Knowledge catalogue
arXiv:2604.19618v1 Announce Type: new Abstract: Reliable pipeline inspection is critical to safe energy transportation, but is constrained by long distances, complex terrain, and risks to human inspec
Amazon Web Services Inc. said today it’s taking steps toward simplifying the creation of the underlying infrastructure for new autonomous artificial intelligence agents. With an update to its Amazon B
B8881 is a release of llama.cpp, an open-source C/C++ project for LLM inference . The project aims to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardwa
b8882 is a release of llama.cpp, a C/C++ implementation for LLM inference . The project follows a rapid release cycle with frequent updates to support multiple hardware architectures and platforms. Th
b8883 is a release build of llama.cpp, an open-source software library that performs inference on various large language models, co-developed alongside the GGML tensor library. This intermediate build
llama.cpp is a C/C++ implementation that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cloud. Release b8884 is a build/versio
The search results don't contain specific information about release b8885. Based on the llama.cpp release numbering pattern visible in the results, b8885 is an intermediate build release of llama.cpp,
B8886 is a release version from llama.cpp, the C/C++ project for LLM inference. The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide rang
Based on the available information, b8888 appears to be a build release version identifier for llama.cpp. While I couldn't locate specific details about this particular release, llama.cpp release vers
b8891 is a release version of llama.cpp, which is a tool for LLM inference in C/C++. llama.cpp is a free and open-source tool that allows users to run AI models locally on Windows, Linux and macOS. Th
arXiv:2604.19133v1 Announce Type: new Abstract: Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning be
arXiv:2604.19508v1 Announce Type: new Abstract: This paper introduces extit{Bangla Key2Text}, a large-scale dataset of 2.6 million Bangla keyword--text pairs designed for keyword-driven text generatio
arXiv:2601.11037v2 Announce Type: replace Abstract: RL-based agentic search enables LLMs to solve complex questions via dynamic planning and external search. While this approach significantly enhances
arXiv:2512.03467v2 Announce Type: replace Abstract: Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a
arXiv:2604.19532v1 Announce Type: cross Abstract: Tokenizing music to fit the general framework of language models is a compelling challenge, especially considering the diverse symbolic structures in
arXiv:2508.21184v3 Announce Type: replace-cross Abstract: We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather informa
been using claude design and loving it it's basically claude code under the hood but with a very opinionated ontology for input and output - the input ontology is logo, colors, fonts, etc. - the outpu
Jeremy Howard shares positive impressions of working with Kimi Moonshot's K2.6 model for two hours, noting it performed well despite being used somewhat unexpectedly. The post suggests K2.6 demonstrat
benchmarking agents by their ability to play video games 🎮 We are excited to launch VideoGameBench on Antim Labs, created by @a1zhang, Thomas L. Griffiths (@cocosci_lab), @karthik_r_n, and @OfirPress
arXiv:2506.06414v2 Announce Type: replace-cross Abstract: Existing language model safety evaluations focus on overt attacks and low-stakes tasks. In reality, an attacker can easily subvert existing sa
arXiv:2509.12483v4 Announce Type: replace Abstract: This study compares the Boundary Element Method (BEM) and Physics-Informed Neural Networks (PINNs) for solving the two-dimensional Helmholtz equatio
arXiv:2604.18837v1 Announce Type: cross Abstract: Quantum kernel methods have been proposed as a promising approach for leveraging near-term quantum computers for supervised learning, yet rigorous ben
arXiv:2604.19196v1 Announce Type: new Abstract: Face Anti-Spoofing (FAS) remains challenging due to the requirement for robust domain generalization across unseen environments. While recent trends lev
arXiv:2604.19724v1 Announce Type: cross Abstract: Despite the remarkable success of Vision Transformers (ViTs) across a wide range of vision tasks, recent studies have revealed that they remain vulner
arXiv:2507.00451v2 Announce Type: replace-cross Abstract: We present an efficient and generalised procedure to accurately identify the best (or near best) performing algorithm for each sub-task in a m
arXiv:2604.18972v1 Announce Type: cross Abstract: We study finite-horizon continuous-time policy evaluation from discrete closed-loop trajectories under time-inhomogeneous dynamics. The target value s
arXiv:2604.18751v1 Announce Type: cross Abstract: Nonlinear machine-learning models are increasingly used to discover causal relationships in time-series data, yet the interpretation of their outputs
arXiv:2604.18663v1 Announce Type: cross Abstract: Existing jamming attacks on Retrieval-Augmented Generation (RAG) systems typically induce explicit refusals or denial-of-service behaviors, which are
arXiv:2604.18697v1 Announce Type: cross Abstract: Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to
arXiv:2512.22673v3 Announce Type: replace Abstract: Travel planning is a natural real-world task to test large language models' (LLMs) planning and tool-use abilities. Although prior work has studied
