All entries

arXiv:2510.13759v3 Announce Type: replace Abstract: Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. E

arXiv:2602.09130v3 Announce Type: replace Abstract: Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning an

arXiv:2604.16678v1 Announce Type: new Abstract: Contrastive objectives power state-of-the-art multimodal models, but their training remains slow, relying on long stochastic optimization. We propose a

arXiv:2604.17850v1 Announce Type: new Abstract: Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, le

arXiv:2507.21545v3 Announce Type: replace Abstract: Robotic task planning in real-world environments requires reasoning over implicit constraints from language and vision. While LLMs and VLMs offer st

arXiv:2512.20249v3 Announce Type: replace-cross Abstract: Multimodal brain decoding aims to reconstruct semantic information that is consistent with visual stimuli from brain activity signals such as

arXiv:2604.16914v1 Announce Type: new Abstract: Clinical ultrasound analysis demands models that generalize across heterogeneous organs, views, and devices, while supporting interpretable workflow-lev

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:2604.16325v1 Announce Type: new Abstract: Multivariate time series forecasting is fundamental to numerous domains such as energy, finance, and environmental monitoring, where complex temporal de

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:2506.03157v4 Announce Type: replace-cross Abstract: Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their

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:2604.18264v1 Announce Type: new Abstract: Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. Howe

arXiv:2603.23404v2 Announce Type: replace-cross Abstract: Existing Multimodal Large Language Models (MLLMs) struggle with 3D spatial reasoning, as they fail to construct structured abstractions of the

arXiv:2604.18000v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models report impressive success rates on standard robotic benchmarks, fueling optimism about general-purpose physic

This post expresses a positive personal assessment of Grok 4.3's chatbot capabilities, suggesting it performs well enough to potentially replace a Gemini subscription. The statement reflects a user's

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:2602.21468v4 Announce Type: replace-cross Abstract: The spin-1/2 J_1-J_2 Heisenberg model on the square lattice exhibits a debated intermediate phase between Neel antiferromagnetic and stripe or

arXiv:2604.16875v1 Announce Type: new Abstract: A central question in computational neuroscience is whether the learning rule used to train a neural network determines how well its internal representa

arXiv:2604.17477v1 Announce Type: new Abstract: Advanced deepfake technologies are blurring the lines between real and fake, presenting both revolutionary opportunities and alarming threats. While it

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:2508.15815v3 Announce Type: replace Abstract: Modern large language models (LLMs) are typically trained and deployed using structured role tags (e.g. system, user, assistant, tool) that explicit

arXiv:2604.18463v1 Announce Type: cross Abstract: Large language models are increasingly used as planners for robotic systems, yet how safely they plan remains an open question. To evaluate safe plann

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

v0.21.1-rc1 is a release candidate for Ollama, an open-source tool for running machine learning models locally. The release candidate phase indicates testing and bug-fixing before the stable v0.21.1 r

VA law REQUIRES that ballot language be 'a neutral explanation.' This language is CLEARLY illegal. A court ruled as much...and Democrats ignored the court and waited for a different court to punt on t

arXiv:2402.13243v2 Announce Type: replace Abstract: Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of plann

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:2510.27462v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) on long chain-of-thought (CoT) trajectories has emerged as a crucial technique for enhancing the reasoning abilities of

arXiv:2604.17248v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing sp

arXiv:2509.23724v2 Announce Type: replace Abstract: Recent Video-Language Models (VLMs) achieve promising results on long-video understanding, but their performance still lags behind that achieved on

arXiv:2604.17656v1 Announce Type: cross Abstract: Video-to-music (V2M) is the fundamental task of creating background music for an input video. Recent V2M models achieve audiovisual alignment by typic

arXiv:2511.11113v2 Announce Type: replace Abstract: Reinforcement fine-tuning (RFT), a two-stage framework consisting of supervised fine-tuning (SFT) and reinforcement learning (RL) has shown promisin

arXiv:2601.15724v2 Announce Type: replace Abstract: Long-form video understanding remains a fundamental challenge for current Video Large Language Models. Most existing models rely on static reasoning

arXiv:2604.17525v1 Announce Type: cross Abstract: Medical imaging AI development is fundamentally dependent on annotated datasets, yet no existing standard provides machine-enforceable validation acro

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:2407.06048v2 Announce Type: replace Abstract: We present Vision-Braille, the first publicly available end-to-end system for translating Chinese Braille extracted from images into written Chinese

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:2604.16388v1 Announce Type: cross Abstract: Rapidly-exploring random trees (RRTs) have been widely adopted for robot motion planning due to their robustness and theoretical guarantees. However,

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:2505.20279v4 Announce Type: replace-cross Abstract: The rapid advancement of Large Multimodal Models (LMMs) for 2D images and videos has motivated extending these models to understand 3D scenes,

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:2508.15229v3 Announce Type: replace Abstract: Small Language Models (SLMs) provide computational advantages in resource-constrained environments, yet memory limitations remain a critical bottlen

arXiv:2604.17727v1 Announce Type: new Abstract: Most existing hyperspectral image super-resolution methods require modifications for different scales, limiting their flexibility in arbitrary-scale rec