gianlucamazza/emotional-memory-benchmarks

Emotional Memory Benchmarks

Benchmark sets for affect-conditioned memory retrieval from the emotional-memory library (Affective Field Theory, AFT). They are controlled research probes assembled by the author. They are not a production leaderboard and not a human-evaluation corpus.

• Author: Gianluca Mazza. I take LLM systems into production: state, recovery, eval, cost.
• Code and full study record: github.com/gianlucamazza/emotional-memory
• Live demo: gianlucamazza/emotional-memory-demo
• Licence: CC-BY-4.0 for every file in this dataset. The library code is MIT. Some sets were drafted with OpenAI models (noted per set below); the author releases them under the same CC-BY-4.0 licence.
• Attribution: please credit the dataset with the citation below and the concept DOI 10.5281/zenodo.19972258.

Read this first: scope and limits

• Built to favour the method being tested (Addendum U). In realistic_recall_v2, 62.5% of queries (125/200) are constructed so that affect can break a semantic tie, and the aggregate AFT advantage is confined to that regime. A win on these sets is not evidence that AFT is better in general.

• Oracle affect. The headline realistic_recall_v2 results use preset valence/arousal at encode and query time. With automatic appraisal on affect-free variants, AFT does not beat cosine (FAIL).

• Negative results recorded in the project:

  • LoCoMo (factual conversational QA): AFT underperforms a naive RAG baseline (F1 0.168 vs 0.271). FAIL.
  • MADial-Bench (third-party): cosine is significantly better (nDCG@5 0.304 vs 0.221). FAIL, inverted: the benchmark rewards supportive, counter-congruent recall.
  • ES-MemEval / EvoEmo (third-party): cosine is significantly better (nDCG@4 0.284 vs 0.133). FAIL, inverted: the gold answer is set by content, not affect.
  • Italian and Spanish with multilingual-e5 at declared power (N=120): FAIL. French (native, 2-session runner) is a PASS.

  Sources: the preregistration closures under benchmarks/ and docs/research/claim_validation_matrix.json.

• Not included here: EmoBank, MADial-Bench, ES-MemEval/EvoEmo, the DailyDialog-derived personas and LoCoMo. They are third-party or third-party-derived and keep their own licences in the GitHub repository.

• Related papers: The project also ran AFT against three published third-party benchmarks: LoCoMo (arXiv:2402.17753), MADial-Bench (arXiv:2409.15240) and ES-MemEval (arXiv:2602.01885). This dataset does not redistribute them. Their results are the FAILs listed above.

Configurations

Config	Language	Size	Origin
affect_reference_v1	en	258 examples	Script-generated from seed texts in generate_dataset.py. Whether the seed texts were drafted with an LLM is not recorded.
realistic_recall_v1	en	50 scenarios, 100 queries	Hand-authored / scripted. LLM involvement is not recorded.
realistic_recall_v2	en	50 scenarios, 200 queries	Hand-authored. Affect labels hand-crafted by the author with AFT in mind. Main benchmark.
realistic_recall_v2_it	it	30 scenarios, 120 queries	Scenarios 1–20 translated (human or LLM: not recorded). Scenarios 21–30 LLM-assisted (gpt-4.1-mini), calls logged in the repo.
realistic_recall_v2_es	es	30 scenarios, 120 queries	Scenarios 1–20 translated (tool not recorded). Scenarios 21–30 translated from the Italian top-up.
realistic_recall_v2_fr	fr	30 scenarios, 120 queries	Hand-authored in native French.
realistic_recall_v3	en	125 scenarios, 500 queries	v2 plus 75 new scenarios assembled by script. How the new texts were drafted is not recorded.
realistic_recall_v3_noAF	en	50 scenarios, 200 queries	v2 with affect labels removed.
realistic_recall_v4_noAF	en	40 scenarios, 160 queries	LLM-assisted (gpt-5-mini), author-blind.
realistic_recall_v5_gate	en	50 scenarios, 200 queries	LLM-assisted (gpt-5-mini), author-blind; 100 affective / 100 affect-free queries.

Full provenance per set, with commits: benchmarks/datasets/DATASET.md.

Structure

• affect_reference_v1: one row per example with id, text, valence, arousal, dominance, expected_label, source.
• realistic_recall_*: one row per scenario, with scenario_id, description and sessions[]. Each session holds events[] and queries[]. The raw JSON files in data/ also keep the top-level name, version, description and license fields that the emotional-memory runner expects.

The files are byte-identical to benchmarks/datasets/ in the GitHub repository at commit 985c0264.

from datasets import load_dataset
ds = load_dataset("gianlucamazza/emotional-memory-benchmarks", "realistic_recall_v2", split="test")

Citation

@software{mazza_emotional_memory,
  author    = {Mazza, Gianluca},
  title     = {emotional-memory: Affective Field Theory for LLM Memory},
  doi       = {10.5281/zenodo.19972258},
  url       = {https://doi.org/10.5281/zenodo.19972258},
  publisher = {Zenodo}
}