id]["total"] += 1 + cross_matrix[jid][model_against_id]["total"] += 1 + + # Compute final stats + result = {} + for jid, js in judge_stats.items(): + n = js["total_judgments"] + result[jid] = { + "total_judgments": n, + "stance_flip_rate": round(js["stance_flips"] / n * 100, 1) if n else 0, + "avg_confidence_initial": round(sum(js["avg_confidence_initial"]) / n, 1) if n else 0, + "avg_confidence_final": round(sum(js["avg_confidence_final"]) / n, 1) if n else 0, + "avg_confidence_delta": round(sum(js["confidence_deltas"]) / n, 1) if n else 0, + "for_vote_pct": round(js["voted_for_count"] / n * 100, 1) if n else 0, + "against_vote_pct": round(js["voted_against_count"] / n * 100, 1) if n else 0, + "undecided_vote_pct": round(js["voted_undecided_count"] / n * 100, 1) if n else 0, + "self_judging": { + "total": js["self_judging_total"], + "favored_self": js["self_judging_favored_self"], + "self_bias_rate": round( + js["self_judging_favored_self"] / js["self_judging_total"] * 100, 1 + ) if js["self_judging_total"] else 0, + "votes": js["self_judging_votes"], + }, + } + + # Cross-model matrix + matrix = {} + for jid in cross_matrix: + matrix[jid] = {} + for did in cross_matrix[jid]: + entry = cross_matrix[jid][did] + matrix[jid][did] = { + "favored": entry["favored"], + "total": entry["total"], + "favor_rate": round(entry["favored"] / entry["total"] * 100, 1) if entry["total"] else 0, + } + + return { + "per_judge": result, + "cross_model_matrix": matrix, + } diff --git a/benchmark/test_debate.py b/benchmark/test_debate.py new file mode 100644 index 0000000..ae646e4 --- /dev/null +++ b/benchmark/test_debate.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +""" +AIΒ² Benchmark β€” Step-by-Step Test Script + +Tests each component individually before running the full benchmark. +Run from workspace root: python -m benchmark.test_debate +""" + +import asyncio +import json +import os +import sys +import time + +# Load env +from dotenv import load_dotenv +load_dotenv(override=True) + +from benchmark.models import MODELS, PERSONA_MAP, TOPICS, get_model_by_id +from benchmark.api_client import chat_completion, print_stats, stats +from benchmark.audience import JudgeAgent, JudgePanel +from benchmark.scoring import compute_debate_score, compute_elo_update, EloTracker + + +async def test_1_api_connectivity(): + """Test 1: Verify API connectivity with cheapest model.""" + print("\n" + "="*60) + print("TEST 1: API Connectivity (Gemini 3 Flash β€” cheapest)") + print("="*60) + + model = get_model_by_id("gemini-3-flash") + result = await chat_completion( + model_id=model["openrouter_id"], + messages=[{"role": "user", "content": "Say 'API working' in exactly 2 words."}], + max_tokens=20, + config=model.get("config"), + ) + print(f" Response: {result['content']}") + print(f" Tokens: {result['usage']}") + assert result["content"], "Empty response!" + print(" βœ… PASSED") + return True + + +async def test_2_all_models_respond(): + """Test 2: Quick ping to all 10 models to verify they work.""" + print("\n" + "="*60) + print("TEST 2: All 10 Models Respond") + print("="*60) + + async def ping(model): + try: + t0 = time.time() + result = await chat_completion( + model_id=model["openrouter_id"], + messages=[{"role": "user", "content": "Reply with just the word 'OK'."}], + max_tokens=50, + config=model.get("config"), + timeout=60.0, + ) + elapsed = time.time() - t0 + content = (result["content"] or "").strip()[:50] + print(f" βœ… {model['display_name']:35s} ({elapsed:.1f}s): {content}") + return True + except Exception as e: + print(f" ❌ {model['display_name']:35s}: {e}") + return False + + results = await asyncio.gather(*[ping(m) for m in MODELS]) + passed = sum(results) + print(f"\n {passed}/10 models responded successfully") + return passed >= 8 # Allow 2 failures + + +async def test_3_judge_vote(): + """Test 3: Single judge agent produces valid JSON vote.""" + print("\n" + "="*60) + print("TEST 3: Judge Agent Vote (Gemini 3 Flash)") + print("="*60) + + model = get_model_by_id("gemini-3-flash") + persona = PERSONA_MAP["gemini-3-flash"] + motion = TOPICS[0] + + agent = JudgeAgent(model, persona, motion) + vote = await agent.initial_vote() + + print(f" Persona: {persona}") + print(f" Motion: {motion}") + print(f" Vote: {json.dumps(vote, indent=2)}") + + assert vote["stance"] in ("FOR", "AGAINST", "UNDECIDED"), f"Bad stance: {vote['stance']}" + assert 0 <= vote["confidence"] <= 100, f"Bad confidence: {vote['confidence']}" + assert vote["judge_model_id"] == "gemini-3-flash" + print(" βœ… PASSED") + return True + + +async def test_4_judge_panel_initial_votes(): + """Test 4: Full 10-judge panel collects initial votes.""" + print("\n" + "="*60) + print("TEST 4: Full Judge Panel (10 judges, initial votes)") + print("="*60) + + motion = TOPICS[0] + panel = JudgePanel(motion) + votes = await panel.collect_initial_votes() + + print(f" Motion: {motion}") + print(f" Votes collected: {len(votes)}") + for v in votes: + print(f" {v['judge_model_id']:35s} [{v['persona']:30s}]: {v['stance']:10s} (conf: {v['confidence']})") + + assert len(votes) == 10, f"Expected 10 votes, got {len(votes)}" + for_count = sum(1 for v in votes if v["stance"] == "FOR") + against_count = sum(1 for v in votes if v["stance"] == "AGAINST") + undecided_count = sum(1 for v in votes if v["stance"] == "UNDECIDED") + print(f"\n Summary: FOR={for_count} AGAINST={against_count} UNDECIDED={undecided_count}") + print(" βœ… PASSED") + return True + + +async def test_5_mini_debate(): + """Test 5: Run a complete mini-debate between the two cheapest models.""" + print("\n" + "="*60) + print("TEST 5: Full Mini-Debate (Gemini 3 Flash vs GPT-5.2 Chat)") + print("="*60) + + from benchmark.debate import run_debate + + model_for = get_model_by_id("gemini-3-flash") + model_against = get_model_by_id("gpt-5.2-chat") + motion = TOPICS[0] + + print(f" FOR: {model_for['display_name']}") + print(f" AGAINST: {model_against['display_name']}") + print(f" Motion: {motion}") + print() + + result = await run_debate(model_for, model_against, motion, debate_id="test-001") + + # Print results + print(f"\n {'='*40}") + print(f" RESULTS") + print(f" {'='*40}") + print(f" Winner: {result.score['winner_side']} ({result.score['winner_model_id']})") + print(f" Initial votes: FOR={result.score['initial']['for']} AGAINST={result.score['initial']['against']} UNDECIDED={result.score['initial']['undecided']}") + print(f" Final votes: FOR={result.score['final']['for']} AGAINST={result.score['final']['against']} UNDECIDED={result.score['final']['undecided']}") + print(f" Delta FOR: {result.score['delta_for']:+.1f}%") + print(f" Delta AGAINST: {result.score['delta_against']:+.1f}%") + print(f" Persuasion FOR: {result.score['persuasion_for']:+d}") + print(f" Persuasion AGAINST: {result.score['persuasion_against']:+d}") + print(f" Elapsed: {result.metadata['elapsed_seconds']}s") + print(f" Transcript entries: {len(result.transcript)}") + + # Save result for inspection + os.makedirs("benchmark/results/debates", exist_ok=True) + with open("benchmark/results/debates/test-001.json", "w") as f: + json.dump(result.to_dict(), f, indent=2) + print(f" Saved to benchmark/results/debates/test-001.json") + + print(" βœ… PASSED") + return True + + +async def test_6_scoring(): + """Test 6: Verify scoring logic with mock data.""" + print("\n" + "="*60) + print("TEST 6: Scoring Logic (Mock Data)") + print("="*60) + + initial = [ + {"stance": "FOR", "confidence": 60, "judge_model_id": "a"}, + {"stance": "AGAINST", "confidence": 70, "judge_model_id": "b"}, + {"stance": "UNDECIDED", "confidence": 40, "judge_model_id": "c"}, + {"stance": "FOR", "confidence": 55, "judge_model_id": "d"}, + {"stance": "AGAINST", "confidence": 80, "judge_model_id": "e"}, + ] + final = [ + {"stance": "FOR", "confidence": 75, "judge_model_id": "a"}, + {"stance": "FOR", "confidence": 60, "judge_model_id": "b"}, # flipped! + {"stance": "FOR", "confidence": 65, "judge_model_id": "c"}, # converted! + {"stance": "FOR", "confidence": 70, "judge_model_id": "d"}, + {"stance": "AGAINST", "confidence": 85, "judge_model_id": "e"}, + ] + + score = compute_debate_score(initial, final, "model_x", "model_y") + print(f" Winner: {score['winner_side']} (model: {score['winner_model_id']})") + print(f" Delta FOR: {score['delta_for']:+.1f}%") + print(f" Delta AGAINST: {score['delta_against']:+.1f}%") + print(f" Persuasion FOR: {score['persuasion_for']}") + + assert score["winner_side"] == "FOR", f"Expected FOR to win, got {score['winner_side']}" + assert score["winner_model_id"] == "model_x" + assert score["delta_for"] > 0 + + # Test ELO + new_a, new_b = compute_elo_update(1500, 1500, "a") + print(f"\n ELO test: 1500 vs 1500, A wins β†’ A={new_a}, B={new_b}") + assert new_a > 1500 + assert new_b < 1500 + + print(" βœ… PASSED") + return True + + +async def main(): + """Run all tests sequentially.""" + print("🏁 AIΒ² Benchmark β€” Test Suite") + print(f" API Key: ...{os.environ.get('OPENROUTER_API_KEY', '')[-6:]}") + print() + + tests = [ + ("Test 1: API Connectivity", test_1_api_connectivity), + ("Test 2: All Models Respond", test_2_all_models_respond), + ("Test 3: Judge Vote", test_3_judge_vote), + ("Test 4: Full Judge Panel", test_4_judge_panel_initial_votes), + ("Test 5: Mini Debate", test_5_mini_debate), + ("Test 6: Scoring Logic", test_6_scoring), + ] + + results = {} + for name, test_fn in tests: + try: + passed = await test_fn() + results[name] = "βœ… PASSED" if passed else "⚠️ PARTIAL" + except Exception as e: + print(f" ❌ FAILED: {e}") + import traceback + traceback.print_exc() + results[name] = f"❌ FAILED: {e}" + + print("\n" + "="*60) + print("TEST SUMMARY") + print("="*60) + for name, status in results.items(): + print(f" {name}: {status}") + + print_stats() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/sync_results.sh b/sync_results.sh new file mode 100755 index 0000000..02a2af1 --- /dev/null +++ b/sync_results.sh @@ -0,0 +1,14 @@ +#!