Local MAP Sampling for Diffusion Models
arXiv:2510.07343v3 Announce Type: replace-cross Abstract: Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from p(x_0 mid y). While posterior
Knowledge catalogue
arXiv:2510.07343v3 Announce Type: replace-cross Abstract: Diffusion Posterior Sampling (DPS) provides a principled Bayesian approach to inverse problems by sampling from p(x_0 mid y). While posterior
arXiv:2605.24019v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited reso
arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-clou
arXiv:2605.25955v1 Announce Type: cross Abstract: Large language models (LLMs) face a dual challenge in creative capability evaluation: existing benchmarks (e.g., Story Cloze Test, HellaSwag) measure
arXiv:2605.25492v1 Announce Type: new Abstract: Pairwise model comparisons drawn from foundation-model benchmarks ('A is safer than B') are read as quantitative verdicts but hinge on harness choices b
arXiv:2605.23949v1 Announce Type: cross Abstract: As Large Language Models (LLMs) evolve into interactive agents, understanding their behavioral alignment within human social dynamics becomes essentia
arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely
System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own
arXiv:2605.24211v1 Announce Type: cross Abstract: Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) conti
arXiv:2605.24846v1 Announce Type: cross Abstract: Large language models (LLMs) display strong comprehensive abilities, yet the internal mechanisms that support these behaviors remain insufficiently un
arXiv:2605.25399v1 Announce Type: new Abstract: Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because ce
arXiv:2605.25969v1 Announce Type: new Abstract: Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and
arXiv:2605.23733v1 Announce Type: cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Traini
arXiv:2512.15767v2 Announce Type: replace-cross Abstract: Simulating complex unsteady physical phenomena relies on detailed mathematical models, simulated for instance by using the Finite Element Meth
ComfyUI-Angelo now supports Qwen-Image-Edit, an advanced image editing model that provides text editing features and the ability to edit both semantics and appearance of images. The model applies Qwen
DeepSeek V4 Flash model has been made available again on the Nous Research portal at no cost for use with Hermes Agent applications. Users can access and utilize this model through the Nous portal's s
arXiv:2605.23089v1 Announce Type: cross Abstract: Model-based reinforcement learning improves sample efficiency by learning a world model. However, existing latent world models such as DreamerV3 do no
arXiv:2512.12677v2 Announce Type: replace-cross Abstract: We explore efficient strategies to fine-tune decoder-only Large Language Models (LLMs) for downstream text classification under resource const
arXiv:2605.22898v1 Announce Type: new Abstract: Federated learning protocols face a structural trilemma: canonical server-based aggregation~ite{mcmahan2017} creates a single point of failure and gradi
arXiv:2605.22885v1 Announce Type: new Abstract: Formal mathematics libraries are rapidly expanding, creating a growing need to refactor verified proofs for maintainability and to improve training data
arXiv:2605.23082v1 Announce Type: cross Abstract: Survival analysis aims to model how covariates and time jointly shape the time-to-event distribution under right censoring. Classical methods such as
arXiv:2605.23657v1 Announce Type: new Abstract: Skills, i.e., structured workflow instructions distilled for large language models (LLMs), are becoming an increasingly important mechanism for improvin
arXiv:2605.23883v1 Announce Type: cross Abstract: Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this wo
arXiv:2605.23168v1 Announce Type: cross Abstract: When practitioners fine-tune LLMs on unvetted datasets, an adversary can exploit the data supply chain through task-level poisoning: inserting a small
arXiv:2605.23522v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become an effective way to improve prompt alignment and perceptual quality in diffusion and flow-matching generators.
