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arXiv:2502.10673v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has become an effective method for enhancing large language models (LLMs) with up-to-date knowledge. Howe

day 1 of recreating iconic tech from my childhood :) I rebuilt Cher’s iconic closet as a full-stack app in @Replit 💛 the frontend uses React + TypeScript, with an Express backend. I used Replit’s Cler

Dean Ball, OpenAI's head of strategic futures, writes: 'I have met people, some of them quite well-resourced, who have told me that it is their intention to deliberately release swarms of self-soverei

arXiv:2608.30271v1 Announce Type: cross Abstract: We study decentralized online optimization of upper-linearizable payoffs over an action set under efficient separation access, with applications to on

arXiv:2608.29465v1 Announce Type: cross Abstract: Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or switching costs. As operating conditions c

arXiv:2607.11577v2 Announce Type: replace-cross Abstract: Conventional Graph Neural Networks (GNNs) couple feature transformation and neighborhood aggregation, which often renders them vulnerable to t

arXiv:2306.11313v5 Announce Type: replace-cross Abstract: Point process models are widely used for continuous-time discrete-event data, where each data point includes time and additional information c

DeepSeek V4 Flash now comes with native vision on Fireworks. Available now on serverless and priority tiers. Better benchmarks than the text-only 0731 version at the same low price: 0.22 input / 0.66

arXiv:2608.28691v1 Announce Type: cross Abstract: Wearable VLM pipelines promise continuous multimodal assistance from egocentric visual capture: a user asks a task-driven question about the surroundi

arXiv:2601.22118v3 Announce Type: replace Abstract: Artificial Intelligence (AI) has been on the rise in many domains, including numerous safety-critical applications. However, for complex systems in

Enterprise defenses hunt the unauthorized: the foreign file, the stolen credential, the behavior nobody sanctioned. Agentic AI security inverts that model, because the agent moving through the busines

arXiv:2608.28837v1 Announce Type: cross Abstract: We conducted an interview study with twelve students on their use of generative AI in academic communication. Students delegated professional messages

arXiv:2310.02423v3 Announce Type: replace Abstract: We present a new algorithm for amortized inference in sparse probabilistic graphical models (PGMs), which we call Delta-amortized inference (Delta-A

arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying

arXiv:2608.29507v1 Announce Type: cross Abstract: Diffusion models are increasingly used not only for sampling from learned data distributions, but also for generating samples that optimize task-speci

arXiv:2608.30405v1 Announce Type: new Abstract: Medical knowledge changes continually, making large language models vulnerable to relying on outdated yet clinically plausible information. We study whe

arXiv:2608.30877v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated exceptional performance in protein-ligand interaction prediction, but state-of-the-art

arXiv:2608.30413v1 Announce Type: new Abstract: Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of ne

arXiv:2602.17690v3 Announce Type: replace-cross Abstract: Graphic design generation demands a delicate balance between high visual fidelity and fine-grained structural editability. However, existing a

arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr

arXiv:2608.29850v1 Announce Type: new Abstract: E-commerce platforms increasingly personalize user experiences through machine learning, yet page layout decisions remain dominated by static rules and

arXiv:2608.29646v1 Announce Type: new Abstract: Large language model (LLM)-based agents have shown strong potential in solving complex tasks through multi-step reasoning, yet they remain vulnerable to

arXiv:2608.30948v1 Announce Type: new Abstract: LLMs can imitate how people write, which raises concerns about impersonation, trust, and detection in social settings. These concerns are especially imp

arXiv:2608.28986v1 Announce Type: new Abstract: LLMs often struggle with modern Korean poetry, producing outputs that resemble 'line-broken prose.' We address two coupled tasks: detecting whether a Ko

arXiv:2608.29307v1 Announce Type: cross Abstract: Language models increasingly answer questions by consulting retrieved documents rather than memory alone, a design now common in search assistants and

arXiv:2608.29956v1 Announce Type: new Abstract: Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete pa

arXiv:2608.29096v1 Announce Type: cross Abstract: The ongoing changes in software engineering requirements have created a substantial need for automated tools which can create secure source code from

arXiv:2608.29043v1 Announce Type: new Abstract: Light-effect contamination poses a significant challenge to nighttime visibility enhancement. Most methods suppress light effects by estimating and deco

arXiv:2608.28970v1 Announce Type: cross Abstract: Current TTS systems typically rely on open-loop, single-pass generation and can produce sporadic local prosodic defects, such as misplaced stress, unn

arXiv:2608.31128v1 Announce Type: new Abstract: Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and cit

arXiv:2608.30209v1 Announce Type: new Abstract: Visual instruction tuning is crucial for advancing the vision-language alignment and instruction-following capabilities of Vision-Language Models (VLMs)

arXiv:2601.18486v3 Announce Type: replace Abstract: Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes w

arXiv:2603.06399v2 Announce Type: replace Abstract: Facial attribute classification relies on large-scale annotated datasets in which many traits, such as age and expression, are inherently ambiguous

arXiv:2608.30532v1 Announce Type: new Abstract: Existing approaches for synthesizing Partial Differential Equation (PDE) solvers predominantly rely on autoregressive models, yet their global left-to-r

arXiv:2608.30603v1 Announce Type: cross Abstract: Robust estimation is a core computer vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampl

arXiv:2608.29907v1 Announce Type: new Abstract: Future wireless systems are expected to transform the surrounding space from a passive propagation medium into a smart electromagnetic environment, wher

arXiv:2608.30205v1 Announce Type: new Abstract: Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with pr

arXiv:2505.16990v3 Announce Type: replace Abstract: In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discre

arXiv:2608.29925v1 Announce Type: new Abstract: Controllable image relighting is an important problem in image editing, and hand-drawn scribbles provide an intuitive interface for specifying the desir

arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context

arXiv:2507.05512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for code generation, making reliable identification of machine-generated code important for

arXiv:2607.02290v2 Announce Type: replace Abstract: Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a know

arXiv:2508.01684v2 Announce Type: replace Abstract: While diffusion models have demonstrated remarkable progress in 2D image generation and editing, extending these capabilities to 3D editing remains

arXiv:2608.28824v1 Announce Type: new Abstract: Currently, in biological systems Neural Correlates of Consciousness (NCCs) are characterized in terms of EEG and FMRI signals. Unfortunately, this chara

arXiv:2506.17007v3 Announce Type: replace Abstract: A major bottleneck in scientific discovery consists of narrowing an exponentially large set of objects, such as proteins or molecules, to a small se

arXiv:2608.29997v1 Announce Type: new Abstract: We propose Discrete Diffusion Bridges (DDB), a novel framework designed to resolve the fundamental spatiotemporal misalignment of standard discrete diff