4 citations · 7 across the 25 of their papers we have counts for
8 papers · 1 filter
DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data
Xin Li, Jin Li, Shoujin Wang +2
Causal discovery from unstructured data is a challenging yet underexplored task in high-expertise domains such as healthcare, finance, and education. Existing methods typically lev…
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong +213
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most wh…
Evaluating Large Language Models on Multimodal Chemistry Olympiad Exams
Yiming Cui, Xin Yao, Yuxuan Qin +3
Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, mole…
InfoAgent: Advancing Autonomous Information-Seeking Agents
Gongrui Zhang, Jialiang Zhu, Ruiqi Yang +15
Building Large Language Model agents that expand their capabilities by interacting with external tools represents a new frontier in AI research and applications. In this paper, we…
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs
Jiliang Ni, Jiachen Pu, Zhongyi Yang +7
Large Language Models (LLMs) have significantly advanced artificial intelligence by optimizing traditional Natural Language Processing (NLP) workflows, facilitating their integrati…
A Survey on Enhancing Causal Reasoning Ability of Large Language Models
Xin Li, Zhuo Cai, Shoujin Wang +2
Large language models (LLMs) have recently shown remarkable performance in language tasks and beyond. However, due to their limited inherent causal reasoning ability, LLMs still fa…
RAPID: Long-Context Inference with Retrieval-Augmented Speculative Decoding
Guanzheng Chen, Qilong Feng, Jinjie Ni +2
The emergence of long-context large language models (LLMs) offers a promising alternative to traditional retrieval-augmented generation (RAG) for processing extensive documents. Ho…
Look Ahead Text Understanding and LLM Stitching
Junlin Julian Jiang, Xin Li
This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human i…