LIVE PULSE

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

papersTODAY 04:00 UTC

Paper tests physics-based assumption in RL simulators and world models

A new arXiv preprint examines the widely held assumption that learned dynamics models which conform to underlying physics produce more accurate predictions. The authors recover exact polynomial invariants from trajectories and canonicalise them as a method for diagnosing faults in reinforcement learning simulators and world models. The work frames these invariants as a diagnostic tool for checking whether a model's learned dynamics actually respect physical structure.

arXiv Model diagnostics Polynomial invariants physics-informed machine learning reinforcement-learning world models

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.AIDiagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants ↗TODAY 04:00 UTC

arXiv cs.LGDiagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants ↗TODAY 04:00 UTC