Update MemAudit code card

README.md

@@ -1,80 +1,90 @@
-# MemAudit
-
-MemAudit is an exact-oracle evaluation protocol for budgeted long-term LLM
-memory writing. The core question is finite and package-conditional:
-
-> Given a fixed storage budget and a finite semantic evidence package, how close
-> is a written memory store to the best package-feasible store?
-
-This repository contains the manuscript, exact-small synthetic benchmarks,
-validity-heavy stress benchmarks, natural support-sliced coverage packages,
-Mem0 diagnostic rescoring artifacts, and reproducibility scripts.
-
-MemAudit is not a runtime memory product. It is an evaluation layer for
-memory writers: it scores finite candidate packages, budgeted representation
-choices, and external written stores against explicit denominators.
-
-## Quickcheck
-
-Run the deterministic tests:
-
-```powershell
-python -m unittest test_oraclemem.py
-```
-
-Run a tiny exact-oracle smoke benchmark:
-
-```powershell
-python run_oraclemem_mvp.py --n-seeds 3 --budgets 4 --distribution base --methods opt,oracle_gvt,density_only --out-dir oraclemem_runs/quickcheck
-```
-
-Expected smoke outputs:
-
-- `oraclemem_runs/quickcheck/raw_results.jsonl`
-- `oraclemem_runs/quickcheck/summary.json`
-- `oraclemem_runs/quickcheck/summary.md`
-
-## Main Artifacts
-
-- `main.tex`: active manuscript.
-- `references.bib`: bibliography.
-- `figures/`: paper figure assets generated from cached experiment summaries.
-- `oraclemem_runs/exact_500`: exact-small 500-instance sweep.
-- `oraclemem_runs/stress_exact_500`: validity-heavy stress sweep.
-- `oraclemem_runs/representative_writers_500`: non-oracle writer diagnostic sweep with Estimated-GVT and A-MAC-like admission.
-- `llm_memory_validation/oraclemem_natural_200_gemini_v2`: Natural-200 support-sliced coverage package.
-- `llm_memory_validation/natural_adjudicated_100_gemini_flash`: stricter adjudicated natural subset.
-- `llm_memory_validation/natural_spotcheck_30_gemini31_flash_lite`: independent Gemini Flash-Lite adjudication spot-check.
-- `llm_memory_validation/human_style_examples`: 100 fictional human-edited/audited natural examples, exported coverage package, exact package evaluation, and actual A-Mem run.
-- `llm_memory_validation/human_style_examples/learned_writer_transfer`: coverage-blind learned writer transfer diagnostic trained on synthetic plus Natural-200 labels and tested on the human-edited package.
-- `llm_memory_validation/human_style_examples/learned_writer_transfer_synth_only` and `llm_memory_validation/human_style_examples/learned_writer_transfer_natural_only`: training-source ablations for the learned writer transfer diagnostic.
-- `llm_memory_validation/human_style_examples/writer_adapters`: denominator-matched Letta/MemGPT-style, A-Mem-style, Mem0-style, and A-MAC-style adapter diagnostics on the exported human-edited coverage package.
-- `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters`: denominator-matched Letta/MemGPT-style and A-Mem-style adapter diagnostics on the adjudicated natural package.
-- `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union`: no-API faithful MemGPT/Letta core/archival baseline scored with a package-plus-written-store union denominator.
-- `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87`: executable Letta server run on 87 adjudicated examples with OpenRouter Gemini, authenticated OpenRouter passage embeddings, archival-memory tools, and the union denominator.
-- `llm_memory_validation/mem0_rescore_adjudicated100_gemini_flash`: Mem0 diagnostic rescoring on the adjudicated subset.
-- `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87`: executable public A-Mem run on 87 adjudicated examples using Gemini Flash and the union denominator.
-- `llm_memory_validation/human_style_examples/actual_amem_gemini_flash_100`: executable public A-Mem run on the human-edited package using Gemini Flash and the union denominator.
-
-See `artifact_manifest.md` for table-to-artifact mapping and full rerun
-commands. See `REPRODUCIBILITY.md` for setup, exact-oracle runs, API runs, and
-known local build limitations.
