| # Reproducibility
|
|
|
| This document records the current reproducibility path for the active root
|
| manuscript, `main.tex`. The repository is intentionally split into deterministic
|
| non-API experiments, cached external LongMemEval artifacts, and API reader runs.
|
|
|
| ## Environment
|
|
|
| Use Python 3.10 or newer.
|
|
|
| ```bash
|
| python -m pip install -r requirements.txt
|
| ```
|
|
|
| Optional dependencies are separated by task:
|
|
|
| ```bash
|
| python -m pip install -r requirements-api.txt
|
| python -m pip install -r requirements-milp.txt
|
| ```
|
|
|
| The exact-small MemAudit benchmark and unit tests use only the Python standard |
| library plus `pytest` for tests. LongMemEval retrieval regeneration uses local |
| ML dependencies and downloads the LongMemEval-S dataset and dense retriever |
| model. API reader runs use OpenRouter and require an API key. |
|
|
| ## LaTeX Build
|
|
|
| On this machine, `latexmk`, `pdflatex`, and `tectonic` were not available on
|
| PATH during the 2026-04-28 local check. The attempted local build is recorded in
|
| `latex_compile_attempt.txt`. A generated `latex_compile.log` also exists
|
| locally, but `*.log` is ignored by the repository.
|
|
|
| If a TeX distribution is installed locally, run one of:
|
|
|
| ```bash
|
| make paper
|
| make paper-pdflatex
|
| make paper-tectonic
|
| ```
|
|
|
| Because local compilation was unavailable here, `.github/workflows/latex.yml`
|
| builds `main.tex` with GitHub Actions on push and pull request.
|
|
|
| ## Unit Tests |
|
|
| ```bash |
| python -m pytest test_oraclemem.py |
| ``` |
|
|
| Current verification on 2026-05-01: both `python -m unittest test_oraclemem.py` |
| and `python -m pytest test_oraclemem.py` ran 17 tests and passed. |
|
|
| ## Quickcheck |
|
|
| Use this before any expensive API or GPU work: |
|
|
| ```bash |
| python -m unittest test_oraclemem.py |
| python run_oraclemem_mvp.py --n-seeds 3 --budgets 4 --distribution base --methods opt,oracle_gvt,density_only --out-dir oraclemem_runs/quickcheck |
| ``` |
|
|
| Expected outputs: |
|
|
| - `oraclemem_runs/quickcheck/raw_results.jsonl` |
| - `oraclemem_runs/quickcheck/summary.json` |
| - `oraclemem_runs/quickcheck/summary.md` |
|
|
| ## Exact-Small Benchmark
|
|
|
| Used by the exact-small budget-sweep figure in `main.tex`. |
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 500 \
|
| --budgets 0.01,0.02,0.05,0.10,0.20 \
|
| --distribution base \
|
| --methods opt,oracle_gvt,density_only,recency_raw,summary_only,fact_only,no_tombstone_gvt,no_tombstone_opt \
|
| --out oraclemem_runs/exact_500
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `oraclemem_runs/exact_500/raw_results.jsonl`
|
| - `oraclemem_runs/exact_500/summary.json`
|
| - `oraclemem_runs/exact_500/summary.md`
|
|
|
| The reported `ratio_to_opt` field is valid only for these exact-small runs where |
| the denominator is an exact certified optimum. |
|
|
| ## Stress Suite |
|
|
| Used by the validity-heavy stress figure in `main.tex`. The manuscript reports |
| the validity-heavy subset `base`, `update_chain`, and `temporal_interval` from |
| the larger stress artifact. |
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 500 \
|
| --budgets 0.02,0.05,0.10,0.20 \
|
| --distribution base,update_chain,temporal_interval,density_trap,scope_shift,summary_tradeoff,redundancy_heavy,abstention_hard \
|
| --methods opt,oracle_gvt,density_only,greedy,recency_raw,reservoir_raw,summary_only,fact_only,no_tombstone_gvt,no_tombstone_opt \
|
| --out oraclemem_runs/stress_exact_500
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `oraclemem_runs/stress_exact_500/raw_results.jsonl`
|
| - `oraclemem_runs/stress_exact_500/summary.json` |
| - `oraclemem_runs/stress_exact_500/summary.md` |
|
|
| ## Representative Non-Oracle Writers |
|
|
| Used by the text diagnostic on Estimated-GVT, A-MAC-like admission, and |
| Mem0-style extraction proxies. These methods use visible candidate features, |
| not hidden coverage labels. |
|
|
| ```bash |
| python run_oraclemem_mvp.py \ |
| --n-seeds 500 \ |
| --budgets 4,6 \ |
| --distribution base,update_chain,temporal_interval \ |
| --methods opt,oracle_gvt,estimated_gvt,amac_admission,mem0_extract,density_only,recency_raw,summary_only,fact_only,no_tombstone_opt \ |
| --out-dir oraclemem_runs/representative_writers_500 |
| ``` |
|
|
| Expected outputs: |
|
|
| - `oraclemem_runs/representative_writers_500/raw_results.jsonl` |
| - `oraclemem_runs/representative_writers_500/summary.json` |
| - `oraclemem_runs/representative_writers_500/summary.md` |
|
|
| ## No-API Proxy Writer Baselines
|
|
|
| Diagnostic only; not a main-paper result after the 9-page compression pass. This |
| local diagnostic addresses the real-system-comparison concern without calling |
| OpenRouter, OpenAI, embedding services, or API reader code. It runs deterministic proxies for |
| MemGPT-style tiering, Mem0-style extraction, A-Mem-style graph/evolving memory,
|
| and A-MAC-style admission under the same MemAudit candidate protocol and exact |
| OPT denominator.
