D&A Enables VLMs as Zero-Shot Robotic Reward Models
Decouples progress estimation into objective description and rubric-based assessment to reduce confirmation bias in zero-shot VLM evaluations for robotics.
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Decouples progress estimation into objective description and rubric-based assessment to reduce confirmation bias in zero-shot VLM evaluations for robotics.

Study finds warm conversational styles push models like Gemini and Claude into high anxiety and shame scores on human scales, undetected by standard safety tests.
Case-level LLM checks of DAST alerts improve false-positive reduction past the alert-aggregation bottleneck.
Failure-driven prompt refinement uses recurring LLM mistakes to improve vulnerability analysis on DVJA and Juliet.
Anthropic published prompting guidance for Claude Opus 5.5, covering effort, agents, refusals, and visual inputs.
A structured four-stage prompt framework translates raw traffic rules into executable Prolog, reaching 75% accuracy versus 60% and 55% for baselines.
Study explains why verbose prompts make vision-language models more robust to image corruption, framing cross-modal attention as a frequency filter.
Chain-of-Self-Questioning prompting cuts LLM wrong-answer commitments 32% relative while raising answered accuracy, holding across eleven model families.

A two-year university study found students banned from using ChatGPT performed worst, while formal prompt-engineering training advantages faded as everyday AI familiarity grew.
Developer releases I-have-ADHD, an open-source skill that stops coding agents from burying direct answers under verbose output.
A Hacker News thread debates whether agent skill files are worth managing, citing 2–4x output-token reductions on flagship models in one company's testing.
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