Author

Jun-Wei Yao

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Preprint Oct 2026

Renegotiable Options Fail to Prevent Hold-Up Between Two Profit-Maximizing Frontier LLM Agents

Do contract terms that protect investment between people also protect it between profit-maximizing language-model agents? In the hold-up problem, a seller can adapt a unit to one buyer at a cost before the price is set. Once that cost is sunk, the buyer can offer almost nothing and the seller will still accept, so a se...

Jun-Wei Yao, Zi-Ke Yang, Yong-Xin Zhou et al. · 0 citations

Preprint Oct 2026

Component Translation and Judge-Labelled Refusal: A Multilingual Safety Evaluation

Whole-prompt translation evaluates a request in different languages but does not compare which component carries a language. We introduce a paired reversal: translate the complete request component or the complete operation component into the same target language, holding behaviour, English frame and translated-span co...

Yong-Xin Zhou, Jun-Wei Yao, Wen-Bo Ye et al. · 0 citations

#artificial intelligence Preprint Oct 2026

One Confidence Threshold, Unequal Coverage: Language Gaps in Typed Decision Interfaces and How Translating Inputs Reduces Them

Deployed classifiers often act on a prediction only when its confidence clears a threshold and pass the rest to a person. We ask whether one threshold serves all languages equally on typed decision interfaces, APIs that return a probability for each option of a fixed multiple-choice question. In a confirmatory study wi...

Jun-Wei Yao, Yong-Xin Zhou, Zi-Ke Yang et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Evaluating Context Compaction on the Questions That Come Later

This work evaluates compaction on later questions of language-model agents, using four corpora that annotate the evidence of several questions per text and recorded agent trajectories, and compares every method with random deletion at the same budget.

Jun-Wei Yao, Yong-Xin Zhou, Zi-Ke Yang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts

Automatic safety judges -- for example Llama Guard, or GPT-4o given a grading prompt -- produce the numbers behind reported jailbreak success rates, defense evaluations, and safety leaderboards. We ask whether these judges grade what a reply contains or how it sounds. We place a fixed piece of text before or after a mo...

Yong-Xin Zhou, Wen-Bo Ye, Yuan-Zhe Liu et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Single Canonical Prompts Underestimate LLM Safety's Surface-Form Sensitivity

A benchmark score is a measurement instrument, yet most benchmarks read each item at a single canonical surface form. We ask whether that reading is faithful: when an item's intent is held fixed and only its meaning-preserving surface form varies, does the canonical-form score estimate model behavior well, and how much...

Yongxin Zhou, Jun-Wei Yao, Yuanzhe Liu et al. · 1 citation