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The paper proposes a multi-agent AI architecture called SOFAI, inspired by Kahneman's dual-system theory, to enhance AI capabilities through metacognition and balancing fast and slow reasoning processes.
The article discusses stress-tests on autonomous research agents, revealing that failures stem from metacognition issues rather than capability and introduces ARFT, a failure taxonomy with 45 patterns.
O artigo descreve as reflexões metacognitivas de um agente de codificação LLM no Devin/Cascade, expondo seu raciocínio e pontuações de confiança durante tarefas de análise de código.
This paper introduces AutoResearchEval, an evaluation framework for AI agents in automated scientific research, revealing a critical lack of metacognitive abilities as a recurring failure pattern across models.
A controlled study evaluates the metacognitive sensitivity of large language models in medical reasoning, finding partial but flawed confidence calibration that varies with evidence strength and conflicting scenarios.
A reflective essay on how AI-assisted reading and coding reduce the cognitive strain once essential for deep learning, and the author's personal efforts to restore that mental challenge through handwriting and code-by-hand.
The paper proposes Sophia, a recursive cognitive refinement architecture for modular artificial consciousness that introduces a metacognitive sublayer to recursively refine intermediate semantic states through coherence checking, contextual synthesis, and memory-aware reinterpretation.
This research paper investigates how AI advice reduces people's willingness to express uncertainty, even when the advice is wrong and accuracy is incentivized, altering metacognitive thresholds.
This paper presents the first comprehensive overview of metacognition in LLMs, arguing that behaviors like confidence calibration and self-verification are facets of a unified metacognitive ability, and taxonomizes methods and benchmarks for evaluating and improving these abilities to enhance LLM reliability and transparency.
This paper presents a comprehensive overview of metacognition in large language models, covering measurement methods, improvement techniques, and future directions.
This paper investigates how confidence-related information evolves during LLM answer generation and introduces future confidence distillation, which trains predictors on pre-solution hidden representations using post-solution correctness probes to achieve reliable and sample-efficient confidence estimation.
A new research paper introduces RLMF (Reinforcement Learning with Metacognitive Feedback), a two-stage approach that uses the model's own self-judgments to calibrate confidence and express uncertainty faithfully, achieving state-of-the-art calibration across diverse tasks while preserving accuracy and surpassing standard RL by up to 63%.
This paper dissociates difficulty registration from deliberation allocation in large reasoning models (LRMs) and humans, finding that LRMs spend more tokens on problems they get wrong while humans spend less time on failures, revealing opposite within-item patterns despite similar cross-item difficulty correlations.
A practitioner discusses the calibration vs. utility tradeoff in LLM agents, sharing experience with a verifier-based pipeline that reduces hallucinated tool calls by ~60% but introduces latency costs and drops easy correct answers.
This research presents probe-targeted fine-tuning (LoRA) to make LLMs verbally express their internal confidence, achieving causal control over confidence outputs and demonstrating that models often know when they are right or wrong but fail to articulate it.
This paper argues that recent claims about LLMs' ability to introspect are not justified, as behavioral evidence alone cannot distinguish genuine introspection from pattern matching on surface-level cues. The authors re-examine two evaluation paradigms and find that models rely on input-level features rather than genuine access to internal states.
This paper investigates whether frontier LLMs exhibit individuated metacognition—the ability to assess their own item-level capabilities beyond shared signals. Through factor analysis and pairwise calibration across 20 models and six benchmarks, the authors find no evidence of such metacognition; confidence differences reduce to a single shared difficulty factor, suggesting models rely on a common difficulty signal rather than model-specific self-knowledge.
A new Google paper argues that LLMs should focus on expressing uncertainty honestly rather than aiming for perfect factuality, proposing 'faithful uncertainty' to build trust.
Introduces Metacognition-as-Reward (MaR), a reinforcement learning framework that guides LLM reasoning via metacognitive knowledge and regulation signals, achieving up to 11% improvement over vanilla methods on reasoning benchmarks.
This essay argues that evaluation is the hardest problem in production AI, not generation, and decomposes AI self-knowledge into calibration, discrimination, and expression, with implications for system design.