/bin/bash +# Sync benchmark results to the web app data directory +# Run this periodically or after benchmark completes + +RESULTS_DIR="/workspace/benchmark/results" +WEB_DATA_DIR="/workspace/web/src/data" + +if [ -f "$RESULTS_DIR/benchmark_summary.json" ]; then + cp "$RESULTS_DIR/benchmark_summary.json" "$WEB_DATA_DIR/benchmark-results.json" + echo "βœ… Synced benchmark results to web app" + echo " Debates: $(python3 -c "import json; d=json.load(open('$WEB_DATA_DIR/benchmark-results.json')); print(d['total_debates'])")" +else + echo "⚠️ No benchmark results found yet" +fi diff --git a/web-assets/hero-banner.png b/web-assets/hero-banner.png new file mode 100644 index 0000000..7200f2b Binary files /dev/null and b/web-assets/hero-banner.png differ diff --git a/web-assets/icon-anthropic.png b/web-assets/icon-anthropic.png new file mode 100644 index 0000000..8593e72 Binary files /dev/null and b/web-assets/icon-anthropic.png differ diff --git a/web-assets/icon-google.png b/web-assets/icon-google.png new file mode 100644 index 0000000..cb6b419 Binary files /dev/null and b/web-assets/icon-google.png differ diff --git a/web-assets/icon-openai.png b/web-assets/icon-openai.png new file mode 100644 index 0000000..7c3ce4e Binary files /dev/null and b/web-assets/icon-openai.png differ diff --git a/web-assets/icon-xai.png b/web-assets/icon-xai.png new file mode 100644 index 0000000..404b64e Binary files /dev/null and b/web-assets/icon-xai.png differ diff --git a/web-assets/logo.png b/web-assets/logo.png new file mode 100644 index 0000000..f116aeb Binary files /dev/null and b/web-assets/logo.png differ diff --git a/web/.gitignore b/web/.gitignore new file mode 100644 index 0000000..5ef6a52 --- /dev/null +++ b/web/.gitignore @@ -0,0 +1,41 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.* +.yarn/* +!.yarn/patches +!.yarn/plugins +!.yarn/releases +!.yarn/versions + +# testing +/coverage + +# next.js +/.next/ +/out/ + +# production +/build + +# misc +.DS_Store +*.pem + +# debug +npm-debug.log* +yarn-debug.log* +yarn-error.log* +.pnpm-debug.log* + +# env files (can opt-in for committing if needed) +.env* + +# vercel +.vercel + +# typescript +*.tsbuildinfo +next-env.d.ts diff --git a/web/AGENTS.md b/web/AGENTS.md new file mode 100644 index 0000000..8bd0e39 --- /dev/null +++ b/web/AGENTS.md @@ -0,0 +1,5 @@ + +# This is NOT the Next.js you know + +This version has breaking changes β€” APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices. + diff --git a/web/CLAUDE.md b/web/CLAUDE.md new file mode 100644 index 0000000..43c994c --- /dev/null +++ b/web/CLAUDE.md @@ -0,0 +1 @@ +@AGENTS.md diff --git a/web/README.md b/web/README.md new file mode 100644 index 0000000..e215bc4 --- /dev/null +++ b/web/README.md @@ -0,0 +1,36 @@ +This is a [Next.js](https://nextjs.org) project bootstrapped with [`create-next-app`](https://nextjs.org/docs/app/api-reference/cli/create-next-app). + +## Getting Started + +First, run the development server: + +```bash +npm run dev +# or +yarn dev +# or +pnpm dev +# or +bun dev +``` + +Open [http://localhost:3000](http://localhost:3000) with your browser to see the result. + +You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file. + +This project uses [`next/font`](https://nextjs.org/docs/app/building-your-application/optimizing/fonts) to automatically optimize and load [Geist](https://vercel.com/font), a new font family for Vercel. + +## Learn More + +To learn more about Next.js, take a look at the following resources: + +- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API. +- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial. + +You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js) - your feedback and contributions are welcome! + +## Deploy on Vercel + +The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js. + +Check out our [Next.js deployment documentation](https://nextjs.org/docs/app/building-your-application/deploying) for more details. diff --git a/web/components.json b/web/components.json new file mode 100644 index 0000000..8d886db --- /dev/null +++ b/web/components.json @@ -0,0 +1,25 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "base-nova", + "rsc": true, + "tsx": true, + "tailwind": { + "config": "", + "css": "src/app/globals.css", + "baseColor": "neutral", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "rtl": false, + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "menuColor": "default", + "menuAccent": "subtle", + "registries": {} +} diff --git a/web/eslint.config.mjs b/web/eslint.config.mjs new file mode 100644 index 0000000..05e726d --- /dev/null +++ b/web/eslint.config.mjs @@ -0,0 +1,18 @@ +import { defineConfig, globalIgnores } from "eslint/config"; +import nextVitals from "eslint-config-next/core-web-vitals"; +import nextTs from "eslint-config-next/typescript"; + +const eslintConfig = defineConfig([ + ...nextVitals, + ...nextTs, + // Override default ignores of eslint-config-next. + globalIgnores([ + // Default ignores of eslint-config-next: + ".next/**", + "out/**", + "build/**", + "next-env.d.ts", + ]), +]); + +export default eslintConfig; diff --git a/web/next.config.ts b/web/next.config.ts new file mode 100644 index 0000000..e9ffa30 --- /dev/null +++ b/web/next.config.ts @@ -0,0 +1,7 @@ +import type { NextConfig } from "next"; + +const nextConfig: NextConfig = { + /* config options here */ +}; + +export default nextConfig; diff --git a/web/package-lock.json b/web/package-lock.json new file mode 100644 index 0000000..dc60e71 --- /dev/null +++ b/web/package-lock.json @@ -0,0 +1,9784 @@ +{ + "name": "web", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "web", + "version": "0.1.0", + 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+ "@tailwindcss/postcss": "^4", + "@types/node": "^20", + "@types/react": "^19", + "@types/react-dom": "^19", + "eslint": "^9", + "eslint-config-next": "16.2.3", + "tailwindcss": "^4", + "typescript": "^5" + } +} diff --git a/web/postcss.config.mjs b/web/postcss.config.mjs new file mode 100644 index 0000000..61e3684 --- /dev/null +++ b/web/postcss.config.mjs @@ -0,0 +1,7 @@ +const config = { + plugins: { + "@tailwindcss/postcss": {}, + }, +}; + +export default config; diff --git a/web/public/file.svg b/web/public/file.svg new file mode 100644 index 0000000..004145c --- /dev/null +++ b/web/public/file.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/public/globe.svg b/web/public/globe.svg new file mode 100644 index 0000000..567f17b --- /dev/null +++ b/web/public/globe.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/public/images/hero-banner.png b/web/public/images/hero-banner.png new file mode 100644 index 0000000..7200f2b Binary files /dev/null and b/web/public/images/hero-banner.png differ diff --git a/web/public/images/icon-anthropic.png b/web/public/images/icon-anthropic.png new file mode 100644 index 0000000..8593e72 Binary files /dev/null and b/web/public/images/icon-anthropic.png differ diff --git a/web/public/images/icon-google.png b/web/public/images/icon-google.png new file mode 100644 index 0000000..cb6b419 Binary files /dev/null and b/web/public/images/icon-google.png differ diff --git a/web/public/images/icon-openai.png b/web/public/images/icon-openai.png new file mode 100644 index 0000000..7c3ce4e Binary files /dev/null and b/web/public/images/icon-openai.png differ diff --git a/web/public/images/icon-xai.png b/web/public/images/icon-xai.png new file mode 100644 index 0000000..404b64e Binary files /dev/null and b/web/public/images/icon-xai.png differ diff --git a/web/public/images/logo.png b/web/public/images/logo.png new file mode 100644 index 0000000..f116aeb Binary files /dev/null and b/web/public/images/logo.png differ diff --git a/web/public/next.svg b/web/public/next.svg new file mode 100644 index 0000000..5174b28 --- /dev/null +++ b/web/public/next.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/public/vercel.svg b/web/public/vercel.svg new file mode 100644 index 0000000..7705396 --- /dev/null +++ b/web/public/vercel.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/public/window.svg b/web/public/window.svg new file mode 100644 index 0000000..b2b2a44 --- /dev/null +++ b/web/public/window.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/web/src/app/arena/page.tsx b/web/src/app/arena/page.tsx new file mode 100644 index 0000000..838d967 --- /dev/null +++ b/web/src/app/arena/page.tsx @@ -0,0 +1,480 @@ +"use client"; + +import { useState, useRef, useCallback } from "react"; +import { Navbar } from "@/components/Navbar"; +import { MODELS, DEBATE_TOPICS, PROVIDER_COLORS } from "@/lib/models"; +import { runDebate, Vote, TranscriptEntry, DebateScore } from "@/lib/debate-engine"; +import { Button } from "@/components/ui/button"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { Badge } from "@/components/ui/badge"; +import { Card } from "@/components/ui/card"; +import { Separator } from "@/components/ui/separator"; +import { ScrollArea } from "@/components/ui/scroll-area"; +import { + Swords, + Play, + Loader2, + Eye, + EyeOff, + Zap, + Brain, + ChevronRight, + Trophy, + ArrowLeftRight, +} from "lucide-react"; + +interface DebateState { + status: "idle" | "running" | "complete" | "error"; + phase: string; + transcript: TranscriptEntry[]; + initialVotes: Vote[]; + finalVotes: Vote[]; + score: DebateScore | null; + error?: string; +} + +export default function ArenaPage() { + const [apiKey, setApiKey] = useState(() => { + if (typeof window !== "undefined") { + return localStorage.getItem("openrouter_api_key") || ""; + } + return ""; + }); + const [showKey, setShowKey] = useState(false); + const [modelForId, setModelForId] = useState(MODELS[0].id); + const [modelAgainstId, setModelAgainstId] = useState(MODELS[MODELS.length - 1].id); + const [topic, setTopic] = useState(DEBATE_TOPICS[0]); + const [customTopic, setCustomTopic] = useState(""); + const [debate, setDebate] = useState({ + status: "idle", + phase: "", + transcript: [], + initialVotes: [], + finalVotes: [], + score: null, + }); + + const abortRef = useRef(null); + + const saveApiKey = useCallback((key: string) => { + setApiKey(key); + if (typeof window !== "undefined") { + localStorage.setItem("openrouter_api_key", key); + } + }, []); + + const startDebate = async () => { + if (!apiKey) { + alert("Please enter your OpenRouter API key"); + return; + } + + const useTopic = customTopic || topic; + const mFor = MODELS.find((m) => m.id === modelForId)!; + const mAgainst = MODELS.find((m) => m.id === modelAgainstId)!; + + abortRef.current = new AbortController(); + + setDebate({ + status: "running", + phase: "Starting...", + transcript: [], + initialVotes: [], + finalVotes: [], + score: null, + }); + + try { + await runDebate(apiKey, mFor, mAgainst, useTopic, (event) => { + setDebate((prev) => { + const next = { ...prev }; + if ("phase" in event && event.type === "phase") next.phase = event.phase; + if (event.type === "transcript") next.transcript = [...prev.transcript, event.entry]; + else if (event.type === "initial_votes") next.initialVotes = event.votes; + else if (event.type === "final_votes") next.finalVotes = event.votes; + else if (event.type === "score") { next.score = event.score; next.status = "complete"; } + else if (event.type === "error") { next.error = event.message; next.status = "error"; } + return next; + }); + }, abortRef.current.signal); + } catch (error) { + if ((error as Error).name === "AbortError") return; + setDebate((prev) => ({ + ...prev, + status: "error", + error: (error as Error).message, + })); + } + }; + + const modelFor = MODELS.find((m) => m.id === modelForId); + const modelAgainst = MODELS.find((m) => m.id === modelAgainstId); + + return ( +
+ + +
+ {/* Header */} +
+

