Knowledge catalogue
arXiv:2604.18067v1 Announce Type: new Abstract: The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable
arXiv:2604.16536v1 Announce Type: new Abstract: Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As re
arXiv:2604.18208v1 Announce Type: new Abstract: Symmetric objects are common in daily life and industry, yet their inherent orientation ambiguities that impede the training of deep learning networks f
arXiv:2604.17067v1 Announce Type: cross Abstract: Linear convergence of first-order methods is typically characterized by global optimization conditions whose constants reflect worst-case geometry of
arXiv:2603.19684v2 Announce Type: replace Abstract: Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing a
arXiv:2604.16542v1 Announce Type: cross Abstract: Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). How
arXiv:2604.17156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a promising framework for solving inverse problems governed by partial differential equations (
arXiv:2511.17699v2 Announce Type: replace Abstract: Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these
arXiv:2604.18389v1 Announce Type: new Abstract: Prompt sensitivity, which refers to how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt, raises con
arXiv:2604.17565v1 Announce Type: new Abstract: Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geomet
arXiv:2604.17472v1 Announce Type: new Abstract: Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D
arXiv:2602.11893v3 Announce Type: replace Abstract: We introduce a universal diffusion-based downscaling framework that lifts deterministic low-resolution weather forecasts into probabilistic high-res
arXiv:2402.02953v2 Announce Type: replace-cross Abstract: With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability t
arXiv:2512.01015v2 Announce Type: replace Abstract: Neural oscillators, originating from second-order ordinary differential equations (ODEs), have demonstrated strong performance in stably learning ca
arXiv:2506.00065v2 Announce Type: replace Abstract: Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly und
arXiv:2604.18035v1 Announce Type: new Abstract: Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a differen
arXiv:2509.16538v3 Announce Type: replace-cross Abstract: We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus
arXiv:2604.17801v1 Announce Type: new Abstract: Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view
arXiv:2604.17623v1 Announce Type: new Abstract: Kinematic rigs provide a structured interface for articulating 3D meshes, but they lack an inherent representation of the plausible manifold of joint co
arXiv:2505.23941v4 Announce Type: replace-cross Abstract: Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may noto
arXiv:2512.01643v2 Announce Type: replace Abstract: Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an o
arXiv:2510.17001v2 Announce Type: replace Abstract: Large language models (LLMs) often encode word-form variation (e.g., walk vs. walked) as linear directions in the embedding space. However, standard
arXiv:2604.18546v1 Announce Type: new Abstract: We propose a distributionally robust approach to risk-sensitive estimation of an unknown signal x from an observed signal y. The unknown signal and obse
arXiv:2603.19880v2 Announce Type: replace Abstract: Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving
arXiv:2508.16464v2 Announce Type: replace Abstract: Entities in discourse vary in salience: main participants, objects and locations stay prominent, while others are quickly forgotten, raising questio
arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos
arXiv:2604.16787v1 Announce Type: new Abstract: We study how informal surface forms degrade NLI accuracy in ELECTRA-small (14M) and RoBERTa-large (355M) across four transforms applied to SNLI and Mult
arXiv:2604.16767v1 Announce Type: new Abstract: Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and liv
arXiv:2601.04609v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used to make visual content accessible via text-based descriptions. In current systems, however, desc
arXiv:2507.13868v2 Announce Type: replace Abstract: Vision-language models (VLMs) increasingly combine visual and textual information to perform complex tasks. However, conflicts between their interna
arXiv:2604.17031v1 Announce Type: new Abstract: The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach this pr
arXiv:2604.17393v1 Announce Type: new Abstract: Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Mos
arXiv:2604.16745v1 Announce Type: cross Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a clo
arXiv:2506.05760v2 Announce Type: replace Abstract: Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited:
arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi
arXiv:2604.15453v1 Announce Type: cross Abstract: Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, token
arXiv:2604.15907v1 Announce Type: new Abstract: Vine-inspired robots achieve large workspace coverage through tip eversion, enabling safe navigation in confined and cluttered environments. However, th
arXiv:2604.15617v1 Announce Type: cross Abstract: Hyperbolic conservation laws govern a wide range of transport-driven dynamics featuring shocks, contact discontinuities, and complex wave interactions
arXiv:2504.09792v2 Announce Type: replace Abstract: Although gossip and random walk-based learning algorithms are widely known for decentralized learning, there has been limited theoretical and experi
arXiv:2604.15322v1 Announce Type: cross Abstract: Individuals often align their speaking patterns with their interlocutors, a phenomenon linked to engagement and rapport. While well documented in task
arXiv:2311.00656v4 Announce Type: replace-cross Abstract: Spatial-temporal estimation of signals on graph edges is challenging because most conventional Graph Signal Processing techniques are defined
arXiv:2503.18970v3 Announce Type: replace Abstract: Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Ne
arXiv:2604.15809v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated strong capability in a wide range of tasks such as visual recognition, document parsing, and visual grou
arXiv:2604.15316v1 Announce Type: cross Abstract: With large language models (LLMs) becoming increasingly prevalent in daily life, so too has the tendency to attribute to them human-like minds and emo
arXiv:2603.13829v2 Announce Type: replace-cross Abstract: Human touch depends on the integration of shape, stiffness, and friction, yet existing tactile displays cannot render these cues together as c
arXiv:2604.16056v1 Announce Type: cross Abstract: Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Existing methods rely on task-speci
arXiv:2603.11487v5 Announce Type: replace Abstract: Transformers often display an attention sink: probability mass concentrates on a fixed, content-agnostic position. Are sinks a byproduct of the opti
arXiv:2602.09953v2 Announce Type: replace Abstract: Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet
arXiv:2602.11173v2 Announce Type: replace Abstract: Author response (rebuttal) writing is a critical stage of scientific peer review that demands substantial author effort. In practice, authors posses
arXiv:2604.16217v1 Announce Type: cross Abstract: Large language models are increasingly deployed in settings where reliability matters, yet output-level uncertainty signals such as token probabilitie
arXiv:2604.15829v1 Announce Type: new Abstract: Text-to-image generative models have achieved impressive fidelity and diversity, but can inadvertently produce unsafe or undesirable content due to impl
arXiv:2604.15591v1 Announce Type: cross Abstract: Effective biomedical information retrieval requires modeling domain semantics and hierarchical relationships among biomedical texts. Existing biomedic
arXiv:2604.15503v1 Announce Type: new Abstract: Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language
arXiv:2604.16011v1 Announce Type: new Abstract: Borehole breakouts are stress-induced spalling on the borehole wall, which are identifiable in acoustic image logs as paired zones with near-symmetry az
arXiv:2604.16060v1 Announce Type: cross Abstract: Multimodal Reasoning Models (MRMs) leveraging Chain-of-Thought (CoT) based thinking have revolutionized mathematical and logical problem-solving. Howe
Tech workers in China are being instructed by their bosses to train AI agents to replace them—and it’s prompting a wave of soul-searching among otherwise enthusiastic early adopters. Earlier this mont
arXiv:2604.15647v1 Announce Type: new Abstract: Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conve
arXiv:2604.15847v1 Announce Type: new Abstract: Machine unlearning has gained increasing attention in recent years, as a promising technique to selectively remove unwanted privacy or copyrighted infor
arXiv:2601.05858v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated competitive performance in zero-shot multilingual machine translation (MT). Some follow-up work
arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance