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Stratum: Repository Memory for Coding Agents Beyond the Context Window
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arXiv AI
Oct 28
Stratum: Repository Memory for Coding Agents Beyond the Context Window
The paper introduces Stratum, a repository memory system designed to give coding agents persistent, experience-aware memory beyond the limits of their context windows. It argues that coding agents suffer from a memory-architecture problem, not just context length, and presents three empirical findings from real repositories that motivate a tiered memory approach. Stratum stores knowledge as indexed text rather than in the model’s KV cache, enabling fast retrieval (1.7 ms median for a million nodes) and significantly improving root-cause identification in multi‑session bug‑fix experiments.
arXiv Computation and Language
Sep 22
VibeMemBench: Evaluating Memory Systems for Coding Agents on Real Repository Coding Tasks
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 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.
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 21
MemDev-Bench: Does Memory Help Code Agents?Benchmarking Memory Systems over Real Repository Histories
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.
arXiv AI
Jul 17
Why Git Is the Memory Solution for the Agentic Development Lifecycle
arXiv:2607. 14390v1 Announce Type: cross Abstract: Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session.