1 citations · 1 across the 13 of their papers we have counts for
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REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent
Qian Zhang, Yaoming Li, Zhewen Tan +9
Post-training quantization (PTQ) is essential for deploying large language models (LLMs) under strict resource constraints. State-of-the-art PTQ methods quantize each layer with a…
ScienceArena: Benchmarking LLMs on Latest Scientific Olympiad Competitions
Guangxiang Zhao, Qilong Shi, Xusen Xiao +13
Benchmark saturation and data contamination increasingly obscure genuine scientific reasoning in frontier LLMs. We introduce \textsc{ScienceArena}, an olympiad-style benchmark from…
ArborMem: Navigating Interaction States with Memory Forests
Zongwei Lv, Yuemeng Xu, Yilun Yao +8
Large language models increasingly serve as persistent conversational assistants, requiring memory that preserves relevant experience and maintains continuity across interactions.…
ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
Yongge Ma, Guoan Wang, Feiyu Wang +5
Post-training quantization (PTQ) is widely used to reduce the memory and computational cost of large language models. Existing PTQ methods typically obtain an initial quantized mod…
Hy-MultiTurn: A Six-Dimensional Benchmark for Deep Multi-Turn Dialogue Understanding
Eileen Ye, Jiawen Tao, Yaoming Li +7
Long-running multi-turn interactions with chatbots and agents are now common, and a correct response often depends on remembering earlier details, tracking later revisions, identif…
Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training
Jiawen Tao, Miao Peng, Yaoming Li +7
Synthetic textbook data has improved language model pre-training, but prior work largely treats the benefit as a property of generated content or local rewriting style. We study a…
RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions
Zongwei Lv, Yaoming Li, Zhewen Tan +7
Agent benchmarks should reflect what users actually ask deployed agents to do, yet existing benchmarks often miss key realism properties of real developer-agent sessions. We introd…
A Primer in Post-Training Reasoning Data: What We Know About How It Works
Yaoming Li, Guangxiang Zhao, Qilong Shi +3
Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whether this stage succeeds. Work…
ConMoE: MoE Compression as Expert Selection and Functional Assignment
Yilun Yao, Jiaming Pan, Elsie Dai +4
Sparse Mixture-of-Experts (MoE) models scale model capacity through sparse activation, but storing their full parameters remains costly in GPU memory. Existing expert compression m…
Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows
Yilun Yao, Xinyu Tan, Chao-Hsuan Liu +10
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the…