-
-## Denominator Types
-
-- Package ratio: exact ratio to `OPT_P(B)` for a finite MemAudit candidate package.
-- Union ratio: exact ratio to `OPT_{P^+(Y)}(B)` after adding an external written store to the candidate package.
-- Upper-pruned bound: best budget-feasible subset of an external store, used only to separate extraction quality from budget-aware selection.
-- Retrieval/reader metrics: downstream diagnostics, not MemAudit optimum ratios.
-
-## Caveats
-
-The strongest exact claims are finite-package claims. LongMemEval-derived
-natural coverage packages are model-adjudicated; the separate
-`human_style_examples` package is human-edited/audited but does not include an
-inter-annotator agreement file. LongMemEval reader/retrieval results
-are downstream diagnostics and do not have exact OPT denominators. Mem0 and
-A-Mem rescoring use union-denominator and upper-pruned-bound diagnostics rather
-than claiming deployable optimal pruning policies.
-
-Do not commit API keys. `api.env` is local-only and should stay ignored.
+---
+license: mit
+pretty_name: MemAudit Code Artifact
+tags:
+- llm-memory
+- benchmark
+- evaluation
+- reproducibility
+---
+
+# MemAudit
+
+MemAudit is an exact-oracle evaluation protocol for budgeted long-term LLM
+memory writing. The core question is finite and package-conditional:
+
+> Given a fixed storage budget and a finite semantic evidence package, how close
+> is a written memory store to the best package-feasible store?
+
+This repository contains the manuscript, exact-small synthetic benchmarks,
+validity-heavy stress benchmarks, natural support-sliced coverage packages,
+Mem0 diagnostic rescoring artifacts, and reproducibility scripts.
+
+MemAudit is not a runtime memory product. It is an evaluation layer for
+memory writers: it scores finite candidate packages, budgeted representation
+choices, and external written stores against explicit denominators.
+
+## Quickcheck
+
+Run the deterministic tests:
+
+```powershell
+python -m unittest test_oraclemem.py
+```
+
+Run a tiny exact-oracle smoke benchmark:
+
+```powershell
+python run_oraclemem_mvp.py --n-seeds 3 --budgets 4 --distribution base --methods opt,oracle_gvt,density_only --out-dir oraclemem_runs/quickcheck
+```
+
+Expected smoke outputs:
+
+- `oraclemem_runs/quickcheck/raw_results.jsonl`
+- `oraclemem_runs/quickcheck/summary.json`
+- `oraclemem_runs/quickcheck/summary.md`
+
+## Main Artifacts
+
+- `main.tex`: active manuscript.
+- `references.bib`: bibliography.
+- `figures/`: paper figure assets generated from cached experiment summaries.
+- `oraclemem_runs/exact_500`: exact-small 500-instance sweep.
+- `oraclemem_runs/stress_exact_500`: validity-heavy stress sweep.
+- `oraclemem_runs/representative_writers_500`: non-oracle writer diagnostic sweep with Estimated-GVT and A-MAC-like admission.
+- `llm_memory_validation/oraclemem_natural_200_gemini_v2`: Natural-200 support-sliced coverage package.
+- `llm_memory_validation/natural_adjudicated_100_gemini_flash`: stricter adjudicated natural subset.
+- `llm_memory_validation/natural_spotcheck_30_gemini31_flash_lite`: independent Gemini Flash-Lite adjudication spot-check.
+- `llm_memory_validation/human_style_examples`: 100 fictional human-edited/audited natural examples, exported coverage package, exact package evaluation, and actual A-Mem run.
+- `llm_memory_validation/human_style_examples/learned_writer_transfer`: coverage-blind learned writer transfer diagnostic trained on synthetic plus Natural-200 labels and tested on the human-edited package.
+- `llm_memory_validation/human_style_examples/learned_writer_transfer_synth_only` and `llm_memory_validation/human_style_examples/learned_writer_transfer_natural_only`: training-source ablations for the learned writer transfer diagnostic.
