5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.1 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.8 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.6 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.3 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.8 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.4 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.4 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.4 Fine-Tuning Vision-Language Models with Listener Gaze for Referring Expressions — 1 src1.4 Perceptual Reality Transformer Explores What Illustrations Must Preserve — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src
Paper Proposes Joint Optimization of Prompts and Training Data via Failure Signals
A new arXiv paper addresses automatic prompt optimization, a technique that normally revises prompts using task feedback while leaving the training set unchanged. The authors argue that repeatedly tuning against the same examples limits feedback to already-known weaknesses, and they propose a failure-guided approach in which prompts and training data are improved together. This co-evolution aims to surface new shortcomings rather than only correcting previously identified ones.