Knowledge catalogue
arXiv:2605.18814v1 Announce Type: new Abstract: Trajectory-based data attribution methods estimate the influence of training samples on model predictions by unrolling the training trajectory. They are
arXiv:2605.19156v1 Announce Type: new Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of ho
arXiv:2605.19027v1 Announce Type: new Abstract: Medical foundation models (MedFMs) have emerged as transformative tools in healthcare, demonstrating capabilities across diverse clinical applications.
arXiv:2602.20700v2 Announce Type: replace Abstract: Estimating sewing patterns from images is a practical approach for creating high-quality 3D garments, but it remains challenging due to the scarcity
arXiv:2605.18930v1 Announce Type: cross Abstract: Memory-augmented large language model (LLM) agents use iterative reflection and self-evolution to solve complex tasks, but these mechanisms introduce
arXiv:2605.19173v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly explored for clinical decision support, yet most evaluations are conducted in English, leaving their relia
arXiv:2605.20052v1 Announce Type: cross Abstract: Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical im
arXiv:2605.19663v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are becoming the cornerstone of high-level reasoning for robotic automation, enabling robots to parse natural language com
arXiv:2605.19077v1 Announce Type: cross Abstract: Task-oriented dialogue systems -- handling transactions, reservations, and service requests -- require predictable behavior, yet the moderately-sized
arXiv:2605.19193v1 Announce Type: new Abstract: Multi-agent LLM debate improves factuality and reasoning, but most recipes pick a fixed round count, over-spending on easy items and under-spending on h
arXiv:2605.19338v1 Announce Type: cross Abstract: Frontier AI models and multi-agent systems have led to significant improvements in mathematical reasoning. However, for problems requiring extended, l
arXiv:2511.01482v2 Announce Type: replace Abstract: Text-based automated Cognitive Distortion detection is a challenging task due to its subjective nature, with low agreement scores observed even amon
arXiv:2605.19798v1 Announce Type: new Abstract: As Socially Interactive Agents (SIAs) become increasingly integrated into daily life, the ability to calibrate user trust to an agent's actual capabilit
arXiv:2603.22453v2 Announce Type: replace Abstract: Community Notes have emerged as an effective crowd-sourced mechanism for combating online deception on social media platforms. However, its reliance
arXiv:2605.17046v1 Announce Type: cross Abstract: Autonomous AI coding agents are becoming a core tool for ML practitioners in industry and research alike. Despite this growing adoption, no standardiz
arXiv:2605.16795v1 Announce Type: cross Abstract: Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works
arXiv:2605.16386v1 Announce Type: new Abstract: Multimodal large language models (LLMs) are increasingly explored as automated evaluators in clinical settings, yet their scoring behavior on ordinal cl
arXiv:2603.23069v2 Announce Type: replace-cross Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original tex
arXiv:2605.17971v1 Announce Type: cross Abstract: Despite rigorous safety alignment, Large Language Models (LLMs) remain vulnerable to jailbreak attacks. Existing black-box methods often rely on heuri
arXiv:2605.17416v1 Announce Type: cross Abstract: Anthropic's April 2026 Mythos materials combine benchmark claims with concrete bug-finding stories across OpenBSD, FreeBSD, Linux, FFmpeg, and browser
arXiv:2605.17079v1 Announce Type: cross Abstract: LLMs are increasingly used as ``digital consumers'' to simulate public opinion, pre-test marketing decisions, and anticipate audience response. Howeve
arXiv:2605.17450v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used for automated vulnerability repair (AVR), where repository-level reasoning enables them to ins
arXiv:2602.18895v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in translating model-based explanations into human-readable narratives. This study evaluates w
arXiv:2605.18727v1 Announce Type: cross Abstract: Evaluating embodied systems on real dexterous hardware requires more than isolated primitive skills: an agent must perceive a changing tabletop scene,
arXiv:2510.11391v3 Announce Type: replace-cross Abstract: Recent agentic workflows automate professional document generation but focus narrowly on textual quality, overlooking structural and stylistic
arXiv:2605.16630v1 Announce Type: cross Abstract: Hybrid local--cloud agents enrich user requests with context from persistent working state before delegating capability-intensive subtasks to a cloud
arXiv:2605.16385v1 Announce Type: cross Abstract: Geometric problem solving, as a typical multimodal reasoning problem, has attracted much attention and made great progress recently, however most of w
arXiv:2605.18111v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promisi
