Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits

TL;DR

This paper introduces Gnosis, a lightweight mechanism enabling large language models to internally predict their own failures by analyzing hidden states, improving self-assessment accuracy without external judges or additional compute.

Contribution

Gnosis is a novel, efficient self-awareness method that decodes internal signals of frozen LLMs to predict correctness, outperforming external judges across multiple benchmarks.

Findings

Gnosis improves failure prediction accuracy across various tasks.

It generalizes zero-shot to partial generations for early failure detection.

Operates with minimal additional parameters (~5M) and negligible inference cost.

Abstract

Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample consistency, or text-based self-critique, which incur additional compute or correlate weakly with true correctness. We ask: can LLMs predict their own failures by inspecting internal states during inference? We introduce Gnosis, a lightweight self-awareness mechanism that enables frozen LLMs to perform intrinsic self-verification by decoding signals from hidden states and attention patterns. Gnosis passively observes internal traces, compresses them into fixed-budget descriptors, and predicts correctness with negligible inference cost, adding only ~5M parameters and operating independently of sequence length. Across math reasoning, open-domain question answering, and academic knowledge benchmarks, and over frozen backbones ranging from 1.7B to 20B parameters, Gnosis consistently outperforms strong internal baselines and large external judges in both accuracy and calibration. Moreover, it generalizes zero-shot to partial generations, enabling early detection of failing trajectories and compute-aware control. These results show that reliable correctness cues are intrinsic to generation process and can be extracted efficiently without external supervision.