Knowledge catalogue
arXiv:2605.07046v1 Announce Type: cross Abstract: Evaluation of large language models (LLMs) is increasingly critical, yet standard benchmarking methods rely on average accuracy, overlooking both the
arXiv:2602.19612v3 Announce Type: replace Abstract: Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts
arXiv:2605.07825v1 Announce Type: cross Abstract: Training multimodal large language models has long been limited by the scarcity of high-quality paired multimodal data. Recent studies show that the s
arXiv:2605.07786v1 Announce Type: cross Abstract: As generative models achieve unprecedented visual quality, the gold standard for image evaluation remains traditional feature-distribution metrics (e.
Chance Miller / 9to5Mac: Apple releases iOS 26.5, introducing end-to-end encryption for RCS messaging in beta with supported carriers; the setting is enabled by default — iOS 26.5 is now available to
Georgia Wells / Wall Street Journal: AppMagic: Grok downloads fell to ~8.3M in April, from a high of 20M+ in January; Recon Analytics says Grok paid adoption in the US remains nearly flat YoY in Q2 —
arXiv:2605.07463v1 Announce Type: new Abstract: We explore the expressive power of Transformers by establishing precise approximation error upper and lower bounds for Holder class. Specifically, a new
arXiv:2605.07837v1 Announce Type: cross Abstract: Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabu
arXiv:2605.07653v1 Announce Type: new Abstract: Underwater environments impose severe constraints on conventional imaging systems and demand solutions that balance high-quality sensing with strict res
Across the public sector, the conversation has shifted; we are no longer just talking about the potential of AI, we are already seeing the impact. While visionary leadership and cultural buy-in are cr
arXiv:2509.03736v2 Announce Type: replace Abstract: The impressive capabilities of Large Language Models (LLMs) raise the possibility that synthetic agents can serve as substitutes for real participan
arXiv:2605.07103v1 Announce Type: new Abstract: Reaction feasibility prediction, as a fundamental problem in computational chemistry, has benefited from diverse tools enabled by recent advances in art
arXiv:2605.07204v1 Announce Type: new Abstract: We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an u
arXiv:2601.18681v2 Announce Type: replace-cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uni
arXiv:2605.07937v1 Announce Type: new Abstract: Long-horizon AI agents execute complex workflows spanning hundreds of sequential actions, yet a single wrong assumption early on can cascade into irreve
arXiv:2502.07143v3 Announce Type: replace Abstract: The severe shortage of medical doctors limits access to timely and reliable healthcare, leaving millions underserved. Large language models (LLMs) o
This Reddit post discusses a user request to an AI image generation tool (likely DALL-E or similar) to create an image depicting a biopic movie scene featuring an actor portraying televangelist Kennet
arXiv:2604.01878v2 Announce Type: replace-cross Abstract: Spectral graph contrastive learning often constructs low- and high-frequency views to capture complementary graph signals, but these views are
arXiv:2511.09016v2 Announce Type: replace-cross Abstract: The Kalman filter and Rauch-Tung-Striebel (RTS) smoother are optimal for state estimation in linear dynamic systems. With nonlinear systems, t
arXiv:2605.06387v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy disti
arXiv:2605.07964v1 Announce Type: cross Abstract: Confidence sequences based on test martingales provide time-uniform uncertainty quantification for the mean of bounded IID observations without parame
arXiv:2605.07192v1 Announce Type: new Abstract: 3D reconstruction methods such as 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) achieve impressive photorealism but fail when input ima
arXiv:2605.07308v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face
arXiv:2512.03476v2 Announce Type: replace-cross Abstract: Bridging the gap between theoretical conceptualization and computational implementation is a major bottleneck in Scientific Computing (SciC) a
arXiv:2603.18636v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) achieve strong video generation quality but suffer from high inference cost due to dense 3D attention, motivating spar
arXiv:2605.07191v1 Announce Type: new Abstract: A recent work shows that Attention Transfer, which transfers only the attention patterns from a pre-trained teacher Vision Transformer (ViT) to a random
arXiv:2605.06834v1 Announce Type: new Abstract: Continual learning research attempts to conserve two fundamental capabilities: new knowledge acquisition and the preservation of previously acquired kno
Audi's new Q9 flagship features a spacious, exclusive interior conceived as a sanctuary, offering up to seven seats with electrically powered doors and a large panoramic sunroof with switchable transp
arXiv:2605.07478v1 Announce Type: new Abstract: Speech-driven facial animation requires accurate correspondence between acoustic signals and facial motion, especially for articulation-related mouth mo
Vercel Flags enables automated progressive rollouts, allowing developers to gradually deploy features to increasing percentages of users rather than releasing to everyone simultaneously. This approach
