Knowledge catalogue
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,
The two most repetitive unfounded concerns about technology in history are: - Will it cause mass unemployment? - Will it ruin a generation? You don’t get to just say “it’s different this time” and mov
Unlimited demand for a product or service does not guarantee business viability or economic sustainability, as other critical factors such as cost structure, scalability, profitability, and resource c
Video of my opening keynote for the 2026 World Modeling Workshop at Mila, Quebec AI Institute: simple but powerful ways of using world models and their latent space (sorry for sound problem 0:20-0:43)
Constraints are the catalyst of invention. An infinite search space leads to paralysis. The most creative inventions happen when you are forced to solve a problem within appropriately narrow constrain
if you are the only one who can build this amazing tech and believe it is super dangerous, it’s very easy: you quit for the sake of humanity. if you are not the only one who can do it, you are definit
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 workers aren'
This article outlines Sebastian Raschka's systematic approach to learning and understanding Large Language Model (LLM) architectures, likely covering foundational concepts, key components, and methodo
Mooncake is the serving platform for Kimi developed by Moonshot AI, featuring a KVCache-centric disaggregated architecture that separates prefill and decoding clusters while leveraging underutilized C
The tensor engine was first implemented inside SN3 (before it was called Lush) in 1992 at Bell Labs by Léon Bottom and me. The naming convention has survived to this day in PyTorch and other libraries
Francois Chollet compares programming language philosophies between PHP and Go, likely discussing design trade-offs, developer experience, or use cases that differentiate the two languages. The compar
Francois Chollet discusses a hiring signal for identifying high-quality deep learning candidates based on their choice of machine learning frameworks, suggesting PyTorch proficiency (or lack thereof)
You cannot think your way to a perfect design. Only building and testing, over many iterations, can reveal the flaws in your mental model and provide the feedback you need to create the best design po
arXiv:2601.08831v5 Announce Type: replace Abstract: Video object segmentation methods like SAM2 achieve strong performance through memory-based architectures but struggle under large viewpoint changes
arXiv:2601.04588v2 Announce Type: replace Abstract: Segmentation of the left atrial (LA) wall and endocardium from late gadolinium-enhanced (LGE) MRI is essential for quantifying atrial fibrosis in pa
arXiv:2604.14868v1 Announce Type: new Abstract: 4D millimeter-wave (mmWave) radars are increasingly used in robotics, as they offer robustness against adverse environmental conditions. Besides the usu
arXiv:2604.14263v1 Announce Type: cross Abstract: Accurate detection and segmentation of glomeruli in kidney tissue are essential for diagnostic applications. Traditional deep learning methods primari
arXiv:2604.15215v1 Announce Type: new Abstract: We present a novel hierarchical spatiotemporal action tokenizer for in-context imitation learning. We first propose a hierarchical approach, which consi
arXiv:2510.13829v3 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is par
arXiv:2601.16870v2 Announce Type: replace Abstract: Integrated control of wheelchairs and wheelchair-mounted robotic arms (WMRAs) has strong potential to increase independence for users with severe mo
arXiv:2604.14484v1 Announce Type: new Abstract: Behavior cloning (BC) policies on position-controlled robots inherit the closed-loop response of the underlying PD controller, yet the effect of control
arXiv:2509.06593v2 Announce Type: replace Abstract: Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estima
arXiv:2604.14603v1 Announce Type: cross Abstract: The fundamental limit of natural signal compression has traditionally been characterized by classical rate-distortion (RD) theory through the tradeoff
arXiv:2509.24886v3 Announce Type: replace Abstract: Canonicalization is a widely used strategy in equivariant machine learning, enforcing symmetry in neural networks by mapping each input to a standar