Knowledge catalogue
arXiv:2604.16265v1 Announce Type: new Abstract: Existing multi-hazard susceptibility mapping (MHSM) studies often rely on spatially uniform models, treat hazards independently, and provide limited rep
Friendly reminder, Hinton is the same guy who told us this: 'We should stop training radiologists now. It's just completely obvious that within five years, deep learning is going to do better than rad
arXiv:2506.19840v2 Announce Type: replace Abstract: Large-scale pre-trained video diffusion models have exhibited remarkable capabilities in diverse video generation. However, existing solutions face
Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a rando
arXiv:2604.15400v1 Announce Type: cross Abstract: We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by asymmetric attractor dyn
arXiv:2604.15482v1 Announce Type: cross Abstract: Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning dema
arXiv:2604.15873v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly studied as repositories of linguistic knowledge. In this line of work, models are commonly evaluated both
Human biological limits, like our tiny working memory and shallow calculation depth, are actually a feature. They force us to abstract, compress, intuit. If we had infinite resources, we would never h
arXiv:2604.15642v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promising progress for generating Register Transfer Level (RTL) hardware designs, largely because they can rap
I love Geoff. But he understands even less than Dario about the effects of technological revolutions on the labor market. Again, don't listen to AI scientists, as brilliant as they might be, and even
arXiv:2604.15977v1 Announce Type: new Abstract: M-MIMO is one of the crucial technologies for increasing spectral and energy efficiency of wireless networks. Most of the current works assume that M-MI
arXiv:2604.15607v1 Announce Type: cross Abstract: AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and join
arXiv:2512.07173v4 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic n
In a capitalist economy, absolute levels of value creation and productivity take a backseat to relative competitive advantage. That's why jobs still exist despite 150 years of intense automation. As l
In a hypothetical scenario where all tasks could be automated at zero cost, François Chollet argues that everyone would work in interpersonal roles, suggesting that human value would shift entirely to
arXiv:2604.15529v1 Announce Type: new Abstract: Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not inter
arXiv:2601.02996v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve strong performance on mathematical reasoning tasks, often attributed to their capability to generate explicit
arXiv:2604.15581v1 Announce Type: cross Abstract: Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key compone
arXiv:2604.16278v1 Announce Type: new Abstract: Although most of the automated theorem-proving approaches depend on formal proof systems, informal theorem proving can align better with large language
arXiv:2604.15741v1 Announce Type: cross Abstract: Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model in
arXiv:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differe
arXiv:2602.12370v2 Announce Type: replace Abstract: Recent progress in large models has led to significant advances in unified multimodal generation and understanding. However, the development of mode
LLMは頭の中でコイントスができるか？ ブログ：https://pub.sakana.ai/ssot 論文（#ICLR2026）：https://arxiv.org/abs/2510.21150 一見簡単そうで奥深いこの問題を「プロンプトだけ」で解決した論文 'SSoT: Prompting LLMs for Distribution-Faithful and Diverse Generation
arXiv:2604.15726v1 Announce Type: new Abstract: This position paper argues that large language model (LLM) reasoning should be studied as latent-state trajectory formation rather than as faithful surf
arXiv:2601.02531v2 Announce Type: replace-cross Abstract: Cooking recipes are complex procedures that require not only a fluent and factual text, but also accurate timing, temperature, and procedural
arXiv:2604.15707v1 Announce Type: new Abstract: Spatiotemporal Local Binary Pattern (STLBP) is a widely used dynamic texture descriptor, but it suffers from extremely high dimensionality. To tackle th
arXiv:2604.15363v1 Announce Type: cross Abstract: Motivated behaviour relies on the brain's capacity to evaluate effort and reward. Dysregulation within these processes contributes to a spectrum of co
arXiv:2604.15618v1 Announce Type: new Abstract: We investigate Functional Majority Voting (FMV), a method based on functional consensus for code generation with Large Language Models, which identifies
arXiv:2603.22201v2 Announce Type: replace Abstract: Humanoid robots require diverse motor skills to integrate into complex environments, but bridging the kinematic and dynamic embodiment gap from huma
