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Benchmark Evaluation — Baseline Replication Study

Version: v0.2.0 Date: 2026-03-06 Evaluator: Alaya Benchmark Harness (bench/)

1. Objective

Establish controlled baselines for three memory benchmarks — LoCoMo, LongMemEval, and MemoryAgentBench (MAB) — to characterize the retrieval landscape that motivates Alaya's lifecycle operations. This study does not evaluate Alaya's retrieval pipeline; it replicates the two extreme baselines (full-context injection and naive vector RAG) under identical infrastructure to enable fair future comparison.

2. Benchmarks

2.1 LoCoMo (Maharana et al., 2024)

Long-conversation memory benchmark with 1,540 questions across four categories, drawn from multi-session dialogues averaging 16-26K tokens.

Category Type Questions Description
1 Single-hop factual 282 Direct factual recall from a single conversation turn
2 Temporal reasoning 321 Requires reasoning about when events occurred relative to each other
3 Multi-hop inference 96 Requires combining information from multiple conversation turns
4 Open-domain 841 Broad knowledge questions that reward coverage over precision

Category 5 (adversarial) is excluded per the standard evaluation protocol.

2.2 LongMemEval (Wu et al., 2025)

Extended interaction memory benchmark with 500 questions across six types, drawn from interaction histories averaging ~115K tokens.

Type Questions Description
single-session-user 70 Facts stated by the user in a single session
single-session-assistant 56 Facts stated by the assistant in a single session
single-session-preference 30 User preferences expressed in a single session
multi-session 133 Information spanning multiple sessions
temporal-reasoning 133 Requires reasoning about temporal ordering
knowledge-update 78 Facts that were updated or corrected over time

2.3 MemoryAgentBench — MAB (Hu et al., ICLR 2026)

Lifecycle competency benchmark with 734 questions across four core competencies, evaluating capabilities beyond factual retrieval. Data from HuggingFace ai-hyz/MemoryAgentBench.

Competency Abbrev. Questions Source Dataset Description
Accurate Retrieval AR 500 EventQA Factual recall from long documents
Test-Time Learning TTL 100 ICL-NLU Learning task patterns from in-context examples
Long-Range Understanding LRU 34 DetectiveQA Cross-document reasoning and inference
Conflict Resolution CR 100 FactConsolidation Resolving contradictions between earlier and later information

MAB is the first benchmark to explicitly test forgetting quality (CR) and in-context learning (TTL) — capabilities invisible to LoCoMo and LongMemEval.

3. Methodology

3.1 Infrastructure

Component Configuration
Generator Gemini-2.0-Flash-001 via OpenRouter, temperature 0.0, max_tokens 512
Judge GPT-4o-mini via OpenRouter, temperature 0.0, max_tokens 10
Scoring Binary yes/no LLM-as-Judge (Maharana et al., 2024 protocol)
RAG backend ChromaDB with all-MiniLM-L6-v2 embeddings, top-10 cosine similarity
Retry policy 5 attempts with exponential backoff (1/2/4/8/16s)

3.2 Systems Under Test

Full-context injection. The entire conversation history is stuffed into the generator prompt. No retrieval, no filtering. This is the upper bound on available information and the lower bound on signal-to-noise ratio for long conversations.

Naive RAG. The question is embedded with all-MiniLM-L6-v2 and the top-10 most similar chunks are retrieved from ChromaDB via cosine similarity. No reranking, no graph traversal, no fusion. This represents the simplest possible retrieval pipeline.

3.3 Scoring Protocol

The LLM judge receives the question, gold answer, and system prediction. It outputs "yes" or "no" (score 1.0 or 0.0). A score >= 0.5 counts as correct. This follows the LongMemEval-style binary scoring protocol standardized in Maharana et al. (2024).

3.4 Statistical Methods

  • Confidence intervals: Wilson score interval (95% CI) for binomial proportions, appropriate for accuracy metrics near boundaries.
  • Significance testing: McNemar's test for paired nominal data, applied to the 2x2 contingency table of per-question agreement/disagreement between systems.
  • Effect size: Difference in proportions with confidence interval.

