environment-aware

EnvShip-Bench is a new benchmark for short-term vessel trajectory prediction, built from large-scale AIS data with standardized protocols and environmental context extensions.

This paper presents the first systematic analysis of how large language models can learn to adapt query formulation strategies for different retrievers using reinforcement learning, revealing distinct optimal query styles and introducing a branching-based rollout technique for multi-retrieval-step training stability.

SABER introduces a benchmark for evaluating the operational safety of LLM coding agents in realistic stateful project workspaces, showing that even the best model has over a 54% harmful safety-violation rate, indicating insufficient alignment for real-world environments.

AJ-Bench introduces a benchmark to evaluate Agent-as-a-Judge systems that interact with environments to verify agent behaviors across 155 tasks in search, data systems, and GUI domains.