# Hidden State Probing

Answer probes on chain-of-thought hidden states are often read as evidence that a model has settled its final answer, yet a hidden state also carries the candidate whose name is about to be written. Emission-controlled dual-target probing (ECDP) fits the same low-rank reader separately to the next candidate mention and to the final answer at identical positions, with target-specific nulls and thresholds fixed in advance. On DecisionDepth, Qwen3.5-4B decodes the next mention at .966 (null .234) but the final answer only at .221 (null .186), an emission gap of .696; Llama-3.1-8B gives .575. The order is not universal: in a Qwen3-8B MMLU setting the final answer is read more strongly.

Answer probes on chain-of-thought hidden states are often read as evidence that a model has settled its final answer, yet a hidden state also carries the candidate whose name is about to be written. Emission-controlled dual-target probing (ECDP) fits the same low-rank reader separately to the next candidate mention and to the final answer at identical positions, with target-specific nulls and thresholds fixed in advance. On DecisionDepth, Qwen3.5-4B decodes the next mention at .966 (null .234) but the final answer only at .221 (null .186), an emission gap of .696; Llama-3.1-8B gives .575. The order is not universal: in a Qwen3-8B MMLU setting the final answer is read more strongly.

The work introduces an internal hidden-state framework for fine-grained, span-level hallucination detection that inspects layer-wise activation patterns to locate the onset and continuation tokens of hallucinations in large language model generations; experiments show substantial improvements in Precision-Recall AUC over random baselines despite extreme class imbalance, and it further proposes a cross-model detection framework in which one model observes the internal representations elicited by another model's generation, finding that an external observer can match or exceed the generator's self-detection of its own hallucination onsets, including when the observer is the smaller model.

The work introduces an internal hidden-state framework for fine-grained, span-level hallucination detection that inspects layer-wise activation patterns to locate the onset and continuation tokens of hallucinations in large language model generations; experiments show substantial improvements in Precision-Recall AUC over random baselines despite extreme class imbalance, and it further proposes a cross-model detection framework in which one model observes the internal representations elicited by another model's generation, finding that an external observer can match or exceed the generator's self-detection of its own hallucination onsets, including when the observer is the smaller model.