Knowledge catalogue
arXiv:2608.28640v1 Announce Type: cross Abstract: In this paper, we present PromptKWS, a novel Prompt-guided keyword spotting (KWS) framework to improve the accuracy of open vocabulary KWS systems. In
arXiv:2508.02208v3 Announce Type: replace-cross Abstract: Evaluating the mathematical capability of Large Language Models (LLMs) is a critical yet challenging frontier. Existing benchmarks fall short,
arXiv:2608.25631v1 Announce Type: cross Abstract: Continuous-time Markov chains (CTMCs) provide the backbone for modeling discrete stochastic dynamics across applied, physical, and biological sciences
arXiv:2608.30874v1 Announce Type: cross Abstract: This paper considers decentralized contingency MPC for multi-agent control under a state-only information pattern, with particular focus on limited se
arXiv:2608.28433v2 Announce Type: replace Abstract: Proof assistants such as Lean 4 promise the paradigm of formally verified mathematics, but large-scale formalization projects have faced major barri
arXiv:2608.30344v1 Announce Type: new Abstract: Most shape reconstruction methods assume measurements defined over planar sensing domains, such as RGB images or depth maps. In this paper, we use a cur
arXiv:2608.29765v1 Announce Type: new Abstract: Structured pruning uses surrogate objectives because direct task evaluation over every feasible mask is too expensive. Most evaluations report average s
arXiv:2608.28622v1 Announce Type: cross Abstract: Large language model training corpora grow through successive, often redundant releases, so each release must be deduplicated against both itself and
Pure scaling of LLMs did actually hit a wall; neurosymbolic AI rescued it. AI didn’t hit a plateau; pure LLMs did — until people used harnesses and tools to complement their weaknesses. Which was alwa
arXiv:2608.30652v1 Announce Type: new Abstract: Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embeddin
Python 3.15.0 candidate 2 is here! Hugo van Kemenade (release manager for Python 3.14 and 3.15) announces the final release candidate for Python 3.15, scheduled for release in October: Entering the re
arXiv:2608.30564v1 Announce Type: cross Abstract: Mixed-precision quantization (MPQ) assigns a different bitwidth to each linear layer of a large language model (LLM) to minimize the quantization-indu
arXiv:2603.12165v3 Announce Type: replace Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult t
arXiv:2608.29253v1 Announce Type: cross Abstract: Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structures that produce weak boundaries and mixed
Qdrant has released **Qdrant‑Fineweb‑10B**, a public benchmark dataset comprising dense and sparse embeddings for 10 billion documents from the FineWeb corpus and exact results for 120,000 queries to
arXiv:2603.00620v3 Announce Type: replace Abstract: Multilingual NLP research increasingly involves hundreds or thousands of languages across different datasets. Managing, discovering, and reporting l
arXiv:2608.30229v1 Announce Type: new Abstract: Korean introduces an additional typographical perturbation level not captured by ordinary character-level edit models: because syllable blocks are inter
arXiv:2608.29144v1 Announce Type: new Abstract: Synthetic data generation has become a cornerstone for advancing large language models. However, the lack of the quantitative analysis for error toleran
arXiv:2608.30393v1 Announce Type: new Abstract: Biomedical artificial intelligence (AI) systems increasingly extract, organize, and reuse scientific claims from literature, clinical trials, and regula
arXiv:2608.28827v1 Announce Type: cross Abstract: We establish quantitative convergence to the target and uniform-in-time propagation of chaos for Langevin-regularized Stein variational gradient desce
arXiv:2608.30633v1 Announce Type: new Abstract: Timely post-disaster building damage assessment from satellite imagery is a critical engineering decision support task, yet it remains constrained by cl
I only started using some vLLM forks recently in a 4 x 48GB 4090 system. DS4F - ~5000pp/180tg (DSpark) Qwen3.8 Flash next - ~7500pp/135tg (MTP) This is amazing, like having the API in my house. But it
I am absolutely blown away. Yes my setup is crap but the fact that it managed to do this in a single take is unbelievable (and I'm a developer). Hardware used: - Windows PC with 4070ti (12GB VRAM, 32G
if you are on Qwen Flash Next make sure to update your build often https://preview.redd.it/zoq3j9gnjvmh1.jpg?width=900&format=pjpg&auto=webp&s=bb21bc405696df94fd5ce38c22f4ca6dc596135f merged already (
arXiv:2608.29798v1 Announce Type: cross Abstract: The same Persona behavior can be beneficial in one context but harmful in another, causing static Persona elicitation to perform inconsistently across
arXiv:2608.29263v1 Announce Type: new Abstract: Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While integ
arXiv:2608.30896v1 Announce Type: new Abstract: Automotive mmWave radar can develop vibration, antenna misalignment, radome blockage, and receive-channel degradation that corrupt the signal before per
arXiv:2608.29280v1 Announce Type: cross Abstract: Standard clothing asset generation---restoring forward-facing flat-lay garment images from diverse real-world contexts---holds immense commercial valu
arXiv:2608.30727v1 Announce Type: cross Abstract: Small-object detection under long-tailed data distributions is a fundamental yet challenging problem in multimedia. Railway Foreign Object Detection (
