4 citations · 15 across the 17 of their papers we have counts for
18 papers
GLM-5: from Vibe Coding to Agentic Engineering
GLM-5-Team, :, Aohan Zeng +184
We present GLM-5, a next-generation foundation model designed to transition the paradigm of vibe coding to agentic engineering. Building upon the agentic, reasoning, and coding (AR…
Chaining the Evidence: Robust Reinforcement Learning for Deep Search Agents with Citation-Aware Rubric Rewards
Jiajie Zhang, Xin Lv, Ling Feng +2
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binary outcome rewards, w…
DocDancer: Towards Agentic Document-Grounded Information Seeking
Qintong Zhang, Xinjie Lv, Jialong Wu +8
Document Question Answering (DocQA) focuses on answering questions grounded in given documents, yet existing DocQA agents lack effective tool utilization and largely rely on closed…
PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding
Penghao Wang, Yiyang He, Xin Lv +4
Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress…
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…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
Yushi Bai, Shangqing Tu, Jiajie Zhang +9
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world…
T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling
Zhenyu Hou, Xin Lv, Rui Lu +6
Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks. However, existing approaches mainly rely on imitation learning and struggle to ac…
LongReward: Improving Long-context Large Language Models with AI Feedback
Jiajie Zhang, Zhongni Hou, Xin Lv +7
Though significant advancements have been achieved in developing long-context large language models (LLMs), the compromised quality of LLM-synthesized data for supervised fine-tuni…
Pre-training Distillation for Large Language Models: A Design Space Exploration
Hao Peng, Xin Lv, Yushi Bai +4
Knowledge distillation (KD) aims to transfer knowledge from a large teacher model to a smaller student model. Previous work applying KD in the field of large language models (LLMs)…
LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-context QA
Jiajie Zhang, Yushi Bai, Xin Lv +8
Though current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their…
LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
Yushi Bai, Jiajie Zhang, Xin Lv +6
Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through c…
SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
Qianchao Zhu, Jiangfei Duan, Chang Chen +6
Large language models (LLMs) now support extremely long context windows, but the quadratic complexity of vanilla attention results in significantly long Time-to-First-Token (TTFT)…
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models
Yantao Liu, Zijun Yao, Xin Lv +5
Providing knowledge documents for large language models (LLMs) has emerged as a promising solution to update the static knowledge inherent in their parameters. However, knowledge i…
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
Hongzheng Li, Ruojin Wang, Ge Shi +7
Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Res…
LongAlign: A Recipe for Long Context Alignment of Large Language Models
Yushi Bai, Xin Lv, Jiajie Zhang +6
Extending large language models to effectively handle long contexts requires instruction fine-tuning on input sequences of similar length. To address this, we present LongAlign --…
Probabilistic Tree-of-thought Reasoning for Answering Knowledge-intensive Complex Questions
Shulin Cao, Jiajie Zhang, Jiaxin Shi +5
Large language models (LLMs) are capable of answering knowledge-intensive complex questions with chain-of-thought (CoT) reasoning. However, they tend to generate factually incorrec…
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
Taehoon Kim, Pyunghwan Ahn, Sangyun Kim +39
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed t…