4 citations · 10 across the 19 of their papers we have counts for
20 papers
QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering
Yujia Chen, Yuhui Wang, Junzhe Wang +15
DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue intent-misaligned, under-constra…
Agents in the Large: Perception-Centered Architecture for Persistent Agents
Shihan Dou, Haoxiang Jia, Shichun Liu +14
Cognitive language agents have achieved substantial progress by equipping language models with memory, tools, and decision-making procedures, enabling agents to reason and act in i…
A Token-Level Analysis of Sampled-Token Reverse-KL On-Policy Distillation
Bing Shao, Jiazheng Zhang, Long Ma +12
On-policy distillation (OPD) supervises a student on its own trajectories with token-level signals from a frozen teacher, yet how a sampled loss allocates updates across tokens rem…
SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information
Junjie Ye, Zhuohui Sheng, Shaofan Liu +12
Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps)…
HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers
Ming Zhang, Qiyuan Peng, Yinxi Wei +16
Existing logic benchmarks primarily measure models' ability to answer reasoning questions directly. Scalable benchmarks often generate text from formal structures, which makes answ…
CL-bench Life: Can Language Models Learn from Real-Life Context?
Shihan Dou, Yujiong Shen, Chenhao Huang +35
Today's AI assistants such as OpenClaw are designed to handle context effectively, making context learning an increasingly important capability for models. As these systems move be…
JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees
Yuhui Wang, Zhixiong Yang, Ming Zhang +10
In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by la…
CL-bench: A Benchmark for Context Learning
Shihan Dou, Ming Zhang, Zhangyue Yin +24
Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…
SciAgentGym: Benchmarking Multi-Step Scientific Tool-use in LLM Agents
Yujiong Shen, Yajie Yang, Zhiheng Xi +17
Scientific reasoning inherently demands integrating sophisticated toolkits to navigate domain-specific knowledge. Yet, current benchmarks largely overlook agents' ability to orches…
OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment
Ming Zhang, Kexin Tan, Yueyuan Huang +20
Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, a…
TaxoBench: Can Deep Research Agents Retrieve and Organize? Evaluating the Synthesis Gap with Expert Taxonomies
Ming Zhang, Jiabao Zhuang, Wenqing Jing +19
Deep Research Agents increasingly automate survey writing, yet existing benchmarks do not jointly test whether they retrieve the papers experts consider essential and organize thos…
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…
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models
Ming Zhang, Yujiong Shen, Jingyi Deng +19
Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting, critical issues that obscure true model ca…
LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation
Ming Zhang, Yujiong Shen, Zelin Li +13
Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three…
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations
Shuo Li, Jiajun Sun, Guodong Zheng +10
Recently, multimodal large language models (MLLMs) have demonstrated remarkable performance in visual-language tasks. However, the authenticity of the responses generated by MLLMs…
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts
Ming Zhang, Yuhui Wang, Yujiong Shen +16
Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large La…
Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training
Changhao Jiang, Ming Zhang, Yifei Cao +12
The GPT-4 technical report suggests that downstream performance can be predicted from pre-training signals, but offers little methodological detail on how to quantify this. This wo…
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities
Ming Zhang, Caishuang Huang, Yilong Wu +10
Task-oriented dialogue (TOD) systems aim to efficiently handle task-oriented conversations, including information collection. How to utilize TOD accurately, efficiently and effecti…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
Exploring the Potential of Flexible 8-bit Format: Design and Algorithm
Zhuoyi Zhang, Yunchen Zhang, Gonglei Shi +6
Neural network quantization is widely used to reduce model inference complexity in real-world deployments. However, traditional integer quantization suffers from accuracy degradati…