Inference-time scaling of LLM reasoning relies on a confidence score over each partial trace, yet existing estimators treat prefix quality as intrinsic to the prefix. A given prefix can succeed at very different rates and rankings under different continuation policies. Reasoning confidence is therefore not policy-invariant, and a squared-loss lower bound shows that policy-blind predictors retain residual error proportional to cross-policy disagreement. Calibration must condition on the continuation model and inference budget. To bridge this gap, we propose the Comparable Policy-Conditioned Calibrator (CPCC), built from a shared frozen-LLM prefix encoder, comparable scalar policy features under per-policy z-score normalization, and a Beta–Binomial head trained on bootstrap success counts. Across six continuation policies and three reasoning benchmarks (GSM8K, SVAMP, MATH-500), all evaluated under majority-vote aggregation, the CPCC family attains the lowest Brier on every evaluation cell, and CPCC is strongest on the held-out model families. It also avoids the held-out-family collapse of raw-hidden conditioning, lowers ECE by 13 to 26 percent on out-of-distribution math, and reduces downstream selective risk by 10 to 30 percent.

Automated clinical risk prediction from electronic health records (EHRs) demands modeling both structured diagnostic codes and unstructured narrative notes. However, most prior approaches either handle these modalities separately or rely on simplistic fusion strategies that ignore the directional, hierarchical causal interactions by which narrative observations precipitate diagnoses and propagate risk across admissions. In this paper, we propose THCM-CAL, a Temporal-Hierarchical Causal Model with Conformal Calibration. Our framework constructs a multimodal causal graph where nodes represent clinical entities from two modalities: textual propositions extracted from notes and ICD codes mapped to textual descriptions. Through hierarchical causal discovery, THCM-CAL infers three clinically grounded interactions: intra-slice same-modality sequencing, intra-slice cross-modality triggers, and inter-slice risk propagation. To enhance prediction reliability, we extend conformal prediction to multi-label ICD coding, calibrating per-code confidence intervals under complex co-occurrences. Experimental results on MIMIC-III and MIMIC-IV demonstrate the superiority of THCM-CAL.

User reviews on e-commerce platforms exhibit dynamic sentiment patterns driven by temporal and contextual factors. Traditional sentiment analysis methods focus on static reviews, failing to capture the evolving temporal relationship between user sentiment rating and textual content. Sentiment analysis on streaming reviews addresses this limitation by modeling and predicting the temporal evolution of user sentiments. However, it suffers from data sparsity, manifesting in temporal, spatial, and combined forms. In this paper, we introduce SynGraph, a novel framework designed to address data sparsity in sentiment analysis on streaming reviews. SynGraph alleviates data sparsity by categorizing users into mid-tail, long-tail, and extreme scenarios and incorporating LLM-augmented enhancements within a dynamic graph-based structure. Experiments on real-world datasets demonstrate its effectiveness in addressing sparsity and improving sentiment modeling in streaming reviews.