Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoni
arXiv:2604.21018v1 Announce Type: new Abstract: While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute
arXiv:2604.27536v1 Announce Type: new Abstract: In black-box large language model (LLM) services, response reliability is often only partially observable at decision time, while
arXiv:2603.28135v1 Announce Type: new Abstract: Recent test-time reasoning methods improve performance by generating more candidate chains or searching over larger reasoning
arXiv:2610.03894v1 Announce Type: new Abstract: A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action
arXiv:2608.11994v1 Announce Type: new Abstract: We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability
arXiv:2605.27768v1 Announce Type: new Abstract: Production AI systems often operate with incomplete, conflicting, or insufficient evidence. Forced classifiers collapse such cases
arXiv:2610.01006v1 Announce Type: new Abstract: Decision models return probabilities intended for routing, abstention and automated action. Calibration makes those probabilities
arXiv:2605.04215v1 Announce Type: cross Abstract: Diffusion-based Large Language Models (D-LLMs) represent a promising frontier in generative AI, offering fully parallel token
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