Knowledge catalogue
arXiv:2608.15533v1 Announce Type: cross Abstract: Linear attention models eliminate the quadratic prefix computation and context-growing KV cache of softmax attention by replacing pairwise token inter
arXiv:2608.14974v1 Announce Type: new Abstract: This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simulation, and d
arXiv:2608.14600v1 Announce Type: cross Abstract: We present a demonstration for generative multicasting with on-device, intent-aware semantic decomposition. At the transmitter, DNN-based segmentation
arXiv:2608.15254v1 Announce Type: new Abstract: Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI). We measure a
arXiv:2608.14632v1 Announce Type: cross Abstract: Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs rem
arXiv:2608.16180v1 Announce Type: new Abstract: To combat oversmoothing in Graph Convolutional Networks, Sheaf Neural Networks (SNNs) were proposed as a generalization by equipping the graph with a sh
arXiv:2608.15851v1 Announce Type: cross Abstract: LLM-based query expansion improves retrieval by generating document-like passages. In hybrid retrieval, however, most evaluations fuse fixed top-L den
arXiv:2608.16506v1 Announce Type: cross Abstract: Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropi
arXiv:2608.15881v1 Announce Type: new Abstract: The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source finite-element framework for building multiphysics simulation applicat
arXiv:2608.16367v1 Announce Type: new Abstract: To date, all natural scene skeleton detection follows the paradigm of taking RGB images as the sole input; despite notable progress, methods under this
arXiv:2608.16014v1 Announce Type: new Abstract: Achieving reliable single-shot high dynamic range (HDR) imaging under extreme illumination conditions remains a long-standing challenge, yet no comprehe
arXiv:2603.23924v2 Announce Type: replace Abstract: Text-to-image models often struggle to synthesize correct occlusion relationships among multiple objects, especially in densely overlapping regions.
arXiv:2608.16728v1 Announce Type: new Abstract: Designing large soft robots capable of generating high forces for physical human-robot interaction remains a significant challenge in soft robotics. Pri
arXiv:2608.15461v1 Announce Type: new Abstract: Reconfigurable robots offer significant potential for adapting to diverse tasks; however, conventional centralized architectures often require dedicated
arXiv:2608.15447v1 Announce Type: new Abstract: Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activi
arXiv:2601.04798v2 Announce Type: replace Abstract: Robust long-term tracking of drone is a critical requirement for modern surveillance systems, given their increasing threat potential. While detecto
arXiv:2608.14935v1 Announce Type: cross Abstract: Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning
Apparently a second version of DFlash from the original authors of DFlash GGUF quants are already made available with an accompanying llama.cpp PR: https://github.com/ggml-org/llama.cpp/pull/27342 sub
DFlash 2 is here! Qwen3.8-27B at 70 tok/s on an M5 Max MacBook Pro. ⚡ Up to 4.6× the speed of autoregressive decoding, with the same output. This is the next generation of DFlash, seeded at Z Lab and
arXiv:2608.14655v1 Announce Type: cross Abstract: Omni-Large Language Models (Omni-LLMs) power complex multi-modal reasoning in applications like World Action Models and autonomous agents. However, th
arXiv:2608.16805v1 Announce Type: cross Abstract: Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Gen
arXiv:2505.14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing v
arXiv:2603.11249v4 Announce Type: replace Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorp
arXiv:2603.27151v2 Announce Type: replace-cross Abstract: Radiance field reconstruction aims to recover high-quality 3D representations from multi-view RGB images. Recent advances, such as 3D Gaussian
arXiv:2504.00944v2 Announce Type: replace-cross Abstract: We propose a numerical method of searching for parameters with experimental constraints in generic flavor models by utilizing diffusion models
arXiv:2608.16159v1 Announce Type: cross Abstract: Digital Twins (DTs) are increasingly used to monitor and analyze Cyber Physical Systems (CPS). However, in adversarial environments, the fidelity of a
arXiv:2405.07780v3 Announce Type: replace-cross Abstract: This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbit
arXiv:2510.00358v2 Announce Type: replace-cross Abstract: Soft snake robots offer remarkable flexibility and adaptability in complex environments, yet their control remains challenging due to highly n
arXiv:2608.14851v1 Announce Type: new Abstract: Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can requir
arXiv:2608.14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification. For each candi
arXiv:2608.14823v1 Announce Type: new Abstract: Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare? Some existing work frames classification of
arXiv:2607.01503v2 Announce Type: replace Abstract: In this paper, we study depth perception of vision-language models (VLMs) to isolate the effects of pictorial depth cues and disentangle vision and
arXiv:2608.14916v1 Announce Type: cross Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or ot
arXiv:2608.15090v1 Announce Type: new Abstract: Studies of industrial visual inspection commonly report the area under the receiver operating characteristic curve (AUROC) and the overlap between anoma
arXiv:2608.15303v1 Announce Type: new Abstract: Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps remains poorly u
arXiv:2608.15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities. A c
arXiv:2608.14729v1 Announce Type: new Abstract: Fake/synthetic images are increasingly prevalent, but it remains unclear whether Convolutional Neural Networks (CNNs) process real and fake images in th
arXiv:2608.14982v1 Announce Type: cross Abstract: Transformers applied to spatial imperfect-information games must represent map geometry while tracking hidden entities through time. We ask whether ge
arXiv:2608.15507v1 Announce Type: new Abstract: A consistent concept of the current time is important for temporal reasoning, yet how language models represent the current time is not well understood.
