Knowledge catalogue
arXiv:2605.18570v1 Announce Type: new Abstract: Cross-domain knowledge alignment is essential for integrating heterogeneous medical systems, yet existing approaches typically treat entity alignment as
arXiv:2602.12633v2 Announce Type: replace Abstract: Reconstructing physically valid 3D scenes from single-view observations is a prerequisite for bridging the gap between visual perception and robotic
arXiv:2601.09413v2 Announce Type: replace-cross Abstract: We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult
arXiv:2605.15203v1 Announce Type: cross Abstract: We introduce Agent4POI, the first POI recommendation framework that generates context-conditioned multimodal representations at recommendation time, r
arXiv:2605.15978v1 Announce Type: cross Abstract: Law enforcement reports contain structured fields and written narratives. However, many incident facts that are needed for review, police training, an
arXiv:2605.14857v1 Announce Type: new Abstract: Harmonized System (HS) tariff classification is a high-stakes, expert-level task in which a free-form product description must be mapped to a specific s
arXiv:2510.02837v2 Announce Type: replace Abstract: Although recent tool-augmented benchmarks involve complex requests, evaluation remains limited to answer matching, neglecting critical trajectory as
arXiv:2601.21174v2 Announce Type: replace Abstract: Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs
arXiv:2605.13854v1 Announce Type: new Abstract: Multi-person 3D reconstruction is pivotal for real-world interaction analysis, yet remains challenging due to severe occlusions and depth ambiguity. Cur
arXiv:2605.14403v1 Announce Type: new Abstract: Dermatological diagnosis requires integrating fine-grained visual perception with expert clinical knowledge. Although Multimodal Large Language Models (
arXiv:2605.15071v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied to cultural heritage materials, from digital archives to educational platforms. This work ident
arXiv:2605.14040v1 Announce Type: new Abstract: We audit the multimodal-physics evaluation pipeline end-to-end and document three undetected construction practices that distort how the field measures
arXiv:2605.13077v1 Announce Type: cross Abstract: Responsibility allocation -- determining the extent to which agents are accountable for outcomes -- is a fundamental challenge in the design and analy
arXiv:2605.12542v1 Announce Type: cross Abstract: Large foundation models (FMs) are transforming Earth science by integrating heterogeneous multimodal data, such as multi-platform imagery, gridded rea
arXiv:2605.13321v1 Announce Type: new Abstract: VLN has achieved remarkable progress by scaling data and model capacity. However, the assumption of a static environment breaks down in real-world indoo
arXiv:2605.11258v1 Announce Type: cross Abstract: Autonomous science promises to augment scientific discovery, particularly in complex fields like biomedicine. However, this requires AI systems that c
arXiv:2605.09153v1 Announce Type: cross Abstract: Closed-loop traffic simulation requires agents that are both scalable and behaviorally realistic. Recent self-play reinforcement learning approaches d
arXiv:2605.08404v1 Announce Type: cross Abstract: This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to chara
arXiv:2605.10410v1 Announce Type: new Abstract: Large language models can score well on named game-theory benchmarks while failing on the same strategic computation once semantic cues are removed. We
arXiv:2605.09419v1 Announce Type: new Abstract: While experience replay is essential for data efficiency in reinforcement learning (RL), standard methods treat the replay buffer as a passive memory sy
arXiv:2605.10806v1 Announce Type: cross Abstract: Generative world models are increasingly used for video generation, where learned simulators are expected to capture the physical rules that govern re
arXiv:2605.09343v1 Announce Type: new Abstract: Decision making in large-scale complaint handling systems increasingly relies on heterogeneous evidence, including complaint narratives, screenshots, or
arXiv:2605.10247v1 Announce Type: new Abstract: Using Large Language Models (LLMs) to process graph-structured data is an active research area, yet current state-of-the-art approaches typically rely o
arXiv:2605.09900v1 Announce Type: new Abstract: A vision-language model can look at a knot diagram and report what it sees, yet fail to act on that structure. KnotBench pairs an 858,318-image corpus f
arXiv:2605.07103v1 Announce Type: new Abstract: Reaction feasibility prediction, as a fundamental problem in computational chemistry, has benefited from diverse tools enabled by recent advances in art
arXiv:2510.01569v3 Announce Type: replace Abstract: We present InvThink, a training and prompting framework that requires the model to enumerate, analyze, and constrain potential failures before gener
arXiv:2605.01566v1 Announce Type: new Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw
