Knowledge catalogue
arXiv:2608.28283v1 Announce Type: new Abstract: In computational argumentation, obtaining arguments that explicitly support or attack given claims is a critical precursor to downstream reasoning tasks
arXiv:2608.28046v1 Announce Type: cross Abstract: Collective behaviour in living systems is usually modelled as the outcome of a direct social drive: agents are rewarded, or hard-wired, to align with
arXiv:2608.28161v1 Announce Type: new Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are often
arXiv:2603.06955v2 Announce Type: replace Abstract: Collaborative aerial transportation of tethered payloads is fundamentally limited by space, power, and weight constraints. Conventional approaches r
arXiv:2608.27925v1 Announce Type: new Abstract: Entity-Memory graph retrieval keeps dialogue turns as verbatim Memory nodes, links repeated mentions through shared Entities, and connects adjacent Memo
arXiv:2602.02745v2 Announce Type: replace-cross Abstract: The ethics of human-robot interaction (HRI) have been discussed extensively based on three traditional frameworks: deontology, consequentialis
arXiv:2608.28425v1 Announce Type: new Abstract: Fourier neural operators (FNOs) provide an efficient framework for learning mappings between function spaces as they are, by construction, independent o
arXiv:2305.12474v4 Announce Type: replace Abstract: Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensive
arXiv:2608.27844v1 Announce Type: new Abstract: Existing evaluations of harmful content detection rely predominantly on static benchmarks, which struggle to reflect the interactive adversarial ecosyst
arXiv:2608.28096v1 Announce Type: new Abstract: Learning-based image-to-point-cloud (I2P) registration has garnered increasing attention in recent years. Nevertheless, existing methods still struggle
arXiv:2608.28007v1 Announce Type: new Abstract: Hanneke, Moran, Shlimovich and Yehudayoff (COLT 2025) posed the following open problem. A selector sees a finite dataset D subseteq R^d imes R, picks at
arXiv:2509.20191v2 Announce Type: replace-cross Abstract: Approximating solutions to partial differential equations (PDEs) is fundamental for the modeling of dynamical systems in science and engineeri
arXiv:2608.28207v1 Announce Type: new Abstract: Diabetic retinopathy (DR) is a major cause of preventable blindness, creating a need for accurate and trustworthy automated screening. This study invest
arXiv:2608.28052v1 Announce Type: new Abstract: Artificial intelligence has strong potential to support clinical decision-making, yet its adoption in healthcare remains limited due to a lack of trust.
arXiv:2608.28191v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) are widely used in computational pathology but remain sensitive to domain shifts arising from variations in staining, ti
arXiv:2608.27763v1 Announce Type: cross Abstract: Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state tran
arXiv:2608.27856v1 Announce Type: new Abstract: Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeling
arXiv:2602.18154v3 Announce Type: replace Abstract: Jailbreaking poses a significant risk to the deployment of Large Language Models (LLMs) and Vision Language Models (VLMs). VLMs are particularly vul
arXiv:2608.28439v1 Announce Type: new Abstract: One model passed our fidelity check without ever opening the datasheet. We found it while qualifying models for an internal extraction service: a struct
arXiv:2608.28155v1 Announce Type: new Abstract: Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM
arXiv:2608.27672v1 Announce Type: new Abstract: We present Qwen-GuidePlay-2B, a 2B-parameter language model for dialogue-game interaction. We fine-tune Qwen3.5-2B using three steps: a) SFT on only suc
arXiv:2602.22056v3 Announce Type: replace-cross Abstract: Generative manipulation policies can fail catastrophically under deployment-time distribution shift, yet many failures are near-misses: the ro
arXiv:2608.28218v1 Announce Type: new Abstract: Vision-language models (VLMs) are rapidly progressing and offer promising capabilities for assistive technologies supporting persons with blindness or l
arXiv:2608.27860v1 Announce Type: new Abstract: Vision foundation models are capable of generalizing across 3-dimensional (3D) scenes with high-fidelity estimates; their empirical success can be attri
arXiv:2608.28154v1 Announce Type: new Abstract: People naturally chat while collaborating and share personal information (i.e., self-disclose) to build rapport and maintain social connections. As robo
arXiv:2608.27531v1 Announce Type: cross Abstract: The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the m
arXiv:2608.28302v1 Announce Type: new Abstract: Diffusion-based inpainting models modify only a localized part of an image, while many AI-image detectors rely on global artifacts and do not localize.
