5 citations · 6 across the 15 of their papers we have counts for
8 papers · 1 filter
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Shaoxiong Zhan, Shi Hu, Boyu Feng +7
Real repository issues routinely include visual evidence such as screenshots, error dialogs, rendered UI states, and logs, yet repository-level issue localization is evaluated most…
Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
Hai Lin, Hoilam Pao, Shaoxiong Zhan +1
Large language models are undergoing a transition from model technology to system technology. Engineering challenges like cache reuse, context capacity, agent scheduling, and permi…
ARBOR: Online Process Rewards via a Reusable Rubric Buffer for Search Agents
Zheng Liu, Longxiang Zhang, Xintong Wang +8
LLM-based search agents are trained predominantly with outcome-only reward, leaving the search process itself unsupervised. This signal degenerates on outcome-homogeneous groups wh…
Experience-Driven Dynamic Exits for LLMs with Reinforcement Learning
Yanyu Zhu, Hoilam Pao, Niu Hu +6
Large Language Models suffer from slow autoregressive inference. While self-speculative decoding accelerates this process, its efficiency is hampered by static configurations like…
JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
Aichen Cai, Anmeng Zhang, Anyu Li +66
We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency in the sub-50B…
SemantiCache: Efficient KV Cache Compression via Semantic Chunking and Clustered Merging
Shunlong Wu, Hai Lin, Shaoshen Chen +5
Existing KV cache compression methods generally operate on discrete tokens or non-semantic chunks. However, such approaches often lead to semantic fragmentation, where linguistical…
3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
Shaoxiong Zhan, Yanlin Lai, Zheng Liu +6
Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks like block counting. This capability m…
R-Align: Enhancing Generative Reward Models through Rationale-Centric Meta-Judging
Yanlin Lai, Mitt Huang, Hangyu Guo +11
Reinforcement Learning from Human Feedback (RLHF) remains indispensable for aligning large language models (LLMs) in subjective domains. To enhance robustness, recent work shifts t…