2 citations · 3 across the 11 of their papers we have counts for
11 papers
PixelEyes: Decoupling Perception and Reasoning for Pinpoint Visual Evidence Seeking
Dengxian Gong, Yuanzheng Wu, Haobo Yuan +11
This paper explores multi-turn visual reasoning and observes that MLLMs repeatedly fail to localize the target, leading to long, redundant trajectories. We attribute this failure t…
HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
HyperAI Team, Yuchen Liu, Kaiyang Han +26
Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy direc…
Agent-Kernel: A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs
Yuren Mao, Peigen Liu, Xinjian Wang +11
Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks…
Explainable AI-Generated Image Detection RewardBench
Michael Yang, Shijian Deng, William T. Doan +4
Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces…
Can World Simulators Reason? Gen-ViRe: A Generative Visual Reasoning Benchmark
Xinxin Liu, Zhaopan Xu, Ming Li +3
While Chain-of-Thought (CoT) prompting enables sophisticated symbolic reasoning in LLMs, it remains confined to discrete text and cannot simulate the continuous, physics-governed d…
Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting
Alexandre Andre, Gauthier Roy, Eva Dyer +1
Large Language Models (LLMs) are increasingly used for recommendation tasks due to their general-purpose capabilities. While LLMs perform well in rich-context settings, their behav…
When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
Kai Wang, Yihao Zhang, Meng Sun
The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans…
Delayed-KD: Delayed Knowledge Distillation based CTC for Low-Latency Streaming ASR
Longhao Li, Yangze Li, Hongfei Xue +4
CTC-based streaming ASR has gained significant attention in real-world applications but faces two main challenges: accuracy degradation in small chunks and token emission latency.…
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought
Tencent Hunyuan Team, Ao Liu, Botong Zhou +248
As Large Language Models (LLMs) rapidly advance, we introduce Hunyuan-TurboS, a novel large hybrid Transformer-Mamba Mixture of Experts (MoE) model. It synergistically combines Mam…
OTCE: Hybrid SSM and Attention with Cross Domain Mixture of Experts to construct Observer-Thinker-Conceiver-Expresser
Jingze Shi, Ting Xie, Bingheng Wu +2
Recent research has shown that combining Mamba with Transformer architecture, which has selective state space and quadratic self-attention mechanism, outperforms using Mamba or Tra…
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation
Kohei Uehara, Nabarun Goswami, Hanqin Wang +10
The increasing demand for intelligent systems capable of interpreting and reasoning about visual content requires the development of large Vision-and-Language Models (VLMs) that ar…