arXiv:2602.20205v3 Announce Type: replace
Abstract: Multi-modal large language models (MLLMs) achieve strong visual-language reasoning but suffer from high inference cost due to redundant visual tokens. Recent work explores visual token pruning to acc…
arXiv:2505.17760v3 Announce Type: replace-cross
Abstract: LLM-as-a-judge is widely used as a scalable substitute for human evaluation, yet current approaches rely on black-box access and struggle to detect subtle dishonesty, such as sycophancy and man…
arXiv:2604.00901v1 Announce Type: new
Abstract: Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard queries that require multiple steps and sources, or complex reasoning. Existing approaches, ho…
arXiv:2604.00455v1 Announce Type: cross
Abstract: Recent Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across various multimodal tasks that require understanding both visual and linguistic inputs. However, object halluc…
arXiv:2603.30031v2 Announce Type: replace
Abstract: Autonomous tool-using agents operating in networked environments must decide which information source to query and when to stop querying and act. Without principled bounds on information-acquisition …
arXiv:2601.10001v3 Announce Type: replace
Abstract: Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the two most prevalent and incurable neurodegenerative diseases (NDs) worldwide, for which early diagnosis is critical to delay their progres…
arXiv:2604.00890v1 Announce Type: cross
Abstract: Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language models because it requires the combination of diagrammatic understanding, symbolic manipulatio…
arXiv:2604.00452v1 Announce Type: new
Abstract: Recent Tracking-by-Query-Propagation (TBP) methods have advanced Multi-Object Tracking (MOT) by enabling end-to-end (E2E) pipelines with long-range temporal modeling. However, this reliance on query prop…
arXiv:2602.19837v2 Announce Type: replace
Abstract: Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle to replicate due to their reliance on task-specific train…
arXiv:2604.00842v1 Announce Type: new
Abstract: Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existi…