Unmatched Does Not Mean False: Incomplete Reference Sets Can Reverse Calibration Rankings in Open-Ended Theory-of-Mind Tracking

arXiv:2608.25654 · cs.CL, cs.AI · Submitted 2026-08-26 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Calibration — 28× in this paper · explained in 7 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Provenance — 5× in this paper · explained in 4 episodes
• AUROC — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Open-ended Theory-of-Mind — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Agent Memory — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Granularity Mismatch — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• MemGPT: Towards LLMs as Operating Systems
• Verbosity Bias in Preference Labeling by Large Language Models
• Large Language Models Fail on Trivial Alterations to Theory-of-Mind Tasks
• Large Language Models are not Fair Evaluators
• When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs

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