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MemDev-Bench is a new benchmark that evaluates how memory systems influence code agents across diverse repository histories. It pairs each task with relevance‑annotated earlier tasks from the same repository, creating 4,869 historical tasks for 555 base tasks and an automated pipeline that ensures these tasks are executable and verifiable. The study finds that memory mainly improves fine‑grained localization and editing, but can also cause failures when agents reuse memory without reassessing its applicability, and that providing raw historical trajectories reduces steps more effectively than compressed memories.
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:2609.23570v1 Announce Type: cross Abstract: Coding agents operate on real repository coding tasks, and persistent memory systems promise to reuse experience across tasks. Yet existing evaluatio...
arXiv:2606. 00619v1 Announce Type: cross Abstract: Long-horizon autonomous agents require memory systems to retain historical information, track evolving states, and reuse relevant knowledge beyond finite context windows.
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%.
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.