Knowledge catalogue
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:2604.15715v1 Announce Type: cross Abstract: The development of general-purpose agents requires a shift from executing simple instructions to completing complex, real-world productivity workflows
arXiv:2409.08036v2 Announce Type: replace Abstract: Heterogeneous graphs, whose nodes and edges may belong to different types and feature spaces, arise in a wide variety of real-world domains such as
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:2604.15713v1 Announce Type: cross Abstract: Type annotations are essential when printing terms in a way that preserves their meaning under reparsing and type inference. We study the problem of c
arXiv:2412.19363v3 Announce Type: replace Abstract: Large Language Models (LLMs) have transformed artificial intelligence by excelling in complex natural language processing tasks. Their ability to ge
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:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on challenging partial differe
arXiv:2604.15671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated significant potential for embodied decision-making; however, their application in complex chemical
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
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Meet Kimi K2.6 agent - Video hero section, WebGL shaders, real backends. From one prompt. 🔹 Video hero sections - cinematic aesthetic, auto-composited 🔹 WebGL shader animations - native GLSL / WGSL, l
arXiv:2604.15774v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks. Speci
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:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T
arXiv:2604.15455v1 Announce Type: new Abstract: Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to
arXiv:2604.15773v1 Announce Type: cross Abstract: We introduce a novel characterization of phase transitions based on hypothesis testing. In our formulation, a phase transition is defined as the break
arXiv:2604.15483v1 Announce Type: new Abstract: We present a new robotic foundation model, called {pi}_{0.7}, that can enable strong out-of-the-box performance in a wide range of scenarios. {pi}_{0.7}
arXiv:2604.15494v1 Announce Type: cross Abstract: Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balan
arXiv:2602.13088v2 Announce Type: replace-cross Abstract: The distinction between genuine grassroots activism and automated influence operations is collapsing. While contemporary policy debates priori
arXiv:2604.15838v1 Announce Type: new Abstract: Distribution shift severely degrades the performance of deep forecasting models. While this issue is well-studied for individual time series, it remains
arXiv:2604.15856v1 Announce Type: cross Abstract: Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav
arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo
arXiv:2604.15499v1 Announce Type: cross Abstract: Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabli
arXiv:2602.11327v2 Announce Type: replace-cross Abstract: The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent
arXiv:2604.15794v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from performance degra
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: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:2604.16086v1 Announce Type: cross Abstract: One of the dominant paradigms in self-supervised learning (SSL), illustrated by MoCo or DINO, aims to produce robust representations by capturing feat
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:2604.15333v1 Announce Type: cross Abstract: Human-AI romantic relationships are increasingly common, yet little is understood about how public discourse around them emerges and shifts over time.
arXiv:2604.15468v1 Announce Type: cross Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engin
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:2604.15370v1 Announce Type: cross Abstract: Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount
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:2604.15705v1 Announce Type: new Abstract: Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with comple
arXiv:2604.16280v1 Announce Type: new Abstract: Explaining Machine Learning (ML) results in a transparent and user-friendly manner remains a challenging task of Explainable Artificial Intelligence (XA
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
Video hero sections, built right in. K2.6 agent calls video generation APIs to create real cinematic footage for your hero, not stock placeholders. Composited into the page, synced to scroll, with sha
arXiv:2604.15613v1 Announce Type: cross Abstract: We present VoodooNet, a non-iterative neural architecture that replaces the stochastic gradient descent (SGD) paradigm with a closed-form analytic sol
arXiv:2604.15574v1 Announce Type: cross Abstract: Large language models are prone to hallucinating factually incorrect statements. A key source of these errors is exposure to new factual information t
Reuters: At the Beijing half-marathon, several humanoid robots beat human winners by 10+ minutes; a robot made by Honor beat the human world record held by Jacob Kiplimo — Dozens of Chinese-made human
Vercel, a major development platform that hosts and deploys web apps, was compromised, and the hackers are attempting to sell stolen data. A person claiming to be a member of ShinyHunters, which was b
And yes, Lush/SN used a homegrown Lisp interpreter, to which a compiler was added in the early 1990s. @TradeTexasBig Oh, it wasn't even CL, it was its own language! https://lush.sourceforge.net/lush-m
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:2604.13042v1 Announce Type: cross Abstract: Semantic data harmonisation is a central requirement in the ILIAD project, where heterogeneous environmental data must be harmonised according to the
arXiv:2511.18107v2 Announce Type: replace Abstract: Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional
Agent Memory is a newly announced managed service designed to address 'context rot'—the issue of degraded performance in sophisticated AI agents due to the limitations of context window size. This ser
arXiv:2604.14661v1 Announce Type: cross Abstract: Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, r
arXiv:2603.07053v2 Announce Type: replace Abstract: Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure a
arXiv:2604.15075v1 Announce Type: cross Abstract: Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as
arXiv:2604.15059v1 Announce Type: new Abstract: Motion artifacts present a significant challenge in structural MRI (sMRI), often compromising clinical diagnostics and large-scale automated analysis. W
arXiv:2509.05367v4 Announce Type: replace-cross Abstract: Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classificati
arXiv:2511.10909v2 Announce Type: replace-cross Abstract: Modern AI accelerators rely on matrix multiply-accumulate units (MMAUs), such as NVIDIA Tensor Cores and AMD Matrix Cores, to accelerate deep
arXiv:2512.22174v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) deployed in practical and safety-critical settings are increasingly susceptible to bit-flip faults caused by hard
arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalat
arXiv:2511.21025v2 Announce Type: replace Abstract: Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic infe
arXiv:2604.14645v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature di