Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

A new technical paradigm called ExpSeek has been introduced, enhancing web agents' interaction capabilities by enabling proactive experience seeking rather than passive experience injection. This approach utilizes step-level entropy thresholds to optimize intervention timing and tailor-designed experience content, demonstrating significant performance improvements in Qwen3-8B and Qwen3-32B models across various benchmarks.

ToolRM has been introduced as a new family of lightweight reward models specifically designed for tool-use scenarios, addressing the limitations of existing reward models in aligning large language models (LLMs) with human preferences. This development includes a novel pipeline for generating high-quality preference data and a benchmark for evaluating these models on tool-calling tasks.

KVzap has been introduced as a fast and adaptive method for key-value (KV) cache pruning in transformer-based language models, addressing the critical inference bottleneck caused by growing context lengths. This method achieves 2-4 times KV cache compression with minimal accuracy loss, demonstrating state-of-the-art performance on the KVpress leaderboard.