arXiv:2412.19098v4 Announce Type: replace Abstract: Model merging combines independently trained models into a single multi-task model. However, most existing approaches focus primarily on avoiding ta
arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-dema
arXiv:2602.12506v3 Announce Type: replace Abstract: Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivatin
arXiv:2601.22365v2 Announce Type: replace-cross Abstract: The Gilbert-Pollak Conjecture itep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in th
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:2509.00303v3 Announce Type: replace-cross Abstract: In this work, we present the exttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical imp
arXiv:2506.11060v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based coding agents have shown promising results on coding benchmarks, but their effectiveness on systems code rema
arXiv:2605.22247v1 Announce Type: new Abstract: Idioms pose a fundamental challenge for language models, as their meaning cannot be inferred from surface form alone. Understanding such expressions, th
arXiv:2605.20442v1 Announce Type: cross Abstract: Generative AI systems are increasingly deployed as interactive agents in online environments, such as a social network called Moltbook. In Moltbook, l
arXiv:2605.20423v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. E
arXiv:2511.07820v3 Announce Type: replace-cross Abstract: Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humano
arXiv:2601.05106v4 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a singl
arXiv:2605.22096v1 Announce Type: new Abstract: Capsule endoscopy event detection is challenging because clinically relevant findings are sparse, visually heterogeneous, and evaluated at the event lev
arXiv:2605.21273v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning,
arXiv:2605.20761v1 Announce Type: new Abstract: The rapid proliferation of AI-generated text has introduced significant challenges in maintaining the integrity of digital content. Advanced generative
arXiv:2605.20296v1 Announce Type: new Abstract: Fine-tuning a language model for a target task routinely degrades capabilities the training data never explicitly threatened. We study this phenomenon,
arXiv:2506.08277v3 Announce Type: replace-cross Abstract: Recent voxel-wise multimodal brain encoding studies have shown that multimodal large language models (MLLMs) exhibit a higher degree of brain
arXiv:2505.19075v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable general capabilities, but enhancing skills such as reasoning often demands substanti
arXiv:2605.20676v1 Announce Type: new Abstract: Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimoda
arXiv:2605.18891v1 Announce Type: cross Abstract: Evaluations of unlearning on reasoning models sometimes show a bypass pattern. The answer side looks unlearned, but the model's own thinking trace kee
arXiv:2603.17305v2 Announce Type: replace Abstract: We propose CRAFT, a red-teaming alignment framework that leverages model reasoning capabilities and hidden representations to improve robustness aga
arXiv:2605.19666v1 Announce Type: cross Abstract: Accurate cardiac output (CO) estimation from photoplethysmography (PPG) is promising for unobtrusive hemodynamic monitoring, but remains difficult sin
arXiv:2605.19743v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly applied to engineering design tasks, yet existing evaluation frameworks do not adequately address mul
arXiv:2605.19111v1 Announce Type: cross Abstract: Existing text-to-image (T2I) evaluation metrics mainly assess whether generated images align with information explicitly stated in the prompt, but oft
arXiv:2605.19522v1 Announce Type: new Abstract: Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, bu
Cohere announced Command A+, their most advanced large language model to date, designed with optimization for efficient hardware requirements to enable broader deployment and accessibility. The model
arXiv:2605.19416v1 Announce Type: new Abstract: Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an ex
arXiv:2601.03645v2 Announce Type: replace Abstract: Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affe
arXiv:2605.18774v1 Announce Type: cross Abstract: In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true
arXiv:2605.19359v1 Announce Type: new Abstract: Deep learning methods have demonstrated promising results in predicting BI-RADS scores from mammography images. However, the interpretation of these ima
arXiv:2605.19127v1 Announce Type: new Abstract: LLM agents increasingly have access to private user data and act on the user's behalf when interacting with third-party systems. The user defines what m
arXiv:2605.19462v1 Announce Type: cross Abstract: The success of self-supervised learning (SSL) in vision and NLP has motivated its rapid adoption for time series. However, research has focused primar
arXiv:2605.19799v1 Announce Type: cross Abstract: We present a semi-supervised framework for joint segmentation and classification of fetal cardiac ultrasound images. Built upon the EchoCare multi-tas
arXiv:2605.19320v1 Announce Type: new Abstract: Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and f
arXiv:2605.19377v1 Announce Type: cross Abstract: As jailbreaks, adversarially crafted inputs that bypass safety constraints, continue to be discovered in Large Language Models, practitioners increasi