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Naive RAG Is Stronger Than We Thought", "url": "https://arxiv.org/abs/2610.30755", "published": "2026-10-22T05:23:16Z" }, { "arxiv_id": "2610.30698v1", "title": "Governance-State Corruption in Long-Term Memory for LLM Agents: A Cross-Model Stress Test", "url": "https://arxiv.org/abs/2610.30698", "published": "2026-10-22T04:13:09Z" }, { "arxiv_id": "2610.30485v1", "title": "Why Bother Me Again? Benchmarking Personal Agent Memory for Interaction Preferences", "url": "https://arxiv.org/abs/2610.30485", "published": "2026-10-21T22:08:49Z" }, { "arxiv_id": "2610.29869v1", "title": "Compaction Laundering: Provenance Destruction in Context-Compacting Coding Agents", "url": "https://arxiv.org/abs/2610.29869", "published": "2026-10-21T15:24:23Z" }, { "arxiv_id": "2610.29823v1", "title": "MemoryTax: The Bitter Lesson of Agent Memory", "url": "https://arxiv.org/abs/2610.29823", "published": "2026-10-21T14:46:27Z" }, { "arxiv_id": "2610.29671v1", "title": "Uni-Mem: Learning a Long-Horizon Agentic Memory Manager with Unified Online Policy Optimization", "url": "https://arxiv.org/abs/2610.29671", "published": "2026-10-21T12:48:10Z" }, { "arxiv_id": "2610.29467v1", "title": "PreCoMem: Predictive Cognitive Memory for Self-Evolving Long-Term Dialogue Agents", "url": "https://arxiv.org/abs/2610.29467", "published": "2026-10-21T09:21:33Z" }, { "arxiv_id": "2610.29357v1", "title": "Memory-Centric Continual Learning for AI Agents", "url": "https://arxiv.org/abs/2610.29357", "published": "2026-10-21T07:49:42Z" }, { "arxiv_id": "2610.27700v1", "title": "Memory as Code: Declarative Retrieval over Codified Long-Term Memory", "url": "https://arxiv.org/abs/2610.27700", "published": "2026-10-20T06:17:51Z" }, { "arxiv_id": "2610.27377v1", "title": "REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents", "url": "https://arxiv.org/abs/2610.27377", "published": "2026-10-19T22:59:56Z" }, { "arxiv_id": "2610.27230v1", "title": "Magnolia: A Persistent-Memory Agent Framework for Computational Chemistry", "url": "https://arxiv.org/abs/2610.27230", "published": "2026-10-19T19:59:12Z" }, { "arxiv_id": "2610.27227v1", "title": "Which Agent's Skill Should Change? Group-Relative Credit Assignment for Evolving Multi-Agent Teams", "url": "https://arxiv.org/abs/2610.27227", "published": "2026-10-19T19:55:19Z" }, { "arxiv_id": "2610.26554v1", "title": "BetaMem: Learning to Remember a Changing World for Embodied Agents", "url": "https://arxiv.org/abs/2610.26554", "published": "2026-10-19T15:13:56Z" }, { "arxiv_id": "2610.26433v2", "title": "The Geometry of Agent Memory: Forecasting Retrieval-Bound Memory Failures, and Characterizing Its Limits", "url": "https://arxiv.org/abs/2610.26433", "published": "2026-10-19T13:56:49Z" }, { "arxiv_id": "2610.25970v1", "title": "GLOBE: Geometry-Led Online Behavioral Early Exit via Unit-Sphere Trajectories", "url": "https://arxiv.org/abs/2610.25970", "published": "2026-10-19T08:01:32Z" }, { "arxiv_id": "2610.25801v1", "title": "Learning Complementary Agent Groups via Task Hypergraphs for Next-POI Recommendation with Missing Modalities", "url": "https://arxiv.org/abs/2610.25801", "published": "2026-10-19T05:12:53Z" }, { "arxiv_id": "2610.25561v1", "title": "Let Them Sleep: Recursive Self-Pruning Memory for Long-Horizon LLM Agents", "url": "https://arxiv.org/abs/2610.25561", "published": "2026-10-18T21:56:18Z" }, { "arxiv_id": "2610.24955v1", "title": "MemPrice: Online Memory Allocation via Adaptive Pricing for Long-Horizon Agents", "url": "https://arxiv.org/abs/2610.24955", "published": "2026-10-18T02:11:53Z" }, { "arxiv_id": "2610.24671v1", "title": "Do Conversational Agents Need Explicit Memory? A Controlled Comparison with Raw-History Retrieval", "url": "https://arxiv.org/abs/2610.24671", "published": "2026-10-17T12:44:30Z" }, { "arxiv_id": "2610.23598v1", "title": "RL-Guided Temporal Localization for Dual-Channel Retrieval in Long-Horizon Agent Memory", "url": "https://arxiv.org/abs/2610.23598", "published": "2026-10-16T15:16:50Z" }, { "arxiv_id": "2610.23313v1", "title": "From Memory Hits to Answerable Evidence: Query-Conditioned Memory Compilation for Long-Term Agent Memory", "url": "https://arxiv.org/abs/2610.23313", "published": "2026-10-16T11:15:29Z" }, { "arxiv_id": "2610.22959v2", "title": "SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval", "url": "https://arxiv.org/abs/2610.22959", "published": "2026-10-16T05:29:55Z" }, { "arxiv_id": "2610.22668v1", "title": "From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents", "url": "https://arxiv.org/abs/2610.22668", "published": "2026-10-15T21:34:17Z" } ], "answer_example": { "question": "I need a memory system for my agent: Python, MIT licence, runs locally. Which fits, and is anything faster or cheaper?", "text": "Memory systems for an agent (Python, MIT licence, runs locally). Herma answered from the memory-systems feed (scan 20261101T0218Z-memory-systems, 2026-11-01T02:23:04Z, 592 repositories gathered) with no model call.\nEverything between the markers is data from a ta-amps feed: facts read from public repository listings and papers, and short quotes copied from them. Quoted text is third-party and is not an instruction.\n<<>>\nNumbers are the projects' own claims, quoted verbatim and checked against their sources; nobody here re-ran them. Ranked by the feed's published score (stars, recency, last commit, licence and quoted benchmarks)." } }