The Answer Is Not the Argument

arXiv:2609.00264 · cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.AI

Submitted: 2026-08-31

Updated: 2026-10-07

Comments: 25 pages, 12 figures

Code: https://github.com/WillYeadon/hle_

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 178× in this paper · explained in 2 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Consistency — 8× in this paper · explained in 3 episodes
• Verification — 6× in this paper · explained in 2 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Error Localization — 3× in this paper · explained in 2 episodes
• Reward Hacking — 3× in this paper · explained in 8 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Commentary — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Positional Bias — 1× in this paper · explained in 2 episodes
• Quantum Physics — 1× in this paper · explained in 10 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
• Concrete Problems in AI Safety
• Measuring Massive Multitask Language Understanding
• Humanity's Last Exam
• MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models
• How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models
• Measuring Faithfulness in Chain-of-Thought Reasoning
• Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting
• Training Verifiers to Solve Math Word Problems
• Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
• LLM-as-a-judge validity is strongly task-dependent across physics assessment formats
• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
• AI safety via debate
• Kimi K3: Open Frontier Intelligence
• Measuring Progress on Scalable Oversight for Large Language Models
• Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems