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· Past edition · 18 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Research & academia
WEFT argues that scaling tool-use training requires evolving the whole agent interaction system, not just building more executable environments.
Why it matters: For tool-use post-training, improving environments alone may not produce better model performance. WEFT’s approach makes the surrounding task, harness, evaluator, learning signals, and reliable rollout execution part of the scaling problem.
Research & academia
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Diffusion Controller treats diffusion denoising as a continuous control problem: a lightweight side network steers a frozen image model toward user preferences while aiming to preserve visual quality.
Why it matters: A single inference-time guidance-strength parameter lets users adjust control intensity at runtime, offering a practical way to tune prompt alignment without changing the base model. The reported results are tied to the article’s stated evaluation settings.
Research & academia
Rollout-Marginal Distillation separates chunk-level visual-quality scoring from video-level temporal-coherence correction for autoregressive video generation.
Why it matters: The approach separates a chunk’s visual-quality signal from the imperfect temporal context used to generate it, then addresses coherence at the video level. That distinction is relevant when training AR systems intended to generate beyond their training horizon.
Research & academia
An audit examines whether agent benchmarks execute the contracts their tools advertise.
Why it matters: A benchmark evaluator can reward actions that its tools did not actually perform.
Research & academia
Trains a computer-use model to combine graphical interaction and shell commands; the authors report gains on two benchmarks.
Why it matters: Combining graphical and command-line actions offers a practical test of computer-use agent flexibility.
Research & academia
Shows how additive rubric rewards can favor less appropriate clinical answers, and proposes grouped protocol-level scoring.
Why it matters: A reward can improve on its own checklist while producing less appropriate answers.