2 citations · 3 across the 9 of their papers we have counts for
11 papers
Contextual Credit Policy Optimization
Huiwen Han, Ziyao Zeng, Zihao Zhu +6
Group-based reinforcement learning enables large language model agents to learn from sparse outcome rewards in long-horizon tasks. Recent methods derive step-level credit by compar…
Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale
Mikhail L. Arbuzov, Karan Dave, Evgeniya Dontsova +5
Enterprise conversation analytics asks many questions of millions of interactions. Each question can require reconstructing what people mean and identifying which information matte…
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…
Estimated Dynamic Equilibrium Model: Supply and Demand as a Sample Path of a Stochastic Process
Mikhail L. Arbuzov, Sisong Bei, Alexey Shvets
We introduce the Estimated Dynamic Equilibrium Model (EDEM), an agent-based framework that treats supply and demand as a coupled stochastic process driven by heterogeneous, noisy a…
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…
Rethinking the Value of Multi-Agent Workflow: A Strong Single Agent Baseline
Jiawei Xu, Arief Koesdwiady, Sisong Bei +8
Recent advances in LLM-based multi-agent systems (MAS) show that workflows composed of multiple LLM agents with distinct roles, tools, and communication patterns can outperform sin…
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…