most citedFrom Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs

Ponkrit Kaewsawee, Chaklam Silpasuwanchai, Chutiporn Anutariya

Institutional policies stay in natural language while the systems that check compliance demand machine-readable constraints. Bridging that gap is still done by hand. PolicyKG close…

cs.HC20261 cited

From Overload to Convergence: Supporting Multi-Issue Human-AI Negotiation with Bayesian Visualization

Mehul Parmar, Chaklam Silpasuwanchai

As AI systems increasingly mediate negotiations, understanding how the number of negotiated issues impacts human performance is crucial for maintaining human agency. We designed a…

cs.CL2026

Sycophancy as a Multilingual Alignment Failure: How Safety Degrades Across Languages, Topics, and Models

Arya Shah, Himanshu Beniwal, Mayank Singh +1

Safety-aligned large language models often exhibit sycophancy, which is the tendency to affirm users' opinions regardless of factual accuracy. Although well-studied in English, its…

cs.CV2026

SycoPhantasy: Quantifying Sycophancy and Hallucination in Small Open Weight VLMs for Vision-Language Scoring of Fantasy Characters

Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai

Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring alignment between images and…

cs.CV2026

Gaslight, Gatekeep, V1-V3: Early Visual Cortex Alignment Shields Vision-Language Models from Sycophantic Manipulation

Arya Shah, Vaibhav Tripathi, Mayank Singh +1

Vision-language models are increasingly deployed in high-stakes settings, yet their susceptibility to sycophantic manipulation remains poorly understood, particularly in relation t…

cs.CL2026

Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models

Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai

Large language models increasingly serve as conversational agents that adopt personas and role-play characters at user request. This capability, while valuable, raises concerns abo…