1 citations · 1 across the 7 of their papers we have counts for
10 papers
Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Minghao Guo, Qingyue Jiao, Xiujin Liu +77
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities…
Knowing When Thinking Is Not Enough: Teaching Small Reasoning Models to Reason Beyond Their Parametric Knowledge
Chanuk Lee, Minki Kang, Sangwoo Park +3
Scaling test-time computation is a powerful way to improve language-model reasoning, and is particularly appealing for small reasoning models (sRMs) that are cheap to serve. Howeve…
Surprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoning
Woongyeong Yeo, Minki Kang, Chanuk Lee +3
Reinforcement learning with verifiable rewards (RLVR) enhances reasoning in large language models (LLMs) through outcome-level feedback, yet recent approaches to finer-grained cred…
OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources
Jinheon Baek, Soyeong Jeong, Sangwoo Park +5
Real-world information needs require access to structurally diverse knowledge sources, from unstructured text and relational tables to knowledge graphs and property graphs. Existin…
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
Sangwoo Park, Woongyeong Yeo, Seanie Lee +6
Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given context. As large language…
HINT-SD: Targeted Hindsight Self-Distillation for Long-Horizon Agents
Woongyeong Yeo, Yumin Choi, Taekyung Ki +1
Training long-horizon LLM agents with reinforcement learning is challenging because sparse outcome rewards reveal whether a task succeeds, but not which intermediate actions caused…
MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents
Kangsan Kim, Yanlai Yang, Suji Kim +4
As embodied models become powerful, humans will collaborate with multiple embodied AI agents at their workplace or home in the future. To ensure better communication between human…
WorldMM: Dynamic Multimodal Memory Agent for Long Video Reasoning
Woongyeong Yeo, Kangsan Kim, Jaehong Yoon +1
Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highl…
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities
Woongyeong Yeo, Kangsan Kim, Soyeong Jeong +2
Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However,…
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Kangsan Kim, Geon Park, Youngwan Lee +2
Recent advancements in video large multimodal models (LMMs) have significantly improved their video understanding and reasoning capabilities. However, their performance drops on ou…