{"total":18,"rows":[{"id":"2604.11806","title":"Detecting Safety Violations Across Many Agent Traces","created":"2026-04-13","cited_by_count":14,"area":null},{"id":"2604.16812","title":"Introspection Adapters: Training LLMs to Report Their Learned Behaviors","created":"2026-04-18","cited_by_count":11,"area":null},{"id":"2605.28591","title":"Models That Know How Evaluations Are Designed Score Safer","created":"2026-05-27","cited_by_count":4,"area":null},{"id":"2605.15164","title":"Position: Behavioural Assurance Cannot Verify the Safety Claims Governance Now Demands","created":"2026-05-14","cited_by_count":2,"area":null},{"id":"2607.19321","title":"ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D","created":"2026-07-21","cited_by_count":2,"area":null},{"id":"2605.26045","title":"Confidence and Calibration of Activation Oracles for Reliable Interpretation of Language Model Internals","created":"2026-05-25","cited_by_count":1,"area":null},{"id":"2607.03640","title":"Revealing Hidden Model Behaviors with Task-Specific Self-Reports","created":"2026-07-03","cited_by_count":1,"area":null},{"id":"2609.04166","title":"From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research","created":"2026-09-03","cited_by_count":1,"area":null},{"id":"2604.12875","title":"AISafetyBenchExplorer: A Metric-Aware Catalogue of AI Safety Benchmarks Reveals Fragmented Measurement and Weak Benchmark Governance","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2604.22109","title":"Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2605.00994","title":"Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2607.01208","title":"Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.10030","title":"Causal Behavioral Evaluation of AI Agents at Scale via Automated Behavioral Science","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.25460","title":"Training Alignment Auditors via Reinforcement Learning","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2608.31105","title":"BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2610.00767","title":"Pre-training interventions, ex post facto: Grafting model beliefs across checkpoints","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.11012","title":"Distillation for Incrimination and Distillation for Capabilities","created":"2026-10-07","cited_by_count":0,"area":null},{"id":"2610.18727","title":"On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}