Author

Yongxin Zhou

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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

#natural language process... Jun 2026

Observable Patterns Are Not Explanations: A Causal-Geometric Analysis of Latent Reasoning Models

Evaluating two LRMs against controls lacking the proposed recurrence or curriculum, it is found these patterns also appear in the controls and do not consistently affect behavior, and latent thoughts should be treated as hidden computation, not hidden explanation.

Darpan Aswal, Thomas Palmeira Ferraz, Yong-Xin Zhou et al. · arXiv.org · 7 citations · ⚡1

#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