activity
20232026
most citedInternLM2 Technical Report

29 citations · 34 across the 15 of their papers we have counts for

collaborators
Showing 2025Show all

8 papers · 1 filter

cs.CL2025

Rectifying LLM Thought from Lens of Optimization

Junnan Liu, Hongwei Liu, Songyang Zhang +1

Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which ena…

cs.CL2025

ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

Hongwei Liu, Junnan Liu, Shudong Liu +33

The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Co…

cs.LG2025

Intern-S1: A Scientific Multimodal Foundation Model

Lei Bai, Zhongrui Cai, Yuhang Cao +173

In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…

cs.CL2025

CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward

Shudong Liu, Hongwei Liu, Junnan Liu +8

Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward m…

cs.CL2025

When Tools Hurt LLM Reasoning: State-Dependent Belief Revision under External Evidence

Yufeng Zhao, Junnan Liu, Hongwei Liu +4

Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when irrelevant. We show that this assumption…

cs.CL2025

Deciphering Trajectory-Aided LLM Reasoning: An Optimization Perspective

Junnan Liu, Hongwei Liu, Linchen Xiao +5

We propose a novel framework for comprehending the reasoning capabilities of large language models (LLMs) through the perspective of meta-learning. By conceptualizing reasoning tra…

cs.CL2025

OpenHuEval: Evaluating Large Language Model on Hungarian Specifics

Haote Yang, Xingjian Wei, Jiang Wu +18

We introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics. OpenHuEval is constructed from a vast collection of Hungarian-specific mater…

cs.CL2025

Exploring the Limit of Outcome Reward for Learning Mathematical Reasoning

Chengqi Lyu, Songyang Gao, Yuzhe Gu +14

Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series…