13 citations · 22 across the 22 of their papers we have counts for
23 papers
Positional Failures in Long-Context LLMs: A Blind Spot in Reasoning Benchmarks
Chuyifei Zhang, Hongyu Cui, Xiaowen Huang +1
Position-controlled evaluation is standard for retrieval tasks such as Needle-in-a-Haystack and RULER, but mainstream reasoning benchmarks do not control positional placement of ta…
Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning
Shuyu Wei, Jian Sun, Delai Qiu +6
Entropy-based deep reasoning has emerged as a promising direction for improving the reasoning capabilities of Large Language Models (LLMs), but existing methods often either increa…
AI Deception: Risks, Dynamics, and Controls
Boyuan Chen, Sitong Fang, Jiaming Ji +56
As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an…
Reasoning Shapes Alignment: Investigating Cultural Alignment in Large Reasoning Models with Cultural Norms
Yuhang Wang, Yanxu Zhu, Jitao Sang
The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving…
Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI
Jitao Sang, Jinlin Xiao, Jiarun Han +5
The rapid evolution of agentic AI marks a new phase in artificial intelligence, where Large Language Models (LLMs) no longer merely respond but act, reason, and adapt. This survey…
ReInAgent: A Context-Aware GUI Agent Enabling Human-in-the-Loop Mobile Task Navigation
Haitao Jia, Ming He, Zimo Yin +3
Mobile GUI agents exhibit substantial potential to facilitate and automate the execution of user tasks on mobile phones. However, exist mobile GUI agents predominantly privilege au…
VAPO: End-to-end Slide-Enhanced Speech Recognition with Omni-modal Large Language Models
Rui Hu, Delai Qiu, Yining Wang +2
Omni-modal large language models (OLLMs) offer a promising end-to-end solution for slide-enhanced speech recognition due to their inherent multimodal capabilities. However, we foun…
MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions
Yanxu Zhu, Shitong Duan, Xiangxu Zhang +7
Recently Multimodal Large Language Models (MLLMs) have achieved considerable advancements in vision-language tasks, yet produce potentially harmful or untrustworthy content. Despit…
Mobile-Agent-V: A Video-Guided Approach for Effortless and Efficient Operational Knowledge Injection in Mobile Automation
Junyang Wang, Haiyang Xu, Xi Zhang +4
The exponential rise in mobile device usage necessitates streamlined automation for effective task management, yet many AI frameworks fall short due to inadequate operational exper…
Investigating and Enhancing Vision-Audio Capability in Omnimodal Large Language Models
Rui Hu, Delai Qiu, Shuyu Wei +4
Omnimodal Large Language Models (OLLMs) have shown significant progress in integrating vision and text, but still struggle with integrating vision and audio, often exhibiting subop…
Mobile-Agent-V: A Video-Guided Approach for Effortless and Efficient Operational Knowledge Injection in Mobile Automation
Junyang Wang, Haiyang Xu, Xi Zhang +4
The exponential rise in mobile device usage necessitates streamlined automation for effective task management, yet many AI frameworks fall short due to inadequate operational exper…
OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning
Yuxiang Zhang, Yuqi Yang, Jiangming Shu +3
OpenAI's recent introduction of Reinforcement Fine-Tuning (RFT) showcases the potential of reasoning foundation model and offers a new paradigm for fine-tuning beyond simple patter…
o1-Coder: an o1 Replication for Coding
Yuxiang Zhang, Shangxi Wu, Yuqi Yang +4
The technical report introduces O1-CODER, an attempt to replicate OpenAI's o1 model with a focus on coding tasks. It integrates reinforcement learning (RL) and Monte Carlo Tree Sea…
Don't Command, Cultivate: An Exploratory Study of System-2 Alignment
Yuhang Wang, Yuxiang Zhang, Yanxu Zhu +2
The o1 system card identifies the o1 models as the most robust within OpenAI, with their defining characteristic being the progression from rapid, intuitive thinking to slower, mor…
ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
Yahan Tu, Rui Hu, Jitao Sang
Hallucination poses a persistent challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are generally static, which may…
KG-FPQ: Evaluating Factuality Hallucination in LLMs with Knowledge Graph-based False Premise Questions
Yanxu Zhu, Jinlin Xiao, Yuhang Wang +1
Recent studies have demonstrated that large language models (LLMs) are susceptible to being misled by false premise questions (FPQs), leading to errors in factual knowledge, know a…
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
Junyang Wang, Haiyang Xu, Haitao Jia +6
Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their train…
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
Rui Hu, Yahan Tu, Shuyu Wei +2
Despite achieving outstanding performance on various cross-modal tasks, current large vision-language models (LVLMs) still suffer from hallucination issues, manifesting as inconsis…
Improving Weak-to-Strong Generalization with Scalable Oversight and Ensemble Learning
Jitao Sang, Yuhang Wang, Jing Zhang +5
This paper presents a follow-up study to OpenAI's recent superalignment work on Weak-to-Strong Generalization (W2SG). Superalignment focuses on ensuring that high-level AI systems…
Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception
Junyang Wang, Haiyang Xu, Jiabo Ye +5
Mobile device agent based on Multimodal Large Language Models (MLLM) is becoming a popular application. In this paper, we introduce Mobile-Agent, an autonomous multi-modal mobile d…
Language-assisted Vision Model Debugger: A Sample-Free Approach to Finding and Fixing Bugs
Chaoquan Jiang, Jinqiang Wang, Rui Hu +1
Vision models with high overall accuracy often exhibit systematic errors in specific scenarios, posing potential serious safety concerns. Diagnosing bugs of vision models is gainin…
CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
Yuhang Wang, Yanxu Zhu, Chao Kong +4
As the scaling of Large Language Models (LLMs) has dramatically enhanced their capabilities, there has been a growing focus on the alignment problem to ensure their responsible and…
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Junyang Wang, Yuhang Wang, Guohai Xu +8
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to…