arXiv:2604.15360v1 Announce Type: new Abstract: This study presents a triadic analysis of energy storage operation under multi-stage model predictive control, investigating the interplay between data
arXiv:2601.05201v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) are highly capable, yet often hallucinate by favoring textual prompts over visual evidence. We study this
arXiv:2512.03053v2 Announce Type: replace-cross Abstract: We show for invertible problems that transform data from a source domain (for example, Logic Condition Tables (LCTs)) to a destination domain
arXiv:2510.09065v2 Announce Type: replace-cross Abstract: We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By l
arXiv:2507.20058v3 Announce Type: replace-cross Abstract: Predicting Parkinson's Disease (PD) progression is crucial for personalized treatment, and voice biomarkers offer a promising non-invasive met
arXiv:2509.17292v3 Announce Type: replace-cross Abstract: Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual
arXiv:2604.15694v1 Announce Type: new Abstract: Discrete diffusion models based on continuous-time Markov chains (CTMCs) have shown strong performance on language and discrete data generation, yet exi
arXiv:2604.15941v1 Announce Type: new Abstract: Recent years have witnessed the rapid emergence of 3D Gaussian splatting (3DGS) as a powerful approach for 3D reconstruction and novel view synthesis. I
arXiv:2604.16232v1 Announce Type: cross Abstract: Understanding natural and engineered systems often relies on symbolic formulations, such as differential equations, which provide interpretability and
arXiv:2604.15718v1 Announce Type: cross Abstract: Visual speaker recognition based on lip motion offers a silent, hands-free, and behavior-driven biometric solution that remains effective even when ac
arXiv:2510.21783v2 Announce Type: replace-cross Abstract: Diffusion models have demonstrated powerful performance in generating high-quality images. A typical example is text-to-image generator like S
arXiv:2604.15439v1 Announce Type: cross Abstract: We study dynamic measure transport for generative modelling in the setting of a stochastic process X_ullet whose marginals interpolate between a sourc
arXiv:2604.15549v1 Announce Type: new Abstract: We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communication
arXiv:2604.15364v1 Announce Type: cross Abstract: Edge intelligence is constrained by the energy and latency costs of shuttling data through electronic memory hierarchies. Optical systems offer a fund
arXiv:2604.15830v1 Announce Type: new Abstract: Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy pr
arXiv:2604.02393v2 Announce Type: replace Abstract: Vanishing gradients and overfitting are central problems in machine learning, yet are typically analyzed in asymptotic regimes that obscure their dy
arXiv:2604.15538v1 Announce Type: cross Abstract: Supervised No Free Lunch Theorems (NFLTs) are well studied, yet unsupervised NFLTs remain underexplored. For elliptical distributions, we prove that t
arXiv:2604.15728v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting servic
arXiv:2510.24887v4 Announce Type: replace Abstract: This paper examines the feasibility of utilizing lightweight body landmark detection for recognizing isolated signs in Brazilian Sign Language (LIBR
arXiv:2602.15143v2 Announce Type: replace Abstract: Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, una
arXiv:2604.15398v1 Announce Type: new Abstract: We explore the possibility of solving Partial Differential Equations (PDEs) using discrete weak formulations. We propose a programming environment for d
arXiv:2604.15945v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely used to augment the input to Large Language Models (LLMs) with external information, such as recent or do
arXiv:2604.16034v1 Announce Type: new Abstract: For head and neck cancer (HNC) patients, prognostic outcome prediction can support personalized treatment strategy selection. Improving prediction perfo
This article analyzes the performance differences between open-source and closed-source AI models in the current landscape, examining factors that influence their relative capabilities and market posi
arXiv:2604.15459v1 Announce Type: cross Abstract: Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising
arXiv:2410.01252v2 Announce Type: replace-cross Abstract: Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities.
arXiv:2604.15621v1 Announce Type: cross Abstract: Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving s
arXiv:2601.11374v2 Announce Type: replace Abstract: Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage
arXiv:2604.15890v1 Announce Type: new Abstract: Multi-UAV inspection missions require spare drones to replace active drones during recharging cycles. Existing fleet-sizing approaches often assume stea
arXiv:2602.07006v2 Announce Type: replace Abstract: Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine d
arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear