Text-Vision Co-Instructed Image Editing
arXiv:2606.16767v2 Announce Type: replace Abstract: Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are se
Knowledge catalogue
arXiv:2606.16767v2 Announce Type: replace Abstract: Existing image editing methods can be generally categorized into textual instruction-based and visual prompt-based ones. Textual instructions are se
arXiv:2610.17655v1 Announce Type: cross Abstract: Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key. The recent textGrai
arXiv:2610.18655v1 Announce Type: new Abstract: Texture interpolation asks what should lie between two textures. Standard methods typically blend pixels or features at corresponding spatial locations,
arXiv:2610.14281v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success in image super-resolution (SR) by leveraging strong generative priors to enhance perceptual quality. H
arXiv:2610.14657v1 Announce Type: new Abstract: Can autoregressive video diffusion be accelerated further when only four denoising steps remain? Existing feature caching approaches struggle in this re
arXiv:2610.17444v1 Announce Type: new Abstract: Language model (LM) agents can fail for different reasons, requiring different explanations and interventions. We study how state beliefs, action values
arXiv:2610.16828v1 Announce Type: new Abstract: When does a shared representation become insufficient for jointly supporting multiple tasks? This question lies at the heart of multi-task learning mode
arXiv:2610.18104v1 Announce Type: new Abstract: Hybrid attention is rapidly becoming the dominant architectural choice for frontier models. Despite their successes, such architectures complicate model
arXiv:2610.18771v1 Announce Type: new Abstract: International challenges have become the standard for developing and benchmarking biomedical image analysis methods using centralized multi-institutiona
arXiv:2404.14855v2 Announce Type: replace Abstract: We characterize the geometry of the set of all weight vectors for which a fully-connected linear neural network computes the same linear transformat
arXiv:2610.15589v1 Announce Type: new Abstract: Many deployed distillation pipelines show the student a truncated view of the teacher: keep the top-k probabilities, renormalize, and minimize a diverge
arXiv:2610.17746v1 Announce Type: new Abstract: A robot may know how to perform a task yet fail when a human asks for it naturally. We study this human--VLA communication gap by separating an informat
arXiv:2511.08328v2 Announce Type: replace Abstract: Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can b
arXiv:2610.16565v1 Announce Type: new Abstract: Human gaze is the primary high-bandwidth interface through which we parse the semantics of our environment. While often modeled as a sequence, the degre
arXiv:2610.18845v1 Announce Type: new Abstract: Earth observation (EO) models are still built by hand. For each new task, experts design a training recipe and tune it against a benchmark. We show that
arXiv:2610.16749v1 Announce Type: new Abstract: Recent foundation models have drastically improved the generalization of monocular relative depth estimation, yet recovering accurate metric depth from
arXiv:2610.15058v1 Announce Type: new Abstract: Larger sample budgets benefit from higher sampling temperatures in LLMs, but what temperature tuning optimizes and why it works remain poorly understood
arXiv:2610.15629v1 Announce Type: new Abstract: Choosing a mixed-precision configuration for large language model (LLM) serving trades speed against accuracy. Allocators that use average bit-width or
arXiv:2610.14279v1 Announce Type: new Abstract: Learning long-range dependencies with recurrent neural networks (RNNs) is difficult because of the vanishing gradient problem. Gated recurrent neural ne
arXiv:2610.14363v1 Announce Type: new Abstract: A predictor is multicalibrated with respect to a class of subpopulations (formally, of functions on the inputs, called auditors) if, for every subpopula
arXiv:2610.17283v1 Announce Type: new Abstract: The success of autoregressive language models trained with Chain-of-Thought~(CoT) supervision raises a fundamental question: How simple can a next-token
arXiv:2610.17636v1 Announce Type: new Abstract: The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text sampl
arXiv:2610.16977v1 Announce Type: new Abstract: Visual in-context learning (ICL) enables vision-language models (VLMs) to adapt at test time via demonstration examples, but its effectiveness depends c
arXiv:2508.04451v2 Announce Type: replace Abstract: Red teaming is essential for securing Large Language Models, yet current automated methods remain limited by templates and single-turn attacks. To s
arXiv:2610.16165v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in solving complex tasks in coding, mathematical logic, and iterative reasoning
arXiv:2610.18447v1 Announce Type: cross Abstract: World models have emerged as a promising paradigm for embodied AI, yet existing systems often remain fragmented in the modalities they model and the f
arXiv:2610.14265v1 Announce Type: new Abstract: Recent advances in multi-modal large language models have shifted toward the 'thinking with images' paradigm to improve reasoning faithfulness, yet exis
arXiv:2606.20310v2 Announce Type: replace Abstract: Video reward models are central to aligning video diffusion models with human preferences, yet existing ones are external vision-language models tha
arXiv:2610.16383v1 Announce Type: new Abstract: Elite athletics throwing provides a structured biomechanical action sequence together with an objective continuous outcome, namely throw distance, makin