+ + Live Debate Arena + +

+

+ Pick two models, enter your OpenRouter API key, and watch them debate live. +

+
+ + {/* Setup Panel */} + {debate.status === "idle" && ( +
+ {/* API Key */} + + +

+ Your key is stored in your browser only and sent directly to OpenRouter from your browser. + It never touches any server. +

+
+
+ saveApiKey(e.target.value)} + placeholder="sk-or-v1-..." + className="pr-10 font-mono text-sm" + /> + +
+
+ + + {/* Model Selection */} + + +
+ {/* FOR */} +
+
+
+ FOR (Proposition) +
+ +
+ + {/* VS */} +
+ + VS +
+ + {/* AGAINST */} +
+
+
+ AGAINST (Opposition) +
+ +
+
+ + + {/* Topic */} + + +
+ {DEBATE_TOPICS.map((t) => ( + + ))} +
+ + + setCustomTopic(e.target.value)} + placeholder="This house believes that..." + className="text-sm" + /> + + + {/* Start Button */} + + {modelForId === modelAgainstId && ( +

+ Please select two different models +

+ )} +
+ )} + + {/* Live Debate View */} + {debate.status !== "idle" && ( +
+ {/* Phase Indicator */} +
+ {debate.status === "running" && ( + + )} + + {debate.status === "complete" + ? "πŸ† Debate Complete" + : debate.status === "error" + ? `❌ Error: ${debate.error}` + : debate.phase} + +
+ + {/* Combatants Header */} +
+
+
+ + {modelFor?.display_name} + + FOR + +
+
+ +
+
+ + {modelAgainst?.display_name} + + AGAINST + +
+
+
+ + {/* Transcript */} + +
+ {debate.transcript.map((entry, idx) => ( +
+
+ + {entry.phase.toUpperCase()} + + {entry.speaker} + {entry.type && ( + + {entry.type} + + )} +
+

+ {entry.content} +

+
+ ))} + {debate.status === "running" && ( +
+ +
+ )} +
+ + + {/* Jury Panel */} + {(debate.initialVotes.length > 0 || debate.finalVotes.length > 0) && ( +
+

+ + Jury Panel ({debate.finalVotes.length > 0 ? "Final" : "Initial"} Votes) +

+
+ {(debate.finalVotes.length > 0 ? debate.finalVotes : debate.initialVotes).map( + (vote, idx) => { + const isDebater = + vote.judge_model_id === modelForId || + vote.judge_model_id === modelAgainstId; + const model = MODELS.find((m) => m.id === vote.judge_model_id); + return ( +
+

+ {model?.display_name || vote.judge_model_id} + {isDebater && " βš”οΈ"} +

+

+ {vote.persona} +

+ + {vote.stance} ({vote.confidence}) + +
+ ); + } + )} +
+
+ )} + + {/* Score */} + {debate.score && ( + +
+ +

+ Winner:{" "} + + {debate.score.winner_side === "FOR" + ? modelFor?.display_name + : modelAgainst?.display_name} + +

+

+ {debate.score.winner_side} side won with{" "} + Ξ”{" "} + {debate.score.winner_side === "FOR" + ? debate.score.delta_for + : debate.score.delta_against} + % vote conversion +

