4 citations · 5 across the 15 of their papers we have counts for
18 papers
Mixture of Layers: Dynamic Layer Routing for Visual Reasoning
Jeonghwan Kim, Sofia Stoica, Jiwan Chung +5
Pre-trained vision encoders contain layer-wise visual representations that differ in spatial granularity, semantic abstraction, and sensitivity to local details. However, most Mult…
MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
Guangzhi Xiong, Xinyuan Zhang, Xiao Yang +14
Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spanning months or years, reprocessi…
Evaluating the Hidden Costs of Personalization in Large Language Models
Yumeng Wang, Yuchen Wu, Cheng Qian +6
While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative respo…
Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment
Dwip Dalal, Shivansh Patel, Chahit Jain +8
Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. Howe…
Securing Multimodal AI through Internal Information Decomposition
Jehyeok Yeon, Hyeonjeong Ha, Qiusi Zhan +1
Multimodal large language models introduce attack surfaces absent in unimodal systems: adversaries can distribute malicious intent across modalities to evade unimodal safeguards. T…
Trimming the Long-Tail of Visual World Modeling Evaluation
Bingxuan Li, Yining Hong, Cheng Qian +6
Physical interactions follow a long-tailed distribution: a set of common and regular interactions dominates human experience and visual data, while a broad spectrum of rare and irr…
Advancing Creative Physical Intelligence in Large Multimodal Models
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…
MemGuard: Preventing Memory Contamination in Long-Term Memory-Augmented Large Language Models
Hyeonjeong Ha, Jeonghwan Kim, Cheng Qian +7
Memory-augmented large language models extend reasoning beyond a fixed context window by maintaining long-term memory across interactions. However, existing memory systems often co…
CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains under…
NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding
Hyeonjeong Ha, Jinjin Ge, Bo Feng +2
Multimodal large language models (MLLMs) have achieved impressive progress in vision-language reasoning, yet their ability to understand temporally unfolding narratives in videos r…
BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning
Qiusi Zhan, Hyeonjeong Ha, Rui Yang +7
Recent advances in Vision-Language Models (VLMs) have propelled embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. How…
Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping
Dwip Dalal, Gautam Vashishtha, Utkarsh Mishra +6
Multimodal large language models (MLLMs) often miss small details and spatial relations in cluttered scenes, leading to errors in fine-grained perceptual grounding. We introduce At…
Energy-Based Transformers are Scalable Learners and Thinkers
Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam +7
Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer…
Perception-Aware Policy Optimization for Multimodal Reasoning
Zhenhailong Wang, Xuehang Guo, Sofia Stoica +8
Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with robust multi-step reasoning abilit…
PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding
Ansel Blume, Jeonghwan Kim, Hyeonjeong Ha +7
Real-world objects are composed of distinctive, object-specific parts. Identifying these parts is key to performing fine-grained, compositional reasoning-yet, large multimodal mode…
Must Read: A Comprehensive Survey of Computational Persuasion
Nimet Beyza Bozdag, Shuhaib Mehri, Xiaocheng Yang +7
Persuasion is a fundamental aspect of communication, influencing decision-making across diverse contexts, from everyday conversations to high-stakes scenarios such as politics, mar…
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks
Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim +6
Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its relianc…
SYNTHIA: Novel Concept Design with Affordance Composition
Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim +7
Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design. While recent studies focus on generating semantic and stylistic variations of g…