MUSE is a memory management framework for scientific coding agents that combines multi-level memory—global, approach-level, and implementation-level—with selective runtime injection. The agent starts with global and problem-matched approach-level memory, then during execution it retrieves implementation-level memory and an LLM gate decides whether to inject relevant experience as review advice. Experiments on four benchmarks in numerical problem solving, kernel optimization, and scientific software repair show that MUSE outperforms retrieval and memory baselines, and ablations confirm the benefits of the multi-level design and gated injection.
arXiv:2606. 05646v1 Announce Type: cross Abstract: Large language models (LLMs) have enabled powerful software engineering (SE) agents capable of navigating complex codebases and resolving real-world issues.
arXiv:2608. 12522v1 Announce Type: new Abstract: LLM-based program evolution systems such as FunSearch and AlphaEvolve have shown strong ability to discover novel algorithms, but typically optimize each task in isolation, discarding search experience after completion.
The paper introduces cognitive extensions for a dual‑process language agent, adding an Adaptive Memory Module (AMM) for episodic storage and a Self‑Reflection Module (SRM) for execution‑time validation. Implemented as feature‑flagged extensions, these modules allow controlled ablations on the ScienceWorld benchmark. Results show the full system outperforms baselines, with SRM providing the strongest individual improvement, indicating that execution‑time control is the key bottleneck while episodic memory aids once the runtime loop stabilizes.
Language agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps. We extend SwiftSage, a dual-proc...
arXiv:2605. 18421v2 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution.
SWE-Opt is a new benchmark that evaluates large language models (LLMs) on repository‑level software performance optimization. It is built from 102 expert‑written optimizations from open‑source projects and measures runtime, peak memory, and Time‑Weighted Memory Usage (TWMU) under varied input data, execution conditions, and noise‑aware measurements. The study finds that current LLMs achieve few runtime gains and almost no memory improvements, whereas expert implementations deliver a 15.5× speedup and 171.3× memory reduction.
arXiv:2607. 24772v1 Announce Type: new Abstract: Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation.
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. 28434v1 Announce Type: cross Abstract: Long-horizon software engineering agents often need to manage lengthy and noisy interaction histories under limited context budgets.
arXiv:2606. 01138v2 Announce Type: replace-cross Abstract: Agent-memory frameworks -- mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor -- each ship their own SDK, storage layout, and operational vocabulary.
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.
The paper introduces KOPE, an experience‑driven framework that records hardware kernel optimization trajectories in an Experience Graph Memory and uses Active Context Management and Injection to retrieve relevant past decisions under a fixed token budget. KOPE preserves decision order, outcomes, and alternative branches, enabling evidence from completed runs to inform future optimization steps. In experiments, KOPE achieves a 1.54× speedup over the strongest baseline, raises pass rates from 60.0% to 84.6%, and reduces token consumption dramatically, demonstrating the benefits of continual learning from external experience while keeping the foundation model unchanged.