AI Agent Papers2026
Alternates harness and policy optimization so a coding agent uses its own policy to propose harness revisions that target evolving failure modes. Combines harness search (H-step) with RL-based policy updates (P-step), improving resolve rates on multiple coding benchmarks and generalizing out-of-distribution.
AI Agent Papers2026
Provides a training-free multimodal reasoning harness that combines heterogeneous visual tools, persistent multimodal memory, and code execution so MLLMs can iteratively acquire and compose grounded evidence. Improves accuracy on 22 benchmarks by about 9.7–12.6 points while cutting comparative token costs.