Applying STP at consecutive semantic reasoning steps achieves 168x more accurate multi-step latent prediction on ProcessBench than frozen baselines, with trajectories forming smooth curves best captured by non-linear predictors.
Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality through theoretically grounded geometric kinematics. By decomposing reasoning traces into Progress (displacement) and Stability (curvature), we reveal a distinct topological divergence: correct reasoning manifests as high-progress, stable trajectories, whereas hallucinations are characterized by low-progress, unstable patterns (stalled displacement with high curvature fluctuations). Leveraging these signatures, our probabilistic framework achieves competitive performance and superior robustness across diverse benchmarks. Crucially, TRACED bridges geometry and cognition by mapping high curvature to ''Hesitation Loops'' and displacement to ''Certainty Accumulation'', offering a physical lens to decode the internal dynamics of machine thought.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Density ridge scoring on 6D kinematic features from hidden states yields 5-20 point AUROC gains over Semantic Entropy and log-probability baselines for hallucination detection under a 200-query calibration protocol across six QA benchmarks and eight models.
GeoFaith proposes a spatio-temporal framework leveraging latent geometry and entropy to diagnose faithful CoT, with a bootstrapped 20k-sample detector and faithfulness-aware RL that yields shorter accurate chains.
citing papers explorer
-
Semantic Step Prediction: Multi-Step Latent Forecasting in LLM Reasoning Trajectories via Step Sampling
Applying STP at consecutive semantic reasoning steps achieves 168x more accurate multi-step latent prediction on ProcessBench than frozen baselines, with trajectories forming smooth curves best captured by non-linear predictors.
-
Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
Density ridge scoring on 6D kinematic features from hidden states yields 5-20 point AUROC gains over Semantic Entropy and log-probability baselines for hallucination detection under a 200-query calibration protocol across six QA benchmarks and eight models.
-
GeoFaith: A Spatio-Temporal Dual View of Faithful Chain-of-Thought
GeoFaith proposes a spatio-temporal framework leveraging latent geometry and entropy to diagnose faithful CoT, with a bootstrapped 20k-sample detector and faithfulness-aware RL that yields shorter accurate chains.