mlsanityData to 13 Oct 2026

Strategic Reflectivism In Intelligent Systems

arXiv 2505.22987 ↗First posted 29 May 2025cs.AI cs.HC econ.THcited by 0

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Abstract (arXiv)

By late 20th century, the rationality wars had launched debates about the nature and norms of intuitive and reflective thinking. Those debates drew from mid-20th century ideas such as bounded rationality, which challenged more idealized notions of rationality observed since the 19th century. Now that 21st century cognitive scientists are applying the resulting dual pro-cess theories to artificial intelligence, it is time to dust off some lessons from this history. So this paper synthesizes old ideas with recent results from experiments on humans and machines. The result is Strategic Reflec-tivism, the position that one key to intelligent systems (human or artificial) is pragmatic switching between intuitive and reflective inference to opti-mally fulfill competing goals. Strategic Reflectivism builds on American Pragmatism, transcends superficial indicators of reflective thinking such as model size or chains of thought, applies to both individual and collective intelligence systems (including human-AI teams), and becomes increasingly actionable as we learn more about the value of intuition and reflection.

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References · 18

  1. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,156
  2. 2025Understanding R1-Zero-Like Training: A Critical Perspectivecited by 1,304
  3. 2025Reasoning Models Don't Always Say What They Thinkcited by 404
  4. 2022Thinking Fast and Slow in Large Language Modelscited by 136
  5. 2025Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Timecited by 48
  6. 2024Prompting Techniques for Reducing Social Bias in LLMs through System 1 and System 2 Cognitive Processescited by 39
  7. 2025Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinkingcited by 31
  8. 2025Assessing Judging Bias in Large Reasoning Models: An Empirical Studycited by 30
  9. 2025ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflectioncited by 30
  10. 2025Incentivizing Dual Process Thinking for Efficient Large Language Model Reasoningcited by 21
  11. 2025THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Modelscited by 19
  12. 2025Unmasking Conversational Bias in AI Multiagent Systemscited by 17
6 more
  1. 2025ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancycited by 17
  2. 2025Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinkingcited by 15
  3. 2024Synergy-of-Thoughts: Eliciting Efficient Reasoning in Hybrid Language Modelscited by 13
  4. 2025Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Modelscited by 10
  5. 2025Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potential Mitigationscited by 6
  6. 2023From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Modelscited by 5

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