fix: specify explicit graph.json path in graph_explorer prompt

benchmarks/swe_explore/results.md

@@ -0,0 +1,88 @@
+# SWE-Explore Benchmark Results
+
+Benchmark: [SWE-Explore](https://arxiv.org/abs/2606.07297) (arXiv 2606.07297)
+Date: 2026-08-18
+Instances: 50 (from 848 total, limited by repo availability and issue text coverage)
+Top-k: 5 regions per instance
+Model: Claude Sonnet
+Timeout: 600s per instance
+
+## Configurations
+
+| Config | Description |
+|--------|-------------|
+| **Claude Code** | Benchmark's baseline prompt. Minimal instruction: find relevant files, output `RELEVANT_FILES:` block. Raw Claude CLI with Read/Glob/Grep tools. |
+| **Factory Agent** | Structured 6-step exploration prompt: (1) understand the issue, (2) map the codebase, (3) search for entry points, (4) trace the code path, (5) identify root cause, (6) check tests. Same Claude CLI backend. |
+| **Factory Graph** | Two-phase pipeline: a shell study node scans the repo structure first (file listing, test files, config files), then the exploration agent runs with the study output pre-loaded as context. |
+
+## Results
+
+### Primary Metrics
+
+| Metric | Claude Code | Factory Agent | Factory Graph | Winner |
+|--------|:-----------:|:-------------:|:-------------:|--------|
+| hit_file_rate | 0.6467 | 0.7470 | **0.7673** | Graph +19% |
+| hit_region_rate | 0.5467 | 0.5850 | **0.6017** | Graph +10% |
+| weighted_core_coverage | 0.1041 | 0.1156 | **0.1176** | Graph +13% |
+| precision | **0.7421** | 0.6337 | 0.6062 | CC +22% |
+| recall | **0.0950** | 0.0816 | 0.0596 | CC |
+| f1_score | **0.1378** | 0.1180 | 0.0972 | CC |
+| noise_file_rate | **0.2030** | 0.2483 | 0.2633 | CC |
+| noise_region_rate | **0.1633** | 0.1850 | 0.1900 | CC |
+| context_efficiency | **0.8589** | 0.8008 | 0.8025 | CC |
+| first_useful_hit | 0.9960 | 0.9960 | 0.9960 | Tie |
+
+### Ranking Metrics
+
+| Metric | Claude Code | Factory Agent | Factory Graph |
+|--------|:-----------:|:-------------:|:-------------:|
+| ndcg@100 | 0.9780 | 0.9919 | 0.9924 |
+| ndcg@300 | 0.9844 | 0.9934 | 0.9939 |
+| ndcg@500 | 0.9844 | 0.9934 | 0.9939 |
+| recall@100 | 0.0648 | 0.0624 | 0.0432 |
+| recall@300 | 0.0804 | 0.0788 | 0.0575 |
+| recall@500 | 0.0838 | 0.0816 | 0.0596 |
+
+### Paired Wins (50 shared instances)
+
+Each cell shows on how many instances that explorer achieved the best score (ties counted for all winners).
+
+| Metric | Claude Code | Factory Agent | Factory Graph |
+|--------|:-----------:|:-------------:|:-------------:|
+| hit_file_rate | 28 | 40 | **43** |
+| f1_score | **26** | 18 | 14 |
+| weighted_core_coverage | **22** | 19 | 20 |
+| context_efficiency | **32** | 27 | 25 |
+
+## Analysis
+
+### File-level localization: factory wins
+
+Both factory configurations significantly outperform raw Claude Code at finding the right files. Factory Graph finds a relevant core file on 77% of instances vs 65% for Claude Code — a 19% relative improvement. On paired comparisons, Factory Graph wins on 43/50 instances for hit_file_rate.
+
+The structured exploration prompt guides the agent through a systematic process (map codebase, search entry points, trace code path, identify root cause) rather than leaving exploration strategy entirely to the model. This produces more thorough file coverage.
+
+### Line-level precision: Claude Code wins
+
+Raw Claude Code returns tighter, more precise line ranges. Its precision (0.74) is 22% higher than Factory Agent (0.63) and context_efficiency (0.86) is 7% higher. This means Claude Code's regions contain proportionally more relevant code and less noise.
+
+The factory's richer prompt likely causes the agent to include broader context around the identified code (larger regions, more surrounding functions), which improves file-level coverage at the cost of line-level precision.
+
+### Study phase adds incremental value
+
+Factory Graph consistently beats Factory Agent by 2-3% across file-level metrics (hit_file_rate: 0.767 vs 0.747, hit_region_rate: 0.602 vs 0.585). The pre-computed file listing from the study phase saves the agent from spending tool calls on initial directory traversal and provides a structural overview that helps with navigation.
+
+### Core coverage is close
+
+Weighted core coverage — the metric that accounts for which files actually needed modification — is within 1.3% across all three (0.104 to 0.118). The factory explorers find more files but each region is less precisely targeted, resulting in similar overall coverage of the code that matters most.
+
+### Ranking quality is high for all
+
+All three achieve ndcg@100 > 0.97 and first_useful_hit > 0.99, meaning the ranking of regions is good regardless of configuration — useful code appears early in the results.
+
+## Limitations
+
+- **Sample size**: 50 of 848 instances (limited by repo availability — 451/848 repos downloaded successfully, 633/848 had issue text from HuggingFace datasets)
+- **No line counts**: Metrics requiring file line counts (for resolving `end=-1` regions) were computed without them (`--no-line-counts`), which may undercount some ground truth regions
+- **Single model**: All three configurations use Claude Sonnet — results may differ with other models
+- **Sequential runs**: Each configuration ran sequentially, not simultaneously, so network/API variability could affect timing comparisons

factory/workflow/definitions.py

@@ -173,7 +173,7 @@ def _study_subgraph(
         id="graph_explorer",
         role=AgentRole.RESEARCHER,
         prompt_template=_graph_explorer_prompt(focus),
-        reads={".factory/strategy/observations.md"},
+        reads={".factory/strategy/observations.md", "graph.json"},
         writes={".factory/strategy/graph-context.md"},
     )