2 citations · 2 across the 6 of their papers we have counts for
7 papers
miniTE: Diagnosing and Bridging the Execution Gap in Native NVFP4 Reinforcement Learning
Zhen Li, Mingfa Feng, Yiming Zhang +6
Native NVFP4 promises to accelerate reinforcement learning (RL) for large language models (LLMs), but achieving stable learning across separate training and sampling engines remain…
ADE: Agentic Data Evolution Framework for Human-Centered Objectives
Yang Yu, Yilin Jiang, Zexuan Fei +6
Aligning large language models to human-centered objectives is difficult when targets are non-executable and context-dependent, limiting reliable verification and scalable supervis…
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
Yilin Jiang, Xiaorong Zhu, Fei Tan +9
Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators. These roles place demands that ordinary question answering does…
Performance Manipulation: Labor Market Implications in AI-assisted Era
Xiaoyun Qiu, Yang Yu, Haifeng Xu
Performance manipulation arises when agents exploit easily measurable, routine tasks to inflate observable outcomes without contributing genuine innovation or expert judgment. We f…
InfiCoEvalChain: A Blockchain-Based Decentralized Framework for Collaborative LLM Evaluation
Yifan Yang, Jinjia Li, Kunxi Li +7
The rapid advancement of large language models (LLMs) demands increasingly reliable evaluation, yet current centralized evaluation suffers from opacity, overfitting, and hardware-i…
InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios
Chenglin Yu, Yang Yu, Songmiao Wang +5
Large Language Model (LLM) agents have demonstrated remarkable capabilities in organizing and executing complex tasks, and many such agents are now widely used in various applicati…
InfiR : Crafting Effective Small Language Models and Multimodal Small Language Models in Reasoning
Congkai Xie, Shuo Cai, Wenjun Wang +17
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have made significant advancements in reasoning capabilities. However, they still face challenges such as…