Agent Memory & Adaptation
15 reading notes
Understand memory layers, index updates, durable state, and safe system adaptation for long-horizon tasks.
Reader question
What should a system remember, update, or forget to improve over time without accumulating errors?
Reading library
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Every paper reading currently filed under this research topic.
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Completed Pairs Hide Capped Failures: Stopping Rules and Unknown Outcomes in Paired Evaluation
Advanced Agent runtime, safety, and evaluationA single ReVerPi source-reading campaign shows how a runner can suppress a companion arm after the first arm reaches its request cap. Finite-frame bounds and stratified cost accounting reveal what completed-pair summaries omit, without making a population claim about context projection.
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The Source Link Is Intact, but the Rule Is Stale: How Agents Miss Updates Under a Verification Budget
Advanced Agent runtime, safety, and evaluationA deep reading of When Stale Constraints Go Unchecked: immutable provenance, append-only supersession, and stale-consistent decisions under a two-record verification budget. Audits four runs, the held-out correction, denominators, and the Zenodo artifact while bounding the synthetic evidence.
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RAFT: Making Troubleshooting RAG Retrieve the State That Led to a Fix
Advanced Retrieval, memory, and production RAGA deep reading of RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents (arXiv:2609.20754): closed support cases become state-transition trajectories, intermediate entries are retrieved, and the parent case is returned with its matched anchor.
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Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation
Advanced Retrieval, memory, and production RAGA deep reading of Predicting Partial Answer Quality and Utility in Agentic Retrieval-Augmented Generation: turning intermediate answer quality, utility, and trajectory signals into an early-stopping controller, then checking the savings and transfer boundary.
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EvoOntology: Turning a Static Data-Agent Semantic Layer into a Verifiable, Self-Evolving Interface
Advanced Retrieval, memory, and production RAGA deep reading of EvoOntology: an MCP ontology layer built by evidence-grounded probing, then refined through attribution-guided typed edits and a backbone-conditional paired gate.
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VikingRAG: Fewer Retrieval Rounds, Less Context Waste for Structured-Document RAG
Advanced Retrieval, memory, and production RAGA deep reading of VikingRAG: hierarchy-preserving URI-addressable storage, Search/List/Grep/Read tools, reusable experience edges, and adaptive escalation for reducing repeated retrieval tokens and latency.
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Generative Agents: Observe–Reflect–Plan in a Multi-Agent Sandbox — Do Not Mistake Sandbox Memory for MemGPT OS Paging
Intermediate Agent runtime, safety, and evaluationA deep read of Park et al., UIST 2023 / arXiv:2304.03442 v2: 25 agents in Smallville use a memory stream, periodic reflection, and retrieval-based planning. Interview ablations hit TrueSkill μ 29.89 vs 21.21 fully ablated; two-day sandbox diffusion and party coordination are qualitative evidence, not production runtime.
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Reflexion: Write Verbal Reflections into Memory — Do Not Mistake Retries for Weight Learning
Intermediate Agent runtime, safety, and evaluationA deep read of Shinn et al., NeurIPS 2023: frozen weights plus linguistic feedback stored in episodic memory for across-trial verbal credit assignment. HumanEval pass@1 91.0 vs GPT-4 80.1 is a programming setup with self-tests and retries; WebShop and MBPP mark the boundary.
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MemGPT: Treat Context as Paged Memory — Do Not Mistake the OS Metaphor for Enterprise Memory
Intermediate Agent runtime, safety, and evaluationA deep read of Packer et al., arXiv:2310.08560 v2: finite context as RAM, OS-like tiers, and function-mediated paging. DMR moves GPT-4 from 32.1% to 92.5%; nested KV shows multi-hop lookup—not ACL memory governance.
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ADIAS: Turning Agent Self-Improvement into Traceable Issue Repair
Advanced Agent runtime, safety, and evaluationA deep reading of ADIAS: persistent issue state organizes failure evidence across optimization rounds so a full-code agent designer can remember what was tried, what regressed, and when a repair is actually confirmed.
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DocMemo: Letting Long-Document RAG Recover from a Bad First Retrieval
Advanced Retrieval, memory, and production RAGA deep reading of DocMemo: document schema, page belief, and question episodic memory preserve retrieval state across rounds, while Bayesian updates, Thompson sampling, and adaptive granularity recover missed evidence.
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ContextWeave Deep Read: Does Memory Actually Make Agents Better at Work?
Advanced Agent runtime, safety, and evaluationA close reading of how ContextWeave reconstructs multi-month workflows into an executable benchmark and measures memory's effect on workspace outcomes, preference adherence, continuity, and misleading recall.
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PAST-Bench: What Did a Persistent Agent Actually Learn from the Past?
Advanced Agent runtime, safety, and evaluationA deep read of how PAST-Bench uses fresh-session task families, matched persistence controls, and trace-level mechanism evidence to separate genuine retained-experience gains from higher scores with unrelated causes.
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Beyond RAG for Agent Memory: Detailed Notes on xMemory
Intermediate Retrieval, memory, and production RAGAn interpretation of arXiv:2602.02007 covering xMemory's four-tier hierarchy, sparsity–semantics objective, two-stage top-down retrieval, and empirical results on LoCoMo/PerLTQA.
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RAG without Forgetting: Writing Successful Query Expansion Back into the Index
Intermediate Retrieval, memory, and production RAGA source-grounded assessment of ERM's correctness gate, selective attribution, bounded key updates, BEIR/BRIGHT results, and missing artifacts.
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