collaborators

8 papers

cs.CL2026

Anatomy of LLM Sycophancy: What a Flip Rate Hides

Haonan Huang

A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modular replay protocol, we test h…

cs.MA2026

Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation

Haonan Huang, Joey Xiao

Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no as…

cs.MA2026

Communication Shapes Collective Inference in Self-Adapting LLM Societies: Evidence from Mafia

Haonan Huang, Joey Xiao

When does communication help a group identify hidden adversaries, and how does its value change as the group adapts? In Mafia, an informed minority hides inside an uninformed major…

cs.CL2026

The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment

Haonan Huang

Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wo…

cs.AI2026

Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics

Haonan Huang

Autonomous-research agents have demonstrated end-to-end LLM automation in machine-learning sandboxes where execution provides calibration. Frontier physical science differs categor…

cs.LG2026

RPS: Information Elicitation with Reinforcement Prompt Selection

Tao Wang, Jingyao Lu, Xibo Wang +5

Large language models (LLMs) have shown remarkable capabilities in dialogue generation and reasoning, yet their effectiveness in eliciting user-known but concealed information in o…

physics.comp-ph2026

Grounded autonomous scrutiny at scale: emergent critique from reproduction of published computational physics papers

Haonan Huang

Autonomous LLM agents now produce complete research artifacts in machine-learning sandboxes, but real computational physics is harder: experiments are first-principles calculations…

physics.comp-ph2026

From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics

Haonan Huang

While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researc…