|
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 50 \
|
| --distribution base,update_chain,scope_shift_v2,density_trap_v2,temporal_interval \
|
| --budgets 4,6 \
|
| --methods opt,oracle_gvt,memgpt_tiered,mem0_extract,amem_graph,amac_admission,generic_candidate_opt,no_tombstone_opt \
|
| --out-dir oraclemem_runs/proxy_writer_baselines_50 \
|
| --enable-retrieval \
|
| --retrieval fixed,oracle
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `oraclemem_runs/proxy_writer_baselines_50/raw_results.jsonl`
|
| - `oraclemem_runs/proxy_writer_baselines_50/summary.json`
|
| - `oraclemem_runs/proxy_writer_baselines_50/summary.md`
|
| - `oraclemem_runs/proxy_writer_baselines_50/REPORT.md`
|
|
|
| The report is explicit that these local ratios are synthetic exact-small ratios
|
| for proxy writers. A real-system comparison still requires running the actual
|
| systems with budget-matched memory generation, storage accounting, retrieval
|
| configuration, and evaluation traces.
|
|
|
| ## Gemini Natural Coverage Pilot |
|
|
| Superseded by the Natural-200 and adjudicated-subset results in `main.tex`. |
| This run builds a smaller LongMemEval-S support-slice MemAudit coverage package |
| using Gemini through OpenRouter. It requires `api.env` with |
| `OPENROUTER_API_KEY`. Candidate generation receives only support sessions plus |
| distractors; query/gold-answer fields are used only in the separate labeling |
| step. |
|
|
| ```bash
|
| python llm_memory_validation/gemini_natural_oraclemem.py \
|
| --limit 50 \
|
| --distractors-per-example 2 \
|
| --budgets 30,60,100 \
|
| --out-dir llm_memory_validation/gemini_natural_oraclemem_50 \
|
| --request-sleep 0.02
|
|
|
| python scripts/audit_coverage_artifacts.py \
|
| --no-defaults \
|
| --artifact gemini_natural_50=llm_memory_validation/gemini_natural_oraclemem_50/coverage_package \
|
| --output-dir llm_memory_validation/gemini_natural_oraclemem_50/coverage_audit
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `llm_memory_validation/gemini_natural_oraclemem_50/REPORT.md`
|
| - `llm_memory_validation/gemini_natural_oraclemem_50/coverage_resolved_summary.json`
|
| - `llm_memory_validation/gemini_natural_oraclemem_50/coverage_package/`
|
| - `llm_memory_validation/gemini_natural_oraclemem_50/coverage_audit/REPORT.md`
|
|
|
| The first uncached 50-example run used 248 API calls, 502,698 total tokens, and |
| about `$0.286` in OpenRouter-reported cost. Cached reruns use zero additional |
| API calls. This run is a pilot: 30/50 examples are coverage-resolved and the |
| labels are single-model annotations rather than human adjudications. |
|
|
| ## Natural-200 And Model-Adjudicated Subsets |
|
|
| Used by the natural package reliability table and the model-adjudicated subset |
| table in `main.tex`. |
|
|
| Primary Natural-200 package: |
|
|
| ```bash |
| python llm_memory_validation/gemini_natural_oraclemem.py \ |
| --limit 200 \ |
| --distractors-per-example 0 \ |
| --max-session-words 1800 \ |
| --budgets 30,60,100 \ |
| --out-dir llm_memory_validation/oraclemem_natural_200_gemini_v2 \ |
| --request-sleep 0.02 |
| |
| python scripts/audit_coverage_artifacts.py \ |
| --no-defaults \ |
| --artifact natural_200_gemini_v2=llm_memory_validation/oraclemem_natural_200_gemini_v2/coverage_package \ |
| --output-dir llm_memory_validation/oraclemem_natural_200_gemini_v2/coverage_audit |
| ``` |
|
|
| Gemini Flash adjudicated subset: |
|
|
| ```bash |
| python llm_memory_validation/adjudicate_natural_package.py \ |
| --primary-package-dir llm_memory_validation/oraclemem_natural_200_gemini_v2/coverage_package \ |
| --out-dir llm_memory_validation/natural_adjudicated_100_gemini_flash \ |
| --model google/gemini-2.5-flash \ |
| --limit 100 \ |
| --budgets 30,60,100 \ |
| --secondary-agreement-rows llm_memory_validation/natural50_annotation_agreement_gemini31_vs_gemini25/agreement_rows.jsonl \ |
| --mem0-raw-results llm_memory_validation/mem0_natural200_actual/raw_results.jsonl \ |
| --request-sleep 0.02 |
| ``` |
|
|
| Gemini 3.1 Flash-Lite spot-check: |
|
|
| ```bash |
