b9172
b9172 is a release of llama.cpp that includes binaries for macOS, Linux, Android, Windows, and openEuler platforms with support for various hardware configurations including CPU, Vulkan, CUDA, ROCm, O
Knowledge catalogue
b9172 is a release of llama.cpp that includes binaries for macOS, Linux, Android, Windows, and openEuler platforms with support for various hardware configurations including CPU, Vulkan, CUDA, ROCm, O
arXiv:2605.14883v1 Announce Type: cross Abstract: Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages. Oculomotor dysfunction is a well-e
arXiv:2605.14555v1 Announce Type: cross Abstract: Current methods for creating drum loop audio in digital music production, such as using one-shot samples or resampling, often demand non-trivial effor
arXiv:2602.18435v2 Announce Type: replace Abstract: Clustering is widely used for unsupervised structure discovery, yet it offers limited insight into how reliable each individual assignment is. Diagn
arXiv:2605.14799v1 Announce Type: new Abstract: In recent years, computer vision has witnessed remarkable progress, fueled by the development of innovative architectures such as Convolutional Neural N
arXiv:2603.24586v2 Announce Type: replace-cross Abstract: As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial
arXiv:2605.14495v1 Announce Type: cross Abstract: Multimedia verification requires not only accurate conclusions but also transparent and contestable reasoning. We propose a contestable multi-agent fr
arXiv:2605.15079v1 Announce Type: new Abstract: Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON-LD-based format that makes dataset discovery,
arXiv:2602.08874v2 Announce Type: replace Abstract: Long-context LLMs can infer objectives that are not stated explicitly. This capability is useful for reasoning over documents, code, retrieved evide
arXiv:2605.14249v1 Announce Type: new Abstract: We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducin
arXiv:2605.15085v1 Announce Type: cross Abstract: Nowadays refinery optimization utilizes sheer amounts of data, which can be handled with modern Linear Programming (LP) software, but the interpreting
arXiv:2605.15181v1 Announce Type: new Abstract: Modern image editing models produce realistic results but struggle with abstract, multi step instructions (e.g., ``make this advertisement more vegetari
arXiv:2605.14475v1 Announce Type: new Abstract: Interpreting ultra-high-resolution (UHR) remote sensing images requires models to search for sparse and tiny visual evidence across large-scale scenes.
arXiv:2602.06718v2 Announce Type: replace-cross Abstract: Citations provide the basis for trusting scientific claims; when they are invalid or fabricated, this trust collapses. With the advent of Larg
arXiv:2605.15157v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are prone to compounding errors in dexterous manipulation, where high-dimensional action spaces and contact-rich d
arXiv:2605.14355v1 Announce Type: new Abstract: As AI agents improve, the central question is no longer whether they can solve isolated well-defined financial tasks, but whether they can reliably carr
arXiv:2605.14259v1 Announce Type: new Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning. Exis
arXiv:2605.14352v1 Announce Type: new Abstract: Elections represent a crucial milestone in a nation's ongoing development. To better understand the political rhetoric from various movements, ranging f
arXiv:2605.14831v1 Announce Type: new Abstract: One of the bottlenecks on the way towards recursively self-improving systems is the challenge of interestingness: the ability to prospectively identify
arXiv:2602.12105v2 Announce Type: replace-cross Abstract: We propose a system for differentiating through solutions to geometry processing problems. Our system differentiates a broad class of geometri
arXiv:2605.14828v1 Announce Type: cross Abstract: Existing clustering methods for functional data often prioritize partitioning accuracy over interpretability, making it challenging to extract meaning
arXiv:2605.13873v1 Announce Type: cross Abstract: Web accessibility aims to ensure that web content and services are usable by people with diverse abilities. In recent years, Large Language Models (LL
arXiv:2605.14552v1 Announce Type: new Abstract: Recent advances in generative models have empowered impressive layered image generation, yet their success is largely confined to graphic design domains
arXiv:2605.14421v1 Announce Type: cross Abstract: We introduce MemLineage, a defense for LLM agent memory that attaches both cryptographic provenance and LLM-mediated derivation lineage to every entry
arXiv:2605.15172v1 Announce Type: cross Abstract: Backdoor attacks pose a serious security threat to large language models (LLMs), which are increasingly deployed as general-purpose assistants in safe
Grok, through a standard subscription, now offers multiple AI capabilities beyond text chat, including image generation, web search via X, and image-to-video conversion. This represents an expansion o
arXiv:2605.14240v1 Announce Type: new Abstract: The recent large-scale emergence of LLMs has left an open space for dealing with their consequences, such as plagiarism or the spread of false informati
PipelineIQ is a Databricks solution that leverages data analytics and AI to provide predictive sales intelligence, enabling sales teams to forecast pipeline outcomes and identify actionable opportunit
arXiv:2605.14002v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long context question answering into op
arXiv:2605.14991v1 Announce Type: cross Abstract: Ovarian cancer is the most lethal gynecologic malignancy: around 60% of patients are diagnosed at an advanced stage, with an associated 5-year surviva
arXiv:2605.14502v1 Announce Type: cross Abstract: Power electronics systems are increasingly exposed to cyber threats due to their integration with digital controllers and communication networks. Howe
Retrieval-Augmented Generation (RAG) is a technique that enhances large language models (LLMs) by enabling them to access and utilize external knowledge sources during response generation. This likely
arXiv:2605.14075v1 Announce Type: cross Abstract: Large language models (LLMs) have revolutionized natural language processing. Understanding their internal mechanisms is crucial for developing more i
arXiv:2511.13026v3 Announce Type: replace Abstract: Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied
arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans
arXiv:2605.14440v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are the standard framework for decision-making under uncertainty. While sampling-based methods s
In just a short few months, we have witnessed several artificial intelligence technology events that deserve the overused “unprecedented” descriptor: a highly complex supply chain attack by TeamPCP, A
arXiv:2605.14824v1 Announce Type: new Abstract: Topological clustering, and its main algorithm ToMATo, is a clustering method from Topological Data Analysis (TDA) which has been applied successfully i
arXiv:2605.14607v1 Announce Type: new Abstract: Any new medium, once it emerges, is used for more than the transmission of overt content alone. The information it carries typically operates on two lev
arXiv:2605.14972v1 Announce Type: cross Abstract: A fundamental limitation of Text-to-Code is that no guarantee can be obtained about the correctness of the generated code. Therefore, to ensure its co
arXiv:2605.14283v1 Announce Type: cross Abstract: Watermarking techniques for large language models (LLMs), which encode hidden information in the output so its source can be verified, have gained sig
arXiv:2605.14578v1 Announce Type: new Abstract: Partial Dependence Plots (PDPs) visualize how changes in a single feature affect the average model prediction. They are widely used in practice to inter
arXiv:2605.12706v1 Announce Type: new Abstract: RSNet is an open-source R package that provides a resampling-based framework for robust and interpretable network inference, designed to address the lim
arXiv:2605.12925v1 Announce Type: cross Abstract: Evaluation of software engineering (SWE) agents is dominated by a binary signal: whether the final patch passes the tests. This outcome-only view trea
arXiv:2605.13357v1 Announce Type: cross Abstract: Foundation models have transformed automated code generation, yet autonomous software-engineering agents remain unreliable in realistic development se
This AINews roundup from Latent Space likely covers updates on OpenAI's Codex model gaining prominence in code generation applications, alongside news about Anthropic's Claude implementing usage meter
almost all LLM “thought” is derivative. below just sounds like hype to me 📁 Jensen Huang, CEO of NVIDIA, says GPT was never just about generating images or text but about generating thought itself. Th
Cisco announced nearly 4,000 job cuts as part of a restructuring to shift investment toward artificial intelligence while simultaneously raising its annual revenue forecast after strong hyperscaler de
deepagents v0.6 is our biggest release yet!!! it’s all about perf - at the model layer w harness profiles, agent layer w code interpreter, and at scale w streaming and delta channels context hub backe
arXiv:2605.12832v1 Announce Type: cross Abstract: Single-arm trials are an important study design for evaluating drug efficacy and safety without enrolling patients into a control arm. Although they d
arXiv:2605.12574v1 Announce Type: cross Abstract: Vision-language models (VLMs) are trained on large-scale image-text corpora that may contain private, copyrighted, or otherwise sensitive data, motiva
everything we build at LangChain is provider/model agnostic for these reasons! our harness is open source and easily customizable! On topic: there is a mistake I saw large companies make in late 2025/
arXiv:2508.09320v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susce
Databricks announced expanded interoperability capabilities for Unity Catalog through open APIs, enabling third-party applications and platforms to integrate with and access Unity Catalog's data gover
Zaheer Kachwala / Reuters: Figma reports Q1 revenue up 46% YoY to 333.4M, vs. 313.2M est., and forecasts Q2 revenue above estimates, citing AI monetization; FIG jumps 8%+ after hours — Figma (FIG.N) r
arXiv:2605.13193v1 Announce Type: new Abstract: Fine-grained recognition in everyday life is often not a closed-book classification problem: when encountering unfamiliar objects, humans actively searc
arXiv has implemented a new policy imposing a 1-year ban on users who submit papers containing hallucinated or fabricated references, reflecting concerns about AI-generated content and citation integr
arXiv:2505.14587v2 Announce Type: replace-cross Abstract: Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of boots
Interesting position paper on agentic AI as a foreseeable pathway to AGI. (bookmark it) There has been strong debate on whether a larger single model get us there or a multi-agent system. The authors
Anyscale has introduced a new Agent Skill designed to enhance LLM post-training capabilities, likely enabling developers to build and train agentic systems more effectively using Ray's distributed com