arXiv:2608.30709v1 Announce Type: cross Abstract: Railway foreign object detection (RFOD) is critical to safe railway operation, yet scarce real positive samples incompletely represent task-relevant v
arXiv:2606.23687v2 Announce Type: replace Abstract: Large language models (LLMs) are typically pretrained on short sequences and then extended to work on longer sequences with additional training. How
arXiv:2608.28922v1 Announce Type: new Abstract: A login service can receive its usual number of failed sign-ins while one source grows from 2% to 30% of them. The same pattern appears in system logs w
arXiv:2608.29189v1 Announce Type: cross Abstract: We propose a neurobiologically plausible model of cognition that combines the advantages of connectionist and symbolic systems, and that can explain a
arXiv:2401.15719v5 Announce Type: replace-cross Abstract: We prove a non-asymptotic central limit theorem for vector-valued martingale differences using Stein's method, and use Poisson's equation to e
arXiv:2608.30041v1 Announce Type: cross Abstract: Large language model agents place outputs from external skills into their execution context, allowing attacker-controlled data to influence later priv
arXiv:2608.30156v1 Announce Type: new Abstract: Plane geometry problem (PGP) solving has become a critical benchmark for multimodal reasoning because it requires accurate visual perception and precise
arXiv:2608.30270v1 Announce Type: new Abstract: Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent can- not be inferred from surface form alone. Prior text-based
arXiv:2608.30609v1 Announce Type: cross Abstract: Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address
arXiv:2601.13206v2 Announce Type: replace Abstract: Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiat
arXiv:2608.30400v1 Announce Type: new Abstract: High-dynamic-range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particular
arXiv:2608.31074v1 Announce Type: cross Abstract: We propose a lightweight two-stage framework for real-time video anomaly detection. The first stage employs YOLO v11n-pose to detect persons and extra
arXiv:2608.30617v1 Announce Type: new Abstract: Reconstructing editable Computer-Aided Design (CAD) models from images is essential for downstream modification, manufacturing, and design reuse. Howeve
arXiv:2608.30820v1 Announce Type: new Abstract: Occlusion boundaries (OBs) are pixel-level image boundaries corresponding to surface visibility discontinuities caused by occlusion. Through precise bou
arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT
arXiv:2608.30778v1 Announce Type: new Abstract: Physical learning lets a trainable material or network use its own physical response to carry error signals, reducing the need for a separately programm
arXiv:2608.29109v1 Announce Type: new Abstract: Large language models often answer structurally unanswerable questions, such as computing cot(-540{eg}) or evaluating (1).startswith('1'), instead of ab
arXiv:2608.31077v1 Announce Type: new Abstract: Outcome-based reinforcement learning provides verified feedback for language-model agents, but assigns trajectory-level advantage uniformly to all decis
arXiv:2608.28895v1 Announce Type: new Abstract: We introduce ReconSplat, a feed-forward model for 3D scene reconstruction that aims to address the longstanding trade-off between plausible view generat
arXiv:2608.28838v1 Announce Type: cross Abstract: Cryo-electron microscopy (cryo-EM) determines the structures of proteins and macromolecular assemblies at near-atomic resolution, and the final 3D rec
arXiv:2512.18718v3 Announce Type: replace Abstract: Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep lea
arXiv:2608.28643v1 Announce Type: cross Abstract: Rubric-based evaluations of deep-research (DR) systems often obscure fine-grained factual failures in generated reports. We introduce CLAIMPROBE, a cl
arXiv:2608.28656v1 Announce Type: cross Abstract: Behavior-cloned Vision-Language-Action (VLA) driving policies struggle with rare rule-governed maneuvers at signalized intersections. Braking and laun
arXiv:2608.30627v1 Announce Type: new Abstract: As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token predi
arXiv:2608.29458v1 Announce Type: cross Abstract: Sandbagging, in which a model deliberately underperforms on an evaluation despite retaining the underlying capability, threatens the safety evaluation
arXiv:2608.28607v1 Announce Type: new Abstract: Pharmaceutical sponsors developing a drug for both the United States and the European Union must reconcile guidance issued independently by the FDA and
arXiv:2608.29809v1 Announce Type: new Abstract: Real-world image generation often involves multi-turn editing, where users iteratively modify small regions while most image content remains unchanged.
arXiv:2607.16325v2 Announce Type: replace Abstract: Foundation models provide powerful representations for brain MRI analysis, but their predictions remain difficult to interpret in anatomically meani
arXiv:2505.03654v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-speci
arXiv:2608.29899v1 Announce Type: cross Abstract: Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models
arXiv:2608.30162v1 Announce Type: new Abstract: We present a reinforcement-learning agent that solves symbolic equations step by step, covering both nonlinear closed equations (radicals, exponentials,