| python llm_memory_validation/adjudicate_natural_package.py \ |
| --primary-package-dir llm_memory_validation/oraclemem_natural_200_gemini_v2/coverage_package \ |
| --out-dir llm_memory_validation/natural_spotcheck_30_gemini31_flash_lite \ |
| --model google/gemini-3.1-flash-lite-preview \ |
| --limit 30 \ |
| --budgets 30,60,100 \ |
| --methods opt,oracle_gvt,estimated_gvt,amac_admission,summary_only,fact_only,recency_raw \ |
| --secondary-agreement-rows llm_memory_validation/natural50_annotation_agreement_gemini31_vs_gemini25/agreement_rows.jsonl \ |
| --mem0-raw-results llm_memory_validation/mem0_natural200_actual/raw_results.jsonl \ |
| --request-sleep 0.02 \ |
| --skip-existing |
| |
| python scripts/audit_coverage_artifacts.py \ |
| --no-defaults \ |
| --artifact natural_spotcheck_30_gemini31_flash_lite=llm_memory_validation/natural_spotcheck_30_gemini31_flash_lite/coverage_package \ |
| --output-dir llm_memory_validation/natural_spotcheck_30_gemini31_flash_lite/coverage_audit |
| ``` |
|
|
| The Flash-Lite spot-check attempted 30 examples, exported 29 |
| accepted/corrected examples, rejected 1, used 201,301 total tokens, and cost |
| `$0.0639` through OpenRouter. It is model adjudication, not human validation. |
|
|
| ## Human-Edited Natural Seed Package |
|
|
| This package is a fictional 100-example natural-memory seed set that was |
| manually edited/audited after generation. It is used as an artifact-validity |
| check for manual annotation plus exact finite-package scoring. It is not an |
| inter-annotator agreement study. |
|
|
| Validate the canonical JSONL: |
|
|
| ```bash |
| python scripts/validate_human_style_examples.py llm_memory_validation/human_style_examples/examples_100.jsonl |
| ``` |
|
|
| Evaluate the finite package with an exact dynamic-programming denominator: |
|
|
| ```bash |
| python llm_memory_validation/evaluate_human_style_examples.py \ |
| --examples-jsonl llm_memory_validation/human_style_examples/examples_100.jsonl \ |
| --out-dir llm_memory_validation/human_style_examples/eval_package_100 \ |
| --budgets 150,300,600,1000 \ |
| --methods opt,oracle_gvt,estimated_gvt,memgpt_tiered,amem_graph,amac_admission,mem0_extract,density_only,greedy,fact_only,summary_only,recency_raw,no_tombstone_opt |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/human_style_examples/eval_package_100/raw_results.jsonl` |
| - `llm_memory_validation/human_style_examples/eval_package_100/summary.json` |
| - `llm_memory_validation/human_style_examples/eval_package_100/summary.md` |
| - `llm_memory_validation/human_style_examples/eval_package_100/REPORT.md` |
|
|
| Current verification on 2026-05-01: validation passed with 100 records and no |
| structural errors. The evaluator reports the denominator as |
| `exact_human_audited_package_dp`. |
|
|
| Export the same examples to the shared coverage-package schema: |
|
|
| ```bash |
| python llm_memory_validation/export_human_style_coverage_package.py \ |
| --examples-jsonl llm_memory_validation/human_style_examples/examples_100.jsonl \ |
| --out-dir llm_memory_validation/human_style_examples/coverage_package |
| |
| python scripts/audit_coverage_artifacts.py \ |
| --no-defaults \ |
| --artifact human_style_coverage=llm_memory_validation/human_style_examples/coverage_package \ |
| --output-dir llm_memory_validation/human_style_examples/coverage_package_audit |
| ``` |
|
|
| Run actual public A-Mem on the exported human-edited package: |
|
|
| ```bash |
| python llm_memory_validation/run_actual_amem_natural_baseline.py \ |
| --package-dir llm_memory_validation/human_style_examples/coverage_package \ |
| --out-dir llm_memory_validation/human_style_examples/actual_amem_gemini_flash_100 \ |
| --limit 100 \ |
| --budgets 150,300,600,1000,5000 \ |
| --amem-model google/gemini-2.5-flash \ |
| --coverage-model google/gemini-2.5-flash \ |
| --request-sleep 0.02 \ |
| --amem-max-tokens 3000 |
| ``` |
|
|
| Current actual A-Mem human-edited run: 85 query-resolved examples, 456 cached API |
| prompts, 269,742 tokens, estimated OpenRouter cost `$0.233`. Full A-Mem notes |