arXiv:2509.26238v4 Announce Type: replace Abstract: Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. However, tra
arXiv:2601.15755v3 Announce Type: replace Abstract: Large language models are increasingly used to represent human opinions, values, or beliefs, and their steerability towards these ideals is an activ
arXiv:2604.18724v1 Announce Type: new Abstract: Users typically interact with and evaluate language models via single outputs, but each output is just one sample from a broad distribution of possible
arXiv:2604.19502v1 Announce Type: new Abstract: The rapid adoption of Large Language Models (LLMs) has spurred interest in automated peer review; however, progress is currently stifled by benchmarks t
arXiv:2604.19281v1 Announce Type: cross Abstract: The use of Large Language Models (LLMs) to support patients in addressing medical questions is becoming increasingly prevalent. However, most of the m
arXiv:2604.17457v2 Announce Type: replace-cross Abstract: Dynamic programming is one of the most fundamental methodologies for solving Markov decision problems. Among its many variants, Q-value iterat
arXiv:2604.16323v2 Announce Type: replace-cross Abstract: As autonomous coding agents become deeply embedded in software development workflows, their high operational velocity introduces a critical ov
Databricks discusses how its platform enables CFOs in financial services to move beyond traditional spreadsheet-based processes by leveraging data analytics and unified data management capabilities. T
arXiv:2601.03512v2 Announce Type: replace-cross Abstract: Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data pair
arXiv:2511.21893v2 Announce Type: replace Abstract: Multi-modal foundation models align images, text, and other modalities in a shared embedding space but remain vulnerable to adversarial illusions [3
arXiv:2604.18957v1 Announce Type: new Abstract: Extracting standardized metallurgical metrics from microscopy images remains challenging due to complex grain morphology and the data demands of supervi
arXiv:2512.19302v2 Announce Type: replace Abstract: Large Vision--Language Models (LVLMs) hold great promise for advancing optical remote sensing (RS) analysis, yet existing reasoning segmentation fra
arXiv:2603.16497v2 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,
arXiv:2604.19672v1 Announce Type: new Abstract: We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common car
Build your own finance app with Replit using @plaid Plaid is now natively integrated into Replit, giving you secure, real-time access to your financial data. Connect your accounts → prompt what you wa
arXiv:2407.13980v3 Announce Type: replace-cross Abstract: Traditional statistical methods need to be updated to work with modern distributed data storage paradigms. A common approach is the split-and-
arXiv:2604.18781v1 Announce Type: new Abstract: Large-scale automated morphometric analysis of brain MRI is limited by the thick-slice, anisotropic acquisitions prevalent in routine clinical practice.
arXiv:2604.19530v1 Announce Type: new Abstract: Transformer-based scientific foundation models are increasingly deployed in high-stakes settings, but current architectures give deterministic outputs a
arXiv:2601.04925v2 Announce Type: replace Abstract: Large Language Models (LLMs) can generate highly persuasive text, raising concerns about their misuse for propaganda, manipulation, and other harmfu
By default, Ollama binds to port 11434 on localhost, which means it's accessible only on your own machine. Ollama has no built-in authentication and should be secured with firewall rules, VPN, or reve
arXiv:2604.19394v1 Announce Type: new Abstract: This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via c
arXiv:2604.18623v1 Announce Type: new Abstract: Scene Graph Generation (SGG) unifies object localization and visual relationship reasoning by predicting boxes and subject-predicate-object triples. Yet
arXiv:2512.05747v3 Announce Type: replace Abstract: Evaluating and optimising authorial style in long-form story generation remains challenging because style is often assessed with ad hoc prompting an
arXiv:2505.16968v4 Announce Type: replace-cross Abstract: Cross-architecture GPU code transpilation is essential for unlocking low-level hardware portability, yet no scalable solution exists. We intro
arXiv:2604.19414v1 Announce Type: cross Abstract: Sequential Recommendation (SR) aims to predict the next interaction of a user based on their behavior sequence, where complementary relations often pr
arXiv:2604.19052v1 Announce Type: new Abstract: Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relati
arXiv:2604.18589v1 Announce Type: cross Abstract: Thematic analysis is difficult to scale: manual workflows are labor-intensive, while fully automated pipelines often lack controllability and transpar
arXiv:2507.06321v2 Announce Type: replace Abstract: Collecting and annotating images for the purpose of training segmentation models is often cost prohibitive. In the domain of wildland fire science,
arXiv:2511.06168v3 Announce Type: replace Abstract: This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models
Changes to GitHub Copilot Individual plans On the same day as Claude Code's temporary will-they-won't-they 100/month kerfuffle (for the moment, they won't), here's the latest on GitHub Copilot pricing