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· Past edition · 4 stories
Retrospective edition reconstructed from later captures. Paper versions may include later updates; coverage labels describe the evidence used.
Research tests how reasoning models allocate compute, how model-switching evaluations can mislead and what makes agent skills reusable.
Research & academia
A study asks how reasoning models allocate one budget across questions.
Why it matters: A reasoning budget is useful only if effort is allocated effectively across tasks; the results concern the tested models.
Agent frameworks & orchestration
A paper questions static replay as a way to assess agent model switching.
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Why it matters: Static replay may miss later changes in an interactive trajectory, complicating comparisons of model switches.
Agent frameworks & orchestration
Researchers audit packaging and reuse problems in agent skills.
Why it matters: The skill-file analysis offers concrete metadata and organization issues to investigate when improving reuse.
Safety, alignment & governance
A study tests the limits of static checks for malicious agent skills.
Why it matters: Static scanners have test-specific detection limits; the constructed corpus does not establish universal coverage.