| reach union-OPT ratio `0.971` at all reported budgets; metadata-only reaches |
| `0.247`. This result is strong but should be interpreted with the package caveat: |
| the sessions are short enough that full notes fit the 150+ word budgets. |
|
|
| ## Learned Writer Transfer Diagnostic |
|
|
| This local run trains a visible-feature utility estimator on train-only oracle |
| labels from synthetic instances plus the Natural-200 model-annotated package, |
| then evaluates held-out decisions on the human-edited seed package. Hidden |
| coverage is used for train labels only; held-out selection sees visible |
| candidate metadata only. |
|
|
| ```bash |
| python llm_memory_validation/evaluate_learned_writer_transfer.py \ |
| --out-dir llm_memory_validation/human_style_examples/learned_writer_transfer \ |
| --budgets 150,300,600,1000 \ |
| --methods opt,oracle_gvt,estimated_gvt,estimated_utility,memgpt_tiered,amem_graph,amac_admission,mem0_extract,density_only,greedy,fact_only,summary_only,recency_raw,no_tombstone_opt |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/human_style_examples/learned_writer_transfer/raw_results.jsonl` |
| - `llm_memory_validation/human_style_examples/learned_writer_transfer/summary.json` |
| - `llm_memory_validation/human_style_examples/learned_writer_transfer/summary.md` |
| - `llm_memory_validation/human_style_examples/learned_writer_transfer/REPORT.md` |
| - `llm_memory_validation/human_style_examples/learned_writer_transfer/train_manifest.json` |
|
|
| Current run: 1,000 synthetic train instances plus 200 natural train instances |
| with 22,106 train candidates. Estimated-GVT reaches held-out exact package-OPT |
| ratios `0.933/0.926/0.854/0.792` at budgets `150/300/600/1000`. This is a |
| deployable-writer diagnostic, not an inter-annotator natural benchmark. |
|
|
| Training-source ablations: |
|
|
| ```bash |
| python llm_memory_validation/evaluate_learned_writer_transfer.py \ |
| --out-dir llm_memory_validation/human_style_examples/learned_writer_transfer_synth_only \ |
| --train-natural-limit 0 \ |
| --budgets 150,300,600,1000 \ |
| --methods opt,oracle_gvt,estimated_gvt,estimated_utility,amac_admission,mem0_extract,density_only,greedy,fact_only,summary_only,recency_raw,no_tombstone_opt |
| |
| python llm_memory_validation/evaluate_learned_writer_transfer.py \ |
| --out-dir llm_memory_validation/human_style_examples/learned_writer_transfer_natural_only \ |
| --n-synthetic-train-seeds 0 \ |
| --budgets 150,300,600,1000 \ |
| --methods opt,oracle_gvt,estimated_gvt,estimated_utility,amac_admission,mem0_extract,density_only,greedy,no_tombstone_opt |
| ``` |
|
|
| Current ablations: synthetic-only Estimated-GVT reaches |
| `0.667/0.778/0.792/0.833`; Natural-200-only reaches |
| `0.000/0.074/0.375/0.486`. The combined run is therefore the paper-facing |
| learned-writer result because it is strongest at tight and medium budgets. |
|
|
| ## Natural Writer Adapter Diagnostic |
|
|
| This local run scores Letta/MemGPT-style archival/recency and A-Mem-style graph |
| adapters on the adjudicated natural package under the same exact package OPT |
| denominator. It does not call an API and does not run Letta or A-Mem itself. |
|
|
| ```bash |
| python llm_memory_validation/evaluate_coverage_package_writers.py \ |
| --package-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/coverage_package \ |
| --out-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters \ |
| --budgets 30,60,100 \ |
| --methods opt,oracle_gvt,memgpt_tiered,amem_graph,mem0_extract,amac_admission,estimated_gvt,density_only,summary_only,fact_only,recency_raw |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters/raw_results.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters/summary.json` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters/summary.md` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters/REPORT.md` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters/run_manifest.json` |
|
|
| Current run: 87 accepted/corrected adjudicated examples, zero API calls. |
