107 citations · 107 across the 9 of their papers we have counts for
11 papers
GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning
Qingsong Xie, Jiahao Tian, Jiacheng Tang +5
Constructing robust GUI agents relies on multimodal visual grounding: the ability to precisely map natural language intents to actionable on-screen components. Despite abundant des…
Autocata: Reaction-network-guided autonomous catalyst screening via capability-centric AI
Ruili Li, Rui Qi, Qingqing Mao +3
Artificial intelligence (AI) has expanded the accessible space for catalyst discovery, yet executing complex screening campaigns still requires constant human intervention to bridg…
Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation
Wenhui Chen, Zhifeng Li, Jie Zhou +5
A compressed student has two shapes that need not agree: the weight it deploys at inference and the weight family its training can reach. We show that a state-of-the-art weight-inh…
How Do Agents Fail on AutoResearch: End-to-End Diagnostic Evaluation on 100 Real-World Frontier Research Tasks
Yanlin Fei, Nazhou Liu, Xinmiao Yu +7
AI has long assisted scientific research, but the rapid advance of LLMs and agentic scaffolds is reshaping the landscape; a single system can now carry whole-stage research from an…
Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
Shaolong Chen, Yanlin Fei, Nazhou Liu +7
Can a language model recover the true research idea of a published paper when given only that paper's pre-publication bibliography? We introduce Reconstruction, a blind idea-recove…
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration
Ruili Li, Rui Qi, Shuoqi Zhang +5
Catalyst discovery for ammonia synthesis is inherently a reaction-network challenge because catalytic performance is governed not by a single adsorbed intermediate, but by a surfac…
Interpretability-Guided Layer Selection over Subspace Projection: SAEs as Stethoscopes, Not Scalpels, for Raw Task Vector Model Editing
Li Lei, Madalina Ciobanu, Qingqing Mao +1
LLMs increasingly require surgical model editing to enhance domain-specific capabilities without incurring the computational cost or catastrophic forgetting associated with full fi…
AdaDPO: Self-Adaptive Direct Preference Optimization with Balanced Gradient Updates
Shaolong Chen, Madalina Ciobanu, Qingqing Mao +1
DPO has become a widely adopted alternative to RLHF for aligning LLMs with human preferences, eliminating the need for a separate reward model or RL loop. Recent theoretical analys…
State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation
Navan Preet Singh, Anurag Garikipati, Ahmed Abulkhair +6
This paper introduces Arabic-DeepSeek-R1, an application-driven open-source Arabic LLM that leverages a sparse MoE backbone to address the digital equity gap for under-represented…
Application-Driven Pedagogical Knowledge Optimization of Open-Source LLMs via Reinforcement Learning and Supervised Fine-Tuning
Navan Preet Singh, Xiaokun Wang, Anurag Garikipati +3
We present an innovative multi-stage optimization strategy combining reinforcement learning (RL) and supervised fine-tuning (SFT) to enhance the pedagogical knowledge of large lang…
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models
Jenish Maharjan, Anurag Garikipati, Navan Preet Singh +7
LLMs have become increasingly capable at accomplishing a range of specialized-tasks and can be utilized to expand equitable access to medical knowledge. Most medical LLMs have invo…