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GraphForge, an evidence-graph based framework that grounds both the task and its verification in real files, and rejects fine-tuning on the SFT model's own rollouts yields further improvements on all three benchmarks, suggesting that the rubrics provide a useful selection signal.
This work proposes AsyncTool, a benchmark for assessing LLM-based agents in interactive multi-task tool-use environments with delayed tool feedback, and constructs a diverse asynchronous multitasking dataset that covers multiple scenarios and tool-use patterns.
Video-DR is introduced, featuring a decoupled perception-exploration pipeline with stage-wise tool unlocking that compels exhaustive cross-frame visual grounding prior to web retrieval, enabling autonomous exploration that breaks the imitation-learning ceiling.
VideoCoCo, an agentic dual-engine framework in which executable Blender code serves as a process-level chain of thought, demonstrates that executable code provides an effective, controllable, and inspectable intermediate representation for physically consistent video generation.
These results establish source-intent preservation and shared executable-state grounding as key principles for scalable terminal-task synthesis as key principles for scalable terminal-task synthesis.
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