| Letta/MemGPT-style reaches `0.638/0.433/0.431`, A-Mem-style reaches |
| `0.481/0.374/0.377`, and density-only reaches `0.991/0.955/0.962` at budgets |
| `30/60/100`. The density result is a warning that this copied-candidate natural |
| denominator is unusually density-friendly. |
|
|
| ## Human-Edited Writer Adapter Diagnostic |
|
|
| This local run scores the same Letta/MemGPT-style, A-Mem-style, Mem0-style, and |
| A-MAC-style adapters on the exported human-edited coverage package. It is a |
| zero-API denominator-matched check. It does not run the Letta service or MemGPT |
| controller; the checked-out Letta repository requires a service/API/model |
| configuration for a true production run. |
|
|
| ```bash |
| python llm_memory_validation/evaluate_coverage_package_writers.py \ |
| --package-dir llm_memory_validation/human_style_examples/coverage_package \ |
| --out-dir llm_memory_validation/human_style_examples/writer_adapters \ |
| --budgets 150,300,600,1000 \ |
| --methods opt,oracle_gvt,memgpt_tiered,amem_graph,mem0_extract,amac_admission,estimated_gvt,density_only,summary_only,fact_only,recency_raw |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/human_style_examples/writer_adapters/raw_results.jsonl` |
| - `llm_memory_validation/human_style_examples/writer_adapters/summary.json` |
| - `llm_memory_validation/human_style_examples/writer_adapters/summary.md` |
| - `llm_memory_validation/human_style_examples/writer_adapters/REPORT.md` |
| - `llm_memory_validation/human_style_examples/writer_adapters/run_manifest.json` |
|
|
| Current run: 85 query-resolved examples, zero API calls. Letta/MemGPT-style |
| reaches `0.847`, A-Mem-style reaches `0.876`, Mem0-style reaches `0.753`, and |
| A-MAC-style reaches `0.835` across budgets `150/300/600/1000`. Density-only is |
| `1.000` on this per-query exported package, so this row is a MemGPT-style |
| adapter reproducibility check rather than the strongest algorithmic separation. |
|
|
| ## Faithful MemGPT/Letta Union Baseline |
|
|
| This no-API runner is the current MemGPT/Letta-strengthened baseline on the |
| adjudicated natural package. It checks the local `external_repos/letta` checkout |
| metadata, records that the actual Letta import path is not available without the |
| full service dependency stack, then simulates the relevant core/archival/recall |
| memory tiers over exported package candidates. Writing and retrieval use visible |
| metadata only; hidden coverage is used only for scoring, except in the |
| analysis-only upper-pruned bound row. |
|
|
| ```bash |
| python llm_memory_validation/run_faithful_memgpt_letta_baseline.py \ |
| --package-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/coverage_package \ |
| --out-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union \ |
| --budgets 30,60,100 \ |
| --limit 87 |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union/raw_results.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union/summary.json` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union/REPORT.md` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union/written_stores.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union/run_manifest.json` |
|
|
| Current run: 87/87 examples, zero API calls. Archival-search pruning reaches |
| `0.746/0.739/0.866` ratio to union OPT at budgets `30/60/100`; recency pruning |
| reaches `0.642/0.700/0.877`; the analysis-only upper-pruned bound reaches |
| `0.829/0.907/0.939`. |
|
|
| ## Actual Letta OpenRouter Passage Run |
|
|
| This runs the checked-out Letta server (`external_repos/letta`, version |
| `0.16.7`) with Postgres/pgvector, OpenRouter Gemini, and authenticated |
| OpenRouter passage embeddings. Apply |
| `llm_memory_validation/patches/letta_openrouter_embedding_auth.patch` to the |
| Letta checkout before starting the server; without it, OpenRouter passage |
| search uses the wrong API key path. |
|
|
| ```powershell |
| .\.venv_letta_prod\Scripts\python.exe llm_memory_validation\run_actual_letta_openrouter_baseline.py ` |
| --package-dir llm_memory_validation\natural_adjudicated_100_gemini_flash\coverage_package ` |