+
+ + {/* New debate button */} +
+ +
+ + )} +
+ )} +
+
+ ); +} diff --git a/web/src/app/debate/[id]/page.tsx b/web/src/app/debate/[id]/page.tsx new file mode 100644 index 0000000..2cf3b5e --- /dev/null +++ b/web/src/app/debate/[id]/page.tsx @@ -0,0 +1,74 @@ +import { Navbar } from "@/components/Navbar"; +import { DebateReplay } from "@/components/debate/DebateReplay"; +import { notFound } from "next/navigation"; +import fs from "fs"; +import path from "path"; + +interface Props { + params: Promise<{ id: string }>; +} + +async function getDebateData(id: string) { + // Try to load from benchmark results + const debatePath = path.join( + process.cwd(), + "..", + "benchmark", + "results", + "debates", + `${id}.json` + ); + + try { + if (fs.existsSync(debatePath)) { + const raw = fs.readFileSync(debatePath, "utf-8"); + return JSON.parse(raw); + } + } catch { + // fall through + } + + return null; +} + +export default async function DebateReplayPage({ params }: Props) { + const { id } = await params; + const debate = await getDebateData(id); + + if (!debate) { + notFound(); + } + + return ( +
+ +
+ +
+
+ ); +} + +// Generate static paths for completed debates +export async function generateStaticParams() { + const debatesDir = path.join( + process.cwd(), + "..", + "benchmark", + "results", + "debates" + ); + + try { + if (fs.existsSync(debatesDir)) { + return fs + .readdirSync(debatesDir) + .filter((f) => f.endsWith(".json") && !f.startsWith("test")) + .map((f) => ({ id: f.replace(".json", "") })); + } + } catch { + // fall through + } + + return []; +} diff --git a/web/src/app/favicon.ico b/web/src/app/favicon.ico new file mode 100644 index 0000000..718d6fe Binary files /dev/null and b/web/src/app/favicon.ico differ diff --git a/web/src/app/globals.css b/web/src/app/globals.css new file mode 100644 index 0000000..c56032b --- /dev/null +++ b/web/src/app/globals.css @@ -0,0 +1,130 @@ +@import "tailwindcss"; +@import "tw-animate-css"; +@import "shadcn/tailwind.css"; + +@custom-variant dark (&:is(.dark *)); + +@theme inline { + --color-background: var(--background); + --color-foreground: var(--foreground); + --font-sans: var(--font-sans); + --font-mono: var(--font-geist-mono); + --font-heading: var(--font-sans); + --color-sidebar-ring: var(--sidebar-ring); + --color-sidebar-border: var(--sidebar-border); + --color-sidebar-accent-foreground: var(--sidebar-accent-foreground); + --color-sidebar-accent: var(--sidebar-accent); + --color-sidebar-primary-foreground: var(--sidebar-primary-foreground); + --color-sidebar-primary: var(--sidebar-primary); + --color-sidebar-foreground: var(--sidebar-foreground); + --color-sidebar: var(--sidebar); + --color-chart-5: var(--chart-5); + --color-chart-4: var(--chart-4); + --color-chart-3: var(--chart-3); + --color-chart-2: var(--chart-2); + --color-chart-1: var(--chart-1); + --color-ring: var(--ring); + --color-input: var(--input); + --color-border: var(--border); + --color-destructive: var(--destructive); + --color-accent-foreground: var(--accent-foreground); + --color-accent: var(--accent); + --color-muted-foreground: var(--muted-foreground); + --color-muted: var(--muted); + --color-secondary-foreground: var(--secondary-foreground); + --color-secondary: var(--secondary); + --color-primary-foreground: var(--primary-foreground); + --color-primary: var(--primary); + --color-popover-foreground: var(--popover-foreground); + --color-popover: var(--popover); + --color-card-foreground: var(--card-foreground); + --color-card: var(--card); + --radius-sm: calc(var(--radius) * 0.6); + --radius-md: calc(var(--radius) * 0.8); + --radius-lg: var(--radius); + --radius-xl: calc(var(--radius) * 1.4); + --radius-2xl: calc(var(--radius) * 1.8); + --radius-3xl: calc(var(--radius) * 2.2); + --radius-4xl: calc(var(--radius) * 2.6); +} + +:root { + --background: oklch(1 0 0); + --foreground: oklch(0.145 0 0); + --card: oklch(1 0 0); + --card-foreground: oklch(0.145 0 0); + --popover: oklch(1 0 0); + --popover-foreground: oklch(0.145 0 0); + --primary: oklch(0.205 0 0); + --primary-foreground: oklch(0.985 0 0); + --secondary: oklch(0.97 0 0); + --secondary-foreground: oklch(0.205 0 0); + --muted: oklch(0.97 0 0); + --muted-foreground: oklch(0.556 0 0); + --accent: oklch(0.97 0 0); + --accent-foreground: oklch(0.205 0 0); + --destructive: oklch(0.577 0.245 27.325); + --border: oklch(0.922 0 0); + --input: oklch(0.922 0 0); + --ring: oklch(0.708 0 0); + --chart-1: oklch(0.87 0 0); + --chart-2: oklch(0.556 0 0); + --chart-3: oklch(0.439 0 0); + --chart-4: oklch(0.371 0 0); + --chart-5: oklch(0.269 0 0); + --radius: 0.625rem; + --sidebar: oklch(0.985 0 0); + --sidebar-foreground: oklch(0.145 0 0); + --sidebar-primary: oklch(0.205 0 0); + --sidebar-primary-foreground: oklch(0.985 0 0); + --sidebar-accent: oklch(0.97 0 0); + --sidebar-accent-foreground: oklch(0.205 0 0); + --sidebar-border: oklch(0.922 0 0); + --sidebar-ring: oklch(0.708 0 0); +} + +.dark { + --background: oklch(0.145 0 0); + --foreground: oklch(0.985 0 0); + --card: oklch(0.205 0 0); + --card-foreground: oklch(0.985 0 0); + --popover: oklch(0.205 0 0); + --popover-foreground: oklch(0.985 0 0); + --primary: oklch(0.922 0 0); + --primary-foreground: oklch(0.205 0 0); + --secondary: oklch(0.269 0 0); + --secondary-foreground: oklch(0.985 0 0); + --muted: oklch(0.269 0 0); + --muted-foreground: oklch(0.708 0 0); + --accent: oklch(0.269 0 0); + --accent-foreground: oklch(0.985 0 0); + --destructive: oklch(0.704 0.191 22.216); + --border: oklch(1 0 0 / 10%); + --input: oklch(1 0 0 / 15%); + --ring: oklch(0.556 0 0); + --chart-1: oklch(0.87 0 0); + --chart-2: oklch(0.556 0 0); + --chart-3: oklch(0.439 0 0); + --chart-4: oklch(0.371 0 0); + --chart-5: oklch(0.269 0 0); + --sidebar: oklch(0.205 0 0); + --sidebar-foreground: oklch(0.985 0 0); + --sidebar-primary: oklch(0.488 0.243 264.376); + --sidebar-primary-foreground: oklch(0.985 0 0); + --sidebar-accent: oklch(0.269 0 0); + --sidebar-accent-foreground: oklch(0.985 0 0); + --sidebar-border: oklch(1 0 0 / 10%); + --sidebar-ring: oklch(0.556 0 0); +} + +@layer base { + * { + @apply border-border outline-ring/50; + } + body { + @apply bg-background text-foreground; + } + html { + @apply font-sans; + } +} \ No newline at end of file diff --git a/web/src/app/layout.tsx b/web/src/app/layout.tsx new file mode 100644 index 0000000..9749620 --- /dev/null +++ b/web/src/app/layout.tsx @@ -0,0 +1,33 @@ +import type { Metadata } from "next"; +import { Geist, Geist_Mono } from "next/font/google"; +import "./globals.css"; + +const geistSans = Geist({ + variable: "--font-geist-sans", + subsets: ["latin"], +}); + +const geistMono = Geist_Mono({ + variable: "--font-geist-mono", + subsets: ["latin"], +}); + +export const metadata: Metadata = { + title: "AIΒ² β€” Artificial Intelligence Squared", + description: + "LLM Debate Benchmark: Watch top AI models debate head-to-head, judged by AI jury panels. ELO rankings, judge tendency analysis, and live debate arena.", +}; + +export default function RootLayout({ + children, +}: Readonly<{ + children: React.ReactNode; +}>) { + return ( + + + {children} + + + ); +} diff --git a/web/src/app/model/[id]/page.tsx b/web/src/app/model/[id]/page.tsx new file mode 100644 index 0000000..e778220 --- /dev/null +++ b/web/src/app/model/[id]/page.tsx @@ -0,0 +1,78 @@ +import { Navbar } from "@/components/Navbar"; +import { ModelDetail } from "@/components/leaderboard/ModelDetail"; +import { notFound } from "next/navigation"; +import { MODELS } from "@/lib/models"; +import { getBenchmarkData, getModelStats } from "@/lib/benchmark-data"; + +interface Props { + params: Promise<{ id: string }>; +} + +export default async function ModelDetailPage({ params }: Props) { + const { id } = await params; + const model = MODELS.find((m) => m.id === id); + + if (!model) { + notFound(); + } + + const data = getBenchmarkData(); + const stats = getModelStats(id); + + // Get debates involving this model + const debates = data.debates.filter( + (d) => d.model_for.id === id || d.model_against.id === id + ); + + // Get judge tendencies for this model + const judgeProfile = data.judge_tendencies?.per_judge?.[id] || null; + + // Build head-to-head record + const h2h: Record< + string, + { wins: number; losses: number; ties: number; opponent_name: string } + > = {}; + for (const d of debates) { + const isFor = d.model_for.id === id; + const opponentId = isFor ? d.model_against.id : d.model_for.id; + const opponentName = isFor + ? d.model_against.display_name + : d.model_for.display_name; + + if (!h2h[opponentId]) { + h2h[opponentId] = { wins: 0, losses: 0, ties: 0, opponent_name: opponentName }; + } + + if (d.score.winner_side === "TIE") { + h2h[opponentId].ties++; + } else if (d.score.winner_model_id === id) { + h2h[opponentId].wins++; + } else { + h2h[opponentId].losses++; + } + } + + // Get the ELO for this model + const leaderboardEntry = data.leaderboard.find((e) => e.model_id === id); + + return ( +
+ +
+ +
+
+ ); +} + +export function generateStaticParams() { + return MODELS.map((m) => ({ id: m.id })); +} diff --git a/web/src/app/page.tsx b/web/src/app/page.tsx new file mode 100644 index 0000000..e88f924 --- /dev/null +++ b/web/src/app/page.tsx @@ -0,0 +1,188 @@ +import { Navbar } from "@/components/Navbar"; +import { LeaderboardTable } from "@/components/leaderboard/LeaderboardTable"; +import { RecentDebates } from "@/components/leaderboard/RecentDebates"; +import { JudgeTendencyHeatmap } from "@/components/analytics/JudgeTendencyHeatmap"; +import { getLeaderboard, getJudgeTendencies, getModelStats, getDebates } from "@/lib/benchmark-data"; +import { MODELS } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; +import { Swords, Users, Brain, BarChart3, ArrowRight } from "lucide-react"; +import Link from "next/link"; + +export default function HomePage() { + const leaderboard = getLeaderboard(); + const judgeTendencies = getJudgeTendencies(); + const debates = getDebates(); + + // Compute per-model stats + const modelStats: Record = {}; + for (const m of MODELS) { + modelStats[m.id] = getModelStats(m.id); + } + + const totalDebates = debates.length; + const totalVotes = totalDebates * 10; // 10 judges per debate + + return ( +
+ + + {/* Hero Section */} +
+ {/* Background gradient */} +
+
+ +
+
+ {/* Logo */} +
+
+ AIΒ² +
+
+ +

