Metacognitive self- and co-regulation loops improve LLM agent performance in engineering design by mitigating fixation and enabling better exploration of design options.
Self-Reflection in Large Language Model Agents: Effects on Problem-Solving Performance
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5representative citing papers
Adversarial competition between attacker and defender teams generates diverse multi-turn conversational data that improves LLM performance on secure code generation benchmarks by 18-29%.
IntentScore learns intent-conditioned action scores from offline GUI trajectories and raises task success by 6.9 points on an unseen agent and environment.
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
SAT reduces reasoning tokens by up to 40% across multiple large reasoning models and benchmarks by adaptively pruning steps based on difficulty while maintaining or improving accuracy.
citing papers explorer
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Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design
Metacognitive self- and co-regulation loops improve LLM agent performance in engineering design by mitigating fixation and enabling better exploration of design options.
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Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition
Adversarial competition between attacker and defender teams generates diverse multi-turn conversational data that improves LLM performance on secure code generation benchmarks by 18-29%.
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IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
IntentScore learns intent-conditioned action scores from offline GUI trajectories and raises task success by 6.9 points on an unseen agent and environment.
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LLM4C2Rust: Large Language Models for Automated Memory-Safe Code Transpilation
A RAG-enhanced LLM pipeline with segmentation improves C-to-Rust transpilation correctness and eliminates raw pointer dereferences and unsafe type casts in several Coreutils programs.
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SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking
SAT reduces reasoning tokens by up to 40% across multiple large reasoning models and benchmarks by adaptively pruning steps based on difficulty while maintaining or improving accuracy.