| --out-dir llm_memory_validation\natural_adjudicated_100_gemini_flash\actual_letta_openrouter_gemini_passage_87 ` |
| --limit 87 ` |
| --budgets 30,60,100 ` |
| --include-salience-pruned ` |
| --include-oracle-pruned-upper ` |
| --max-steps 12 ` |
| --message-retries 2 ` |
| --request-sleep 0.02 |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/raw_results.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/summary.json` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/REPORT.md` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/written_stores.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/coverage_scoring_calls.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87/salience_scoring_calls.jsonl` |
|
|
| Current run: 87/87 examples, zero failed instances. Letta writes archival |
| passages for 85 examples and core-memory atoms for 30 examples. The combined |
| core+archival store reaches union-OPT ratios `0.652/0.696/0.734` with salience |
| pruning, `0.219/0.260/0.342` with recency pruning, and `0.723/0.763/0.765` for |
| the analysis-only upper-pruned bound at budgets `30/60/100`. |
|
|
| ## Actual A-Mem Gemini-Flash Pilot |
|
|
| This runs the checked-out public `external_repos/AgenticMemory` implementation, |
| using Gemini Flash through OpenRouter for A-Mem metadata/evolution calls and for |
| post-hoc coverage scoring. It reports a finite union denominator over package |
| candidates plus A-Mem-written memories. The full-memory rows score A-Mem's actual |
| stored notes; the metadata rows are a compact diagnostic serialization of |
| A-Mem-generated context/keywords/tags/links. |
|
|
| ```bash |
| python llm_memory_validation/run_actual_amem_natural_baseline.py \ |
| --package-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/coverage_package \ |
| --out-dir llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87 \ |
| --limit 87 \ |
| --budgets 30,60,100,5000 \ |
| --amem-model google/gemini-2.5-flash \ |
| --coverage-model google/gemini-2.5-flash \ |
| --request-sleep 0.02 \ |
| --amem-max-tokens 3000 |
| ``` |
|
|
| Expected outputs: |
|
|
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/REPORT.md` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/summary.json` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/raw_results.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/written_stores.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/coverage_scoring_calls.jsonl` |
| - `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87/run_manifest.json` |
|
|
| Current 87-example run: raw full A-Mem notes have mean serialized cost `4446` |
| words and therefore score `0.000/0.000/0.000` at budgets `30/60/100`; at the |
| diagnostic budget `5000`, the raw full-store oracle upper reaches `0.845`. |
| The compact metadata diagnostic has mean cost `66` words and reaches |
| `0.204/0.158/0.180` with oracle pruning at budgets `30/60/100`. The run used |
| 524 cached API prompts, 2,433,021 tokens, and an estimated OpenRouter cost of |
| `$1.576`. |
|
|
| ## Actual Mem0 Smoke |
|
|
| This verifies executable integration with the public Mem0 codebase. It is not a |
| benchmark and should not be reported as a budget-matched Mem0 comparison. |
|
|
| Prerequisites from this environment:
|
|
|
| ```bash
|
| python -m pip install qdrant-client==1.12.2 rank-bm25==0.2.2 litellm==1.83.7
|
| python -m pip install -e external_repos/mem0
|
| python -m pip install "huggingface-hub>=0.34,<1.0"
|
| ```
|
|
|
| Run:
|
|
|
| ```bash
|
| python llm_memory_validation/mem0_actual_smoke.py \
|
| --api-env api.env \
|
| --out-dir llm_memory_validation/mem0_actual_smoke
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `llm_memory_validation/mem0_actual_smoke/search_result.json`
|
| - `llm_memory_validation/actual_system_repo_audit/REPORT.md`
|
|
|
| ## LongMemEval-S Retrieval Transfer
|
|
|
| Diagnostic only after the 9-page compression pass. This report is |
| retrieval-only: no answer generation, no abstention scoring, and no exact OPT
|
| denominator.