+- `llm_memory_validation/human_style_examples/writer_adapters`: denominator-matched Letta/MemGPT-style, A-Mem-style, Mem0-style, and A-MAC-style adapter diagnostics on the exported human-edited coverage package.
+- `llm_memory_validation/natural_adjudicated_100_gemini_flash/writer_adapters`: denominator-matched Letta/MemGPT-style and A-Mem-style adapter diagnostics on the adjudicated natural package.
+- `llm_memory_validation/natural_adjudicated_100_gemini_flash/faithful_memgpt_letta_union`: no-API faithful MemGPT/Letta core/archival baseline scored with a package-plus-written-store union denominator.
+- `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_letta_openrouter_gemini_passage_87`: executable Letta server run on 87 adjudicated examples with OpenRouter Gemini, authenticated OpenRouter passage embeddings, archival-memory tools, and the union denominator.
+- `llm_memory_validation/mem0_rescore_adjudicated100_gemini_flash`: Mem0 diagnostic rescoring on the adjudicated subset.
+- `llm_memory_validation/natural_adjudicated_100_gemini_flash/actual_amem_gemini_flash_87`: executable public A-Mem run on 87 adjudicated examples using Gemini Flash and the union denominator.
+- `llm_memory_validation/human_style_examples/actual_amem_gemini_flash_100`: executable public A-Mem run on the human-edited package using Gemini Flash and the union denominator.
+
+See `artifact_manifest.md` for table-to-artifact mapping and full rerun
+commands. See `REPRODUCIBILITY.md` for setup, exact-oracle runs, API runs, and
+known local build limitations.
+
+## Denominator Types
+
+- Package ratio: exact ratio to `OPT_P(B)` for a finite MemAudit candidate package.
+- Union ratio: exact ratio to `OPT_{P^+(Y)}(B)` after adding an external written store to the candidate package.
+- Upper-pruned bound: best budget-feasible subset of an external store, used only to separate extraction quality from budget-aware selection.
+- Retrieval/reader metrics: downstream diagnostics, not MemAudit optimum ratios.
+
+## Caveats
+
+The strongest exact claims are finite-package claims. LongMemEval-derived
+natural coverage packages are model-adjudicated; the separate
+`human_style_examples` package is human-edited/audited but does not include an
+inter-annotator agreement file. LongMemEval reader/retrieval results
+are downstream diagnostics and do not have exact OPT denominators. Mem0 and
+A-Mem rescoring use union-denominator and upper-pruned-bound diagnostics rather
+than claiming deployable optimal pruning policies.
+
+Do not commit API keys. `api.env` is local-only and should stay ignored.

scripts/prepare_submission_artifacts.py

@@ -152,7 +152,35 @@ def write_readmes() -> None:
         "SOFTWARE.\n",
         encoding="utf-8",
     )
+    code_readme = (CODE / "README.md").read_text(encoding="utf-8")
+    if code_readme.startswith("---"):
+        code_readme = code_readme.split("---", 2)[2].lstrip()
+    (CODE / "README.md").write_text(
+        "---\n"
+        "license: mit\n"
+        "pretty_name: MemAudit Code Artifact\n"
+        "tags:\n"
+        "- llm-memory\n"
+        "- benchmark\n"
+        "- evaluation\n"
+        "- reproducibility\n"
+        "---\n\n"
+        + code_readme,
+        encoding="utf-8",
+    )
     (DATASET / "README.md").write_text(
+        "---\n"
+        "license: cc-by-4.0\n"
+        "pretty_name: MemAudit Benchmark Artifacts\n"
+        "task_categories:\n"
+        "- question-answering\n"
+        "- text-retrieval\n"
+        "tags:\n"
+        "- llm-memory\n"
+        "- benchmark\n"
+        "- evaluation\n"
+        "- croissant\n"
+        "---\n\n"
         "# MemAudit Dataset Artifact\n\n"
         "This dataset contains finite memory-writing packages, exact/verified solver outputs, "
         "model-adjudicated natural support-slice packages, human-edited fictional seed packages, "