Daybreak is an automated security system that detects potential security threats, validates their legitimacy, and executes response actions with minimal human intervention. Developed by OpenAI, it rep
arXiv:2510.00436v2 Announce Type: replace Abstract: Automated approaches to answer patient-posed health questions are rising, but selecting among systems requires reliable evaluation. The current gold
arXiv:2604.01619v3 Announce Type: replace-cross Abstract: Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their enviro
Release b9102 adds support for the im2col_3d operator to the SYCL backend in llama.cpp, expanding GPU-accelerated tensor operations for Intel GPU inference. The im2col_3d operation converts 3D input v
B9103 is a recent release of llama.cpp , a C/C++ implementation for large language model inference. This release represents one of the latest versions in the project's active development cycle, buildi
Release b9105 of llama.cpp includes updates to the AllReduce implementation for CUDA, introducing a NCCL-free provider for tensor parallelism that pipelines data transfers and GPU reduction operations
Release b9106 of llama.cpp includes support for asymmetric FlashAttention in scalar, MMQ, and cooperative matrix paths for Vulkan. The release provides builds for multiple platforms including macOS, L
Release b9109 of llama.cpp introduces refinements to the CUDA tensor parallelism AllReduce implementation, including renaming the --allreduce flag to --reduction-provider and updates to NCCL-free AllR
The search results show recent llama.cpp releases and general information but don't contain specific details about the b9110 release. Based on the search patterns and similar recent releases documente
Release b9112 of llama.cpp introduces a NCCL-free AllReduce implementation for LLAMA_SPLIT_MODE_TENSOR using a single-phase CUDA kernel, and adds an --allreduce flag to llama-bench to select between A
arXiv:2605.07394v1 Announce Type: cross Abstract: Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the
Bare Metal: Z-Image Turbo - Flux.2 Klein 9b - Wan 2.2 discusses comparisons between these lightweight AI image generation models, with Flux.2 Klein 9B showing good prompt adherence and natural composi
arXiv:2505.22842v4 Announce Type: replace Abstract: Transformer-based language models rely on positional encoding (PE) to handle token order and support context length extrapolation. However, existing
arXiv:2602.03331v2 Announce Type: replace Abstract: We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk
arXiv:2605.07706v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large models by decomposing weight updates into low-rank matrices, significantly r
arXiv:2605.07993v1 Announce Type: new Abstract: Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis hel
arXiv:2512.05439v2 Announce Type: replace Abstract: As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify model
arXiv:2605.07903v1 Announce Type: cross Abstract: Discovering structure in biological signals without supervision is a fundamental problem in computational intelligence, yet existing bioacoustic metho
arXiv:2605.07021v1 Announce Type: new Abstract: Reasoning in Large Language Models (LLMs) poses a challenge for oversight as many misaligned behaviors do not surface until reasoning concludes. To addr
arXiv:2605.05583v2 Announce Type: replace Abstract: LLM agents that operate over long context depend on external memory to accumulate knowledge over time. However, existing methods typically store eac
arXiv:2512.23694v2 Announce Type: replace-cross Abstract: Reliable long-horizon value prediction is difficult in offline reinforcement learning because fitted value methods combine bootstrapping, func
arXiv:2605.06856v1 Announce Type: cross Abstract: Generative AI systems achieve impressive performance on standard benchmarks yet fail to deliver real-world utility, a disconnect we identify across 28
arXiv:2605.07731v1 Announce Type: cross Abstract: This report benchmarks the performance of ENGINEERING Ingegneria Informatica S.p.A.'s EngGPT2MoE-16B-A3B LLM, a 16B parameter Mixture of Experts (MoE)
arXiv:2605.07749v1 Announce Type: new Abstract: The rapid proliferation of open-source medical foundation models (FMs) raises a practical question: how well do their pre-trained representations transf
arXiv:2510.19788v4 Announce Type: replace Abstract: World models are central to building AI agents capable of flexible reasoning and planning. Yet current evaluations (i) test only properties measurab
With 32GB of RAM, 30B Q4 models run comfortably on M5 MacBook Air , and popular choices include Qwen 3.6 27B (requiring 18GB+ VRAM) and Qwen 3.6 9B (lightweight, runs on 8GB) . Ollama's macOS implemen
arXiv:2605.06879v1 Announce Type: new Abstract: Protein sequence data from nature exhibits survivorship bias: we only observe data from those organisms that survive and reproduce, while non-functional
arXiv:2605.07806v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved fro
arXiv:2605.07590v1 Announce Type: new Abstract: Despite extensive progress in point cloud robustness, existing methods primarily improve performance through augmentation or defense mechanisms, while o
arXiv:2605.07557v1 Announce Type: new Abstract: Semi-supervised learning faces significant challenges in realistic scenarios where labeled data is scarce and unlabeled data follows unknown, arbitrary