Knowledge catalogue
arXiv:2605.31598v1 Announce Type: new Abstract: Video vision-language models (VLMs) are increasingly used in long-horizon and streaming settings, yet most video encoders still rely on spatiotemporal s
arXiv:2605.31492v1 Announce Type: new Abstract: Large language models (LLMs) often solve reasoning problems by generating intermediate traces that explore and revise partial solutions. From a search p
arXiv:2605.30848v1 Announce Type: cross Abstract: Agentic LLMs with web search change the threat model for text anonymization: weak contextual cues can become cross-referenceable evidence for re-ident
arXiv:2409.14583v4 Announce Type: replace Abstract: LLM bias evaluation is critical as large language models (LLMs) increasingly influence high-stakes decisions. This paper provides a comprehensive as
arXiv:2605.31167v1 Announce Type: new Abstract: Assessing whether Large Language Models outputs are factually grounded, epistemically calibrated, and methodologically reproducible is a prerequisite fo
arXiv:2605.31381v1 Announce Type: new Abstract: We evaluate the consistency of automated judges in conducting a multi-dimensional safety evaluation in a reference-free setup. Our results indicate that
arXiv:2510.03415v3 Announce Type: replace-cross Abstract: Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pr
arXiv:2605.30385v1 Announce Type: cross Abstract: The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. Very recently, there has been a sig
arXiv:2605.30476v1 Announce Type: cross Abstract: We study privately estimating the sum of n user-held values in the presence of an honest-but-curious server. This motivates requiring privacy not only
arXiv:2605.30936v1 Announce Type: new Abstract: We study the problem of learning Gaussian mixture models under overparameterization. Prior work has shown that while overparameterization is essential f
arXiv:2603.04946v2 Announce Type: replace Abstract: In local-life service platforms, query suggestion reduces user effort by generating candidate queries from input prefixes. Traditional multi-stage s
arXiv:2605.31345v1 Announce Type: cross Abstract: Compositional data must be analysed through log-ratios: scale invariance, the defining axiom of the field, leaves no alternative. The centred log-rati
arXiv:2605.30434v1 Announce Type: cross Abstract: Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability
arXiv:2605.31584v1 Announce Type: cross Abstract: Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive di
arXiv:2507.16362v3 Announce Type: replace Abstract: Chinese License Plate Recognition (CLPR) faces numerous challenges in unconstrained and complex environments, particularly due to perspective distor
arXiv:2605.31603v1 Announce Type: cross Abstract: Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelit
arXiv:2605.31057v1 Announce Type: new Abstract: Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and bey
arXiv:2605.30514v1 Announce Type: cross Abstract: Machine unlearning evaluation is structurally skewed: Why-type questions, which probe causal and relational knowledge, comprise less than 0.06% of Cou
arXiv:2605.30857v1 Announce Type: new Abstract: Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuni
arXiv:2605.31199v1 Announce Type: cross Abstract: Capturing dynamic malware behavior in a practical but still semantically precise manner remains a significant challenge in cyber threat intelligence.
arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone
arXiv:2509.16187v3 Announce Type: replace-cross Abstract: Code translation transforms source code from one programming language (PL) to another. Validating the functional equivalence of translation an
arXiv:2605.30700v1 Announce Type: new Abstract: This work introduces mathematical morphology-an established visual computing theory-into machine learning to exploit shape and density aspects often ove
arXiv:2605.30738v1 Announce Type: new Abstract: Generalization across agentic tool-calling environments remains a central challenge for reliable agentic reasoning systems. Although large language mode
arXiv:2605.30526v1 Announce Type: cross Abstract: Aligned language models often exhibit a recognizable AI-like style, yet its connection to post-training and internal representations remains poorly un
arXiv:2510.00845v4 Announce Type: replace-cross Abstract: Mechanistic Interpretability (MI) aims to reverse-engineer model behaviors by identifying functional sub-networks. Yet, the scientific validit
arXiv:2605.30794v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant achievements in general visual question answering (VQA) tasks. However, they re
arXiv:2602.07905v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While
arXiv:2509.12440v3 Announce Type: replace-cross Abstract: Deploying Large Language Models (LLMs) in medical applications requires fact-checking capabilities to ensure patient safety and regulatory com
arXiv:2605.31268v1 Announce Type: new Abstract: We present Mellum 2, an open-weight 12B-parameter Mixture-of-Experts (MoE) language model with 2.5B active parameters per token. Mellum 2 is a general-p
arXiv:2605.30571v1 Announce Type: cross Abstract: Physical AI systems, including robots, autonomous vehicles, embodied agents and edge copilots, often run a different inference workload from cloud LLM
arXiv:2605.31163v1 Announce Type: cross Abstract: We introduce the design-model framework: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writ
arXiv:2602.13069v2 Announce Type: replace-cross Abstract: On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints
arXiv:2605.30365v1 Announce Type: cross Abstract: Retrieval-augmented text-to-music (TTM) systems augment underspecified user prompts using captions retrieved from a music caption dataset. This design
arXiv:2605.30904v1 Announce Type: new Abstract: Most visual tokenizers for image generation are bifurcated into two families with complementary limitations: continuous VAEs offer high-fidelity reconst
arXiv:2603.26612v2 Announce Type: replace Abstract: Drones equipped with overhead manipulators offer unique capabilities for inspection, maintenance, and contact-based interaction. However, the motion
arXiv:2605.31171v1 Announce Type: cross Abstract: Multilingual Information Retrieval (MLIR) reflects real-world search environments in which queries and relevant documents may appear in different lang
arXiv:2605.31173v1 Announce Type: cross Abstract: Reconstructing continuous speech from non-invasive neural recordings is a fundamental problem for probing human auditory perception and building safe,
arXiv:2605.30931v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to susta
arXiv:2411.00759v5 Announce Type: replace Abstract: Discrete flow matching, a recent framework for modeling categorical data, has shown competitive performance with autoregressive models. However, unl
arXiv:2605.30437v1 Announce Type: new Abstract: Generative AI (GenAI) image editors, such as Nano Banana, produce visually compelling results for retouching tasks, enabling non-experts to edit images
arXiv:2602.02886v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs ty
arXiv:2511.19433v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the extb
arXiv:2605.30889v1 Announce Type: cross Abstract: Constructing production-quality machine-learned interatomic potentials (MLIPs) requires balancing accuracy, dynamical stability, and computational thr
arXiv:2605.31036v1 Announce Type: cross Abstract: Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms.
arXiv:2605.31443v1 Announce Type: cross Abstract: We present a regression-adjustment framework designed for the estimation of longitudinal treatment effects in randomized experiments under static regi
arXiv:2605.31302v1 Announce Type: cross Abstract: Undersampled magnetic resonance imaging (MRI) reconstruction seeks to recover temporally or contrast-varying image series from incomplete multicoil k-
arXiv:2605.31010v1 Announce Type: new Abstract: Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge
arXiv:2602.23280v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains ch
arXiv:2605.30727v1 Announce Type: new Abstract: Deep research agents increasingly combine private local documents with external tools like web retrieval, creating a privacy risk: an agent's external q
arXiv:2506.23768v2 Announce Type: replace Abstract: We introduce a novel musculoskeletal model of a dog, procedurally generated from accurate 3D muscle meshes. Accompanying this model is a motion capt
arXiv:2605.31296v1 Announce Type: cross Abstract: Therapeutic mRNA design requires coordinating multiple interacting sequence features across the full transcript, where codon usage, untranslated regio
arXiv:2602.01011v4 Announce Type: replace-cross Abstract: Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-spe
arXiv:2605.31027v1 Announce Type: new Abstract: We propose a novel neural network architecture, termed Multi-Scale Separable Fourier Neural Networks (MS-SFNN), for the accurate and efficient solution
arXiv:2605.31387v1 Announce Type: new Abstract: Robots operating in diverse environments rely on visual input to interpret objects and spatial layouts. In human-collaborative tasks, they are expected
arXiv:2605.30925v1 Announce Type: new Abstract: Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However
arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t
arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often disto
arXiv:2511.16940v3 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individua
arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema