Knowledge catalogue
arXiv:2511.16361v3 Announce Type: replace Abstract: Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-qualit
arXiv:2605.16998v1 Announce Type: cross Abstract: The Quantum Fourier Transform (QFT) is required by hidden subgroup problem (HSP) algorithms, including Shor's algorithm for factoring. The circuit dep
arXiv:2605.16391v1 Announce Type: cross Abstract: Inertial measurement units (IMUs) are fundamental sensing components in multi-source integrated navigation systems, and their performance directly det
arXiv:2605.18609v1 Announce Type: new Abstract: Accelerating stochastic gradient methods with classical momentum schemes, such as Polyak's heavy ball, has proven highly successful in training large-sc
arXiv:2605.18333v1 Announce Type: cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in mult
arXiv:2605.16429v1 Announce Type: cross Abstract: We introduce extbf{QuantFPFlow}, a reinforcement learning framework that integrates quantum amplitude estimation into the Fokker--Planck~(FP) formulat
arXiv:2605.16454v1 Announce Type: new Abstract: Seismic prediction remains challenging due to the highly nonlinear and chaotic dynamics of earthquake signals. While classical deep learning models such
arXiv:2605.18197v1 Announce Type: cross Abstract: Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This li
arXiv:2510.26635v3 Announce Type: replace-cross Abstract: Summary: SAMRI is an MRI-specialized adaptation of the Segment Anything Model achieving superior whole-body MRI segmentation, particularly for
arXiv:2501.01046v4 Announce Type: replace Abstract: Dataset deduplication is widely recognized as a crucial preprocessing step that enhances data quality and improves the performance of large language
arXiv:2602.08167v2 Announce Type: replace-cross Abstract: Embodied Chain-of-Thought (CoT) reasoning has significantly enhanced Vision-Language-Action (VLA) models, yet current methods rely on rigid te
arXiv:2605.17633v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory.
arXiv:2605.18003v1 Announce Type: cross Abstract: Deploying adaptive intelligence at the edge remains challenging due to the high computational and energy cost of training neural models. Spiking Neura
arXiv:2605.17745v1 Announce Type: cross Abstract: Quantization is essential for reducing the computational cost and memory usage of deep neural networks, enabling efficient inference on low-precision
arXiv:2505.17674v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. However, existing SNNs still exhibit a signif
arXiv:2605.17336v1 Announce Type: cross Abstract: Tactile sensing is a fundamental modality for embodied intelligence, offering unique and direct feedback on contact geometry, material properties, and
I put together these annotated slides from my five minute lightning talk at PyCon US 2026, using the latest iteration of my annotated presentation tool. # I presented this lightning talk at PyCon US 2
arXiv:2605.17217v1 Announce Type: cross Abstract: Marine oil spills require rapid detection to mitigate severe ecological and economic damage. While satellite-based Synthetic Aperture Radar (SAR) prov
arXiv:2605.16443v1 Announce Type: cross Abstract: Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses seri
arXiv:2605.16338v1 Announce Type: cross Abstract: The large-scale digitization of historical archives has created a paradox: 'dark data'-digital objects lacking metadata for retrieval. Manual archival
arXiv:2511.06516v3 Announce Type: replace Abstract: Many LLM applications require only narrow capabilities, yet standard post-training quantization (PTQ) methods allocate precision without considering
arXiv:2605.15510v1 Announce Type: new Abstract: This paper presents a quadratic unconstrained binary optimization-based formulation framework for robot design optimization using kinematic structure-le
arXiv:2511.18225v2 Announce Type: replace Abstract: Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty qu
arXiv:2605.16015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) for quadrotor flight control typically relies on Domain Randomization (DR) for sim-to-real transfer, resulting in ov
arXiv:2605.15206v1 Announce Type: cross Abstract: Autonomous agents powered by large language models (LLMs) are increasingly used to automate complex, multi-step tasks such as coding or web-based ques
arXiv:2605.15362v1 Announce Type: new Abstract: Half a billion citation edges extracted from 100.7 million Ukrainian court decisions reveal that judicial citation structure encodes legal domain bounda
arXiv:2605.16255v1 Announce Type: cross Abstract: Demand for AI accelerators is rapidly increasing rack power density, with projections approaching 1MW per deployment by 2027. This poses a major chall
arXiv:2605.15410v1 Announce Type: cross Abstract: Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Q
arXiv:2602.01568v2 Announce Type: replace-cross Abstract: Multi-robot coordination often exhibits hierarchical structure, with some robots' decisions depending on the planned behaviors of others. Whil
arXiv:2605.15684v1 Announce Type: new Abstract: The Diffusion Transformer (DiT) architecture is the state-of-the-art paradigm for high-fidelity image generation, underpinning models like Stable Diffus
arXiv:2605.15355v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, makin
