arXiv AI
ECHO: An Auditable Dual-Path Memory System for Long-Horizon Agents
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arXiv AI
Aug 25
ECHO: A Cognitively Inspired, Auditable Memory Plane for Long-Horizon Agents
arXiv:2608.21755v1 Announce Type: new Abstract: Long-horizon agents need memory that identifies relevant experience, resolves revisions, and exposes checkable provenance. We present ECHO (Embodied Co...
arXiv AI
Jul 29
Addressable Recall Compaction for Long Context-Window Control in AI Agents
arXiv:2607. 25066v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate reasoning traces, actions, and tool observations that can eventually exceed a model's fixed context window.
arXiv AI
Aug 5
LeanMem: Simple and Efficient Long-Term Memory for LLM Agents
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
arXiv AI
6d ago
From Memory Hits to Answerable Evidence: Query-Conditioned Memory Compilation for Long-Term Agent Memory
The paper introduces MemCompile, a query‑conditioned evidence compilation framework designed to improve long‑term conversational memory. It uses an Address Plane to locate relevant support via compact descriptors and capacity‑aware cells, then normalizes retrieval hits into stable SourcePacket identities. An Evidence Plane allocates a bounded evidence budget across packets, admits limited additional support, and produces answer‑ready context, achieving 80.52% Judge Accuracy on LoCoMo and improving the quality‑cost Pareto frontier.
arXiv AI
Oct 15
Memory Echo in Single-Agent Long-Term Memory: Lifecycle Evaluation and Provenance-Guided Mitigation
arXiv:2610.17763v1 Announce Type: new Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions an...
arXiv AI
Sep 10
When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
The paper introduces MERIT, a benchmark that evaluates the marginal benefit of long‑term memory for tool‑using large language model agents while explicitly accounting for cost. MERIT provides episodic tool‑use tasks across three domains, verifies dependence on earlier‑episode facts, and measures memory operations in tokens and dollars. Experiments on GPT‑4.1‑mini, Claude Haiku 4.5, and Claude Sonnet 5 show that memory can significantly improve task success, but its utility varies widely across models and memory implementations, and full replay is rarely cost‑effective.