Search: “inference”

arXiv:2605.23381v1 Announce Type: new Abstract: Though rectified flow models have achieved remarkable performance in image, video, and 3D generation, their practical deployments are challenged by slow

arXiv:2602.07399v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations r

arXiv:2605.22876v1 Announce Type: new Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize an

LongCat-Video-Avatar 1.5 is an upgraded open-source framework that prioritizes extreme empirical optimization and production-readiness for audio-driven human video generation. The v1.5 release replace

v0.30.0-rc24 is a pre-release version that changes Ollama's architecture to directly support llama.cpp instead of building on top of GGML, enabling compatibility with the GGUF file format. MLX is used

arXiv:2605.21916v1 Announce Type: cross Abstract: Dynamic link prediction is important for modeling evolving interactions in complex systems, including social, communication, financial, and transporta

arXiv:2605.22275v1 Announce Type: new Abstract: Kernel methods are typically formulated under the assumption of exact, noise-free access to the Gram matrix. However, in emerging settings such as quant

The search results show information about llama.cpp releases but don't contain specific details about build b9297. Based on the context and numbering (b9297 is a recent build after b9294), b9297 is li

arXiv:2605.22468v1 Announce Type: new Abstract: Cross-subject generalization in biomedical time-series refers to training on data from some subjects and testing on unseen subjects.The key challenge is

arXiv:2605.22075v1 Announce Type: new Abstract: Diabetes is a global health burden, and early detection is critical for timely intervention. This study explores a non-invasive, data-driven framework t

arXiv:2605.22221v1 Announce Type: new Abstract: Backtracking search underlies classical constraint solvers, planners, and theorem provers. Recent transformer-based reasoning systems explore search tre

arXiv:2605.21963v1 Announce Type: new Abstract: Long-horizon clinical simulation -- predicting how a patient's physiology evolves over years under specified interventions -- is central to chronic-dise

arXiv:2605.21742v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) have achieved exceptional performance on tabular classification tasks. However, like other classifiers, their performa

arXiv:2605.21951v1 Announce Type: new Abstract: Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting pr

arXiv:2605.22537v1 Announce Type: new Abstract: Reinforcement learning methods such as GRPO have seen great popularity in LLM post-training. In GRPO, models produce completions to a set of prompts, wh

arXiv:2605.22596v1 Announce Type: new Abstract: Robotic tasks are typically specified by a tuple of factors, such as the object to be grasped, the obstacles to be avoided, the color of the target, and

arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over ti

arXiv:2603.02604v2 Announce Type: replace Abstract: We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem th

arXiv:2605.22335v1 Announce Type: new Abstract: In-context learning for tabular data sets strong predictive standards in observational settings; it however primarily relies on correlational structure,

arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vecto

arXiv:2506.21035v5 Announce Type: replace Abstract: Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based M

arXiv:2605.22775v1 Announce Type: new Abstract: Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications s

arXiv:2605.21751v1 Announce Type: new Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coeffi

arXiv:2605.22338v1 Announce Type: new Abstract: Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states th

arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus o

arXiv:2605.22097v1 Announce Type: cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challen

arXiv:2605.22334v1 Announce Type: new Abstract: Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curve

arXiv:2605.22331v1 Announce Type: new Abstract: Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by syste

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

arXiv:2605.21813v1 Announce Type: new Abstract: Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of

Nemotron-Labs Diffusion Language Models represent NVIDIA's approach to achieving faster text generation through diffusion-based architectures, potentially offering significant speed improvements over

arXiv:2603.21717v4 Announce Type: replace Abstract: Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating

arXiv:2502.13822v3 Announce Type: replace-cross Abstract: We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Ma

arXiv:2605.22765v1 Announce Type: new Abstract: Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynami

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

arXiv:2605.22257v1 Announce Type: new Abstract: Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equi

arXiv:2605.22216v1 Announce Type: new Abstract: This report presents our solution for the WeatherProof Dataset Challenge, namely CVPR 2026 8th UG2+ Challenge Track 2: Semantic Segmentation in Adverse

arXiv:2605.22183v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures m

arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy

arXiv:2605.21845v1 Announce Type: new Abstract: Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information

After 15 months of incremental updates, leaks, and rumored leaks, DeepSeek released version 4. It arrived without the fanfare R1 and R1-preview commanded in early 2025. That quiet reception is the mos

arXiv:1709.03806v2 Announce Type: replace Abstract: Modern vision models have achieved strong object-recognition performance, yet it remains unclear whether their representations encode object-level s

arXiv:2605.21649v1 Announce Type: cross Abstract: Long-context decoding is increasingly limited by KV-cache memory traffic since each generated token attends over a cache whose size grows linearly wit

arXiv:2605.22208v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly selecting

arXiv:2605.22020v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is funda

arXiv:2605.22413v1 Announce Type: new Abstract: Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multi