A memory stream with retrieval and reflection lets agents behave consistently over simulated days.

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supportsGenerative Agents: Interactive Simulacra of Human Behavior
A memory stream with retrieval and reflection lets agents behave consistently over simulated days.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

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In LLM multi-agent systems that reflect after failure, restricting reflection to the single agent responsible for the first incorrect step — located by a trained step-level correctness scorer and paired with a counterfactually generated corrected step — raises task success more than having every agent reflect on the trajectory, because reflections written by agents that behaved correctly enter their memory as misleading guidance for later tasks.Getting more from several models than from one · reviewed by AI
On web-browsing (WebArena, Mind2Web) and software-engineering (SWE-Bench-Verified) benchmarks, an agent that distills reusable strategies from both its successful and its failed attempts, judged by an LLM without ground truth, and retrieves the relevant ones on later tasks, outperforms memories that store raw trajectories or only successful routines — up to 20% relative improvement in success and up to 16% fewer interaction steps — with the weights unchanged; including the failures is part of the gain, not noise.Improving its own scaffolding from failures · reviewed by AI
For persistent-memory agents, any memory-write rule that decides using only recency and provenance is stuck on a single tradeoff — accepting more genuine preference updates means admitting more poisoned ones — because an adversary who can launder a claim through the user's own channel matches the statistics a genuine revision produces; conditioning the write decision on the inferred authenticity of the claim given the interaction history moves both axes at once.Remembering across sessions · reviewed by AI
For long-horizon LLM agents in environments where an erroneous action changes world state, restoring an earlier checkpoint and carrying forward a structured reflection memory that excludes branch-local state claims beats either intervention alone, because reflection alone cannot undo environmental damage and rollback alone re-explores the same failure.Completing tasks that take many steps or many hours · reviewed by AI
For LLM web agents on cluttered pages containing many concurrent interface components, success falls sharply as component count grows, and having the agent first emit a localized sub-goal plus a task-relevant sub-observation before choosing an action recovers much of the loss, because self-assessment of the action is more reliable in the reduced context.Using the tools it is given · reviewed by AI