Autoregressive Visual Generation Needs a Prologue
arXiv:2605.06137v2 Announce Type: replace-cross Abstract: In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead
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arXiv:2605.06137v2 Announce Type: replace-cross Abstract: In this work, we propose Prologue, an approach to bridging the reconstruction-generation gap in autoregressive (AR) image generation. Instead
arXiv:2605.31468v1 Announce Type: new Abstract: Scientific research has traditionally been human-intensive, requiring researchers to coordinate literature, ideas, experiments, manuscripts, and review
b9451 is the latest release of llama.cpp, published on June 1, 2026 . Llama.cpp is an LLM inference project implemented in C/C++ that enables efficient local execution of large language models. This r
llama.cpp b9452 is a release of the LLM inference C/C++ project from the ggml-org repository. This release likely includes updates to the software's core functionality, bug fixes, or new features that
llama.cpp release b9453 added support for EXAONE 4.5 model implementations with vision capabilities, including markers, projector paths, and routing through a Qwen2.5-VL-style encode path with window
Release b9455 is a build of llama.cpp, a C/C++ implementation for LLM inference . This release represents a specific commit or version update to the llama.cpp project, which is used for running large
Release b9459 is a version of llama.cpp, the C/C++ implementation for LLM inference. The specific release would contain bug fixes, optimizations, or new features for the llama.cpp project, which enabl
Release b9464 is a version of llama.cpp, a project that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. This specific release likely contains upd
A Reddit user shares a nostalgic story about creating homemade Yu-Gi-Oh cards with their brothers in 2002 when they couldn't afford official cards, then used ChatGPT to generate designs making them re
arXiv:2511.19394v2 Announce Type: replace Abstract: Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architec
arXiv:2605.31246v1 Announce Type: cross Abstract: Prompt learning is a new machine learning paradigm that has attracted ample attention due to its simplicity and proven efficacy. Despite its growing a
arXiv:2605.31484v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multi
arXiv:2605.30892v1 Announce Type: new Abstract: We consider a federated learning (FL) system in which Industrial Internet-of-Things (IIoT) devices collaboratively train a global model over wireless ch
arXiv:2602.16305v2 Announce Type: replace-cross Abstract: Probing is widely adopted in computer vision to faithfully evaluate self-supervised learning (SSL) embeddings, as finetuning may misrepresent
arXiv:2605.31481v1 Announce Type: new Abstract: As robot control shifts toward large-scale reinforcement learning with in-loop dynamics computation, the community's reliance on CPU-bound libraries suc
arXiv:2605.30976v1 Announce Type: cross Abstract: We study stochastic linear bandits under a natural combination of batching and communication constraints: the time horizon is partitioned into batches
arXiv:2605.30860v1 Announce Type: cross Abstract: A central aim of deep learning theory is to characterize how neural networks make predictions in the regime of simultaneously large model and training
arXiv:2605.05520v2 Announce Type: replace Abstract: Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate grou
I cannot provide an accurate summary as the title appears to be truncated and the full content is not accessible. Based on the available text, this likely discusses internal practices at Anthropic reg
arXiv:2605.31256v1 Announce Type: new Abstract: Embodied Large Language Models (LLMs) are increasingly used as reasoning modules in robotic control pipelines to improve human-robot interaction, but th
arXiv:2605.31212v1 Announce Type: cross Abstract: AI systems are increasingly used to support educational content creation, yet it remains unclear whether they can generate outputs that faithfully rep
arXiv:2605.30585v1 Announce Type: cross Abstract: Effective prognostics and health management of modern engines relies on accurate turbine gas temperature predictions and robust uncertainty quantifica
arXiv:2605.30987v1 Announce Type: new Abstract: The tasks of object removal and inpainting 3D Gaussian Splatting (3DGS) scenes face challenges such as 3D consistency across camera views. In comparing
arXiv:2508.04457v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior
arXiv:2605.31483v1 Announce Type: new Abstract: Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LL
Harrison Chase compares LangSmith Engine to Tesla's 'Full Self-Driving' moment, suggesting it represents a significant advancement in AI engineering capabilities and automation. The post indicates Lan
Bernie Sanders / New York Times: Bernie Sanders says the wealth AI creates “must benefit humanity”, calling for a sovereign wealth fund that would hold ownership stakes in the top AI companies — Artif
Bernini is a unified framework for video editing and video generation , built using Wan2.2-A14B as its renderer . The model covers complementary task families that demonstrate its capabilities as a un
arXiv:2605.31050v1 Announce Type: new Abstract: Gaussian process-based Bayesian optimization (BO) is a popular approach for expensive black-box optimization, but its performance often degrades on comp
arXiv:2605.30734v1 Announce Type: cross Abstract: Malaria remains a leading cause of mortality in sub-Saharan Africa, where scarce diagnostic infrastructure makes timely, accurate diagnosis particular
arXiv:2605.31200v1 Announce Type: new Abstract: Interpretable machine learning requires models that are accurate and structurally faithful to the data.Existing explainability methods rely heavily on a
arXiv:2605.30826v1 Announce Type: cross Abstract: Biomedical NER is deceptively simple for modern LLMs: plausible biomedical mentions are easy to surface, but corpus-convention correctness depends on
arXiv:2605.31229v1 Announce Type: cross Abstract: While retrieval is a core function of vision-language models, continually updating these models for retrieval tasks remains critically underexplored.
