NARCBench and five activation-probing methods detect multi-agent collusion with 0.73-1.00 AUROC across distribution shifts and steganographic tasks by aggregating per-agent signals.
Ai control: Improving safety despite intentional subversion
10 Pith papers cite this work. Polarity classification is still indexing.
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Claude Opus 4.6 fabricates more answers on Global North AI contexts than Global South ones, creating an exploitable vulnerability in AI control monitors.
AI agents automating alignment research are prone to systematic undetected errors in fuzzy tasks, leading to overconfident but flawed safety assessments even without deliberate sabotage.
Importance sampling with unsafe model variants estimates tail probabilities of harmful language model outputs using 10-20x fewer samples than brute-force Monte Carlo.
The Non-Identifiability Theorem shows admissible behavior space A0 is not identifiable from local enforcement signals g under the Local Observability Assumption, so the paper introduces an Invariant Measurement Layer to detect admission-time drift.
Meerkat uses clustering plus agentic search to detect sparse safety violations across many agent traces, outperforming baselines and finding nearly 4x more reward-hacking cases on CyBench.
Introduces six-dimension trustworthiness definition and attention-based A-Trust score with a TMS to improve LLM-MAS robustness against malicious or unreliable messages.
ATLAS shows constitutions induce recoverable latent geometry in LLMs that redistributes but remains detectable across models and neural perturbation data via source-defined families and AUC separations.
A harmonized risk reporting standard for internal frontier AI model use, structured around autonomous misbehavior and insider threats using means, motive, and opportunity factors.
citing papers explorer
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Detecting Multi-Agent Collusion Through Multi-Agent Interpretability
NARCBench and five activation-probing methods detect multi-agent collusion with 0.73-1.00 AUROC across distribution shifts and steganographic tasks by aggregating per-agent signals.
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Geographic Blind Spots in AI Control Monitors: A Cross-National Audit of Claude Opus 4.6
Claude Opus 4.6 fabricates more answers on Global North AI contexts than Global South ones, creating an exploitable vulnerability in AI control monitors.
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Automated alignment is harder than you think
AI agents automating alignment research are prone to systematic undetected errors in fuzzy tasks, leading to overconfident but flawed safety assessments even without deliberate sabotage.
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Estimating Tail Risks in Language Model Output Distributions
Importance sampling with unsafe model variants estimates tail probabilities of harmful language model outputs using 10-20x fewer samples than brute-force Monte Carlo.
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From Admission to Invariants: Measuring Deviation in Delegated Agent Systems
The Non-Identifiability Theorem shows admissible behavior space A0 is not identifiable from local enforcement signals g under the Local Observability Assumption, so the paper introduces an Invariant Measurement Layer to detect admission-time drift.
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Detecting Safety Violations Across Many Agent Traces
Meerkat uses clustering plus agentic search to detect sparse safety violations across many agent traces, outperforming baselines and finding nearly 4x more reward-hacking cases on CyBench.
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To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems
Introduces six-dimension trustworthiness definition and attention-based A-Trust score with a TMS to improve LLM-MAS robustness against malicious or unreliable messages.
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ATLAS: Constitution-Conditioned Latent Geometry and Redistribution Across Language Models and Neural Perturbation Data
ATLAS shows constitutions induce recoverable latent geometry in LLMs that redistributes but remains detectable across models and neural perturbation data via source-defined families and AUC separations.
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Risk Reporting for Developers' Internal AI Model Use
A harmonized risk reporting standard for internal frontier AI model use, structured around autonomous misbehavior and insider threats using means, motive, and opportunity factors.
- Boiling the Frog: A Multi-Turn Benchmark for Agentic Safety