MemCoRe is a memory system for large language model agents that organizes factual knowledge into a compression hierarchy, progressively reducing redundancy while preserving retrieval structure. The hierarchy compresses detailed records into keywords and then into topic groups, allowing evidence to be located by searching across levels. Experiments show that MemCoRe outperforms current state‑of‑the‑art baselines in retrieving relevant evidence for downstream reasoning.
arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.
Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks. However, as interactions accumulate, the memory store grows without bound and fills with redundant entries that inflate storage cost and degrade retrieval by crowding out the most useful evidence.
arXiv:2610.15642v1 Announce Type: new Abstract: Under a limited context budget, a long-term agent must recall not merely memories that are individually relevant, but evidence that is collectively suf...
arXiv:2606. 10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions.
arXiv:2608. 10676v1 Announce Type: new Abstract: Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments.
Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments. However, providing complete execution trajectories to the LLM causes unbounded context growth and introduces noise.
MemoryLACE (MemLACE) is a lightweight memory framework that explicitly models the lifecycle of textual evidence—capturing sparse merge, supersession, and contradiction relations—while preserving atomic natural‑language memories and their provenance. Unlike traditional systems that retrieve memories independently, MemLACE reconstructs relation‑aware evidence units that expose current, historical, supporting, and conflicting evidence for downstream reasoning. In benchmark evaluations (BEAM and StructMemEval) using both open‑weight and proprietary LLM backbones, MemLACE achieves the highest overall performance among same‑backbone comparisons and reduces BEAM runtime by 66.6% compared to the strongest reflective‑memory baseline, Hindsight.
arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.
MemReread is a new agentic long‑context reasoning method that uses memory‑guided rereading instead of intermediate retrieval. By decomposing questions and selectively rereading when the final memory is insufficient, it recovers indirect evidence that might otherwise be lost during memory overwriting. A reinforcement‑learning framework further improves length extrapolation and adaptively controls the number of rereading passes, allowing MemReread‑4B to outperform strong baselines on long‑context tasks while keeping linear time complexity.
arXiv:2608.12990v2 Announce Type: replace Abstract: Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager c...
MemFit is a long‑term memory system designed for conversational agents that stores each dialogue turn verbatim in an append‑only store, enabling near‑instantaneous, LLM‑free insertion. It indexes turns using segment summaries and employs an LLM‑free, multi‑path retrieval strategy that blends lexical and semantic signals with cross‑encoder reranking over caption‑augmented episodes. Experiments on LoCoMo, MemGallery, and LongMemEval‑S demonstrate state‑of‑the‑art performance while drastically reducing memory construction time and cost.
arXiv:2609.08273v1 Announce Type: new Abstract: Agent memory systems have demonstrated significant potential in long-term dialogue, personalized assistants, and video understanding. However, continuo...