27 citations · 27 across the 6 of their papers we have counts for
6 papers
Do Multimodal LLMs See Before They Read? Diagnosing Contextual Sycophancy
Yi-Cheng Lai, Hen-Hsen Huang
External text can override conflicting image evidence in multimodal large language models, a failure we call multimodal contextual sycophancy. We introduce a 998-case diagnostic th…
MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems
Mao-xun Huang, Jerry Wang, Yi-Cheng Lai +3
Large language model-based multi-agent systems improve complex problem solving through task decomposition, agent specialization, information exchange, and intermediate validation.…
AR: Adversarial Reinforcement Learning for Abstract Reasoning in Large Language Models
Cheng-Kai Yeh, Hsing-Wang Lee, Chung-Hung Kuo +1
Abstraction--the ability to recognize and distill essential computational patterns from complex problem statements--is a foundational skill in computer science, critical both for h…
SmartSpatial: Enhancing the 3D Spatial Arrangement Capabilities of Stable Diffusion Models and Introducing a Novel 3D Spatial Evaluation Framework
Mao Xun Huang, Brian J Chan, Hen-Hsen Huang
Stable Diffusion models have made remarkable strides in generating photorealistic images from text prompts but often falter when tasked with accurately representing complex spatial…
Efficient Beam Search for Large Language Models Using Trie-Based Decoding
Brian J Chan, MaoXun Huang, Jui-Hung Cheng +2
This work presents a novel trie (prefix-tree)-based parallel decoding method that addresses the memory inefficiency of batch-based beam search. By sharing a single KV cache across…
Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
Brian J Chan, Chao-Ting Chen, Jui-Hung Cheng +1
Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. However, RAG introduces cha…