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AgentSWE is introduced, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target.
Results show that long-horizon reflective data is an effective route toward self-improving agents, and synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration.
StepLearn is introduced, a nonparametric framework that separates immediate use from persistent trust, while requiring prospective validation before reuse across episodes, and turns informative transitions into hypotheses that can guide the next step.
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.
DisCo is presented, a skill-powered research agent that creates skills and uses them during research, and yields the AREX-Skill Library, with 5,000+ verified skills distilled from 1,000 widely used ML repositories and organized into 20 areas and 178 capability families.
This work introduces AREX, a family of Recursively Self-Improving (RSI) deep research agents that substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters.
On-Policy Omni Distillation (OPOD), which consolidates text, image, and audio teachers into one omni model, and surpasses the base model and pooled RL training on all twelve benchmarks, and ranks first or second on eleven even when the teachers are included.
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