arXiv:2409.02428v4 Announce Type: replace-cross Abstract: Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and m
arXiv:2603.16947v2 Announce Type: replace-cross Abstract: Although vision-language navigation (VLN) has progressed rapidly, zero-shot VLN in continuous environments (VLN-CE) remains highly challenging
arXiv:2605.18176v1 Announce Type: cross Abstract: This report presents MARS, short for Multimodal Agentic Reasoning with Source selection, our system for the CASTLE Challenge at EgoVis 2026. Participa
arXiv:2605.16409v1 Announce Type: cross Abstract: Optical character recognition (OCR) and multilingual text understanding remain major failure modes of multimodal large language models (MLLMs), partic
arXiv:2503.20981v2 Announce Type: replace-cross Abstract: Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey
arXiv:2605.17187v1 Announce Type: cross Abstract: Social media are shifting towards pluralism -- community-governed platforms where groups define their own norms. What violates rules in one community
arXiv:2510.18941v2 Announce Type: replace-cross Abstract: Evaluating progress in large language models (LLMs) is often constrained by the challenge of verifying responses, limiting assessments to task
arXiv:2508.00901v4 Announce Type: replace-cross Abstract: Large language models may encounter factual knowledge during pre-training yet fail to reliably use that knowledge after fine-tuning. Despite g
arXiv:2512.19134v2 Announce Type: replace Abstract: Dynamic Retrieval-Augmented Generation adaptively determines when to retrieve during generation to mitigate hallucinations in large language models
arXiv:2605.17610v1 Announce Type: cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deplo
arXiv:2605.16986v1 Announce Type: cross Abstract: LLM agents benefit from reusable skills, yet test-time tasks often require guidance more specific than a static skill library can provide. We propose
arXiv:2605.18401v1 Announce Type: cross Abstract: Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills a
arXiv:2605.17789v1 Announce Type: cross Abstract: Memory systems for AI assistants were built for single-user dialogue and fail characteristically when applied to multi-party social group settings. Th
arXiv:2605.18232v1 Announce Type: cross Abstract: Somali is a Cushitic language of the Horn of Africa with ~25 million speakers, yet no documented dedicated Somali pretraining corpus with a companion
arXiv:2504.13217v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have become increasingly embedded in organizational workflows. This has raised concerns over their energy consump
arXiv:2605.18106v1 Announce Type: cross Abstract: A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich s
arXiv:2605.18109v1 Announce Type: new Abstract: In real home deployments, household agents must often operate from a complete household scene and a situated household request, rather than from a clean
arXiv:2605.17691v1 Announce Type: cross Abstract: Automating the classification of negative treatment in legal precedent is a critical yet nuanced NLP task where misclassification carries significant
arXiv:2605.17467v1 Announce Type: new Abstract: Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agents remain a key bottleneck to system-level reliabil
arXiv:2605.16481v1 Announce Type: cross Abstract: Long video understanding requires more than large context windows. It also needs a memory mechanism that decides what visual evidence to retain, keeps
arXiv:2510.16252v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) for web agents demands environments that are both effective for evaluation and efficient enough for large-scale on
arXiv:2605.16288v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in clinical and care settings. This exploratory study investigates whether LLMs exhibit sycophantic
arXiv:2605.16077v1 Announce Type: new Abstract: Accurate assessment of cognitive decline from spontaneous speech remains challenging due to limited dataset size and class imbalance. In this work, we p
arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such
arXiv:2605.15513v1 Announce Type: new Abstract: Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-ti
arXiv:2605.15759v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term memory to leverage information from past interactions. However, existing memory systems often face a
arXiv:2605.15377v1 Announce Type: new Abstract: As AI systems are increasingly deployed in autonomous agentic settings at scale, it is important to ensure the actions they take are safe and aligned wi
arXiv:2410.02832v2 Announce Type: replace-cross Abstract: This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we
arXiv:2605.15338v1 Announce Type: cross Abstract: Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for pe
arXiv:2605.15486v1 Announce Type: cross Abstract: This study introduces intelligent frameworks that use Large Language Models (LLMs) to improve task scheduling for construction robots. The LLM is fed
arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage con
arXiv:2605.16079v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring p