Confidence Calibration in Large Language Models
1 distinct publishers across 2 articles (some outlets filed more than once).
Ownership mix: Other: 2
1 publishers · 2 articles · switch to 1-minute for disagreement and framing.
What happened
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, like people, too sure they are right:…
Why the coverage differs
AI-assisted comparison · labeled · generated Sep 9, 2026, 12:09 AM · not a verdict
What happened: GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
Where coverage diverges: Center: 2 (arXiv cs.AI, arXiv cs.AI).
Comparison summary
AI-assisted · Cerebras / Llama · Sep 9, 2026, 12:09 AM · inspect sources below rather than trusting this alone
What happened: GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
Where coverage diverges: Center: 2 (arXiv cs.AI, arXiv cs.AI).
What's missing: AI bias-comparison is temporarily offline. Configure Cerebras in admin to enable rich comparison summaries.
Report timeline
Oldest → newest among clustered members. Gaps may mean delayed pickup, not silence.
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May 24, 2026, 9:00 PMarXiv cs.AI · CenterGENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
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May 25, 2026, 9:00 PMarXiv cs.AI · CenterConfidence Calibration in Large Language Models
Coverage by perspective
Perspective labels are external consensus ratings (AllSides / Ad Fontes / MBFC-style), not WeSearch truth scores. Center is not automatically more accurate.
Center · 2
Headline framing
Vocabulary fingerprints · not a political endorsement
AI framing analysis temporarily offline. Configure Cerebras in admin to enable framing comparison.
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