13 papers
Asking What a Director Sees Can Change What Vision-Language Models Choose
Jiaxuan Guo, Xingyu Ren, Kejia Zhang +3
Vision-language models are now routinely tested on theory of mind: whether they can work out what another person sees, knows, and means. A common design asks the model to report th…
Scivot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents
Youran Sun, Xingyu Ren, Chugang Yi +15
Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research also requires scientific judgme…
AutoSR: Automatic Symbolic Regression by Searching Research States
Kejia Zhang, Youran Sun, Xinyu Ren +2
We introduce Automatic Symbolic Regression (AutoSR), a fully automated system that instantiates Research-Space Symbolic Regression by searching persistent scientific investigations…
AgonAlpha: Autonomous Alpha Discovery via Prompt Economy and Scalable Agentic Search
Weicheng Ye, Youran Sun, Xingyu Ren +3
Language models can propose many plausible trading factors, but an autonomous research system must also allocate its evaluation budget, verify its own evidence, and preserve how ea…
Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation
Xingyu Ren, Youran Sun, Chugang Yi +1
Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific content on model output. We firs…
Cross-Task Dissociation in Frontier Vision-Language Model Theory of Mind
Kejia Zhang, Youran Sun, Chugang Yi +1
Do frontier vision-language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that profile fragment from one par…
Effective Parameters, Real Behavior: Renormalization for Robotics -- From Infinite Electron Mass to Sim-to-Real Gap
Youran Sun, Jiaxuan Guo, Xingyu Ren +2
Bridging the sim-to-real gap is a central problem in robotics, and the prevailing approach is to build increasingly accurate simulators. Here, we propose another approach based on…
RouterVLA: Budgeted Commissioning and Expert Onboarding for Growing VLA Pools
Xingyu Ren, Chugang Yi, Youran Sun
Robotic teams often maintain several vision-language-action policies but still deploy one global winner. We study two recurring decisions: which expert to deploy for a new conditio…
PerspectiveGap: A Benchmark for Multi-Agent Orchestration Prompting
Youran Sun, Xingyu Ren, Kejia Zhang +2
Real-world LLM applications are moving beyond single-agent workflows toward orchestrated multi-agent systems, yet current models still struggle to determine what each sub-agent nee…
Agon: An Autonomous Large-Scale Omnidisciplinary Research System Built on Prompt Economy
Youran Sun, Xingyu Ren, Chugang Yi +4
Large language models are making research production scalable, shifting the bottleneck from producing artifacts to judging claims. We present \textsc{Agon}, a research orchestrator…
Semantic Data Processing with Holistic Data Understanding
Youran Sun, Sepanta Zeighami, Bhavya Chopra +2
Semantic operators have increasingly become integrated within data systems to enable processing data using Large Language Models (LLMs). Despite significant recent effort in improv…
Correcting Mean Bias in Text Embeddings: A Refined Renormalization with Training-Free Improvements on MMTEB
Xingyu Ren, Youran Sun, Haoyu Liang
We find that current sentence-embedding models produce outputs with a consistent bias: every embedding decomposes as , where the mean is near-identical across…
Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models
Haoyu Liang, Youran Sun, Yunfeng Cai +2
The security issue of large language models (LLMs) has gained wide attention recently, with various defense mechanisms developed to prevent harmful output, among which safeguards b…