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Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds

BACKGROUND
1 SIGNALFIRST DETECTED 16 June 2026
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How the story developed
16 Jun
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
12 May
Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria

arXiv:2605.08354v1 Announce Type: new Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional,

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
25 Jun
Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval

arXiv:2606.24976v1 Announce Type: new Abstract: Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes

REPORT · WHERE · ArXiv CS.AIOPEN STORY
31 Mar
Reward Hacking as Equilibrium under Finite Evaluation

arXiv:2603.28063v1 Announce Type: new Abstract: We prove that under five minimal axioms -- multi-dimensional quality, finite evaluation, effective optimization, resource

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
11 May
CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains

REPORT · WHAT · ArXiv CS.AIOPEN STORY
16 Apr
Numerical Instability and Chaos: Quantifying the Unpredictability of Large Language Models

arXiv:2604.13206v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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