Knowledge catalogue
arXiv:2605.08222v1 Announce Type: cross Abstract: Handwritten archival tables contain rich historical information, yet transforming them into structured representations, such as Knowledge Graphs, requ
arXiv:2605.08767v1 Announce Type: new Abstract: Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition mol
arXiv:2605.08934v1 Announce Type: new Abstract: Mechanistic interpretability aims to explain neural model behaviour by reverse-engineering learned computational structure into human-understandable com
arXiv:2605.08819v1 Announce Type: new Abstract: Deep learning techniques have revolutionised medical imaging, improving diagnostic accuracy and enabling both more accurate and earlier disease detectio
arXiv:2605.09290v1 Announce Type: new Abstract: Prediction-based approaches are widely used in neural architecture search (NAS), where a predictor estimates the performance of candidate architectures
arXiv:2605.09949v1 Announce Type: new Abstract: Understanding how chemical language models (CLMs) learn chemical meaning from molecular string representations, rather than only surface-level string pa
arXiv:2605.09147v1 Announce Type: cross Abstract: Part-of-speech (POS) tagging for Medieval Romance languages remains challenging due to orthographic variation, morphological complexity, and limited a
arXiv:2605.08952v1 Announce Type: new Abstract: In LiDAR-based environment perception systems, ground segmentation is a key preprocessing step supporting various applications such as mapping and navig
arXiv:2509.15519v2 Announce Type: replace Abstract: This paper studies fully decentralized cooperative multi-agent reinforcement learning, where each agent solely observes the states, its local action
arXiv:2605.09335v1 Announce Type: new Abstract: Sparse goal-conditioned reinforcement learning can produce policies whose failures are hidden by aggregate success rates. We analyze trained goal-condit
arXiv:2405.09570v2 Announce Type: replace-cross Abstract: Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmur
arXiv:2503.12333v2 Announce Type: replace Abstract: Safe, agile, and socially compliant multi-robot navigation in cluttered and constrained environments remains a critical challenge. This is especiall
arXiv:2605.10124v1 Announce Type: cross Abstract: The recent growth of on-device Large Language Model (LLM) inference has driven significant interest in device-edge collaborative LLM inference. As a p
arXiv:2605.10525v1 Announce Type: new Abstract: Video depth estimation extends monocular prediction into the temporal domain to ensure coherence. However, existing methods often suffer from spatial bl
arXiv:2510.13896v2 Announce Type: replace-cross Abstract: Cellular image segmentation is essential for quantitative biology yet remains difficult due to heterogeneous modalities, morphological variabi
arXiv:2605.10645v1 Announce Type: new Abstract: Data-driven medical AI is traditionally formulated as a discriminative mapping from input X to output Y via a learned function f, which does not general
arXiv:2605.08521v1 Announce Type: new Abstract: Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters. While 2D semantic segmentation provid
arXiv:2605.10457v1 Announce Type: cross Abstract: Real-time Light Detection And Ranging (LiDAR) simulation must find, per emitted ray, the closest intersecting triangle even in dynamic scenes containi
arXiv:2605.10885v1 Announce Type: new Abstract: Cross-domain few-shot medical image segmentation (CD-FSMIS) requires a model to generalise simultaneously to novel anatomical categories and unseen imag
arXiv:2605.10108v1 Announce Type: new Abstract: Joint named entity recognition (NER) and relation extraction (RE) is a fundamental task in natural language processing for constructing knowledge graphs
arXiv:2605.05248v2 Announce Type: replace-cross Abstract: AI systems increasingly synthesize executable structure at runtime: LLMs generate programs, agents construct workflows,self-improving systems
arXiv:2605.09534v1 Announce Type: cross Abstract: Engineering managers increasingly must decide how to introduce generative artificial intelligence (AI), retrieval-augmented generation, and coding age
arXiv:2605.09100v1 Announce Type: new Abstract: Text embedding and generative tasks are usually trained separately based on large language models (LLMs) nowadays. This causes a large amount of trainin
arXiv:2505.15879v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated the efficacy of using Reinforcement Learning (RL) in building reasoning models that articulate chains of thou
arXiv:2605.10893v1 Announce Type: new Abstract: Large vision-language models suffer from visual ungroundedness: they can produce a fluent, confident, and even correct response driven entirely by langu
arXiv:2603.16253v2 Announce Type: replace-cross Abstract: Vision-language process reward models (VL-PRMs) are increasingly used to score intermediate reasoning steps and rerank candidates under test-t
arXiv:2605.10386v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly integrated into autonomous driving (AD) systems; however, they remain vulnerable to diverse sa