+ + Artificial Intelligence + +
+ Squared +

+ +

+ The top 10 LLMs debate head-to-head in structured Intelligence Squared format, + judged by AI jury panels. Every model is both a debater and a judge. +

+ + {/* Stats badges */} +
+ + + {totalDebates > 0 ? totalDebates : 45} Debates + + + + 10 Models + + + + {totalVotes > 0 ? totalVotes : 450} Jury Votes + + + + ELO Ranked + +
+ + {/* CTA */} + + + +
+
+
+ + {/* Leaderboard Section */} +
+
+
+

+ + Debate ELO Leaderboard +

+

+ Rankings based on head-to-head debate performance across {totalDebates > 0 ? totalDebates : 45} matchups +

+
+
+ +
+ + + {/* Recent Debates Sidebar */} +
+

+ + Recent Debates ({debates.length}) +

+ +
+
+ +

+ Arena Score is from the original benchmark. Debate ELO is computed from AIΒ² head-to-head matchups. + Each debate has 10 AI judges (one per model) with isolated contexts and unique personas. +

+
+ + {/* Judge Tendency Section */} + {Object.keys(judgeTendencies.per_judge).length > 0 && ( +
+
+

+ + Judge Tendency Analysis +

+

+ How each model behaves as a judge β€” persuadability, bias, and self-judging patterns +

+
+ + +
+ )} + + {/* How It Works Section */} +
+

How AIΒ² Works

+
+
+
+ +
+

Structured Debates

+

+ Intelligence Squared format: Opening β†’ Rebuttal β†’ Cross-Examination (3 rounds) β†’ Audience Questions β†’ Closing Statements. +

+
+ +
+
+ +
+

Model = Brain, Agent = Person

+

+ Same model can be debater AND judge simultaneously. Each agent has isolated context β€” debater and judge never share memory. +

+
+ +
+
+ +
+

10-Model Jury

+

+ Every debate has 10 judges (one per model), each with a unique persona. Self-judging bias is tracked. Winner = highest vote conversion. +

+
+
+
+ + {/* Footer */} +
+
+

+ AIΒ² β€” Artificial Intelligence Squared β€’ LLM Debate Benchmark β€’{" "} + + Try the Live Arena β†’ + +

+
+
+
+ ); +} diff --git a/web/src/components/Navbar.tsx b/web/src/components/Navbar.tsx new file mode 100644 index 0000000..af76b30 --- /dev/null +++ b/web/src/components/Navbar.tsx @@ -0,0 +1,53 @@ +"use client"; + +import Link from "next/link"; +import { Swords, BarChart3, Zap } from "lucide-react"; +import { Button } from "@/components/ui/button"; + +export function Navbar() { + return ( + + ); +} diff --git a/web/src/components/analytics/JudgeTendencyHeatmap.tsx b/web/src/components/analytics/JudgeTendencyHeatmap.tsx new file mode 100644 index 0000000..1008063 --- /dev/null +++ b/web/src/components/analytics/JudgeTendencyHeatmap.tsx @@ -0,0 +1,211 @@ +"use client"; + +import { JudgeTendencies } from "@/lib/types"; +import { MODELS, PROVIDER_COLORS, PERSONA_MAP } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; + +interface Props { + tendencies: JudgeTendencies; +} + +export function JudgeTendencyHeatmap({ tendencies }: Props) { + const modelIds = MODELS.map((m) => m.id); + const judgeData = tendencies.per_judge; + const matrix = tendencies.cross_model_matrix; + + if (Object.keys(judgeData).length === 0) { + return ( +
+

Judge tendency data will appear after the benchmark completes.

+
+ ); + } + + return ( +
+ {/* Per-Judge Stats Cards */} +
+

Judge Profiles

+
+ {modelIds.map((jid) => { + const stats = judgeData[jid]; + if (!stats) return null; + const model = MODELS.find((m) => m.id === jid)!; + const color = PROVIDER_COLORS[model.provider]; + const persona = PERSONA_MAP[jid]; + + return ( +
+
+
+
+

+ {model.display_name} +

+

+ {persona} +

+
+
+ +
+
+ Flip rate +

+ {stats.stance_flip_rate}% +

+
+
+ Avg Ξ” conf +

0 + ? "text-green-400" + : stats.avg_confidence_delta < 0 + ? "text-red-400" + : "" + }`} + > + {stats.avg_confidence_delta > 0 ? "+" : ""} + {stats.avg_confidence_delta} +

+
+
+ FOR bias +

+ {stats.for_vote_pct}% +

+
+
+ AGAINST +

+ {stats.against_vote_pct}% +

+
+
+ + {/* Self-judging */} + {stats.self_judging.total > 0 && ( +
+
+ Self-bias + 60 + ? "destructive" + : "secondary" + } + className="text-[10px] px-1.5 py-0" + > + {stats.self_judging.self_bias_rate}% + {stats.self_judging.self_bias_rate > 60 ? " ⚠️" : ""} + +
+
+ )} +
+ ); + })} +
+
+ + {/* Cross-Model Matrix */} + {Object.keys(matrix).length > 0 && ( +
+

+ Cross-Model Favor Matrix +

+

+ Cell shows how often the judge (row) voted in favor of the debater (column). + Higher = more favorable. Self-judging cells marked with βš”οΈ. +