4. Results

4.1 Overall Accuracy

System LoCoMo (n=1,540) 95% CI LongMemEval (n=500) 95% CI
Full-context 61.36% (945/1540) [58.9, 63.8] 46.20% (231/500) [41.9, 50.6]
Naive RAG 25.97% (400/1540) [23.9, 28.2] 54.60% (273/500) [50.2, 58.9]
Delta (FC - RAG) +35.39 pp -8.40 pp 
xychart-beta
    title "Overall Accuracy: Full-Context vs Naive RAG"
    x-axis ["LoCoMo (1540 Q)", "LongMemEval (500 Q)"]
    y-axis "Accuracy (%)" 0 --> 80
    bar [61.4, 46.2]
    bar [26.0, 54.6]
Loading

4.2 LoCoMo Per-Category Breakdown

Cat Type N FC Correct FC Acc (%) RAG Correct RAG Acc (%) Delta (pp)
1 Single-hop 282 153 54.26 38 13.48 +40.78
2 Temporal 321 62 19.31 21 6.54 +12.77
3 Multi-hop 96 45 46.88 21 21.88 +25.00
4 Open-domain 841 685 81.45 320 38.05 +43.40
All 1540 945 61.36 400 25.97 +35.39 
xychart-beta
    title "LoCoMo Accuracy by Question Category"
    x-axis ["1: Single-hop", "2: Temporal", "3: Multi-hop", "4: Open-domain"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [54.3, 19.3, 46.9, 81.5]
    bar [13.5, 6.5, 21.9, 38.0]
Loading

Observations:

  1. Temporal reasoning is catastrophic for both systems. Category 2 accuracy is 19.3% (FC) and 6.5% (RAG) — well below random guessing on binary questions. Temporal reasoning requires understanding when events occurred relative to each other, which neither context stuffing nor cosine similarity provides.

  2. Open-domain questions mask overall weakness. Category 4 (54.6% of all questions) reaches 81.5% FC accuracy, inflating the overall score. Excluding category 4, FC accuracy drops to 37.2%.

  3. Full-context advantage is strongest on coverage questions. Categories requiring broad coverage (4: open-domain) show the largest absolute gap (+43.4 pp). Categories requiring precise relational reasoning (2: temporal, 3: multi-hop) show smaller gaps, suggesting that full-context's information advantage is partially offset by attention dilution.

4.3 LongMemEval Per-Type Breakdown

Type N FC Correct FC Acc (%) RAG Correct RAG Acc (%) Delta (pp)
single-session-user 70 42 60.00 62 88.57 -28.57
single-session-assistant 56 54 96.43 53 94.64 +1.79
single-session-preference 30 3 10.00 10 33.33 -23.33
multi-session 133 41 30.83 59 44.36 -13.53
temporal-reasoning 133 39 29.32 39 29.32 0.00
knowledge-update 78 52 66.67 50 64.10 +2.57
All 500 231 46.20 273 54.60 -8.40 
xychart-beta
    title "LongMemEval Accuracy by Question Type"
    x-axis ["SS-User", "SS-Asst", "SS-Pref", "Multi-Sess", "Temporal", "K-Update"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [60.0, 96.4, 10.0, 30.8, 29.3, 66.7]
    bar [88.6, 94.6, 33.3, 44.4, 29.3, 64.1]
Loading

Observations:

  1. Single-session-user shows the largest RAG advantage (+28.6 pp). Facts stated by the user in a single session are well-served by embedding similarity — the question and the answer are semantically close and localized.

  2. Temporal reasoning is identical (29.3% for both). Neither approach addresses time-dependent reasoning. This confirms the finding from LoCoMo: temporal reasoning requires structured temporal indexing, not content similarity.

  3. Single-session-preference is poor for both (10.0% FC, 33.3% RAG). Preferences are often implied rather than stated explicitly, making them hard for both approaches. This motivates Alaya's emergent preference crystallization over extraction.

  4. Single-session-assistant is high for both (96.4% FC, 94.6% RAG). Facts stated by the assistant are typically clear, structured responses that both approaches handle well.

4.4 MemoryAgentBench (MAB) Per-Competency Results

Competency N FC Correct FC Acc (%) RAG Correct RAG Acc (%) Delta (pp)
AR (Accurate Retrieval) 500 470 94.00 452 90.40 +3.60
TTL (Test-Time Learning) 100 86 86.00 44 44.00 +42.00
LRU (Long-Range Underst.) 34 28 82.35 23 67.65 +14.71
CR (Conflict Resolution) 100 50 50.00 41 41.00 +9.00
All 734 634 86.38 560 76.29 +10.08 
xychart-beta
    title "MAB Accuracy by Competency"
    x-axis ["AR (Retrieval)", "TTL (Learning)", "LRU (Understanding)", "CR (Forgetting)"]
    y-axis "Accuracy (%)" 0 --> 100
    bar [94.0, 86.0, 82.4, 50.0]
    bar [90.4, 44.0, 67.6, 41.0]
Loading

Observations:

  1. Accurate retrieval is nearly solved. Both baselines exceed 90% on AR, confirming that factual lookup over moderate-length contexts is no longer a discriminating test for memory systems.

  2. Test-time learning shows the largest gap across all benchmarks (+42.0 pp). TTL requires the model to learn task patterns from in-context examples. RAG's top-k retrieval selects semantically similar chunks but destroys the sequential structure that makes in-context learning possible. This echoes the LoCoMo temporal reasoning finding: capabilities that depend on structural rather than semantic properties of conversation history are systematically destroyed by naive retrieval.

  3. Conflict resolution is near chance for both (50.0% FC, 41.0% RAG). Neither full-context injection nor retrieval provides a mechanism for resolving contradictions between earlier and later information. This directly validates the gap identified in the survey: only 8% of systems implement contradiction resolution. CR cannot emerge from retrieval alone — it requires explicit lifecycle management that identifies and resolves conflicting facts.

  4. Long-range understanding benefits from full context but is partially recoverable through retrieval. The moderate gap (82.4% vs 67.6%) indicates that cross-document reasoning benefits from seeing everything, but relevant passages that share vocabulary with the question can still be found by cosine similarity.

5. Statistical Analysis

5.1 McNemar's Test — LoCoMo

Contingency table for 1,529 paired questions (11 duplicate question texts removed):

RAG Correct RAG Wrong
FC Correct 353 583
FC Wrong 44 549
  • McNemar's chi-squared: (583 - 44)^2 / (583 + 44) = 463.35
  • Degrees of freedom: 1
  • p < 10^-5 (highly significant)
  • Interpretation: Full-context is significantly better than naive RAG on LoCoMo. The asymmetry is extreme: 583 questions are correct for FC but wrong for RAG, while only 44 show the reverse.
pie title "LoCoMo Question-Level Agreement (n=1,529)"
    "Both correct" : 353
    "FC only correct" : 583
    "RAG only correct" : 44
    "Both wrong" : 549
Loading

5.2 McNemar's Test — LongMemEval

Contingency table for 500 paired questions (matched by question_id):

RAG Correct RAG Wrong
FC Correct 180 51
FC Wrong 93 176
  • McNemar's chi-squared: (93 - 51)^2 / (93 + 51) = 12.25
  • Degrees of freedom: 1
  • p = 4.7 x 10^-4 (highly significant)
  • Interpretation: Naive RAG is significantly better than full-context on LongMemEval. 93 questions are correct for RAG but wrong for FC, while only 51 show the reverse.
pie title "LongMemEval Question-Level Agreement (n=500)"
    "Both correct" : 180
    "FC only correct" : 51
    "RAG only correct" : 93
    "Both wrong" : 176
Loading

5.3 Wilson Score Confidence Intervals

For binomial proportion p with n trials, the Wilson score interval is:

CI = (p + z^2/2n +/- z * sqrt(p(1-p)/n + z^2/4n^2)) / (1 + z^2/n)

where z = 1.96 for 95% CI.

System Benchmark p n 95% CI Lower 95% CI Upper CI Width
FC LoCoMo 0.6136 1540 0.589 0.638 4.9 pp
RAG LoCoMo 0.2597 1540 0.239 0.282 4.3 pp
FC LongMemEval 0.4620 500 0.419 0.506 8.7 pp
RAG LongMemEval 0.5460 500 0.502 0.589 8.7 pp

The wider CIs for LongMemEval reflect the smaller sample size (500 vs 1,540). Despite this, the crossover is significant by McNemar's test.

5.4 Effect Sizes

Comparison Delta 95% CI of Delta Interpretation
FC vs RAG on LoCoMo +35.39 pp [+32.1, +38.7] Very large effect favoring FC
RAG vs FC on LongMemEval +8.40 pp [+2.3, +14.5] Moderate effect favoring RAG

The LoCoMo effect is massive (>35 pp) and narrow CI. The LongMemEval effect is moderate (~8 pp) with wider CI, but statistically significant.

