What Am I Missing? Question-Answering as Hidden State Probing
Researchers propose using question-asking as a window into LLM reasoning dynamics, training probes on student model hidden states to predict solution correctness before the teacher responds. This work addresses a critical gap in understanding why identical prompts yield different outputs across samples, suggesting that intermediate model states encode measurable signals about reasoning trajectory success. The finding has implications for both interpretability research and potential runtime interventions to steer model behavior toward correct solutions.
Modelwire context
ExplainerThe key insight is using a student model's questions about its own reasoning as a training signal for probes, rather than relying on external annotations or post-hoc explanations. This flips the typical interpretability setup: instead of asking 'what did the model think?', the researchers ask 'what does the model need to know to correct itself?'
This work sits alongside the LongTraceRL paper from the same week, which also focuses on extracting and supervising intermediate reasoning steps in LLMs. Where LongTraceRL uses rubric rewards to guide long-context reasoning, this paper uses hidden state probes to detect when reasoning is going off-track before the model commits to an answer. Both treat the model's internal trajectory as observable and correctable, rather than opaque. The difference is scope: LongTraceRL targets document-level reasoning under noise, while this targets solution correctness signals that could apply to any task where a model generates multiple attempts.
If the probes trained on student hidden states transfer to different model architectures or scales without retraining, that would confirm the signals are genuinely about reasoning quality rather than model-specific artifacts. Otherwise, the approach remains a tool for understanding specific models rather than a general interpretability method.
Coverage we drew on
This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.
MentionsLLMs · chain-of-thought reasoning · hidden state probing · question-answering
Modelwire Editorial
This synthesis and analysis was prepared by the Modelwire editorial team. We use advanced language models to read, ground, and connect the day’s most significant AI developments, providing original strategic context that helps practitioners and leaders stay ahead of the frontier.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.