arXiv AI
Aug 21
Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration
arXiv:2608. 19701v1 Announce Type: new Abstract: Long-term multi-agent systems continuously accumulate the memories produced by different agents.
Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration
Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat retrieved memories as independent evidence and combine them through voting or weighting.
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Hugging Face Trending Papers
Jun 22
RaMem: Contextual Reinstatement for Long-term Agentic Memory
Long-term memory has become increasingly important for LLM agents that operate across extended interactions and evolving task contexts. Recent memory systems have made past experiences more persistent, compact, and retrievable, but retrieval alone does not ensure that a memory provides valid evidence for the current query.
arXiv AI
Jul 17
When Does Belief-Based Agent Memory Help? Reliability-Conditional Updating and Provenance-Capped Poisoning Defense
arXiv:2606. 22030v2 Announce Type: replace Abstract: We investigate when belief-based memory actually improves large language model (LLM) agents.
arXiv AI
Aug 20
MemFuse: Multi-Source Memory Fusion from Fragmented Observations
MemFuseBench is a benchmark for multi‑source memory fusion that generates source‑tagged observations, evidence‑grounded questions, and adversarial distractors using a Scene‑to‑Sensor pipeline. The proposed MemFuse system preserves source‑level evidence in atomic memory and clusters related events into fused memory via a causal fusion graph, enabling traceable retrieval of dispersed observations. Experiments show that MemFuse outperforms other memory systems across all LLM settings, especially on questions requiring cross‑source evidence fusion.
arXiv Machine Learning
Sep 4
MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval
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.
Hugging Face Trending Papers
Aug 19
MemFuse: Multi-Source Memory Fusion from Fragmented Observations
MemFuse introduces a multi‑source memory fusion system and a corresponding benchmark, MemFuseBench, designed to evaluate agents that must integrate fragmented observations from multiple applications, devices, users, and time points. The benchmark uses a Scene‑to‑Sensor pipeline to generate source‑tagged observations, evidence‑grounded questions, and adversarial distractors, enabling systematic assessment of temporal reasoning, cross‑source evidence fusion, and noise robustness. Experiments show that MemFuse outperforms other memory systems across all evaluated LLM settings, especially on questions requiring cross‑source evidence fusion.
arXiv AI
Jun 10
Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents
arXiv:2606. 10616v1 Announce Type: new Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts that exceed their finite context windows, making memory retention a fundamental resource-allocation problem.
arXiv Machine Learning
Aug 4
Stop When Memory Suffices: Evidence-Conditioned Progressive Execution for LLM Agents
arXiv:2608. 01285v1 Announce Type: new Abstract: The continued development of LLMs toward persistent and adaptive intelligence increasingly requires long-term memory mechanisms that preserve and reuse information across interactions.
arXiv Computation and Language
Sep 1
UTILMEM: Benchmarking Evidence Utilization in Long-Term Conversational Memory
UTILMEM is a new diagnostic benchmark that tests how conversational agents use long‑term memory, focusing on reasoning over dense histories, spotting implicitly relevant memories, synthesizing distributed evidence, and resisting interference from similar distractors. It contains 1,717 instances across five domains and evaluates a range of retrieval‑based and memory‑augmented systems. The study shows that strong performance on traditional factual recall does not guarantee effective memory utilization, highlighting a gap between retrieving information and integrating it into coherent, task‑oriented outputs.
arXiv AI
Jul 28
MemChain: Learning Interpretable Memory Traces for Memory-Augmented LLM Agents
arXiv:2607. 24097v1 Announce Type: new Abstract: Memory-augmented LLM agents typically answer queries by retrieving relevant memories and feeding them directly to an answer model.
arXiv AI
Jun 10
Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory
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 AI
Aug 12
MESA:Task-Adaptive Multi-Structure Evidence Selection for Long-Horizon Agent Memory
arXiv:2608. 10108v1 Announce Type: new Abstract: Long-horizon agents accumulate trajectories spanning hundreds of interleaved reasoning, action, and observation steps, where answering a query may depend on evidence buried far back in the history.