Knowledge catalogue
arXiv:2605.17159v1 Announce Type: new Abstract: Document processing automation remains a critical challenge in enterprise environments, where traditional manual approaches are labor-intensive and erro
arXiv:2605.17478v1 Announce Type: new Abstract: Visual Geometry Grounded Transformers (VGGT) have set new benchmarks in high-fidelity 3D scene reconstruction. However, as the sequence length increases
arXiv:2605.18617v1 Announce Type: cross Abstract: Most existing vision-language manipulation research targets rigid robotic arms, whose fixed morphology limits adaptability in cluttered or confined sp
arXiv:2605.16301v1 Announce Type: cross Abstract: Single-turn benchmarks such as AnimalHarmBench (AHB) have established important baselines for measuring animal welfare alignment in large language mod
arXiv:2601.00360v3 Announce Type: replace-cross Abstract: As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed
arXiv:2605.17120v1 Announce Type: new Abstract: arly identification of motor impairment in infancy relies on expert visual assessment of spontaneous movement, motivating the development of automated,
arXiv:2605.17640v1 Announce Type: cross Abstract: Retrieval-augmented generation from videos requires systems to retrieve relevant audiovisual evidence from large corpora and synthesize it into cohere
arXiv:2605.17997v1 Announce Type: cross Abstract: Recently, residual reconstruction-based model quantization methods have achieved promising performance in low-bit post-training quantization (PTQ) by
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:2509.15357v2 Announce Type: replace Abstract: Diffusion models have achieved strong results in text-to-image generation, but important limitations remain as prompts become more structured and mu
arXiv:2603.06984v2 Announce Type: replace-cross Abstract: Many regulatory and analytic problems require that a prohibited variable influence a decision only through a designated allowable channel -- a
arXiv:2605.17431v1 Announce Type: cross Abstract: We propose MATE, a simple yet effective memory architecture for solving Contextual Markov Decision Processes (CMDPs), a family of MDPs parameterized b
arXiv:2010.15538v4 Announce Type: replace-cross Abstract: Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about th
arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio
arXiv:2605.16716v1 Announce Type: cross Abstract: Text-to-video (T2V) generation has rapidly progressed in visual fidelity, yet its ability to faithfully represent multiple cultures within a single pr
arXiv:2605.17230v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the class
arXiv:2605.16290v1 Announce Type: cross Abstract: Predicting the difficulty of multiple-choice questions (MCQs) is important for effective assessment, yet current methods typically assume a unimodal s
arXiv:2605.16284v1 Announce Type: cross Abstract: Student use of Generative AI (GenAI) products in completing their classwork, with or without their professors' knowledge and/or approval, has resulted
arXiv:2605.17091v1 Announce Type: new Abstract: Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationar
arXiv:2605.16468v1 Announce Type: cross Abstract: A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificia
arXiv:2603.05308v2 Announce Type: replace-cross Abstract: Assessing whether an article supports an assertion is essential for hallucination detection and claim verification. While large language model
arXiv:2605.17436v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte
arXiv:2605.16639v1 Announce Type: new Abstract: Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing mod
arXiv:2602.11130v2 Announce Type: replace-cross Abstract: Sparse point clouds are a common input modality for 3D surface reconstruction, including in safety-critical settings such as surgical navigati
arXiv:2605.16445v1 Announce Type: cross Abstract: Masked Diffusion Language Models MDLMs replace autoregressive generation with iterative demasking and their privacy properties are largely unstudied.
arXiv:2605.18652v1 Announce Type: new Abstract: Recent GUI agents have made substantial progress in visual grounding and action prediction, yet they remain brittle in long-horizon tasks that require m
arXiv:2605.17758v1 Announce Type: new Abstract: Synthetic data is widely used in healthcare to create datasets that are similar to original data but without the privacy concerns. Generating and evalua
arXiv:2601.21468v5 Announce Type: replace Abstract: Long-horizon agentic reasoning necessitates effectively compressing growing interaction histories into a limited context window. Most existing memor
arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refi
arXiv:2506.15588v2 Announce Type: replace Abstract: Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead
arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small
arXiv:2605.17444v1 Announce Type: cross Abstract: Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can op
arXiv:2602.12871v2 Announce Type: replace Abstract: Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However,
arXiv:2510.10528v3 Announce Type: replace Abstract: Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this length
arXiv:2601.02353v3 Announce Type: replace Abstract: Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performan
arXiv:2605.17292v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems have shown promise for solving complex tasks through agent collaboration. However, existing frameworks as
arXiv:2507.22057v2 Announce Type: replace Abstract: Difficult few-shot image recognition has significant application prospects, yet remaining the substantial technical gaps with the conventional large
arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa
arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience
arXiv:2605.16887v1 Announce Type: new Abstract: Cross-modality recognition has many important applications in science, law enforcement and entertainment. Popular methods to bridge the modality gap inc
arXiv:2605.17398v1 Announce Type: new Abstract: This paper presents MiniGPT, a compact from-scratch implementation of GPT-style autoregressive language modeling in PyTorch. The aim is to rebuild the c
arXiv:2601.21458v2 Announce Type: replace Abstract: Modern deepfakes have evolved into localized and intermittent manipulations that require fine-grained temporal localization to mitigate severe digit
arXiv:2603.08290v2 Announce Type: replace-cross Abstract: We study the implicit bias of Sharpness-Aware Minimization (SAM) when training L-layer linear diagonal networks on linearly separable binary c
arXiv:2605.17198v1 Announce Type: cross Abstract: To be useful for downstream applications, vision decoding models that are trained to reconstruct seen images from human brain activity must be able to
arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial trai
arXiv:2505.02621v2 Announce Type: replace Abstract: The mean-field Langevin dynamics (MFLD) minimizes an entropy-regularized nonlinear convex functional on the Wasserstein space over R^d, and has gain
arXiv:2601.08118v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as human simulators, both for evaluating conversational systems and for generating fine-tuning da
arXiv:2506.22901v2 Announce Type: replace-cross Abstract: A key challenge in learning from multimodal biological data is missing modalities, where data from one or more modalities are absent for some
arXiv:2507.06384v2 Announce Type: replace-cross Abstract: Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image int
arXiv:2602.03664v3 Announce Type: replace Abstract: Large language models excel as few-shot learners when provided with appropriate demonstrations, yet this strength becomes problematic in multiturn a
arXiv:2605.16378v1 Announce Type: cross Abstract: Masked language models (MLMs) define local conditional distributions over tokens but do not, in general, correspond to any consistent joint distributi
arXiv:2605.16865v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) is widely used to inject new knowledge into language models, but it often degrades pretrained capabilities such as reasonin
arXiv:2506.12119v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token co
arXiv:2605.17598v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enable efficient model scaling, yet expert routing behavior across underrepresented languages remains poorly unde
arXiv:2402.15058v3 Announce Type: replace-cross Abstract: We combine standard persistent homology with image persistent homology to define a novel way of characterizing shapes and interactions between
arXiv:2605.17635v1 Announce Type: cross Abstract: A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern p
arXiv:2605.16616v1 Announce Type: new Abstract: Autonomous research systems capable of generating complete scientific manuscripts have advanced rapidly, yet robust and realistic evaluation frameworks
arXiv:2605.17743v1 Announce Type: new Abstract: Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams while retaining past competence, yet texture
arXiv:2603.19538v2 Announce Type: replace Abstract: Monocular 3D object understanding has largely been cast as a 2D RoI-to-3D box lifting problem. However, emerging downstream applications require ima
arXiv:2605.18483v1 Announce Type: cross Abstract: Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of