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This work forms long-horizon OPD as hierarchical supervision allocation as well as a coarse-to-fine framework that organizes supervision through Locate, Validate, and Refine and suggests that effective long-horizon distillation requires teaching at the right depth, the right decision, and the right token.
This work proposes SkillLift, which decouples skill search from oracle cost by learning an oracle-aligned rubric as a structured evaluation space, and outperforms existing auto-skill methods with 40--70\% less token cost compared to frontier evolving methods.
HazardAuditor is introduced, an execution-grounded framework that runs heterogeneous agents in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision, and observes that token-level post-training objectives create a structural mismatch for generati...
LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers often trust evidence such as test results and execution logs. We identify a response path integrity gap in Bring Your Own Key configurations used by roughly 88 percent of mainstrea...
UI-Venus-2 is presented, a general-purpose foundation GUI agent designed to operate across mobile, web, and desktop environments through a unified closed-loop reasoning-action framework that integrates safety-aware mechanisms to ensure controlled execution of consequential actions.
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