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5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 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 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src5.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.7 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.5 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src2.2 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 VoiceCodeBench arXiv paper proposes benchmark for exact structured-token recovery in speech recognition — 1 src1.4 Arabic-Russian Parallel Corpus and LLM Benchmark for Scientific Text — 1 src1.4 Study Analyzes Self-Reported Limitations in NLP Research — 1 src

#adversarial-robustness

2 curated events

papersTODAY 04:00 UTC

Admissable: Training RL Agents to Withstand Adversarial Missing Features

A new arXiv paper introduces Admissable, a training approach for reinforcement learning agents that must keep operating safely when an adversary deliberately removes or withholds input features. The work targets real-world deployments where sensor dropouts or tampered observations can degrade decision quality. It frames adversarial feature missingness as a distinct safety problem for RL rather than standard robustness to noise.

reinforcement-learning/adversarial-robustness/safety/arxiv

papersSEP 10 04:00 UTC

Training-Free Edge Sanitization Method Defends Graph Neural Networks from Structural Attacks

A new arXiv paper proposes a defense for graph neural networks that strips out attacker-inserted edges from a graph's topology before inference, requiring no retraining of the model. The method relies on kernel-complexity signals to identify edges likely introduced through adversarial manipulation, and the authors support the design with theoretical guarantees. It addresses threats that target graph structure rather than node features.

graph-learning/adversarial-robustness/security/gnns