arXiv:2605.10051v1 Announce Type: cross Abstract: Inference-time guidance is essential for steering generative robot policies toward dynamic objectives without retraining, yet existing methods are lar
arXiv:2605.09971v1 Announce Type: cross Abstract: Text-to-vibration generation converts natural language into haptic feedback, enabling vibration-effect designers to get scenarios-fitted vibrations mo
arXiv:2605.10942v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics. Current WAMs generally follow two parad
arXiv:2605.08456v1 Announce Type: cross Abstract: The realtime analysis and secure transmission of electrocardiogram ECG signals are critical for accurate diagnosis and safeguarding patient privacy in
arXiv:2605.10201v1 Announce Type: cross Abstract: Generalizable manipulation involving cross-type object interactions is a critical yet challenging capability in robotics. To reliably accomplish such
arXiv:2605.10536v1 Announce Type: cross Abstract: Rare semantic innovations in high-dimensional, mission-critical domains are often obscured by dense background contexts, a challenge we define as exti
arXiv:2602.01442v3 Announce Type: replace-cross Abstract: Gradient-based attribution is the workhorse of mechanistic interpretability, yet whether it reliably tracks causal importance at the component
arXiv:2605.08292v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) models scale capacity by routing each token to a small subset of experts. However, their routers exhibit a fundamental
arXiv:2605.08330v1 Announce Type: new Abstract: We present a hierarchical language-driven framework for robotic task and motion planning to improve natural, intuitive human-robot interaction in servic
arXiv:2410.14927v2 Announce Type: replace-cross Abstract: Automated equity trading requires converting noisy market and news signals into executable portfolio decisions under risk, turnover, and trans
arXiv:2512.21815v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, as a measure of model uncertaint
arXiv:2605.08864v1 Announce Type: new Abstract: We study online estimation in latent-variable models by recasting the problem as tracking a moving empirical equilibrium. Standard online EM and stochas
arXiv:2404.18923v5 Announce Type: replace Abstract: We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic
arXiv:2605.09348v1 Announce Type: cross Abstract: Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured kno
arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibiti
arXiv:2504.10766v2 Announce Type: replace-cross Abstract: As the post-training of large language models (LLMs) advances from instruction-following to complex reasoning tasks, understanding how differe
arXiv:2605.10347v1 Announce Type: new Abstract: Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable predi
arXiv:2605.08533v1 Announce Type: new Abstract: Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as int
arXiv:2501.12202v4 Announce Type: replace Abstract: We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two fo
arXiv:2605.10100v1 Announce Type: cross Abstract: We introduce HYPERPOSE, a novel 3D human pose estimation framework that performs spatio-temporal reasoning entirely within the Lorentz model of hyperb
arXiv:2604.15113v2 Announce Type: replace Abstract: Vector Symbolic Architectures (VSAs) provide a well-defined algebraic framework for compositional representations in hyperdimensional spaces. We int
arXiv:2605.08254v1 Announce Type: cross Abstract: As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can
arXiv:2403.18136v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have gained popularity in numerous domains, yet they are vulnerable to backdoor attacks that can compromise their
arXiv:2602.22551v2 Announce Type: replace-cross Abstract: Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these
arXiv:2605.09127v1 Announce Type: new Abstract: Contact-implicit trajectory optimization (CITO) has attracted growing attention as a unified framework for planning and control in contact-rich robotic
arXiv:2512.02012v2 Announce Type: replace Abstract: MeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its ``fastforward'' nature introduces key chal
arXiv:2605.09256v1 Announce Type: cross Abstract: We introduce a use of the (M)-cover (or (M)-layer) transform for machine learning. The method replicates a model (M) times, but instead of coupling th
arXiv:2605.08577v1 Announce Type: new Abstract: In modern GANs, maintaining an Exponential Moving Average (EMA) of the generator's weights is a standard practice, as such an averaged model consistentl
arXiv:2605.08930v1 Announce Type: new Abstract: While explicit Chain-of-Thought (CoT) empowers large reasoning models (LRMs), it enables the generation of riskier final answers. Current alignment para
arXiv:2605.09090v1 Announce Type: cross Abstract: Visual Grounding benchmarks assume that the object described by a referring expression is always present in the image, and grounding models are theref
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typ
arXiv:2603.02676v2 Announce Type: replace-cross Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that
arXiv:2605.08384v1 Announce Type: new Abstract: In this work, we introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in