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Living-Harness enables agents to learn from past failures dynamically, leading to substantial performance improvements in interactive tasks.
LLMs can now be trained to prioritize task constraints intrinsically, resulting in a dramatic improvement in planning reliability.
Trajectory neglect in LLM agents can be significantly reduced using a novel reward mechanism that enhances focus on task goals without sacrificing training stability.
MLLMs can mislead medical professionals by misaligning confidence with accuracy, but a new calibration method cuts Expected Calibration Error by 40%.
MLLMs are often overconfident, but a new confidence-driven training and test-time scaling approach can boost accuracy by 8.8% across benchmarks.
