Knowledge catalogue
arXiv:2606.27737v1 Announce Type: new Abstract: Programming adaptive behaviors at the cellular level is a long-standing goal that raises the question of how probabilistic computation can be implemente
arXiv:2606.27766v1 Announce Type: cross Abstract: Offline reinforcement learning enables policy learning from fixed datasets without additional environment interaction, making it appealing for safety-
Based in the UK, Curve are building a financial super-app, a smart wallet that consolidates all your debit and credit cards into a single app and card, simplifying how millions of users spend, send an
arXiv:2606.27581v1 Announce Type: cross Abstract: Current humanoid reinforcement-learning policies excel at free-space motions but struggle with contact-rich tasks, as pure kinematic tracking cannot r
arXiv:2606.27491v1 Announce Type: new Abstract: Automated instance segmentation of forest LiDAR point clouds is increasingly critical as forest monitoring moves toward scalable, detailed, 3D measureme
arXiv:2606.27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benc
In the agentic era, the role of the database has fundamentally changed. It is no longer a passive repository; it’s a critical context engine designed to ground generative AI apps, models and power aut
arXiv:2508.06115v3 Announce Type: replace Abstract: Semantic segmentation in open-vocabulary scenarios presents significant challenges due to the wide range and granularity of semantic categories. Exi
arXiv:2606.27514v1 Announce Type: new Abstract: Geolocation encoders, which map geographic coordinates to learned representations, are emerging as an effective means of capturing visual and non-visual
arXiv:2606.16974v3 Announce Type: replace Abstract: The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodol
arXiv:2606.28277v1 Announce Type: cross Abstract: Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem pr
arXiv:2606.27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns rega
arXiv:2509.17932v2 Announce Type: replace Abstract: Large language models (LLMs) are prone to generating factually incorrect content, motivating methods for assessing truthfulness from internal model
arXiv:2606.27505v1 Announce Type: new Abstract: AI generated images are proliferating across the Internet. While some are used for entertainment, others are weaponized for fraud and social engineering
arXiv:2602.10179v2 Announce Type: replace-cross Abstract: Recent advances in large image editing models have shifted the paradigm from text-driven instructions to vision-prompt editing, where user int
Build b9829 is an intermediate release of llama.cpp, the open-source C/C++ project that enables users to run large language models on consumer hardware without expensive GPUs or cloud infrastructure.
Release b9831 of llama.cpp includes backend detection improvements and synchronization enhancements, particularly for async CUDA copies and Vulkan backend operations. Llama.cpp is designed to enable L
B9832 is a build release of llama.cpp from the ggml-org project , which is a free and open-source tool that allows you to run AI models locally on Windows, Linux and macOS . The release likely contain
Llama.cpp has merged DFLASH support into its main branch, which is a development update related to optimization or acceleration technology for running large language models locally. The post expresses
MASSIVE NEWS has partnered with NVIDIA to advance local AI deployment as a default option, addressing the shift toward on-device and edge AI processing rather than cloud-dependent models. This collabo
The search results show information about other llama.cpp releases and the project generally, but do not contain specific details about release b9824. Based on the available information about llama.cp
1/ On p (doom) tl;dr a) Everyone is making up the numbers b) nobody knows anything (least of all the experts), c) don't worry about it d) there is nothing you can do to stop it e) most things you can
arXiv:2606.26627v1 Announce Type: cross Abstract: Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a us
As token usage explodes, model choice becomes product strategy. Teams are already testing models like GLM-5.2 because they want frontier quality, better tokenomics, and more control over cost, data, a
arXiv:2606.26396v1 Announce Type: new Abstract: Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data. Yet, real-wo
arXiv:2606.27361v1 Announce Type: cross Abstract: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the dev
Based on available search results, I cannot find specific details about the b9814 release. However, b9814 is a build version identifier from the ggml-org/llama.cpp repository, which is a C/C++ impleme
arXiv:2606.27321v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have become a leading tool for interpreting the representations of vision foundation models, decomposing their polysemantic
arXiv:2606.27114v1 Announce Type: new Abstract: Uplift modeling, crucial for estimating individual treatment effects (ITE), faces dual challenges: flexibly leveraging inter-group similarity to enhance
arXiv:2510.20769v2 Announce Type: replace-cross Abstract: Accurate medium-range precipitation forecasting is essential for hydrometeorological risk management but remains challenging for both numerica
