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ForceDelta-VLA is presented, a correction-distillation framework that constructs an explicit force-correction target using paired predictions from a frozen teacher's force-conditioned and learned force-agnostic modes, and reduces mean peak contact force over successful trials by approximately 26% on both platforms.
Adaptive Relevance-guided Evidence Allocation (AREA) is introduced, a training-free inference-time method that formulates evidence highlighting as adaptive allocation and establishes the best performance among training-free highlighting methods.
StackTok is introduced, a training-free selector that treats query relevance as the objective and visual coverage as budget-calibrated support in vision-language models and ranks first among training-free selectors in every tested model--budget setting.
A dense, decision-level reward is introduced in which an LLM judge evaluates the necessity of each tool call, which effectively suppresses cue-driven tool use while preserving task performance, providing a practical approach to improving the robustness of LLM agent tool-use policies.
Humanoid robots can now perform complex loco-manipulation tasks with more natural and stable movements by decomposing control into VLM-orchestrated expert policies trained with human motion priors.
