How do LLMs detect their own errors without external feedback?

LLMs maintain an internal confidence signal at the post-answer newline (PANL) that operates independently of output probabilities. This second-order evaluative signal can disagree with the model's chosen response, allowing it to recognize when an answer is likely wrong. The signal encodes not only error likelihood but whether the model has the knowledge to fix it, enabling self-correction without human input.

What is the difference between first-order and second-order confidence in LLMs?

First-order confidence derives directly from the generation signal and is always highest for the chosen response, making error detection impossible. Second-order confidence involves a partially independent evaluative signal that can contradict the committed answer. LLMs appear to implement second-order architecture, where internal PANL activations provide an evaluative check separate from output probabilities, enabling genuine error awareness.

Can PANL signals predict which errors an LLM can actually fix?

Yes. Research shows PANL activations predict error correction success better than verbal confidence or log-probabilities alone. The signal encodes not just whether an error exists but whether the model possesses sufficient knowledge to correct it. Causal interventions confirm that PANL signals are necessary for error detection behavior, and when answer information is corrupted, PANL activation can restore correction capability.

ai · 4 min read · Apr 27, 2026

LLMs use hidden confidence signals to detect and fix their own errors

Research shows large language models maintain a second-order evaluative signal that predicts error detection and self-correction beyond what their output probabilities reveal.

Source: arxiv/cs.LG · Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Velickovic, Nathaniel Daw · open original ↗

LLMs detect errors via internal confidence signals independent of output probabilities, enabling self-correction without external feedback.

— Models cache a confidence representation at post-answer newline (PANL) that drives error detection.
— PANL activations predict which errors the model can correct, outperforming verbal confidence signals.
— Second-order confidence architecture mirrors decision neuroscience frameworks with independent evaluative signals.
— Causal interventions show PANL signals rescue error detection when answer information is corrupted.
— Findings replicate across Gemma 3 27B, Qwen 2.5 7B, and tasks like TriviaQA and MNLI.
— Verbal confidence alone fails to predict correctable errors; internal signals encode fixability.
— First-order models cannot explain error detection since confidence would always favor chosen response.

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Knowledge Capture Document the PANL signal concept and second-order confidence framework as a reference model for evaluating LLM reliability in your domain.
Focus Brief Summarize the causal intervention results (PANL rescues error detection when answers are corrupted) as a design principle for robust prompting strategies.
Reading Queue Queue the full arxiv paper and Kumaran et al. (2026) for deeper study of activation patterns and mechanistic interpretability techniques.

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LLMs maintain an internal confidence signal at the post-answer newline (PANL) that operates independently of output probabilities. This second-order evaluative signal can disagree with the model's chosen response, allowing it to recognize when an answer is likely wrong. The signal encodes not only error likelihood but whether the model has the knowledge to fix it, enabling self-correction without human input.

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APA

Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Velickovic, Nathaniel Daw. (2026, April 27). LLMs use hidden confidence signals to detect and fix their own errors. Astrobobo Content Engine (rewrite of arxiv/cs.LG). https://astrobobo-content-engine.vercel.app/article/llms-use-hidden-confidence-signals-to-detect-and-fix-their-own-errors-0bf32d

MLA

Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Velickovic, Nathaniel Daw. "LLMs use hidden confidence signals to detect and fix their own errors." Astrobobo Content Engine, 27 Apr 2026, https://astrobobo-content-engine.vercel.app/article/llms-use-hidden-confidence-signals-to-detect-and-fix-their-own-errors-0bf32d. Based on "arxiv/cs.LG", https://arxiv.org/abs/2604.22271.

BibTeX

@misc{astrobobo_llms-use-hidden-confidence-signals-to-detect-and-fix-their-own-errors-0bf32d_2026,
  author       = {Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Velickovic, Nathaniel Daw},
  title        = {LLMs use hidden confidence signals to detect and fix their own errors},
  year         = {2026},
  url          = {https://astrobobo-content-engine.vercel.app/article/llms-use-hidden-confidence-signals-to-detect-and-fix-their-own-errors-0bf32d},
  note         = {Astrobobo rewrite of arxiv/cs.LG, https://arxiv.org/abs/2604.22271},
}

#llm #confidence #error-detection #neuroscience #interpretability

LLMs use hidden confidence signals to detect and fix their own errors

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