arXiv:2610.15098v1 Announce Type: new Abstract: Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only th
arXiv:2610.17249v1 Announce Type: cross Abstract: Differentially private (DP) pipelines often combine heterogeneous mechanisms, yet their guarantees are best analyzed using different notions of privac
arXiv:2610.16448v1 Announce Type: new Abstract: Diffusion models, particularly Diffusion Transformers, achieve strong image and video generation quality but remain expensive at inference time due to r
arXiv:2610.17065v1 Announce Type: new Abstract: Real-world tables are often heterogeneous, combining numerical features with free-form string columns. Table foundation models (TFMs) excel on numerical
arXiv:2610.16921v1 Announce Type: cross Abstract: Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-length sliding windows that may be misalign
arXiv:2610.18649v1 Announce Type: new Abstract: Message passing neural networks have shown remarkable performance in algorithmic and constraint reasoning, due to their ability to encode the structure
arXiv:2610.14923v1 Announce Type: new Abstract: The TLA+ (temporal logic of actions) formal specification toolkit has seen widespread adoption in modeling modern distributed software systems including
arXiv:2610.18638v1 Announce Type: cross Abstract: Combining pretrained models is a common strategy for building AI systems, whether by aligning a vision encoder with a language model or merging fine-t
arXiv:2610.15721v1 Announce Type: new Abstract: Cross-model KV cache sharing reduces prefill computation by allowing a receiver model to reuse prefix KV states generated by a related sender model. Exi
arXiv:2610.15128v1 Announce Type: new Abstract: Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertai
arXiv:2610.14590v1 Announce Type: cross Abstract: Wheel odometry provides an efficient motion prior for ground robots but is prone to transient violations such as wheel slip or terrain-induced tilt. I
arXiv:2610.14207v1 Announce Type: new Abstract: Under a fixed context budget, which objects and relations should a 3D scene-grounded VLM see first? On ScanNet indoor referring benchmarks (Dai et al.,
arXiv:2603.18091v2 Announce Type: replace Abstract: Test-time sampling can improve Vision-Language-Action (VLA) policies, but only if the model can efficiently select a good action from multiple candi
arXiv:2603.01714v2 Announce Type: replace Abstract: Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Lear
arXiv:2603.25063v2 Announce Type: replace-cross Abstract: Recent agentic systems demonstrate that large language models can generate complex codebases from natural language prompts. Despite this progr
arXiv:2610.15807v1 Announce Type: new Abstract: Generating coherent videos grounded in a unified underlying world remains a major challenge, particularly under large viewpoint changes, where existing
arXiv:2610.15076v1 Announce Type: new Abstract: Plasma experiments observe coupled physical states through sparse, heterogeneous, and incomplete diagnostics. We present a proof of concept toward a mul
arXiv:2510.22833v2 Announce Type: replace-cross Abstract: While reinforcement learning agents can achieve superhuman performance in many complex tasks, they typically do not become more computationall
arXiv:2602.24111v2 Announce Type: replace Abstract: Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagno
arXiv:2610.16334v1 Announce Type: new Abstract: Robot errors can disrupt human-robot collaboration by reducing user trust and willingness to rely on the robot. Robot apologies can support trust recove
arXiv:2601.02759v2 Announce Type: replace Abstract: Some deep learning-based point cloud registration methods struggle with zero-shot generalization, often requiring target-domain retraining, fine-tun
arXiv:2610.17657v1 Announce Type: new Abstract: An embodied brain, or System 2, must turn an abstract goal into actions while discovering relevant scene conditions and checking whether executed skills
arXiv:2610.16762v1 Announce Type: new Abstract: Keypoint detection plays a central role in geometric vision pipelines. Existing detectors exhibit complementary strengths across different scene structu
arXiv:2610.16857v1 Announce Type: new Abstract: Research on video classification models has witnessed significant growth recently, accompanied by a significant increase in the demand for datasets to t
arXiv:2411.14865v2 Announce Type: replace-cross Abstract: Optical flow estimation is a fundamental task in dynamic scene understanding and computer vision. While recent deep models achieve impressive
arXiv:2610.17531v1 Announce Type: new Abstract: While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact
arXiv:2610.16596v1 Announce Type: new Abstract: Seizure prediction and detection, essential for clinical treatment of epilepsy, remain the crucial task of this neurological disease. EEG and iEEG are w
arXiv:2602.01581v2 Announce Type: replace Abstract: Aligning large language models (LLMs) depends on high-quality datasets of human preference labels, which are costly to collect. Although active lear
arXiv:2610.13947v2 Announce Type: replace Abstract: In this paper, we study physically grounded articulated 3D asset generation---moving the goal from reproducing how objects look to capturing how the
arXiv:2610.14846v1 Announce Type: new Abstract: Indoor human perception is the foundation for smart homes and healthcare. However, RGB cameras raise privacy concerns in private indoor environments, wh
arXiv:2610.18508v1 Announce Type: cross Abstract: The safety of LLM-based agents currently rests on alignment training and safety classifiers, neither of which can rule out that a sophisticated indire