arXiv AI
HSRM: Hidden-State Reward Models for Test-Time Verification
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Rethinking Reasoning with MDLMs: Early Exits, Post-hoc Reasoning, and Beyond
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TimeThink: Eliciting Compositional Reasoning in Timeseries Large Language Models
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arXiv Computation and Language
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Small Reward Models via Backward Inference
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Test-time Calibration Learning for Large Language Model Reasoning
The paper introduces Test-Time Calibration Learning (TTCL), a label‑free framework that adapts both reasoning accuracy and verbalized confidence of large language models directly on unlabeled target‑task data. TTCL generates self‑supervision signals from multiple model responses, enabling calibration without ground‑truth labels and proving theoretically as a bounded surrogate for the ideal calibration objective. Experiments on mathematical reasoning and factual question answering show consistent improvements, with base models gaining an average 40.13% in accuracy and 70.80% in ECE reduction across eight benchmarks, and further gains under domain shift.
arXiv AI
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Penelope: Localized Latent Recurrence for Efficient Structured Reasoning
arXiv:2607. 25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens.