activity
20232026
most citedGraphLLM: Boosting Graph Reasoning Ability of Large Language Model

5 citations · 10 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai +398

We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…

cs.CL20254 cited

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

5 Team, Aohan Zeng, Xin Lv +167

We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that s…

cs.CL2024

An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing

Ziwei Chai, Guoyin Wang, Jing Su +8

We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special exper…

cs.CL2024

Can GNN be Good Adapter for LLMs?

Xuanwen Huang, Kaiqiao Han, Yang Yang +4

Recently, large language models (LLMs) have demonstrated superior capabilities in understanding and zero-shot learning on textual data, promising significant advances for many text…

cs.CL2024

InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks

Xueyu Hu, Ziyu Zhao, Shuang Wei +14

In this paper, we introduce InfiAgent-DABench, the first benchmark specifically designed to evaluate LLM-based agents on data analysis tasks. These tasks require agents to end-to-e…

cs.CL20235 cited

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model

Ziwei Chai, Tianjie Zhang, Liang Wu +4

The advancement of Large Language Models (LLMs) has remarkably pushed the boundaries towards artificial general intelligence (AGI), with their exceptional ability on understanding…