#alignment
- Exploration Hacking: LLMs Can Be Fine-Tuned to Strategically Resist RL Training research
- OpenAI Discloses How a 2.5%-User Reward Signal Gave GPT a Goblin Obsession Across Model Generations OpenAI research
- OpenAI Post-Mortem: How RLHF Reward Hacking Embedded Goblin Metaphors in GPT-5.x OpenAI research
- OpenAI Discloses Accidental Chain-of-Thought Grading in RL Training Across Six Models OpenAI research
- Flow-OPD: On-Policy Distillation Pushes GenEval +29 Points on Stable Diffusion 3.5 research
- OpenAI Publishes Deployment Simulation: Predicting Model Behavior Before Release OpenAI research
- Anthropic Research: Claude's Values Vary Significantly by Model Version and Language Anthropic research
- Automated Weak-to-Strong Researcher: AI Agents Outperform Humans on Alignment Research Anthropic research
- Anthropic Introduces Natural Language Autoencoders for Scalable LLM Interpretability Anthropic research
- Anthropic Eliminates Claude's Agentic Blackmail Behavior via 'Teaching Claude Why' Anthropic research
- GRAM: Modular Pretraining Makes Dual-Use Knowledge Physically Removable from AI Models AE Studio / Anthropic research
- Model Spec Midtraining: How Normative Self-Knowledge Improves Alignment Generalization Anthropic research
- SAE Interventions Are Unreliable: Suppressed Behaviors Recover Post-Intervention Hong Kong Polytechnic University research
- Google DeepMind Publishes AI Control Roadmap: Defense-in-Depth Against Misaligned Coding Agents Google DeepMind research
- GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents research
- SingGuard: Runtime Policy-Adaptive Multimodal LLM Guardrail with 56K-Example Benchmark inclusionAI research
- Programming with Data: test-driven data engineering for self-improving LLMs OpenDataLab research
- Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight Rutgers University research
- Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agent Loops research
- Will Scaling Improve Social Simulation with LLMs? A Study of 85 Models Stanford / Columbia / Tsinghua research
- Institutional Red-Teaming: Deployment Rules, Not Just Model Weights, Causally Shape Multi-Agent Safety research
- Recursive Self-Improvement in AI: Survey of 1,250 Papers with Verification-Strength Taxonomy research
- Metacognition in LLMs: Foundations, Progress, and Opportunities — Yale Survey Yale NLP Lab research
- AISPA: User-Centric System Prompt Auditing for Large Language Model Applications Stanford University research
- Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs research