6 citations · 16 across the 15 of their papers we have counts for
16 papers
Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work
Wenhui Chen, Shiwen Cheng, Hao Dong +39
Co-work agents execute complex workflows that combine information gathering, tool use, coding, and file manipulation across many model invocations. Because cost and latency accumul…
The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows
Junbo Li, Boyi Liu, Canwen Xu +5
Recent systems for optimizing prompts, programs, and ML workflows typically rely on explicit outer-loop controllers such as evolutionary search, bandits, or textual-gradient method…
No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
Xu Yang, Zhizhou Sha, Junbo Li +10
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prom…
SARA: A Dual-Stream VAE for High-Fidelity Speech Generation via Integrating Semantic and Acoustic Representations
Peijie Chen, Wenhao Guan, Weijie Wu +7
Zero-shot text-to-speech (TTS) relies on robust speech representations. However, current speech tokenizers face a fundamental trade-off: acoustic codecs preserve high-fidelity audi…
Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
Jianing Zhu, Yeonju Ro, John Robertson +5
Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic syst…
Turn-PPO: Turn-Level Advantage Estimation with PPO for Improved Multi-Turn RL in Agentic LLMs
Junbo Li, Peng Zhou, Rui Meng +3
Reinforcement learning (RL) has re-emerged as a natural approach for training interactive LLM agents in real-world environments. However, directly applying the widely used Group Re…
Neurosymbolic LoRA: Why and When to Tune Weights vs. Rewrite Prompts
Kevin Wang, Neel P. Bhatt, Cong Liu +7
Large language models (LLMs) can be adapted either through numerical updates that alter model parameters or symbolic manipulations that work on discrete prompts or logical constrai…
HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
Geon-Woo Kim, Junbo Li, Shashidhar Gandham +5
Training large language models (LLMs) increasingly relies on geographically distributed accelerators, causing prohibitive communication costs across regions and uneven utilization…
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering
Junnan Zhu, Jingyi Wang, Bohan Yu +4
LLMs have shown impressive progress in natural language processing. However, they still face significant challenges in TableQA, where real-world complexities such as diverse table…
PIPA: Preference Alignment as Prior-Informed Statistical Estimation
Junbo Li, Zhangyang Wang, Qiang Liu
Offline preference alignment for language models such as Direct Preference Optimization (DPO) is favored for its effectiveness and simplicity, eliminating the need for costly reinf…
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
Kevin Wang, Junbo Li, Neel P. Bhatt +4
Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In t…
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
Sukmin Yun, Haokun Lin, Rusiru Thushara +14
Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However…
LLM360: Towards Fully Transparent Open-Source LLMs
Zhengzhong Liu, Aurick Qiao, Willie Neiswanger +25
The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs…
FedNAR: Federated Optimization with Normalized Annealing Regularization
Junbo Li, Ang Li, Chong Tian +3
Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prev…
Hedonic Prices and Quality Adjusted Price Indices Powered by AI
Patrick Bajari, Zhihao Cen, Victor Chernozhukov +8
We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and d…
Exponential Family Model-Based Reinforcement Learning via Score Matching
Gene Li, Junbo Li, Anmol Kabra +3
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family di…