What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
For practitioners deploying LLM forecasters, this work provides a practical method to calibrate and audit model behavior, revealing that reasoning traces can be unreliable while internal representations offer a more faithful window.
The paper shows that probing internal representations of LLM forecasters yields better calibration than chain-of-thought reasoning, and that these probes can detect when reasoning is unfaithful, predicting direction of change in 84% of cases. Pre-reasoning representations already encode the final forecast, enabling token savings of 30-47% without accuracy loss.
Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both. Working with Eternis-Forecaster 8B on OpenForesight, we train representation-pooling probes on intermediate activations and find they achieve substantially better calibration; a result that also holds for GLM-4.7-Flash and GLM-4.5-Air. We then assess CoT faithfulness through evidence ablation and diversionary injection: removing an influential source in the prompt often changes the model's forecast while leaving the reasoning trace untouched. The same probes function as lie detectors: their activations track behavioral shifts far better than the reasoning trace does, and they also predict the direction of change in 84% of cases, including when the CoT conceals the perturbation's influence. Finally, forced answering reveals that forecasts are largely fixed before reasoning begins: a single pre-reasoning pass recovers the committed answer and confidence, and routing questions by the spread of this pre-set answer distribution saves 30-47% of generated tokens, with no loss of accuracy. Together, these results establish probing internal representations as a practical tool for calibrating, auditing, and triaging language model forecasters and reasoning models more broadly.