arXiv:2608.14613v1 Announce Type: new Abstract: Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool access and Go
arXiv:2608.14808v1 Announce Type: new Abstract: When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it,
arXiv:2608.14653v1 Announce Type: cross Abstract: Prediction uncertainty is a widely adopted metric for quantifying model confidence, with downstream applications spanning model explanation, data sele
arXiv:2608.15061v1 Announce Type: new Abstract: Do feed-forward networks (FFNs) in visual grounding decoders add essential computation once a pretrained vision-language model has already encoded image
arXiv:2608.15365v1 Announce Type: new Abstract: Regret minimization (RM) and best-arm identification (BAI) are two fundamental objectives in multi-armed bandits. Among regret-minimizing algorithms, 1/
arXiv:2608.14992v1 Announce Type: new Abstract: Language-model systems increasingly read from stores they also write to, so a claim that was merely written earlier can return looking retrieved. We tes
One line in a new Qwen3.8-27B derivative card caught my eye: the uploader says the vision-language tower and MTP head were preserved through its offline block-FP8 build. The checkpoint is OrcaRouter's
arXiv:2607.14882v2 Announce Type: replace-cross Abstract: With a large controlled vocabulary as the label set, the task of automated subject indexing in a library can be understood as a multi-label cl
arXiv:2608.14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual varia
arXiv:2608.16671v1 Announce Type: new Abstract: The language-model head maps a hidden state of width D to a vocabulary of size V, so its transpose can return at most D independent directions to the Tr
arXiv:2608.15432v1 Announce Type: new Abstract: In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated proof search
Does your context compactor actually save anything? If you built or tuned one, this one is worth your time. (bookmark it) Task completion is the standard way to validate compression, and it hides the
arXiv:2608.16269v1 Announce Type: new Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-trainin
arXiv:2608.14693v1 Announce Type: cross Abstract: With the increasing digitization of personal and corporate communication, the automatic sanitization of textual data has become a crucial component of
arXiv:2608.16161v1 Announce Type: cross Abstract: General-purpose text embedding models are designed to capture semantic similarity but are not optimised for distinguishing entity records that represe
arXiv:2608.16889v1 Announce Type: cross Abstract: Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the
Results up front: I had to prioritize prefill or token generation - there was no happy medium. Using UD-Q4_K_XL, q8 kv cache, and 96k max context: focus on generation (MTP = 2): 350 pp and 36 tg @ 12k
arXiv:2211.14297v4 Announce Type: replace-cross Abstract: We introduce and analyze an improved variant of nearest neighbors (NN) for estimation with missing data in latent factor models. We consider a
arXiv:2608.16640v1 Announce Type: new Abstract: Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address th
arXiv:2608.16632v1 Announce Type: new Abstract: Deep learning-based object detectors are fundamental to intelligent transportation systems, enabling traffic monitoring, vehicle analytics, and infrastr
arXiv:2502.11603v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit strong natural language understanding capabilities but also inherit and amplify societal biases, particul