When most people think of A/B experimentation, they think of button colors, landing page layouts, or checkout flows. At Google, many fundamental infrastructure improvements also need the rigor of A/B
arXiv:2605.15626v1 Announce Type: new Abstract: Large language models deliver strong performance across language and reasoning tasks, but their storage and compute costs remain major barriers to deplo
arXiv:2605.15463v1 Announce Type: new Abstract: As machine learning models grow in complexity, they increasingly struggle with three conflicting demands: the need for high accuracy, the requirement fo
arXiv:2604.02812v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most cu
llama.cpp adds MTP for the Qwen3.6 family This is a significant milestone for the local AI ecosystem. The performance jump with these changes is massive and elevates local inference on commodity hardw
llama.cpp with MTP support makes local models fast enough to use as daily drivers 🚀 Qwen3.6-27B dense generation (on A10G): From 25 tok/s → 45 tok/s (+78%). Two flags on llama-server: --spec-type draf
arXiv:2605.15222v1 Announce Type: cross Abstract: Large language models (LLMs) can often generate functionally correct code, but their ability to produce efficient implementations for performance-crit
arXiv:2511.09884v2 Announce Type: replace Abstract: Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, determinist
arXiv:2605.15517v1 Announce Type: new Abstract: We present a method for training reference-guided, perceptive reinforcement learning locomotion policies for humanoid robots in which reference trajecto
arXiv:2511.02342v3 Announce Type: replace Abstract: Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet
Comfyui-Mesh now supports LTX 2.3 with the ability to distribute models across multiple GPUs or networked machines using Nvenc codec for video generation. The update includes significant VRAM optimiza
This guide demonstrates that modern AI image generation models beyond Stable Diffusion 1.5 can run on a GTX 1060 6GB GPU using optimization techniques and ComfyUI, countering the common misconception
llama.cpp release b9181 updated cpp-httplib to version 0.45.0 and included refactoring of the web UI to use new naming conventions with 'ui' instead of 'webui' throughout the codebase . The release pr
arXiv:2605.15049v1 Announce Type: new Abstract: This paper presents a prototyping framework for distributed control of multi-robot systems, aimed at bridging theory and practical testing of distribute
arXiv:2512.01977v2 Announce Type: replace-cross Abstract: The global capacity for mineral processing must expand rapidly to meet the demand for critical minerals, which are essential for building the
arXiv:2605.14331v1 Announce Type: cross Abstract: Modern edge devices increasingly rely on neural networks for intelligent applications. However, conventional digital computing-based edge inference re
Release b9165 fixes a transform issue with the top entry in the release archive . The release includes pre-built binaries for multiple platforms including macOS, Linux, Android, and Windows with vario
arXiv:2602.19532v2 Announce Type: replace Abstract: Real-world tasks involve nuanced combinations of goal and safety specifications. In high dimensions, the challenge is exacerbated: formal automata b
arXiv:2605.14036v1 Announce Type: new Abstract: In current Large Language Models we can trust the production of smoothly flowing prose on the basis of the principles of machine learning. However, ther
arXiv:2605.14950v1 Announce Type: new Abstract: Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action gener
The Gemini Live Agent Challenge is officially in the books! We challenged developers worldwide to break out of the traditional 'text box' paradigm by building next-generation AI agents. From our initi
arXiv:2602.21302v2 Announce Type: replace Abstract: We introduce a Task-Level Iterative Learning Control method for dynamic manipulation of ropes. We demonstrate this method on a non-planar rope manip
arXiv:2509.14159v3 Announce Type: replace Abstract: As robots become more integrated in society, their ability to coordinate with other robots and humans on multi-modal tasks (those with multiple vali
arXiv:2605.13915v1 Announce Type: cross Abstract: Quantization is essential for efficient large language model (LLM) inference, yet the dequantization step-converting low-bit weights back to high-prec
arXiv:2605.15058v1 Announce Type: cross Abstract: The rapid expansion of spiking neural networks (SNNs) has led to a proliferation of training algorithms that differ widely in biological inspiration,
arXiv:2603.16039v2 Announce Type: replace-cross Abstract: Recent work has made clear that the residual pathway is not mere optimization plumbing; it is part of the model's representational machinery.
This playbook provides step-by-step instructions for running Nous Research's Hermes Agent locally on NVIDIA DGX Spark using Ollama, enabling users to deploy an open-source AI agent entirely on local h
arXiv:2605.15178v1 Announce Type: new Abstract: We introduce SANA-WM, an efficient 2.6B-parameter open-source world model natively trained for one-minute generation, synthesizing high-fidelity, 720p,
arXiv:2605.13989v1 Announce Type: new Abstract: We present VectraYX-Nano, a 41.95M-parameter decoder-only language model trained from scratch in Spanish for cybersecurity, with a Latin-American focus