collaborators

7 papers

cs.CV2026

HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents

Letian Li, Chao Shen, Shuzhao Xie +6

Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on sc…

cs.CL2026

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf

Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner +17

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because revi…

cs.LO2026

Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs

Terry Jingchen Zhang, Wenyuan Jiang, Rongchuan Liu +6

Formal theorem proving (FTP) has emerged as a critical foundation for evaluating the reasoning capabilities of large language models, enabling automated verification of mathematica…

cs.LG2026

ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression

Tingcheng Bian, Yuzhe Zhang, Jing Jin +5

Large reasoning models (LRMs) achieve strong performance via extended chain-of-thought (CoT) reasoning, yet suffer from excessive token consumption and high inference latency. Exis…

cs.AI2026

Test of Time: Rethinking Temporal Signal of Benchmark Contamination

Terry Jingchen Zhang, Gopal Dev, Ning Wang +8

Post-cutoff performance decay of LLMs has been widely interpreted as a temporal signal for benchmark contamination, where public information released before the training cutoff may…

cs.MA2026

Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

Yuzhe Zhang, Feiran Liu, Yi Shan +8

Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. Yet whether agents can reliably co…