6. The Retrieval Crossover

xychart-beta
    title "The Retrieval Crossover"
    x-axis ["LoCoMo (16-26K tokens)", "LongMemEval (~115K tokens)"]
    y-axis "Accuracy (%)" 0 --> 80
    bar [61.4, 46.2]
    bar [26.0, 54.6]
Loading

The crossover occurs because the two benchmarks occupy different regimes:

Property LoCoMo LongMemEval
Avg conversation length 16-26K tokens ~115K tokens
Within context window? Yes (for most models) No (exceeds effective attention)
Full-context advantage Preserves all relational/temporal cues Noise overwhelms signal
RAG advantage Discards relational context Filters noise, improves S/N ratio
Dominant question type Open-domain (54.6%) Multi-session + temporal (53.2%)

Note on model gap: Our full-context LoCoMo accuracy (61.4%) is lower than the GPT-4o baseline (~72.9%) reported in published results. This reflects the model quality gap between Gemini-2.0-Flash and GPT-4o, not a methodological difference. The crossover pattern is architecture-dependent, not model-dependent: within any fixed model, the relative advantage of full-context vs RAG reverses as history length exceeds the model's effective attention window.

7. Implications for Lifecycle Management

Neither baseline addresses what lifecycle management is designed for:

  1. Consolidation reduces episodic volume while preserving signal, potentially avoiding the noise that degrades full-context on long histories while retaining the relational cues that RAG discards.

  2. Forgetting further improves signal-to-noise by suppressing memories that are deeply stored but no longer relevant.

  3. Graph-based retrieval discovers indirect associations that cosine similarity misses, addressing the multi-hop and temporal reasoning failures observed in both baselines.

  4. Emergent ontology provides category-level retrieval that can boost recall for categorically related memories without requiring exact semantic match.

  5. Contradiction resolution requires explicit lifecycle management. The MAB conflict resolution results (50% FC, 41% RAG) prove that neither baseline can handle contradictions. This is Alaya's strongest motivation: the forget() and consolidate() operations are designed precisely to resolve conflicting memories by suppressing outdated information and promoting consistent knowledge.

  6. In-context learning requires structural preservation. The TTL results show that any retrieval approach that fragments sequential context (as RAG does) will fail on tasks requiring the model to learn from examples. Alaya's episodic store preserves temporal ordering, and graph-based retrieval can follow association chains rather than relying on semantic similarity alone.

The hypothesis is that a system with lifecycle management should exceed both baselines simultaneously on all three benchmarks — achieving full-context-level relational reasoning (via graph links and consolidation) with RAG-level noise filtering (via forgetting and ranked retrieval), while also handling contradiction resolution and preserving structural context. Validating this hypothesis is the immediate next step for Alaya's evaluation.

8. Reproducibility

All code, data, and results are in bench/:

bench/
  run.py              # CLI entrypoint
  runners/
    locomo.py         # LoCoMo benchmark runner
    longmemeval.py    # LongMemEval benchmark runner
    mab.py            # MemoryAgentBench runner (HuggingFace dataset)
  adapters/
    full_context.py   # Full-context injection adapter
    naive_rag.py      # ChromaDB + MiniLM adapter
  judge/
    llm_judge.py      # LLM-as-Judge scoring (GPT-4o-mini)
  datasets/           # LoCoMo + LongMemEval data
  results/            # Raw JSON results with per-question scores

To reproduce:

# Install dependencies
pip install -r bench/requirements.txt

# Run LoCoMo full-context baseline
python bench/run.py --benchmark locomo --system full-context

# Run LongMemEval naive RAG baseline
python bench/run.py --benchmark longmemeval --system naive-rag

# Run MAB full-context baseline (all 4 competencies)
python bench/run.py --benchmark mab --system full-context

# Run MAB for specific competencies only
python bench/run.py --benchmark mab --system naive-rag -c AR,CR

Results are saved as timestamped JSON files in bench/results/ with per-question scores enabling post-hoc statistical analysis.

9. References

  • Maharana, A., Lee, D., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024). LoCoMo: Long-Context Conversation Memory Benchmark.
  • Wu, Y. et al. (2025). LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory.
  • Hu, Y., Zhang, Y., & Zhao, H. (2025). MemoryAgentBench: Benchmarking LLM-based Agent Memory Systems. ICLR 2026.
  • McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157.
  • Wilson, E.B. (1927). Probable inference, the law of succession, and statistical inference. JASA, 22(158), 209-212.
  • Cormack, G.V., Clarke, C.L.A., & Buettcher, S. (2009). Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods.