When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep? Two natural heuristics keep the rarest claims or the claims relevant to the de
When compressing multi-agent discussions for final decisions, memory selection by rarity or relevance both fail due to decoy inclusion and social-proof bias, and even full transcripts reduce accuracy compared to no notes.
When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep? Two natural heuristics keep the rarest claims or the claims relevant to the decision criteria. On hidden-profile tasks both look attractive, because how widely a fact is held is confounded with whether it helps. We introduce CrossProfile, a diagnostic benchmark of 60 templates with automatically verified answer keys, in which the ownership of supporting, decoy, and irrelevant facts is crossed while their text is held fixed (480 tasks). Across Qwen2.5-7B, Llama-3.1-8B, and two DeepSeek models, both heuristics fail in predictable places. Selecting by relevance readmits…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions was first seen on Hugging Face · Daily papers on Oct 29, 2026. Since then it has appeared in 4 checks on 2 different days. Its best position was #28 of 30, reached on Oct 30, 01:28 UTC. It dropped off the list after Oct 30, 05:28 UTC.
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#28 on Hugging Face · Daily papers, on Oct 30, 01:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 29, 2026 and has been on the list on 2 days (4 checks) since.