+
+ + + + + {modelIds.map((did) => { + const dm = MODELS.find((m) => m.id === did)!; + return ( + + ); + })} + + + + {modelIds.map((jid) => { + const jm = MODELS.find((m) => m.id === jid)!; + const judgeRow = matrix[jid] || {}; + return ( + + + {modelIds.map((did) => { + const entry = judgeRow[did]; + const isSelf = jid === did; + if (!entry || entry.total === 0) { + return ( + + ); + } + const rate = entry.favor_rate; + // Color: green for high favor, red for low + const intensity = Math.abs(rate - 50) / 50; + const bg = + rate > 50 + ? `rgba(34, 197, 94, ${intensity * 0.4})` + : `rgba(239, 68, 68, ${intensity * 0.4})`; + return ( + + ); + })} + + ); + })} + +
+ Judge ↓ / Debater β†’ + + +
+
+
+ + {jm.display_name} + +
+
+ β€” + m.id === did)?.display_name} in ${entry.favored}/${entry.total} judgments (${rate}%)${isSelf ? " [SELF-JUDGING]" : ""}`} + > + + {rate}%{isSelf ? " βš”οΈ" : ""} + +
+
+
+ )} +
+ ); +} diff --git a/web/src/components/debate/DebateReplay.tsx b/web/src/components/debate/DebateReplay.tsx new file mode 100644 index 0000000..5a97852 --- /dev/null +++ b/web/src/components/debate/DebateReplay.tsx @@ -0,0 +1,389 @@ +"use client"; + +import { DebateResult, Vote } from "@/lib/types"; +import { MODELS, PROVIDER_COLORS } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; +import { Card } from "@/components/ui/card"; +import { Separator } from "@/components/ui/separator"; +import { + Trophy, + Swords, + MessageSquare, + HelpCircle, + Flag, + Users, + ArrowRight, + TrendingUp, +} from "lucide-react"; + +interface Props { + debate: DebateResult; +} + +const phaseLabels: Record = { + opening: { label: "Opening Statement", icon: }, + rebuttal: { + label: "Rebuttal", + icon: , + }, + cross_exam_r1: { + label: "Cross-Examination Round 1", + icon: , + }, + cross_exam_r2: { + label: "Cross-Examination Round 2", + icon: , + }, + cross_exam_r3: { + label: "Cross-Examination Round 3", + icon: , + }, + audience_qa: { + label: "Audience Q&A", + icon: , + }, + closing: { label: "Closing Statement", icon: }, +}; + +function VoteCard({ vote, initialVote }: { vote: Vote; showChange?: boolean; initialVote?: Vote }) { + const model = MODELS.find((m) => m.id === vote.judge_model_id); + const color = PROVIDER_COLORS[model?.provider || ""] || "#888"; + const changed = initialVote && initialVote.stance !== vote.stance; + + return ( +
+
+
+ + {model?.display_name || vote.judge_model_id} + +
+

+ {vote.persona} +

+
+ + {vote.stance} + + + {vote.confidence}% + + {changed && ( + + FLIPPED + + )} +
+ {vote.reasoning && ( +

+ {vote.reasoning} +

+ )} +
+ ); +} + +export function DebateReplay({ debate }: Props) { + const modelFor = MODELS.find((m) => m.id === debate.model_for.id); + const modelAgainst = MODELS.find((m) => m.id === debate.model_against.id); + const score = debate.score; + + return ( +
+ {/* Header */} +
+ + {debate.debate_id} + +

+ {debate.model_for.display_name} + vs + {debate.model_against.display_name} +

+

+ “{debate.motion}” +

+
+ + {/* Score Summary */} + +
+ {/* FOR */} +
+
+
+ + {debate.model_for.display_name} + + {score.winner_side === "FOR" && ( + + )} +
+
+ {score.final.for} +
+
+ {score.initial.for} β†’ {score.final.for} votes +
+
0 ? "text-green-400" : "text-muted-foreground" + }`} + > + {score.delta_for > 0 ? "+" : ""} + {score.delta_for}% +
+
+ + {/* Middle */} +
+ + + {debate.metadata?.elapsed_seconds + ? `${Math.round(debate.metadata.elapsed_seconds / 60)}min` + : ""} + +
+ + {/* AGAINST */} +
+
+
+ + {debate.model_against.display_name} + + {score.winner_side === "AGAINST" && ( + + )} +
+
+ {score.final.against} +
+
+ {score.initial.against} β†’ {score.final.against} votes +
+
0 + ? "text-green-400" + : "text-muted-foreground" + }`} + > + {score.delta_against > 0 ? "+" : ""} + {score.delta_against}% +
+
+
+ + + {/* Initial Votes */} +
+

+ + Initial Jury Votes (Pre-Debate) +

+
+ {debate.initial_votes.map((vote, idx) => ( + + ))} +
+
+ + {/* Transcript */} +
+

+ + Full Debate Transcript +

+
+ {debate.transcript.map((entry, idx) => { + const showPhaseHeader = idx === 0 || entry.phase !== debate.transcript[idx - 1].phase; + const phaseInfo = phaseLabels[entry.phase] || { + label: entry.phase, + icon: null, + }; + + return ( +
+ {showPhaseHeader && ( +
+ {phaseInfo.icon} +

+ {phaseInfo.label} +

+ +
+ )} + +
+
+ + {entry.side} + + + {entry.speaker} + + {entry.type && ( + + {entry.type} + + )} +
+

+ {entry.content} +

+
+
+ ); + })} +
+
+ + {/* Final Votes */} +
+

+ + Final Jury Votes (Post-Debate) +

+
+ {debate.final_votes.map((vote, idx) => { + const initialVote = debate.initial_votes.find( + (v) => v.judge_model_id === vote.judge_model_id + ); + return ( + + ); + })} +
+
+ + {/* Vote Details */} + {score.vote_details && score.vote_details.length > 0 && ( +
+

+ + Vote Shift Analysis +

+
+ + + + + + + + + + + + + {score.vote_details.map((vd, idx) => ( + + + + + + + + + ))} + +
JudgeBefore→AfterΔ ConfSelf?
+ {MODELS.find((m) => m.id === vd.judge_model_id)?.display_name || + vd.judge_model_id} + + {vd.initial_stance} ({vd.initial_confidence}) + + {vd.stance_changed ? ( + + ) : ( + β€” + )} + + {vd.final_stance} ({vd.final_confidence}) + 0 + ? "text-green-400" + : vd.confidence_delta < 0 + ? "text-red-400" + : "text-muted-foreground" + }`} + > + {vd.confidence_delta > 0 ? "+" : ""} + {vd.confidence_delta} + + {vd.is_self_judging ? "βš”οΈ" : "β€”"} +
+
+
+ )} +
+ ); +} diff --git a/web/src/components/leaderboard/LeaderboardTable.tsx b/web/src/components/leaderboard/LeaderboardTable.tsx new file mode 100644 index 0000000..beb7411 --- /dev/null +++ b/web/src/components/leaderboard/LeaderboardTable.tsx @@ -0,0 +1,219 @@ +"use client"; + +import { LeaderboardEntry } from "@/lib/types"; +import { PROVIDER_COLORS, getModelById, PERSONA_MAP } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; +import { + Table, + TableBody, + TableCell, + TableHead, + TableHeader, + TableRow, +} from "@/components/ui/table"; +import { + Trophy, + TrendingUp, + TrendingDown, + Minus, + Brain, + Zap, + Shield, +} from "lucide-react"; + +interface Props { + leaderboard: LeaderboardEntry[]; + modelStats?: Record; +} + +const rankIcons = [ + , + , + , +]; + +function getProviderIcon(provider: string) { + switch (provider) { + case "Anthropic": + return ; + case "Google": + return ; + case "xAI": + return ; + case "OpenAI": + return ; + default: + return null; + } +} + +export function LeaderboardTable({ leaderboard, modelStats }: Props) { + const maxElo = Math.max(...leaderboard.map((e) => e.elo)); + const minElo = Math.min(...leaderboard.map((e) => e.elo)); + const eloRange = maxElo - minElo || 1; + + return ( +
+ + + + # + Model + Provider + + + Debate ELO + + + Arena Score + Win Rate + Persuasion + Judge Persona + + + + {leaderboard.map((entry, idx) => { + const model = getModelById(entry.model_id); + const providerColor = PROVIDER_COLORS[entry.provider] || "#888"; + const stats = modelStats?.[entry.model_id]; + const eloBarWidth = + ((entry.elo - minElo) / eloRange) * 100; + const persona = PERSONA_MAP[entry.model_id] || ""; + + return ( + window.location.href = `/model/${entry.model_id}`} + > + {/* Rank */} + + {idx < 3 ? ( + rankIcons[idx] + ) : ( + {entry.rank} + )} + + + {/* Model Name */} + +
+
+
+
+ {entry.display_name} +
+ {model?.config && Object.keys(model.config).length > 0 && ( + + Extended Thinking + + )} +
+
+ + + {/* Provider */} + + + {getProviderIcon(entry.provider)} + {entry.provider} + + + + {/* ELO */} + +
+ + {Math.round(entry.elo)} + +
+
+
+
+ + + {/* Arena Score */} + + + {entry.arena_score} + + + + {/* Win Rate */} + + {stats ? ( +
+ {stats.win_rate >= 60 ? ( + + ) : stats.win_rate <= 40 ? ( + + ) : ( + + )} + = 60 + ? "text-green-400" + : stats.win_rate <= 40 + ? "text-red-400" + : "text-muted-foreground" + }`} + > + {stats.win_rate}% + +
+ ) : ( + β€” + )} + + + {/* Persuasion */} + + {stats ? ( + 0 + ? "text-green-400" + : stats.avg_persuasion < 0 + ? "text-red-400" + : "text-muted-foreground" + }`} + > + {stats.avg_persuasion > 0 ? "+" : ""} + {stats.avg_persuasion} + + ) : ( + β€” + )} + + + {/* Judge Persona */} + + + {persona} + + + + ); + })} + +
+
+ ); +} diff --git a/web/src/components/leaderboard/ModelDetail.tsx b/web/src/components/leaderboard/ModelDetail.tsx new file mode 100644 index 0000000..4d53f02 --- /dev/null +++ b/web/src/components/leaderboard/ModelDetail.tsx @@ -0,0 +1,411 @@ +"use client"; + +import { Model, DebateSummary, JudgeStats } from "@/lib/types"; +import { PROVIDER_COLORS, PERSONA_MAP, PROVIDER_ICONS } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; +import { Card } from "@/components/ui/card"; +import { Separator } from "@/components/ui/separator"; +import { + Trophy, + Brain, + Swords, + TrendingUp, + TrendingDown, + Minus, + Layers, + Zap, + ArrowRight, + Shield, +} from "lucide-react"; +import Link from "next/link"; +import Image from "next/image"; + +interface Props { + model: Model; + stats: { + wins: number; + losses: number; + ties: number; + total_debates: number; + win_rate: number; + avg_persuasion: number; + }; + debates: DebateSummary[]; + judgeProfile: JudgeStats | null; + h2h: Record< + string, + { wins: number; losses: number; ties: number; opponent_name: string } + >; + elo: number; + rank: number; +} + +export function ModelDetail({ + model, + stats, + debates, + judgeProfile, + h2h, + elo, + rank, +}: Props) { + const color = PROVIDER_COLORS[model.provider] || "#888"; + const persona = PERSONA_MAP[model.id] || "Unknown"; + const iconPath = PROVIDER_ICONS[model.provider]; + + return ( +
+ {/* Header */} +
+ {iconPath && ( +
+ {model.provider} +
+ )} +
+
+