arXiv:2510.20853v2 Announce Type: replace-cross Abstract: Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyd
Enterprise AI adoption at scale requires moving beyond large language models to implement agent logic systems that can handle complex reasoning, planning, and decision-making autonomously. Agent-based
arXiv:2501.04661v3 Announce Type: replace-cross Abstract: The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represente
arXiv:2602.15634v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from overs
arXiv:2605.31086v1 Announce Type: new Abstract: In existing memory benchmarks for Large Language Models (LLMs), the evaluated dialogue sessions often lack long-term semantic consistency, and the under
arXiv:2603.09221v2 Announce Type: replace Abstract: Associative memory has long underpinned the design of sequential models. Beyond recall, humans reason by projecting future states and selecting goal
arXiv:2508.18730v2 Announce Type: replace Abstract: Estimating the quality of register transfer level (RTL) designs is crucial in the electronic design automation (EDA) workflow, as it enables instant
arXiv:2605.31153v1 Announce Type: new Abstract: Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic
arXiv:2602.10117v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We cal
arXiv:2605.30647v1 Announce Type: new Abstract: We focus on the problem of efficient anytime kinodynamic planning for systems with complex dynamics in unstructured environments that make precomputing
arXiv:2605.31241v1 Announce Type: new Abstract: This study presents a novel hybrid prognostic framework for uncertainty-aware Remaining Useful Life (RUL) estimation in turbofan engines using the NASA
Big day for American open models... Nemotron 3 Ultra is now the strongest US open-weight model tested, while apparently serving 300+ tok/s 🤯 Comparable large DeepSeek/Kimi models are usually 50-100 to
arXiv:2605.30900v1 Announce Type: new Abstract: Current multimodal models handle static image recognition well, but intuitive physical reasoning remains a weakness. Predicting how objects will move an
Jeff John Roberts / Fortune: Binance launches trading for 7,000+ US stocks and ETFs for non-US users, with zero commissions and fractional share purchases, as part of its “super app” push — Binance, t
arXiv:2605.30972v1 Announce Type: new Abstract: Accurate 3D medical image segmentation requires both long-range volumetric context and fine boundary preservation. CNN-based methods have limited global
arXiv:2605.30907v1 Announce Type: cross Abstract: We present BlueFin, a benchmark that tasks large language model (LLM) agents with synthesis, manipulation, and comprehension tasks over spreadsheet wo
arXiv:2605.30660v1 Announce Type: new Abstract: Test-time scaling for vision-language-action (VLA) policies, methods such as RoboMonkey, SEAL, MG-Select, and V-GPS, samples K candidate action chunks a
arXiv:2512.19673v3 Announce Type: replace-cross Abstract: Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a unified policy, overlooking their internal mechanisms.
arXiv:2510.11683v3 Announce Type: replace-cross Abstract: A key challenge in applying reinforcement learning (RL) to diffusion large language models (dLLMs) is the intractability of their likelihood f
arXiv:2605.30448v1 Announce Type: cross Abstract: Black-box LLM distillation is usually evaluated as an output-matching problem: a student is considered successful when its responses are semantically
Kalley Huang / New York Times: Box says it created 13 new AI-focused roles, like AI architect and AI solutions manager, and plans to grow its staff to 3,000 by early 2027, up from 2,900 — Box, a Silic
🚨BREAKING: Henry Nowak's father speaks out on the murder of his son: 'He told officers he could not breathe NINE TIMES, he said he had been stabbed FOUR TIMES, but the officer replied saying' 'I don't
arXiv:2411.13865v4 Announce Type: replace-cross Abstract: Modern recommender systems often create information cocoons, restricting users' exposure to diverse content. The central challenge is to balan
arXiv:2602.08885v5 Announce Type: replace-cross Abstract: Symbolic regression (SR) aims to discover interpretable analytical expressions that accurately describe observed data. Amortized SR promises t
arXiv:2605.30652v1 Announce Type: new Abstract: Traditional multi-modal financial forecasting often relies on scalar sentiment scores, which fail to capture the nuances of financial news. To address t
This post outlines the agent development lifecycle, consisting of four key phases: Build, Test, Deploy, and Monitor. The content covers best practices and methodologies for developing AI agents using
arXiv:2605.31110v1 Announce Type: new Abstract: Generalization in robotics requires prior knowledge about how the world is structured, yet this structure changes from one situation to the next. This p