|
|
|
| To regenerate the focus report from the cached retrieval rows:
|
|
|
| ```bash
|
| python llm_memory_validation/longmemeval_focus_report.py \
|
| --summary-json llm_memory_validation/competitor_run_v2/summary.json \
|
| --retrieval-rows-json llm_memory_validation/competitor_run_v2/retrieval_rows.json \
|
| --output-dir llm_memory_validation/longmemeval_focus_report_core4 \
|
| --methods dense_budgeted_bsc,dense_rag_e5,dense_budgeted_replay,fifo_replay
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `llm_memory_validation/longmemeval_focus_report_core4/summary.json`
|
| - `llm_memory_validation/longmemeval_focus_report_core4/REPORT.md`
|
|
|
| The current paper-facing label map is:
|
|
|
| - `dense_budgeted_bsc`: MemAudit writer + dense retrieval |
| - `dense_rag_e5`: Full raw-store dense retrieval
|
| - `dense_budgeted_replay`: Budgeted raw replay + dense retrieval
|
| - `fifo_replay`: FIFO raw replay
|
|
|
| To regenerate the upstream dense retrieval rows, use:
|
|
|
| ```bash
|
| python llm_memory_validation/paper_competitor_suite.py \
|
| --output-dir llm_memory_validation/competitor_run_v2 \
|
| --topk 5 \
|
| --retriever-model intfloat/e5-base-v2
|
| ```
|
|
|
| This upstream regeneration downloads external data/model artifacts and may vary
|
| with model or dataset revisions unless those are pinned outside this repository.
|
|
|
| ## GPT-5.5 Frozen-Context Reader
|
|
|
| Appendix diagnostic only after the 9-page compression pass. The current artifact |
| uses frozen top-5 retrieval contexts, `openai/gpt-5.5` through OpenRouter, and
|
| the `answer_if_supported` prompt.
|
|
|
| Set up `api.env` locally. Do not commit it.
|
|
|
| ```text
|
| OPENROUTER_API_KEY=...
|
| ```
|
|
|
| Then run:
|
|
|
| ```bash
|
| python llm_memory_validation/longmemeval_reader_eval.py \
|
| --dataset-json llm_memory_validation/cache/longmemeval_s_cleaned.json \
|
| --retrieval-rows-json llm_memory_validation/competitor_run_v2/retrieval_rows.json \
|
| --output-dir llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full \
|
| --methods dense_budgeted_bsc,dense_rag_e5,dense_budgeted_replay,fifo_replay \
|
| --focus-only \
|
| --focus-types knowledge-update,temporal-reasoning \
|
| --reader openrouter \
|
| --reader-model openai/gpt-5.5 \
|
| --prompt-style answer_if_supported \
|
| --api-env api.env \
|
| --api-cache llm_memory_validation/openrouter_cache_gpt55_answer_supported_focus_full.json
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/summary.json`
|
| - `llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/REPORT.md`
|
| - `llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/reader_outputs.jsonl`
|
| - `llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/predictions.json`
|
|
|
| The committed/cacheable outputs should be treated as the reproducible artifact
|
| for the paper. Re-running the API may change costs, latency, or model behavior.
|
|
|
| ## Reader Audit
|
|
|
| Appendix diagnostic only after the 9-page compression pass. |
|
|
| ```bash
|
| python llm_memory_validation/longmemeval_reader_eval.py \
|
| --analyze-errors \
|
| --run-dir llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full
|
| ```
|
|
|
| Expected outputs in the same run directory:
|
|
|
| - `ERROR_AUDIT.md`
|
| - `error_audit_summary.json`
|
| - `error_audit_rows.jsonl`
|
| - `failure_examples.jsonl`
|
| - `semantic_audit_sample_50.jsonl`
|
| - `normalized_scoring.json`
|
| - `llm_memory_validation/scoring_audit_gpt55/normalized_scoring_v2.json`
|
|
|
| ## Deterministic Decomposition
|
|
|
| Diagnostic only after the 9-page compression pass. This is a local evidence-only |
| reader path and does not use an API. |
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 300 \
|
| --budgets 0.05,0.10,0.20 \
|
| --distribution base,update_chain,temporal_interval \
|
| --methods opt,oracle_gvt,density_only,greedy,recency_raw,reservoir_raw,summary_only,fact_only,no_tombstone_gvt \
|
| --enable-retrieval \
|
| --retrieval fixed,oracle \
|
| --reader evidence_only \
|
| --out oraclemem_runs/decomp_det_300
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `oraclemem_runs/decomp_det_300/raw_results.jsonl`
|
| - `oraclemem_runs/decomp_det_300/summary.json`
|
| - `oraclemem_runs/decomp_det_300/summary.md`
|
|
|
| ## MILP Verification
|
|
|
| Referenced in the exact-small solver audit text. This optional run requires
|
| `pulp` from `requirements-milp.txt`.
