12 stories tagged with #calibration, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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Common Cog: The Calibration Case Method
Commoncog uses a unique case approach to business education. What it is, how it works, and why it’s superior to the traditional case method.…
Rising Treasury Yields: Recalibration, Not Rupture
Higher Treasury yields appeared to reflect a recalibration driven by growth and term premium, not a rupture in confidence in US debt.…
Confidence Calibration in Large Language Models
We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the current crop of LLMs are, lik…
GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration
While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplore…
Mitigating Object Hallucinations in Vision-Language Models through Region-Aware Attention Recalibration
The generation of factually incorrect objects, commonly known as object hallucination, remains a persistent challenge in Large Vision-Language Models (LVLMs). Current approaches to…
AI and epistemic calibration.
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating long-form Chain-of-Thought (C…
When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems
LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon w…
Enhancing Deep Neural Network Reliability with Refinement and Calibration
Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This…
Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use
We formalize trust calibration for agentic tool use (deciding when an automated agent's proposed action may execute autonomously versus require human approval) as a preference-lear…