Hugging Face Trending Papers
MemShift: Separating Detection from Repair in LLM-Derived Personal Memory
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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....
arXiv AI
Aug 25
MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance
arXiv:2608.21867v1 Announce Type: new Abstract: LLM agents are moving from single-prompt use to long task streams in which reusable memory becomes a core capability for terminal, software-engineering...
arXiv AI
Jul 28
ConsistencyGate: Preventing Memory Contamination in LLM Agents via Self-Consistency Admission Control
arXiv:2607. 22962v1 Announce Type: new Abstract: LLM agents that operate over many turns accumulate facts in an external memory store and reuse them as premises for downstream reasoning.
arXiv AI
Oct 16
MemScope: Evolving Agent Memory Architectures through Localized Code Repair
MemScope introduces a framework for evolving large language model (LLM) agent memory architectures by using localized code repair guided by detailed execution traces and runtime memory states. The Patch Agent identifies precise repair targets and applies bounded, in-place edits while respecting provider identity and memory compatibility constraints. Experiments on GAIA, xBench, and ALFWorld show that MemScope improves accuracy and correct answer rates, achieving higher success rates with a modest median code‑change ratio of 4.4%.
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
Oct 2
Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents
Mem++ is a non‑destructive memory framework for large language model agents that records every document in full, along with its date and author, instead of compressing it at write time. At read time, it retrieves only documents up to the requested time and fuses lexical and semantic rankings, leaving the choice of which version to use to the answering model. Evaluations on OrgMemBench show Mem++ outperforms the strongest baseline by 8.0 to 13.1 points and achieves the best overall score with gpt‑4.1‑mini, while also ranking highly on other benchmarks such as LoCoMo and LongMemEval‑S.