{model.display_name}

+ {rank > 0 && ( + + #{rank} + + )} +
+
+ +
+ {model.provider} + + {Object.keys(model.config).length > 0 && ( + + Extended Thinking + + )} + + Judge persona: {persona} + +
+

+ OpenRouter: {model.openrouter_id} +

+
+
+ + {/* Stats Cards */} +
+ + +

{Math.round(elo)}

+

Debate ELO

+ + + +

{stats.win_rate}%

+

+ Win Rate ({stats.wins}W-{stats.losses}L-{stats.ties}T) +

+ + + +

0 + ? "text-green-400" + : stats.avg_persuasion < 0 + ? "text-red-400" + : "" + }`} + > + {stats.avg_persuasion > 0 ? "+" : ""} + {stats.avg_persuasion} +

+

Avg Persuasion

+ + + +

{model.arena_score}

+

Arena Score

+ +
+ + {/* Model Info */} + +

+ + Model Specs +

+
+
+

Context Window

+

+ {(model.context_window / 1_000_000).toFixed(1)}M tokens +

+
+
+

Input Price

+

+ ${model.pricing.input}/M +

+
+
+

Output Price

+

+ ${model.pricing.output}/M +

+
+
+

Config

+

+ {Object.keys(model.config).length > 0 + ? JSON.stringify(model.config) + : "Default"} +

+
+
+ + + {/* Head-to-Head Record */} +
+

+ + Head-to-Head Record +

+
+ {Object.entries(h2h) + .sort( + ([, a], [, b]) => + b.wins - b.losses - (a.wins - a.losses) + ) + .map(([opponentId, record]) => { + const net = record.wins - record.losses; + return ( + + +
+
+ + vs {record.opponent_name} + +
+
+ 0 + ? "text-green-400" + : net < 0 + ? "text-red-400" + : "text-muted-foreground" + }`} + > + {record.wins}W-{record.losses}L + {record.ties > 0 ? `-${record.ties}T` : ""} + + {net > 0 ? ( + + ) : net < 0 ? ( + + ) : ( + + )} +
+
+ + + ); + })} +
+
+ + {/* Judge Profile */} + {judgeProfile && ( +
+

+ + As a Judge ({persona}) +

+ +
+
+

Total Judgments

+

+ {judgeProfile.total_judgments} +

+
+
+

Stance Flip Rate

+

+ {judgeProfile.stance_flip_rate}% +

+
+
+

Avg Confidence Ξ”

+

0 + ? "text-green-400" + : judgeProfile.avg_confidence_delta < 0 + ? "text-red-400" + : "" + }`} + > + {judgeProfile.avg_confidence_delta > 0 ? "+" : ""} + {judgeProfile.avg_confidence_delta} +

+
+
+

FOR Bias

+

+ {judgeProfile.for_vote_pct}% +

+
+
+ + + + {/* Voting breakdown bar */} +
+

Voting Distribution

+
+
+
+
+
+
+ FOR {judgeProfile.for_vote_pct}% + Undecided {judgeProfile.undecided_vote_pct}% + AGAINST {judgeProfile.against_vote_pct}% +
+
+ + {/* Self-judging */} + {judgeProfile.self_judging.total > 0 && ( + <> + +
+

Self-Judging Bias

+
+

+ When judging debates it was also participating in:{" "} + + voted for itself {judgeProfile.self_judging.favored_self}/ + {judgeProfile.self_judging.total} times + +

+ 60 + ? "destructive" + : "secondary" + } + > + {judgeProfile.self_judging.self_bias_rate}% self-bias + {judgeProfile.self_judging.self_bias_rate > 60 ? " ⚠️" : ""} + +
+
+ + )} + +
+ )} + + {/* Debate History */} +
+

+ + Debate History ({debates.length}) +

+
+ {debates.map((d) => { + const isFor = d.model_for.id === model.id; + const won = d.score.winner_model_id === model.id; + const tied = d.score.winner_side === "TIE"; + const opponentName = isFor + ? d.model_against.display_name + : d.model_for.display_name; + + return ( + + +
+
+ {won ? ( + + ) : tied ? ( + + ) : ( + + )} + + {isFor ? "FOR" : "AGAINST"} + + + vs {opponentName} + +
+
+ + {won ? "WON" : tied ? "TIE" : "LOST"} + + +
+
+

+ {d.motion} +

+ + + ); + })} +
+
+
+ ); +} diff --git a/web/src/components/leaderboard/RecentDebates.tsx b/web/src/components/leaderboard/RecentDebates.tsx new file mode 100644 index 0000000..d65ba89 --- /dev/null +++ b/web/src/components/leaderboard/RecentDebates.tsx @@ -0,0 +1,111 @@ +"use client"; + +import { DebateSummary } from "@/lib/types"; +import { MODELS, PROVIDER_COLORS } from "@/lib/models"; +import { Badge } from "@/components/ui/badge"; +import { ScrollArea } from "@/components/ui/scroll-area"; +import { Trophy, Swords, Clock } from "lucide-react"; +import Link from "next/link"; + +interface Props { + debates: DebateSummary[]; +} + +export function RecentDebates({ debates }: Props) { + if (debates.length === 0) { + return ( +
+ +

Benchmark debates will appear here as they complete.

