PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution
cs.AI, cs.LG
Submitted: 2026-10-03
Updated: 2026-10-10
Code: https://github.com/astral-sh/ruff
Project page: https://www.sqlite.org
Sources
- Measuring Faithfulness in Chain-of-Thought Reasoning
- Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
- Let's Verify Step by Step
- A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
- VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains
- When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors
- Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
- Monitoring Monitorability
- Measuring Progress on Scalable Oversight for Large Language Models
- Teaching Large Language Models to Self-Debug
- Code Execution with Pre-trained Language Models
- What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces
- CWM: An Open-Weights LLM for Research on Code Generation with World Models
- Solving math word problems with process- and outcome-based feedback
- TACO: Topics in Algorithmic COde generation dataset
- Measuring Coding Challenge Competence With APPS
- Program Synthesis with Large Language Models
- Qwen3 Technical Report
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
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