arXiv:2608.27527v1 Announce Type: new Abstract: A major challenge in finger vein recognition is the lack of large-scale public datasets. Existing datasets contain few identities and limited samples pe
arXiv:2608.27971v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scene
arXiv:2608.27989v1 Announce Type: new Abstract: For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to a
arXiv:2608.28567v1 Announce Type: new Abstract: Conventionally, Building Damage Assessment (BDA) is tackled either with dedicated network architectures or by fine-tuning geospatial image foundation mo
arXiv:2608.28209v1 Announce Type: new Abstract: Unlike images and text, applying transfer learning to tabular data is challenging due to heterogeneity in feature types, structures, and semantics acros
arXiv:2608.28116v1 Announce Type: new Abstract: Forecast combination is a reliable way to improve predictive performance when several forecasting models are available. Simple aggregation rules such as
arXiv:2608.28446v1 Announce Type: cross Abstract: For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the minimum-mean-square error estimator takes t
arXiv:2608.28288v1 Announce Type: new Abstract: Existing feed-forward 3D reconstruction methods typically process a bounded number of images and recover cameras and geometry in local or internally nor
arXiv:2608.28361v1 Announce Type: cross Abstract: Machine Unlearning methods for Large Language Models typically assume pre-specified forget and retain sets. In realistic settings, however, requests m
arXiv:2608.28386v1 Announce Type: new Abstract: Existing monocular full-body 3D human-object interaction (HOI) methods do not combine explicit finger-level grasp optimization with category-agnostic ob
arXiv:2608.28113v1 Announce Type: new Abstract: The NVIDIA Blackwell architecture, with native support for the ultra-fine-grained NVFP4 format, opens new opportunities for accelerating large language
arXiv:2608.28158v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) often produces irregular rollout trees with shared histories. Training root-to-leaf trajectories independently recom
arXiv:2506.07188v2 Announce Type: replace Abstract: End-to-end neural networks have become a dominant paradigm in autonomous driving, where reliable deployment requires controllable post-training adap
arXiv:2608.27510v1 Announce Type: new Abstract: Transcoder attribution graphs are usually trained to explain why a model assigns high probability to a particular next token. We introduce Concept-Targe
arXiv:2608.28404v1 Announce Type: new Abstract: Video generation for autonomous driving cannot follow the web-scale route: driving data is expensive to collect, bound by privacy requirements, and cann
arXiv:2608.28482v1 Announce Type: new Abstract: This paper evaluates how reward function choice shapes the performance and behavior of LLM forecasters. We compare five proper scoring rules as training
arXiv:2608.27475v1 Announce Type: cross Abstract: Discovering PDEs in heterogeneous media requires jointly identifying the governing operator and the unknown spatial fields that parameterize it. These
arXiv:2608.28245v1 Announce Type: cross Abstract: We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienobst et al. (2026) from observational to interventional dat
arXiv:2608.27502v1 Announce Type: cross Abstract: Ensuring the reliability of deep learning-based image retrieval systems is a software engineering challenge. This paper presents a dual contribution:
arXiv:2402.05098v5 Announce Type: replace Abstract: We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark s
arXiv:2603.18031v2 Announce Type: replace Abstract: Balancing fine-grained local modeling with long-range dependency capture under computational constraints remains a central challenge in sequence mod
arXiv:2608.27760v1 Announce Type: new Abstract: We consider the ability of transformer-based language models (LLMs) to learn what we call k-antilocal languages, i.e., languages that have no mutual inf
arXiv:2608.27791v1 Announce Type: new Abstract: Short-term load forecasting (STLF) provides essential information for numerous applications in modern power systems. However, accurate STLF often relies
arXiv:2608.27501v1 Announce Type: new Abstract: Mathematical reasoning has seen rapid progress in large language models (LLMs), yet existing methods optimize predominantly for final-answer correctness
arXiv:2512.22392v2 Announce Type: replace Abstract: Accurate, up-to-date sidewalk data is essential for building accessible and inclusive pedestrian infrastructure, yet current approaches to data coll
arXiv:2608.27866v1 Announce Type: new Abstract: Achieving virtual agents capable of automating tasks across diverse digital environments remains a pivotal challenge in Embodied AI. While Multimodal La
arXiv:2608.27985v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) and every-visit Monte Carlo (MC) control are usually presented as different methods. MCTS is described in the language of
arXiv:2608.27848v1 Announce Type: cross Abstract: Prosody plays an important role in speech translation, conveying information such as emphasis, emotion, and intent beyond lexical content. However, de
arXiv:2305.19366v3 Announce Type: replace Abstract: Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the p
arXiv:2608.27661v1 Announce Type: new Abstract: In Retrieval-Augmented Generation (RAG), retrieval may provide insufficient or conflicting information needed to answer a question. The system should no
arXiv:2608.28496v1 Announce Type: new Abstract: Two forms of test-time scaling for Large Language Models (LLMs) have emerged as effective and widely adopted paradigms: sequential, in which later answe
arXiv:2608.28059v1 Announce Type: cross Abstract: In-context learning (ICL) allows a pretrained model to infer a new task from examples supplied in its prompt without updating its parameters. In linea
arXiv:2608.27902v1 Announce Type: new Abstract: Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hiera