1 citations · 1 across the 9 of their papers we have counts for
11 papers
CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research
Victor Li, Yuzhang Xie, Ziwei Dong +6
High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constrained by fragmented, non-researc…
Moral Missions: Sustaining Responsible Practice by Surfacing Practitioner Moral Decision-Making Procedures
Teanna Barrett, B. Biira, Jainaba Jawara +8
A growing ecosystem of techniques, toolkits, and guidelines has been developed to help data scientists consider the social implications of data-driven technologies. However, prior…
OmniCAD: A Large-Scale Benchmark for 3D Spatial Reasoning in Robotics Assemblies
Mingjia Wang, Taiting Lu, Ziwei Dong +25
Recent vision-language models (VLMs) show strong capabilities in robotic perception and spatial reasoning, yet their ability to reason about complex mechanical assemblies remains u…
OmniMech: All-in-one Multimodal Mechanical Benchmark for 3D Reconstruction
Taiting Lu, Runze Liu, Ziwei Dong +18
Recent vision-language models (VLMs) can generate executable CAD programs from images, but existing methods mainly target coarse, general-purpose 3D objects and rarely address the…
OmniRouting: A Semantic-Coupled Multimodal Benchmark for Constraint-Aware Spatial Reasoning in PCB Routing
Taiting Lu, Kaiyuan Lin, Ziwei Dong +18
Recent large language models (LLMs) have demonstrated remarkable progress in constraint-aware navigation, maze reasoning, and graph reasoning. However, their ability to reason abou…
Context Compression Is Not One Thing: Readable Symbolic Re-expression vs. Coherent Summary at Matched Budget
Sisong Bei, Mikhail L. Arbuzov, Ziwei Dong +2
We study context compression for multi-hop question answering with small language models. We propose Telegraph English, a readable symbolic format that rewrites retrieved passages…
The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability
Mikhail L. Arbuzov, Lee Mosbacker, Sisong Bei +3
Universal LLM reliability is not a finite-library problem: across all possible tasks, tools, schemas, knowledge sources, and evaluator expectations, new intervention-distinguishabl…
Telegraph English: Semantic Prompt Compression via Structured Symbolic Rewriting
Mikhail L. Arbuzov, Sisong Bei, Ziwei Dong +2
We introduce Telegraph English (TE), a prompt-compression protocol that rewrites natural language into a symbol-rich, formally-structured dialect. Where token-deletion methods such…
The World Won't Stay Still: Programmable Evolution for Agent Benchmarks
Guangrui Li, Yaochen Xie, Yi Liu +11
LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Yet, most existing benchmarks eva…
Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models
Mikhail L. Arbuzov, Sisong Bei, Ziwei Dong +2
The prevailing assumption of an exponential decay in large language model (LLM) reliability with sequence length, predicated on independent per-token error probabilities, posits an…
Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science
Ziwei Dong, Ameya Patil, Yuichi Shoda +2
Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily…