10 citations · 28 across the 30 of their papers we have counts for
14 papers · 1 filter
Guideline-as-Oracle: Zero-Annotation Training of an Ophthalmic Telephone Triage Agent
Chenyu Wang, Yi Liu, Baoqing Li +2
Scaling supervision for multi-turn medical agents is difficult because expert dialogue annotation is costly and clinical conversations are privacy-restricted. We introduce Guidelin…
ArchEval: Measuring AI Agents as Computer Architects
Chenyu Wang, Zishen Wan, Jeffrey Ma +8
Computer architecture has long used benchmarks to make progress measurable. LLM agents create a different measurement problem: success is not merely writing code or tuning paramete…
AgentDSE: Reasoning-Augmented Architectural Design Space Exploration
Chenyu Wang, Jiahe Caroline Shi, David Kong +4
Traditional architectural design space exploration (DSE) is highly inefficient, typically requiring tens of thousands of simulator evaluations across various optimization methods.…
ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation
Chenyu Wang, Yueyuan Li, Yingmin Liu +1
Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice. We present ConflictR…
SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter
Powei Chang, Jinpeng Zhang, Chaoqun Sun +6
Reinforcement learning with verifiable rewards (RLVR) often adopts GRPO-style group-relative updates, sampling multiple rollouts per prompt to construct normalized learning signals…
Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement
Yunpeng Mei, Jiakai He, Hongjie Cao +12
Large vision-language-action (VLA) policies are increasingly trained as conditional generative models over action chunks. Yet deployment produces mixed-quality experience-successfu…
Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
Zhenting Qi, Huangyuan Su, Ao Qu +13
How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of d…
HRM-Text: Efficient Pretraining Beyond Scaling
Guan Wang, Changling Liu, Chenyu Wang +6
The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foundational research. In contra…
MetaCogAgent: A Metacognitive Multi-Agent LLM Framework with Self-Aware Task Delegation
Chenyu Wang, Yang Shu
Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks assign tasks based on predefin…
Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
Han Li, Jinyu Tian, Rili Feng +10
Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliabil…
BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
Zili Zhang, Chengxu Yang, Shenglong Zhang +8
Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, b…
Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning
Cai Zhou, Zekai Wang, Menghua Wu +6
While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be at…
GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon
Arya Tschand, Chenyu Wang, Zishen Wan +21
Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. Thi…
SPICE: Submodular Penalized Information-Conflict Selection for Efficient Large Language Model Training
Powei Chang, Jinpeng Zhang, Bowen Chen +9
Information-based data selection for instruction tuning is compelling: maximizing the log-determinant of the Fisher information yields a monotone submodular objective, enabling gre…