From the 1 of 8 linked papers with an AI index.
1 citations · 1 across the 4 of their papers we have counts for
8 papers
NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs
Jiarong Zhao, Zhikai Lei, Zhiheng Xi +5
The paper presents NexForge, a requirement‑first framework that automatically turns free‑form capability requirements into executable agent training tasks, scaling data generation…
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Honglin Guo, Qi Zhang, Yu Zhang +6
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating spa…
From Prediction to Justification: Aligning Sentiment Reasoning with Human Rationale via Reinforcement Learning
Shihao Zhang, Ziwei Wang, Jie Zhou +6
While Aspect-based Sentiment Analysis (ABSA) systems have achieved high accuracy in identifying sentiment polarities, they often operate as "black boxes," lacking the explicit reas…
Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark
Yuxuan Cai, Yipeng Hao, Jie Zhou +14
As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we i…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
Code-driven Number Sequence Calculation: Enhancing the inductive Reasoning Abilities of Large Language Models
Kedi Chen, Zhikai Lei, Xu Guo +10
Large language models (LLMs) make remarkable progress in reasoning tasks. Among different reasoning modes, inductive reasoning, due to its better alignment with human learning, att…