Knowledge catalogue
arXiv:2604.15777v1 Announce Type: cross Abstract: Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnos
arXiv:2509.05489v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards has significantly advanced reasoning in large language models (LLMs), but such signals remain coarse,
arXiv:2604.15681v1 Announce Type: new Abstract: Photoacoustic tomography (PAT) is an emerging imaging modality that combines the complementary strengths of optical contrast and ultrasonic resolution.
arXiv:2604.16138v1 Announce Type: new Abstract: We present a dataset and a model for sentiment analysis of German sign language (DGS) fairy tales. First, we perform sentiment analysis for three levels
arXiv:2604.15356v1 Announce Type: cross Abstract: Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer ke
arXiv:2504.16315v4 Announce Type: replace-cross Abstract: The complexity of Sign Language (SL) data processing brings many challenges. The current approach to recognition of SL signs aims to translate
arXiv:2604.15783v1 Announce Type: new Abstract: Urban bike-sharing systems require strategic station expansion to meet growing demand. Traditional allocation approaches rely on explicit demand modelli
arXiv:2603.20410v2 Announce Type: replace Abstract: Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which futu
arXiv:2411.13109v4 Announce Type: replace Abstract: This paper revisits the topic of rotation estimation through the lens of special unitary matrices. We begin by reformulating Wahba's problem using S
arXiv:2604.15862v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and com
arXiv:2604.15651v1 Announce Type: new Abstract: Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-trut
arXiv:2604.15711v1 Announce Type: cross Abstract: Pathological diagnosis is highly reliant on image analysis, where Regions of Interest (ROIs) serve as the primary basis for diagnostic evidence, while
arXiv:2604.15416v1 Announce Type: cross Abstract: Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and
arXiv:2604.15727v1 Announce Type: new Abstract: Large language models exhibit systematic limitations in structured logical reasoning: they conflate hypothesis generation with verification, cannot dist
arXiv:2604.15324v1 Announce Type: cross Abstract: As AI enters creative practice, audiences face growing uncertainty in judging authenticity and value. This study examines the Struggle Premium, the ad
arXiv:2602.05638v3 Announce Type: replace Abstract: While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that w
arXiv:2604.16147v1 Announce Type: cross Abstract: This paper presents SWNet, a bimodal end-to-end cross-spectral network specifically engineered for the detection of camouflaged weeds in dense agricul
arXiv:2604.15706v1 Announce Type: new Abstract: Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented la
arXiv:2512.05722v2 Announce Type: replace Abstract: Chemical reaction mechanisms are the foundation of how chemists evaluate reactivity and feasibility, yet current Computer-Assisted Synthesis Plannin
arXiv:2604.16079v1 Announce Type: new Abstract: The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training
The countries MAGA describes as collapsing and joyless hold positions 1 through 6 in the global happiness rankings. They have held them for years. The country producing the most content about European
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. No one’s sure if synthetic mirror life will kill us all In Feb
arXiv:2604.15372v1 Announce Type: cross Abstract: As generative AI advances, the distinction between authentic and synthetic media is increasingly blurred, challenging the integrity of online informat
arXiv:2604.15490v1 Announce Type: new Abstract: Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks
🤖 This debate is largely a matter of productivity gains-@a_bergeaud's latest book is a must read to easily understand why ! 📈 With @BunelSimon @XJaravel @tmikaelsen @alexandraroulet J.Søgaard we found
arXiv:2504.00966v2 Announce Type: replace Abstract: This article studies the time-optimal path planning problem for a convexified Reeds-Shepp (CRS) vehicle on a unit sphere, capable of both forward an
arXiv:2604.15344v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into design and development workflows, yet decisions about their use are rarely binary or pur
arXiv:2604.15964v1 Announce Type: cross Abstract: Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging proto
arXiv:2604.16114v1 Announce Type: new Abstract: Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-qua
arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such metho
arXiv:2512.07515v4 Announce Type: replace-cross Abstract: Detecting hallucinations in Retrieval-Augmented Generation remains a challenge. Prior approaches attribute hallucinations to a binary conflict
