Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning
Existing explainability methods for Large Language Models (LLMs) typically treat hidden states as static points in activation space, assuming that correct and incorrect inferences can be separated using representations from an individual layer. However, these activations are saturated with polysemantic features, leading to linear probes learning surface-level lexical patterns rather than underlying reasoning structures. We introduce Truth as a Trajectory (TaT), which models the transformer inference as an unfolded trajectory of iterative refinements, shifting analysis from static activations to layer-wise geometric displacement. By analyzing displacement of representations across layers, ...
| Authors | Hamed Damirchi, Ignacio Meza De la Jara, Ehsan Abbasnejad et al. |
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| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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