16 citations · 17 across the 15 of their papers we have counts for
10 papers · 1 filter
Mixture of Layers: Dynamic Layer Routing for Visual Reasoning
Jeonghwan Kim, Sofia Stoica, Jiwan Chung +5
Pre-trained vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local details. However, most Mult…
It Takes Workflows to Evolve Better Workflows
Xuehang Guo, Haoyu Wang, Haifeng Chen +3
Tackling complex real-world tasks can exceed the capabilities of a single large language model (LLM), motivating the use of multi-agent workflows that coordinate specialized agents…
R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing
Xin Wang, Zichuan Ying, Xinna Lin +8
Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsunclear.Markush structures, w…
Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
Brian Wang, Bin Feng, Xiaoman Pan +26
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning
Xuehang Guo, Pingyue Zhang, Ruiyi Zhang +6
Chart question answering (CQA) requires multimodal large language models (MLLMs) to integrate visual comprehension with logical reasoning, yet current models struggle with accurate…
Long-Horizon Embodied Decision-Making via Multimodal Memory Compression
Bingxuan Li, Rui Yang, Cheng Qian +6
Agents are increasingly expected to act not only as task executors, but also as decision-makers on behalf of human users. This shift requires agents to accumulate evidence over lon…
PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems
Jiayu Liu, Qihan Lin, Cheng Qian +8
LLM agents increasingly operate in large tool ecosystems, where real-world tasks require discovering relevant tools, inferring implicit sub-goals, and adapting to dynamic environme…
Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs
Haozhe Zhao, Shuzheng Si, Zhenhailong Wang +6
Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensi…
From Context to Skills: Can Language Models Learn from Context Skillfully?
Shuzheng Si, Haozhe Zhao, Yu Lei +10
Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly lear…
OSExpert: Computer-Use Agents Learning Professional Skills via Exploration
Jiateng Liu, Zhenhailong Wang, Rushi Wang +6
General-purpose computer-use agents have shown impressive performance across diverse digital environments. However, our new benchmark, OSExpert-Eval, indicates they remain far less…