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Agent-World reveals that self-evolving environments can dramatically boost agent performance, outperforming established models by leveraging dynamic task synthesis.
LLMs can now navigate 100-turn multimodal search tasks without context explosion, thanks to a file-based visual representation that slashes token costs.
Autonomous ML research agents achieve significantly better long-horizon performance by maintaining durable state through a shared workspace, suggesting that orchestration and memory are more critical than raw reasoning power.
LLMs' training trajectories in RLVR are more predictable than you think: modeling the non-linear evolution of a rank-1 subspace lets you extrapolate parameters and cut compute by 37.5%.
