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Beyond Content Safety: Real-Time Monitoring for Reasoning Vulnerabilities in Lar

BACKGROUND
1 SIGNALFIRST DETECTED 27 March 2026
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How the story developed
27 Mar
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Other feeds and industry voices
27 Apr
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining

REPORT · WHAT · ArXiv CS.AIOPEN STORY
23 Mar
Stepwise: Neuro-Symbolic Proof Search for Automated Systems Verification

arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet

REPORT · HOW · ArXiv CS.AIOPEN STORY
13 May
The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested

arXiv:2605.11496v1 Announce Type: new Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently

REPORT · WHEN · ArXiv CS.AIOPEN STORY
5 Oct
On the Chain-of-Thought Monitorability of Looped Language Models

arXiv:2610.02741v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring provides a promising approach for detecting undesirable model behavior. Looped language models

REPORT · WHAT · ArXiv CS.AIOPEN STORY
30 Mar
BeSafe-Bench: Unveiling Behavioral Safety Risks of Situated Agents in Functional Environments

arXiv:2603.25747v1 Announce Type: new Abstract: The rapid evolution of Large Multimodal Models (LMMs) has enabled agents to perform complex digital and physical tasks, yet their

REPORT · WHAT · ArXiv CS.AIOPEN STORY
22 Apr
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System

arXiv:2604.18789v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical

REPORT · WHEN · ArXiv CS.AIOPEN STORY
29 May
Robust and Efficient Guardrails with Latent Reasoning

arXiv:2605.29068v1 Announce Type: new Abstract: Maintaining the safety of large language models (LLMs) is crucial as they are increasingly deployed in real-world applications.

REPORT · WHAT · ArXiv CS.AIOPEN STORY
11 May
Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight

arXiv:2605.07021v1 Announce Type: new Abstract: Reasoning in Large Language Models (LLMs) poses a challenge for oversight as many misaligned behaviors do not surface until

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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