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Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
Sci-MMR is introduced, a benchmark for multi-step evidence-grounded scientific reasoning built on structured argument graphs linking scientific claims, citation-grounded knowledge, visual evidence, and supporting regions, and it is found that current answer-centric benchmarks substantially overestimate the evidence-grounded reasoning capabilities of multimodal research agents.
Self-improving search agents thrive when feedback and policy evolution are intertwined, leading to sustained performance gains and reduced hallucinations.
IACM-RL reduces infinite loops and stale context errors by proactively managing dynamic user intents, setting a new standard for robust tool invocation.
LLMs can explore codebases far more effectively by planning test sequences that balance immediate coverage with long-term reachability, boosting branch coverage by up to 77% compared to greedy methods.
By explicitly modeling the interplay between morphology and control, Stackelberg PPO stabilizes training and boosts performance in morphology-control co-design tasks.