|
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 100 \
|
| --budgets 0.02,0.05,0.10,0.20 \
|
| --distribution base,update_chain,temporal_interval \
|
| --methods opt \
|
| --solver milp \
|
| --verify-against exact_stdlib \
|
| --out oraclemem_runs/milp_verify_100_agent4
|
| ```
|
|
|
| Expected outputs:
|
|
|
| - `oraclemem_runs/milp_verify_100_agent4/raw_results.jsonl`
|
| - `oraclemem_runs/milp_verify_100_agent4/summary.json`
|
| - `oraclemem_runs/milp_verify_100_agent4/summary.md`
|
| - `oraclemem_runs/milp_verify_100_agent4/REPORT.md`
|
|
|
| ## Gemini Flash-Lite Diagnostic
|
|
|
| This API run is a robustness diagnostic, not a theorem-facing result. It uses
|
| OpenRouter model `google/gemini-3.1-flash-lite-preview` and requires `api.env`.
|
|
|
| ```bash
|
| python llm_memory_validation/longmemeval_reader_eval.py \
|
| --reader openrouter \
|
| --reader-model google/gemini-3.1-flash-lite-preview \
|
| --prompt-style answer_if_supported \
|
| --focus-only \
|
| --methods dense_budgeted_bsc,fifo_replay \
|
| --api-env api.env \
|
| --api-cache llm_memory_validation/openrouter_cache_gemini31_flash_lite_focus_full_bsc_fifo.json \
|
| --output-dir llm_memory_validation/longmemeval_reader_api_gemini31_flash_lite_focus_full_bsc_fifo \
|
| --api-max-tokens 320 \
|
| --api-timeout 120 \
|
| --temperature 0 \
|
| --request-sleep 0.02 \
|
| --bootstrap 1000 \
|
| --save-prompts
|
| ```
|
|
|
| ## Noisy Estimated-Policy Diagnostic
|
|
|
| This run does not call an API. It records Gemini Flash-Lite as provenance for a
|
| local noisy estimated-utility profile and is useful as a synthetic stress
|
| diagnostic for non-oracle writer evaluation.
|
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 500 \
|
| --distribution scope_shift_v2,density_trap_v2 \
|
| --budgets 4,6 \
|
| --methods opt,oracle_gvt,estimated_gvt,estimated_utility,mem0_extract,amac_admission,no_tombstone_gvt,no_tombstone_opt \
|
| --estimated-model google/gemini-3.1-flash-lite-preview \
|
| --estimated-profile noisy_gemini_flash_lite_v1 \
|
| --enable-retrieval \
|
| --retrieval fixed,oracle \
|
| --export-coverage-matrices \
|
| --coverage-package-limit 4 \
|
| --out-dir oraclemem_runs/estimated_policy_noisy_noapi_1000
|
| ```
|
|
|
| To audit an exported coverage package:
|
|
|
| ```bash
|
| python scripts/audit_coverage_artifacts.py \
|
| --no-defaults \
|
| --artifact exported_oraclemem_package=oraclemem_runs/estimated_policy_noisy_noapi_1000/coverage_instances/scope_shift_v2/seed_0 \
|
| --output-dir oraclemem_runs/estimated_policy_noisy_noapi_1000/coverage_audit
|
| ```
|
|
|
| ## Train/Dev Estimated-Writer Diagnostic
|
|
|
| This local run trains a ridge utility estimator on synthetic train seeds and
|
| evaluates `estimated_*` methods only on held-out dev seeds. It does not call an
|
| API and is diagnostic rather than final deployed-writer evidence.
|
|
|
| ```bash
|
| python run_oraclemem_mvp.py \
|
| --n-seeds 60 \
|
| --train-dev-estimator \
|
| --train-fraction 0.5 \
|
| --distribution base,update_chain,temporal_interval,density_trap,scope_shift,summary_tradeoff,redundancy_heavy,abstention_hard,scope_shift_v2,density_trap_v2 \
|
| --budgets 4,6 \
|
| --methods opt,oracle_gvt,estimated_gvt,estimated_utility,mem0_extract,amac_admission,no_tombstone_gvt,no_tombstone_opt \
|
| --out-dir oraclemem_runs/estimated_policy_train_dev_local_60
|
| ```
|
|
|
| ## Known Non-Reproducible Or External Pieces
|
|
|
| - Local LaTeX compilation depends on a TeX distribution; this machine did not
|
| have `latexmk`, `pdflatex`, or `tectonic` on PATH.
|
| - GPT-5.5 reader outputs require OpenRouter access, model availability, and API
|
| spending. Use the cached reader outputs for paper auditability.
|
| - Gemini natural coverage and actual Mem0 smoke outputs require OpenRouter
|
| access if regenerated from scratch; use cached artifacts for audit where
|
| possible.
|
| - LongMemEval-S retrieval regeneration downloads the dataset and
|
| `intfloat/e5-base-v2`; exact rows can drift if upstream artifacts change.
|
| - API costs in `summary.json` are historical and should not be treated as a
|
| stable price quote.
|
|
|