arXiv:2606.26398v1 Announce Type: new Abstract: High-precision remote perception is often hindered by the severe bandwidth constraints of Vehicle-to-Everything (V2X) networks. We propose extit{DinoLin
arXiv:2606.26686v1 Announce Type: new Abstract: In order to screen a prompt or a response, the recent guardrail methods generate a chain-of-thought (CoT) before they issue a verdict. This design follo
arXiv:2606.26121v1 Announce Type: cross Abstract: Global insect population declines necessitate scalable, continuous monitoring systems, yet existing vision-based solutions remain constrained by high
arXiv:2606.27268v1 Announce Type: cross Abstract: Recently, a few works have made early attempts to study test-time scaling for embodied tasks. However, two major challenges remain unsolved: (1) reaso
arXiv:2606.26273v1 Announce Type: new Abstract: Symmetries are important for many deep learning tasks, ranging from applications in the sciences to medical imaging. However, there is an ongoing debate
arXiv:2606.26515v1 Announce Type: new Abstract: Test Time Training (TTT) is a mechanism in which a model adapts to an incoming test-sample by performing some self-supervised (SSL) task and updating it
arXiv:2606.26287v1 Announce Type: new Abstract: With the increase in model parameters and training data, the instruction following and generalization capabilities of Large VisionLanguage Models (LVLMs
arXiv:2606.26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run-
arXiv:2606.26801v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for generalizable robotic manipulation. During fine-tuning, however, action supervisio
arXiv:2606.26981v1 Announce Type: cross Abstract: Synthesizing human motion from textual descriptions is essential for immersive digital applications, yet existing methods face a persistent trade-off
Incident Report: CVE-2026-LGTM Spectacular hypothetical incident report by Andrew Nesbitt. Day 2, 16:00 UTC --- Two AI review agents from competing vendors, both attached to a downstream pull request
arXiv:2602.07533v2 Announce Type: replace Abstract: Reward models are critical for reinforcement learning from human feedback, as they determine the alignment quality and reliability of generative mod
arXiv:2606.26778v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have driven substantial progress in image and video generation but suffer from prohibitive computational costs. Feature ca
arXiv:2602.18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for
arXiv:2606.27163v1 Announce Type: cross Abstract: I describe my solution to the LeHome Challenge 2026, an ICRA 2026 competition on bimanual garment folding. The system placed 1st of 62 teams in the on
arXiv:2606.26183v1 Announce Type: cross Abstract: Building a generalist robot that can leverage prior knowledge for continuous task adaptation remains a significant challenge. Previous works alleviate
arXiv:2606.27192v1 Announce Type: new Abstract: The prevalent dual-branch paradigm, i.e., training a side network to encode visual conditions and fusing its intermediate-layer features to a frozen pre
arXiv:2606.26609v1 Announce Type: new Abstract: Image restoration aims to reconstruct high-quality images from degraded low-quality inputs. As the computational demands of image restoration models con
Many companies could build their own Workday or whatever, too, using open source alternatives. They don't do that for a wide variety of reasons. Same dynamics apply in AI, only more so (upgrade cycles
arXiv:2604.01958v2 Announce Type: replace Abstract: Infrared and visible video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantic
arXiv:2606.27019v1 Announce Type: new Abstract: The Unigram tokenizer uses an elegant representation which makes it straightforward to edit vocabularies, but its training is comparatively heavy and co
arXiv:2606.26913v1 Announce Type: cross Abstract: Recent work on neural texture compression has demonstrated that it is possible to learn small, per-material texture representations (composed of laten
arXiv:2606.26410v1 Announce Type: new Abstract: We present a self-supervised framework for learning implicit 3D physical dynamics directly from video-derived supervisory signals. While current generat
arXiv:2606.27317v1 Announce Type: new Abstract: We present OctoSense, an open-source sensor platform with stereo RGB and event cameras, LiDAR, a thermal camera, an inertial measurement unit, RTK-corre
arXiv:2601.13300v2 Announce Type: replace Abstract: Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface ar
arXiv:2606.27154v1 Announce Type: new Abstract: Root cause analysis (RCA) poses a holistic test of LLM agentic capabilities, such as long-context understanding, multi-step reasoning, and tool use. How
arXiv:2512.03704v3 Announce Type: replace Abstract: Long-context dialogue systems suffer from state inertia, where models over-attend to history and fail to adapt to evolving intents. We demonstrate t
arXiv:2606.27210v1 Announce Type: new Abstract: We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS
arXiv:2606.26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread
arXiv:2512.02652v2 Announce Type: replace-cross Abstract: Existing methods for expressive music performance rendering, a conditional generation task that aims to generate a human-like performance from