+
+ ); + } + + return ( + +
+ {debates.slice().reverse().map((debate) => { + const modelFor = MODELS.find((m) => m.id === debate.model_for.id); + const modelAgainst = MODELS.find( + (m) => m.id === debate.model_against.id + ); + const winnerSide = debate.score.winner_side; + + return ( + + {/* Header */} +
+ + {debate.score.initial.for}β†’{debate.score.final.for} FOR + + + {debate.score.initial.against}β†’{debate.score.final.against} AGT + + {debate.metadata?.elapsed_seconds && ( + + + {Math.round(debate.metadata.elapsed_seconds / 60)}m + + )} +
+ + {/* Matchup */} +
+
+
+ + {debate.model_for.display_name} + + {winnerSide === "FOR" && ( + + )} +
+ + vs + +
+ {winnerSide === "AGAINST" && ( + + )} + + {debate.model_against.display_name} + +
+
+
+ + {/* Topic snippet */} +

+ {debate.motion} +

+ + ); + })} +
+ + ); +} diff --git a/web/src/components/ui/badge.tsx b/web/src/components/ui/badge.tsx new file mode 100644 index 0000000..b20959d --- /dev/null +++ b/web/src/components/ui/badge.tsx @@ -0,0 +1,52 @@ +import { mergeProps } from "@base-ui/react/merge-props" +import { useRender } from "@base-ui/react/use-render" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const badgeVariants = cva( + "group/badge inline-flex h-5 w-fit shrink-0 items-center justify-center gap-1 overflow-hidden rounded-4xl border border-transparent px-2 py-0.5 text-xs font-medium whitespace-nowrap transition-all focus-visible:border-ring focus-visible:ring-[3px] focus-visible:ring-ring/50 has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 aria-invalid:border-destructive aria-invalid:ring-destructive/20 dark:aria-invalid:ring-destructive/40 [&>svg]:pointer-events-none [&>svg]:size-3!", + { + variants: { + variant: { + default: "bg-primary text-primary-foreground [a]:hover:bg-primary/80", + secondary: + "bg-secondary text-secondary-foreground [a]:hover:bg-secondary/80", + destructive: + "bg-destructive/10 text-destructive focus-visible:ring-destructive/20 dark:bg-destructive/20 dark:focus-visible:ring-destructive/40 [a]:hover:bg-destructive/20", + outline: + "border-border text-foreground [a]:hover:bg-muted [a]:hover:text-muted-foreground", + ghost: + "hover:bg-muted hover:text-muted-foreground dark:hover:bg-muted/50", + link: "text-primary underline-offset-4 hover:underline", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +function Badge({ + className, + variant = "default", + render, + ...props +}: useRender.ComponentProps<"span"> & VariantProps) { + return useRender({ + defaultTagName: "span", + props: mergeProps<"span">( + { + className: cn(badgeVariants({ variant }), className), + }, + props + ), + render, + state: { + slot: "badge", + variant, + }, + }) +} + +export { Badge, badgeVariants } diff --git a/web/src/components/ui/button.tsx b/web/src/components/ui/button.tsx new file mode 100644 index 0000000..09df753 --- /dev/null +++ b/web/src/components/ui/button.tsx @@ -0,0 +1,58 @@ +import { Button as ButtonPrimitive } from "@base-ui/react/button" +import { cva, type VariantProps } from "class-variance-authority" + +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "group/button inline-flex shrink-0 items-center justify-center rounded-lg border border-transparent bg-clip-padding text-sm font-medium whitespace-nowrap transition-all outline-none select-none focus-visible:border-ring focus-visible:ring-3 focus-visible:ring-ring/50 active:not-aria-[haspopup]:translate-y-px disabled:pointer-events-none disabled:opacity-50 aria-invalid:border-destructive aria-invalid:ring-3 aria-invalid:ring-destructive/20 dark:aria-invalid:border-destructive/50 dark:aria-invalid:ring-destructive/40 [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-4", + { + variants: { + variant: { + default: "bg-primary text-primary-foreground [a]:hover:bg-primary/80", + outline: + "border-border bg-background hover:bg-muted hover:text-foreground aria-expanded:bg-muted aria-expanded:text-foreground dark:border-input dark:bg-input/30 dark:hover:bg-input/50", + secondary: + "bg-secondary text-secondary-foreground hover:bg-secondary/80 aria-expanded:bg-secondary aria-expanded:text-secondary-foreground", + ghost: + "hover:bg-muted hover:text-foreground aria-expanded:bg-muted aria-expanded:text-foreground dark:hover:bg-muted/50", + destructive: + "bg-destructive/10 text-destructive hover:bg-destructive/20 focus-visible:border-destructive/40 focus-visible:ring-destructive/20 dark:bg-destructive/20 dark:hover:bg-destructive/30 dark:focus-visible:ring-destructive/40", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: + "h-8 gap-1.5 px-2.5 has-data-[icon=inline-end]:pr-2 has-data-[icon=inline-start]:pl-2", + xs: "h-6 gap-1 rounded-[min(var(--radius-md),10px)] px-2 text-xs in-data-[slot=button-group]:rounded-lg has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 [&_svg:not([class*='size-'])]:size-3", + sm: "h-7 gap-1 rounded-[min(var(--radius-md),12px)] px-2.5 text-[0.8rem] in-data-[slot=button-group]:rounded-lg has-data-[icon=inline-end]:pr-1.5 has-data-[icon=inline-start]:pl-1.5 [&_svg:not([class*='size-'])]:size-3.5", + lg: "h-9 gap-1.5 px-2.5 has-data-[icon=inline-end]:pr-2 has-data-[icon=inline-start]:pl-2", + icon: "size-8", + "icon-xs": + "size-6 rounded-[min(var(--radius-md),10px)] in-data-[slot=button-group]:rounded-lg [&_svg:not([class*='size-'])]:size-3", + "icon-sm": + "size-7 rounded-[min(var(--radius-md),12px)] in-data-[slot=button-group]:rounded-lg", + "icon-lg": "size-9", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +function Button({ + className, + variant = "default", + size = "default", + ...props +}: ButtonPrimitive.Props & VariantProps) { + return ( + + ) +} + +export { Button, buttonVariants } diff --git a/web/src/components/ui/card.tsx b/web/src/components/ui/card.tsx new file mode 100644 index 0000000..40cac5f --- /dev/null +++ b/web/src/components/ui/card.tsx @@ -0,0 +1,103 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +function Card({ + className, + size = "default", + ...props +}: React.ComponentProps<"div"> & { size?: "default" | "sm" }) { + return ( +
img:first-child]:pt-0 data-[size=sm]:gap-3 data-[size=sm]:py-3 data-[size=sm]:has-data-[slot=card-footer]:pb-0 *:[img:first-child]:rounded-t-xl *:[img:last-child]:rounded-b-xl", + className + )} + {...props} + /> + ) +} + +function CardHeader({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardTitle({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardDescription({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardAction({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardContent({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function CardFooter({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +export { + Card, + CardHeader, + CardFooter, + CardTitle, + CardAction, + CardDescription, + CardContent, +} diff --git a/web/src/components/ui/dialog.tsx b/web/src/components/ui/dialog.tsx new file mode 100644 index 0000000..014f5aa --- /dev/null +++ b/web/src/components/ui/dialog.tsx @@ -0,0 +1,160 @@ +"use client" + +import * as React from "react" +import { Dialog as DialogPrimitive } from "@base-ui/react/dialog" + +import { cn } from "@/lib/utils" +import { Button } from "@/components/ui/button" +import { XIcon } from "lucide-react" + +function Dialog({ ...props }: DialogPrimitive.Root.Props) { + return +} + +function DialogTrigger({ ...props }: DialogPrimitive.Trigger.Props) { + return +} + +function DialogPortal({ ...props }: DialogPrimitive.Portal.Props) { + return +} + +function DialogClose({ ...props }: DialogPrimitive.Close.Props) { + return +} + +function DialogOverlay({ + className, + ...props +}: DialogPrimitive.Backdrop.Props) { + return ( + + ) +} + +function DialogContent({ + className, + children, + showCloseButton = true, + ...props +}: DialogPrimitive.Popup.Props & { + showCloseButton?: boolean +}) { + return ( + + + + {children} + {showCloseButton && ( + + } + > + + Close + + )} + + + ) +} + +function DialogHeader({ className, ...props }: React.ComponentProps<"div">) { + return ( +
+ ) +} + +function DialogFooter({ + className, + showCloseButton = false, + children, + ...props +}: React.ComponentProps<"div"> & { + showCloseButton?: boolean +}) { + return ( +
+ {children} + {showCloseButton && ( + }> + Close + + )} +
+ ) +} + +function DialogTitle({ className, ...props }: DialogPrimitive.Title.Props) { + return ( + + ) +} + +function DialogDescription({ + className, + ...props +}: DialogPrimitive.Description.Props) { + return ( + + ) +} + +export { + Dialog, + DialogClose, + DialogContent, + DialogDescription, + DialogFooter, + DialogHeader, + DialogOverlay, + DialogPortal, + DialogTitle, + DialogTrigger, +} diff --git a/web/src/components/ui/input.tsx b/web/src/components/ui/input.tsx new file mode 100644 index 0000000..7d21bab --- /dev/null +++ b/web/src/components/ui/input.tsx @@ -0,0 +1,20 @@ +import * as React from "react" +import { Input as InputPrimitive } from "@base-ui/react/input" + +import { cn } from "@/lib/utils" + +function Input({ className, type, ...props }: React.ComponentProps<"input">) { + return ( + + ) +} + +export { Input } diff --git a/web/src/components/ui/label.tsx b/web/src/components/ui/label.tsx new file mode 100644 index 0000000..74da65c --- /dev/null +++ b/web/src/components/ui/label.tsx @@ -0,0 +1,20 @@ +"use client" + +import * as React from "react" + +import { cn } from "@/lib/utils" + +function Label({ className, ...props }: React.ComponentProps<"label">) { + return ( +