Halt: Residual Probes Achieve Near-Instantaneous LLM Hallucination Risk Estimation

Scientists are tackling the persistent problem of ‘hallucination’ , the generation of factually incorrect information , in large language models (LLMs). Rohan Bhatnagar, Youran Sun, and Chi Andrew Zhang, from the University of Maryland and University of Chicago, alongside et al., present a novel approach called HALT (Hallucination Assessment via Latent Testing) which identifies hallucination risk by analysing intermediate hidden states within LLMs. This research is significant because it offers a computationally efficient method for detecting unreliable responses before they are presented, effectively adding a ‘critic’ within the LLM itself. By reading uncertainty signals directly from these hidden layers, HALT enables near-instantaneous risk estimation with minimal added latency, paving the way for more reliable and trustworthy agentic AI systems.

Residual probes detect LLM hallucination risk

Scientists have developed a novel method for detecting hallucinations in large language models (LLMs), addressing a critical limitation that undermines user trust and restricts deployment in sensitive applications. The research team proposes lightweight “residual probes” that directly assess hallucination risk by analysing intermediate hidden states within question tokens, operating on the principle that these layers retain crucial epistemic signals often lost during final decoding. This innovative approach allows for near-instantaneous hallucination risk estimation, adding effectively zero latency in low-risk scenarios, as the probe’s computational demands are orders of magnitude lower than standard token generation. The study unveils a system where the probe functions as an “agentic critic”, enabling fast selective generation and routing of queries.
LLMs can now confidently answer questions with low predicted risk, while uncertain queries are immediately delegated to more robust verification pipelines, a significant advancement in responsible AI development. Across four question answering benchmarks and utilising multiple LLM families, the method consistently achieves strong AUROC and AURAC scores, demonstrating robust generalisation even when faced with dataset shifts. This performance establishes fast internal uncertainty readout as a principled foundation for building reliable agentic AI systems. Experiments show the additional computation required by this detector is less than 1% of that needed to generate a single token, minimising the impact on overall generation costs.

Crucially, the detector operates in parallel with inference, meaning it doesn’t introduce any extra delay for users receiving confident answers. The team’s motivation stems from recent findings suggesting LLMs perform substantial hidden reasoning during processing, encoding signals in intermediate representations that aren’t always fully expressed in the final output text. This inspired the researchers to directly read out hallucination-related signals from these intermediate layers, rather than relying solely on the generated results. Furthermore, the research reveals interpretable structure within these intermediate representations, analogous to a student’s internal sense of uncertainty before formulating an answer.

The study demonstrates that intermediate layers are often more informative for hallucination detection than final-layer representations, as the latter may discard crucial confidence-related features during decoding. By focusing solely on question-aligned representations, the team balanced detection accuracy with minimal latency, finding it sufficient for accurate risk estimation in most cases. The main contributions of this work include a lightweight, parallelisable hallucination detector, an adaptive LLM router improving answer correctness, and key insights into the properties of intermediate representations within LLMs.

Residual Probes for LLM Hallucination Risk Detection

Scientists developed lightweight residual probes to detect hallucination risk within large language models (LLMs) by directly reading intermediate hidden states of question tokens. Motivated by the hypothesis that these layers retain epistemic signals lost during final decoding, the research team engineered a small auxiliary network, the probe, that requires computation orders of magnitude cheaper than token generation. This probe operates fully in parallel with LLM inference, achieving near-instantaneous hallucination risk estimation with effectively zero added latency when risk is low, a significant methodological innovation. The study pioneers an agentic critic approach, deploying the probe to enable fast selective generation and routing of queries.

Experiments employed four question answering benchmarks and multiple LLM families to rigorously evaluate the method’s performance. The team assessed the system using AUROC and AURAC metrics, demonstrating strong performance and generalisation under dataset shift, revealing interpretable structures within intermediate representations. Crucially, the probe’s parallel processing capability allows LLMs to immediately answer confident queries, while delegating uncertain ones to more robust verification pipelines, a system delivering substantial efficiency gains. This approach reduces latency in low-risk scenarios to zero, and limits additional delay in fallback cases to less than the time required to generate a single token.

Researchers harnessed intermediate representations aligned to the question as input to the detector, balancing detection quality and response latency. This choice stemmed from the observation that final layers discard confidence-related features unnecessary for output generation, making earlier layers more informative. The study revealed that intermediate layers encode uncertainty signals analogous to a student’s internal sense of not knowing an answer, but refraining from writing “I don’t know” on an exam. The team constructed a detector to capture this internal confusion, demonstrating that the probe’s performance often exceeds that of detectors using final-layer representations.

This work positions fast internal uncertainty readout as a principled foundation for reliable agentic AI, offering a novel solution to the critical problem of LLM hallucination. The technique achieves a computational cost less than 1% of generating a single token, and the system’s architecture enables adaptive computation allocation, improving both accuracy and efficiency. The research demonstrates that this method not only detects hallucination but also provides insights into the internal workings of LLMs, furthering our understanding of their reasoning processes.