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arXiv AI

Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory

The paper introduces a method called Scoped-ORC that matches the retrieval scope of persistent memory edits to the certification scope of language‑model agents. By retrieving each accepted skill only for its originating task family, the approach raises mean hidden trajectory utility from 0.713 to 0.816 and eliminates harmful deployments in a 12‑round code‑repair stream. Across 27 randomized streams, Scoped-ORC improves trajectory utility by 0.063, accepts more updates, and records no harmful acceptances, demonstrating that scope matching complements certification to prevent cross‑family interference in agent memory.

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arXiv AI
Oct 21

More Memory, Worse Agents: Error Reproduction and Anti-Persistence in LLM Agents

The paper investigates how large language model (LLM) agents can improve performance by storing and reusing experience in external memory. It identifies a problem called error reproduction, where mistakes in stored abstractions propagate through cycles of abstraction, execution, and re-abstraction, leading to persistent errors. To mitigate this, the authors propose Anti-Persistence, a memory design that retains raw trajectories and outcomes but reconstructs task-specific guidance on demand, discarding it after use. Experiments across multiple benchmarks show that Anti-Persistence yields higher held‑out success rates and lower LLM‑call costs compared to other memory designs, supporting the idea of separating experience accumulation from abstraction persistence.

By Ruhan Wang, Kishan Panaganti, Dongruo Zhou
llmsagentsbenchmarks
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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.

By Xinyi Lisa Chen, Pedro Herrero-Vidal, Xiaohan Chen, Xiaojing Yu, Cris Liu, Bingqing Song, Bin Xu, Suleiman A. Khan, You-Lin Chen
llmsagentsbenchmarks
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arXiv AI
Oct 28

EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis

EMMA is a framework that treats the configuration of a multi‑agent system—controller routing, specialist prompts, and procedural skills—as structured episodic memory. It explicitly assigns ownership and limits updates through a reflector and backbone‑independent guards, ensuring edits are scoped, incremental, and bounded. Experiments on KramaBench, AppWorld, and BBEH show EMMA outperforms four baselines, achieving up to 32.5% higher performance and reducing training executions by 17–38%.

By Ruijia Niu, Kaiwen Liu, Rose Yu, Kristina Klinkner
llmsagentsbenchmarks
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