arXiv:2605.03609v1 Announce Type: cross Abstract: Large language models often display heterogeneous moral preferences across settings. We study inference-time steering toward a desired ethical framewo
arXiv:2605.01345v1 Announce Type: new Abstract: Visual perception in modern Vision-Language Models (VLMs) is constrained by a fundamental perceptual bandwidth bottleneck: a broad field of view inevita
arXiv:2605.02475v1 Announce Type: cross Abstract: Stories hold a reader's attention because they have causes, secrets, and consequences. Shadow-Loom is an experimental open-source framework that turns
arXiv:2605.00270v1 Announce Type: new Abstract: Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to
arXiv:2605.00438v1 Announce Type: cross Abstract: Long-horizon robotic manipulation requires plans that are both logically coherent and geometrically grounded. Existing Vision-Language-Action policies
arXiv:2604.27576v1 Announce Type: cross Abstract: We present BAss (BDD-based ADF symbolic solver), a novel analysis tool for Abstract Dialectical Frameworks (ADFs) based on Binary Decision Diagrams (B
arXiv:2501.19143v2 Announce Type: replace Abstract: As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial a
arXiv:2604.27713v1 Announce Type: new Abstract: The risks posed by AI features are increasing as they are rapidly integrated into software applications. In response, regulations and standards for safe
arXiv:2602.01297v3 Announce Type: replace Abstract: Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges fo
arXiv:2604.23563v1 Announce Type: cross Abstract: Privacy-critical domains require phishing detection systems that satisfy contradictory constraints: near-zero false positives to prevent workflow disr
NVIDIA Nemotron 3 Nano Omni is a multimodal large language model that unifies video, audio, image, and text understanding for enterprise Q&A, summarization, transcription, and document intelligence, w
arXiv:2604.21999v1 Announce Type: cross Abstract: We study learned memory tokens as computational scratchpad for a single-block Universal Transformer (UT) with Adaptive Computation Time (ACT) on Sudok
arXiv:2604.21461v1 Announce Type: new Abstract: Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite
arXiv:2604.21068v1 Announce Type: cross Abstract: The most technologically consequential materials are often the rarest: they occupy narrow regions of chemical space, obey competing physical constrain
arXiv:2604.21873v1 Announce Type: new Abstract: Physical video understanding requires more than naming an event correctly. A model can answer a question about pouring, sliding, or collision from textu
arXiv:2604.21327v1 Announce Type: cross Abstract: Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization sig
arXiv:2510.04225v2 Announce Type: replace-cross Abstract: The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital i
arXiv:2603.29025v2 Announce Type: replace-cross Abstract: Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through
arXiv:2511.03180v2 Announce Type: replace Abstract: As multilingual Large Language Models (LLMs) gain traction across South Asia, their alignment with local ethical norms, particularly for Bengali, sp
arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator
arXiv:2604.18041v1 Announce Type: new Abstract: Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a s
arXiv:2603.26771v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens from a fully masked sequence. Their standard confidence-based
arXiv:2511.21064v2 Announce Type: replace-cross Abstract: Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although
arXiv:2604.17819v1 Announce Type: new Abstract: Large language models (LLMs) perform substantially below human level on existing theory-of-mind (ToM) benchmarks, even when augmented with chain-of-thou
arXiv:2510.05336v2 Announce Type: replace Abstract: Historical archives on weather events are collections of enduring primary source records that offer rich, untapped narratives of how societies have
arXiv:2512.14554v5 Announce Type: replace-cross Abstract: The rapid advancement of large language models (LLMs) has enabled new possibilities for applying artificial intelligence within the legal doma
arXiv:2604.14362v1 Announce Type: new Abstract: Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying naive retrieval often i
arXiv:2604.15244v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose outputs that a stronger target mod
arXiv:2604.14507v1 Announce Type: new Abstract: As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a
arXiv:2410.17448v3 Announce Type: replace Abstract: Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not e
arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing e
Anthropic PBC today opened access to Claude Opus 4.7, the latest addition to its popular line of large language models. The company says that the LLM is significantly better than its predecessor at co
arXiv:2604.12735v1 Announce Type: new Abstract: LLM-based multimodal emotion recognition relies on static parametric memory and often hallucinates when interpreting nuanced affective states. In this p