LAPSE: aspectual flattening in LLM agent memory

LAPSE is a benchmark for one failure of LLM agent memory. A user says "I am working at Corvida." The memory writer stores "The user works at Corvida." The progressive told a later reader that the job might be temporary. The stored note states it as a standing fact, so a later model has less evidence that the fact may have gone stale. We call this aspectual flattening.

Each LAPSE item is a dated conversation excerpt with one user statement. Matched items differ only in the statement's temporal form: progressive ("I'm driving"), simple present ("I drive"), an explicit end date, or a perfect form.

Paper: Memory Consolidation Flattens the Temporal Shape of User Facts. Code, scorer, model outputs and labels: github.com/aimsresearchlab/lapse.

What is in this dataset

Split	Rows
dev	4,791
test	1,125

data/manifest.json is the build manifest (builder version, evaluation date, prompt templates, checksums).

The arm field says what the model is asked to do:

arm	Task
e1	Write memory notes from the conversation.
l2	Write memory notes with an instruction to preserve tense and aspect.
behavioral	Act on the fact directly.
explicit	Say whether the fact still holds.
anchor	Choose to send with the remembered value or to check first.

Other fields: form (temporal form), frame (content domain: lodging, workplace, vehicle, class, household, equipment, affiliate_role, project), cluster (rows sharing frame and cluster differ only in the manipulated factor), gap and gap_date (time between statement and evaluation), witness and wit_token (the invented entity the scorer searches for), and linguistic annotations. The GitHub README describes every field and how to score a new model.

Use

from datasets import load_dataset
ds = load_dataset("vein05/lapse")
notes = ds["test"].filter(lambda r: r["arm"] == "e1")

Score outputs with the rule-based scorer in the GitHub repository (tools/score.py), which labels each note as keeping, flattening or dropping the temporal form.

Notes

• All names, companies, institutions, projects and residences are invented.
• The split is fixed by content (cluster % 5 == 4 is test). Both splits are public, so a model trained after release may have seen them.
• The paper's natural-text checks used WildChat, LoCoMo and LongMemEval. Those texts are not included.

Citation

@misc{panthi2026lapse,
  title         = {Memory Consolidation Flattens the Temporal Shape of User Facts},
  author        = {Panthi, Sugam and Yeamin, Muhaiminul and Luo, Siyan and Abdelfattah, Rabab},
  year          = {2026},
  eprint        = {2609.36457},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  doi           = {10.48550/arXiv.2609.36457},
  url           = {https://arxiv.org/abs/2609.36457}
}