arXiv AI
MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 11
Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs
Fortunate Recall (FR) introduces an ontology-driven policy layer that categorizes personal facts into over ten behavioral types and applies tailored lifecycle rules—such as differential decay, supersession, and event-time validity—to manage memory persistence in large language models. The FR-Bank implementation, independent of underlying infrastructure, achieves a 76.9% pass rate on the new LifecycleBench benchmark and improves LongMemEval-S performance, while significantly reducing confabulation rates compared to prior systems. Ablation studies show that the generic lifecycle metadata drives correctness, whereas the behavioral ontology enhances calibration and reduces downstream hallucinations.
arXiv AI
Oct 9
MemTrace: State-Consistent Memory for Long-Horizon Coding Agents
MemTrace is a provenance‑aware memory system designed to help coding agents handle long‑horizon software evolution tasks that span multiple files and stages. It stores execution history as immutable Memory Traces linked to key artifacts (files, symbols, tests) and organizes them in a Memory Trace Graph to track execution order and dependencies. When context budgets are limited, the system keeps only compact Memory Anchors, from which the agent can reconstruct the latest execution state and retrieve only valid, relevant evidence, improving performance on three long‑horizon coding benchmarks. "whyItMatters":"By preserving consistent task state and verifying evidence validity, MemTrace enables coding agents to outperform existing baselines on long‑horizon benchmarks, achieving significant gains in pass@1, resolved rate, and milestone scores."
arXiv AI
Aug 5
Verifiable Memory: Learning Unified Memory Management with Local and Global Verifiers for Large Language Model Agents
arXiv:2608. 03137v1 Announce Type: new Abstract: Large language model (LLM) agents must retain reusable information, control a bounded active context, and recover earlier evidence during long-horizon interaction.
arXiv Computation and Language
3d ago
MemShift: Separating Detection from Repair in LLM-Derived Personal Memory
arXiv:2611.02338v1 Announce Type: new Abstract: AI assistants store values that a language model derived from a person's records under the person's own definitions, such as their important contacts....
Hugging Face Trending Papers
Sep 9
Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs
Current LLM memory systems treat all personal facts identically, so stores grow without bound while retrieval precision degrades. The core challenge is lifecycle management: which memories should pers...
arXiv AI
Oct 21
REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents
The paper introduces REM (Reusable Experience Memory), a framework that manages the lifecycle of experience for large language model agents performing long-horizon coding tasks. REM consolidates completed trajectories into compact, phase-structured documents, injects them at task start, and evicts them after a single exposure, making the memory system responsive to the agent’s needs. Experiments on repository-level bug fixing show REM outperforms state‑of‑the‑art memory systems in retrieval accuracy and task resolution while reducing cost.