arXiv:2603.06454v2 Announce Type: replace Abstract: We study the training objectives of denoising-based generative models, with a particular focus on loss weighting and output parameterization, includ
arXiv:2604.16145v1 Announce Type: cross Abstract: Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe
arXiv:2604.15448v1 Announce Type: cross Abstract: Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These
arXiv:2604.15756v1 Announce Type: new Abstract: Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representation
arXiv:2604.15542v1 Announce Type: new Abstract: Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Po
arXiv:2604.15339v1 Announce Type: cross Abstract: Uncertainty, vagueness, and ambiguity are closely related and often confused concepts in human-robot interaction (HRI). In earlier studies, these conc
arXiv:2511.02626v3 Announce Type: replace Abstract: Prior works have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect out
arXiv:2604.16119v1 Announce Type: new Abstract: Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. Howe
arXiv:2604.15375v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown significant improvement in RTL code generation. Despite the advances, the generated code is often riddled with
arXiv:2604.15533v1 Announce Type: cross Abstract: We consider the problem of verification modulo tested library contracts as a step towards automating the verification of client programs that use comp
arXiv:2601.05547v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal tasks, but remain susceptible to hallucinations, where gener
arXiv:2510.08480v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text
arXiv:2509.23175v2 Announce Type: replace-cross Abstract: With the rise of Web 2.0 and microservices, the increasing availability of Web APIs has intensified the need for effective recommendation syst
arXiv:2604.15451v1 Announce Type: new Abstract: Large-scale visual learning is increasingly limited by training cost. Existing knowledge distillation methods transfer from a stronger teacher to a weak
arXiv:2604.15940v1 Announce Type: new Abstract: Fixed Radius Near Neighbor (FRNN) search is an alternative to the widely used k Nearest Neighbors (kNN) search. Unlike kNN, FRNN determines a label or a
arXiv:2604.15764v1 Announce Type: cross Abstract: Early-exit neural networks enable adaptive computation by allowing confident predictions to exit at intermediate layers, achieving 2-8imes inference s
arXiv:2604.16027v1 Announce Type: cross Abstract: Post-trained language models produce less varied outputs than their base counterparts. This output diversity collapse undermines inference-time scalin
arXiv:2604.16177v1 Announce Type: new Abstract: We present a three-stage progressive shadow-removal pipeline for the CVPR2026 NTIRE WSRD+ challenge. Built on OmniSR, our method treats deshadowing as i
arXiv:2604.15376v1 Announce Type: cross Abstract: Multi-step zoom-in pipelines are widely used for GUI grounding, yet the intermediate predictions they produce are typically discarded after coordinate
“AI skeptic” v “AI cynic” is going to be an important distinction in the next few years, IMO. I am not a skeptic about AI abilities but I am cynical about the incentives CEOs have to say wildly bombas
这可能是今年AI圈最清醒的一条推文， Yann LeCun 是当今 AI 领域最有影响力的科学家之一，深度学习三大教父之一，2018 年图灵奖获得者， 他直接怼了Anthropic CEO Dario的著名言论， Dario说未来一到五年，一半的科技法律咨询金融岗位会被彻底干掉。 LeCun说你错了，而且不仅Dario错了，所有的AI实验室CEO 和一众大佬包括Sam Altman，Yoshua
Yann LeCun argues that the normalized exponential function exp(x)/sum(exp(x)) should be called 'softargmax' rather than 'softmax,' as it represents a soft approximation of the argmax operation rather
Exactly. The number of iterations you do is far more important than the amount of 'thinking' you do. It doesn't matter how intelligent or knowledgeable you are. Reality will always push back. The worl
Human cognitive friction has long been acting as a regularizer for a lot of digital infrastructure. It made software APIs less terrible and codebases less complex. Now LLM disintermediation is causing
I'd say cognitive friction is even more than just a regularizer: it's an incentive to find the right interface abstractions, and in turn good abstractions are what enables compounding over time. Piles
Indeed. Indeed. The history of tech impact on labor is well-documented, including by those named. It's unpredictable, but usually improves productivity and leads to expansion. Law & white-collar worke
Just got access to Grok 4.3 and the first thing I did was throw the hardest task I usually run on claude to test it gave it a complex research prompt on deepseek: >thought for 13 mins and 17 seconds >
Automated lint: 43 errors, 9 warnings, 3 info
This post draws a humorous parallel between using images of salarymen eating ramen as a test case for AI image generation models in Eastern contexts and the Western 'Will Smith eating spaghetti' meme,