Knowledge catalogue
arXiv:2605.25232v1 Announce Type: cross Abstract: Direct Code2Code transformation remains challenging to control because it can preserve surface-level syntax while introducing semantic drift, hidden b
arXiv:2605.24818v1 Announce Type: cross Abstract: The literature on test set contamination largely focuses on detection, but the correction of contaminated test scores is underexplored. Our core propo
arXiv:2404.08073v3 Announce Type: replace-cross Abstract: Bregman proximal-type algorithms (BPs), such as mirror descent, have become popular tools in machine learning and data science for exploiting
arXiv:2605.26111v1 Announce Type: cross Abstract: Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Ex
arXiv:2605.26000v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is a foundational algorithm for large-scale statistical learning and stochastic optimization. However, statistical i
arXiv:2605.25954v1 Announce Type: cross Abstract: Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution efficiency of tensor programs remains challenging
arXiv:2605.24733v1 Announce Type: new Abstract: We present extbf{StepGap}, a hybrid NLI-LLM decision tree that detects step-level evidence gaps in multi-hop QA and emits one of three typed labels: ext
arXiv:2605.25005v1 Announce Type: new Abstract: Achieving both efficient pushability (propulsion transmission) and proximally concentrated bending for steerability is challenging for magnetically actu
arXiv:2505.02129v3 Announce Type: replace-cross Abstract: Organizing large-scale resources in a multidimensional semantic space is an approach to efficiently managing and querying resources from diffe
arXiv:2605.24690v1 Announce Type: cross Abstract: The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant
arXiv:2605.24416v1 Announce Type: new Abstract: Human cognitive performance is constrained by limited mental resources, yet continuous computational estimation of cognitive capacity dynamics remains a
arXiv:2605.24852v1 Announce Type: new Abstract: Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance
arXiv:2605.24899v1 Announce Type: new Abstract: Ontologies represent the conceptual knowledge of a domain. At the core of an ontology is the taxonomy of concepts and subconcepts that represent specifi
arXiv:2605.24311v1 Announce Type: new Abstract: Planetary rovers operating in extraterrestrial environments often encounter significant mobility challenges due to varying terrain features such as grad
arXiv:2605.25686v1 Announce Type: new Abstract: The recent shift from dedicated NMT systems to general-purpose LLMs has reshaped machine translation, with LLMs reported to produce more fluent, less li
arXiv:2605.24971v1 Announce Type: cross Abstract: The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. Ho
arXiv:2605.25928v1 Announce Type: new Abstract: We describe the winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The task requires produ
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. A reality check on the AI jobs hysteria Despite the growing hy
arXiv:2409.08379v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors cho
arXiv:2509.14250v2 Announce Type: replace Abstract: This paper explores prompts and prompting in large language models (LLMs) as dynamic semiotic phenomena, drawing on Peirce's triadic model of signs,
arXiv:2605.24556v1 Announce Type: cross Abstract: Multilingual retrieval increasingly underpins cross-lingual question answering and retrieval-augmented generation. Strong zero-shot scores on multilin
arXiv:2605.24477v1 Announce Type: new Abstract: The Normalized Maximum Likelihood (NML) codelength, or stochastic complexity, represents a principled criterion for universal coding. While recent coare
arXiv:2605.25880v1 Announce Type: new Abstract: Large-scale transformer training and deployment are increasingly constrained by the transfer of activations, gradients, and optimizer states across acce
The Telegraph: Macron tore up 65 years of doctrine to defend Europe with French nukes, with or without the US. Poland, Germany, the Netherlands, Belgium, Greece, Sweden, Denmark, and now the Czech Rep
arXiv:2605.25868v1 Announce Type: cross Abstract: The speed and accuracy of an artificial teammate fundamentally alter the failure states of Human-AI integration. While high-speed AI interventions ris
arXiv:2605.24718v1 Announce Type: new Abstract: Tokenizer fertility the number of tokens per word imposes a hidden cost on non-English NLP. We measure fertility for ten foundation models across 25 Eur
arXiv:2605.25134v1 Announce Type: cross Abstract: Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is ell_p regularization. H
arXiv:2605.25208v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction (ECPE) was introduced to explain why an emotion occurs, but this goal is now often reduced to binary pair/non-pair predict
arXiv:2605.24489v1 Announce Type: new Abstract: Enzyme-reaction retrieval is a fundamental problem in computational biology, underpinning enzyme characterization, reaction mechanism elucidation, and t
TinkerAPI, developed by TML, is a post-training infrastructure tool that Soumith Chintala recommends as an essential component for new AI companies during their startup phase. The tool appears to addr
arXiv:2605.25046v1 Announce Type: cross Abstract: YOLO-series and DETR-based detectors struggle with tiny-object detection. YOLO-style models benefit from efficient dense prediction, but their large-s
arXiv:2605.25399v1 Announce Type: new Abstract: Objective: Survival analysis is central to medical prediction, yet large language models (LLMs) are rarely used as end-to-end survival models because ce
arXiv:2605.24213v1 Announce Type: cross Abstract: Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result
arXiv:2602.01914v2 Announce Type: replace Abstract: Token attribution methods provide intuitive explanations for language model outputs by identifying causally important input tokens. However, as mode
arXiv:2605.24000v1 Announce Type: new Abstract: Toxicity in online gaming communities remains a persistent challenge, manifesting across genres, platforms, and player interactions. While much research
arXiv:2605.24526v1 Announce Type: cross Abstract: Interactive assistance systems typically provide feedback after an action has been completed, supporting error recovery but not preventing the error i
arXiv:2401.01160v3 Announce Type: replace-cross Abstract: We investigate a framework for train-free MRI segmentation based on Topological Data Analysis. The pipeline proceeds in three steps, first ide
arXiv:2402.08726v2 Announce Type: replace-cross Abstract: We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gates in the limit of infinite width, where the genera
arXiv:2605.24680v1 Announce Type: new Abstract: Gradient-boosted trees achieve strong performance on tabular data, yet often leave a long tail of poorly predicted instances. We introduce a Trajectory-
arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of
arXiv:2605.25969v1 Announce Type: new Abstract: Causal Transformer language models suffer from strictly sequential decoding and a quadratic per-step attention cost. While linear-time causal models and
arXiv:2605.23988v1 Announce Type: cross Abstract: Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacit
arXiv:2605.24292v1 Announce Type: new Abstract: Log-likelihood is a standard metric for evaluating generative models. Unfortunately, in contrast to autoregressive models (ARMs), discrete diffusion mod
arXiv:2601.14340v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely integrated into interactive systems such as dialogue agents and task-oriented assistants. This growing
arXiv:2410.15173v4 Announce Type: replace-cross Abstract: The thematic fit estimation task measures semantic arguments' compatibility with a given semantic role for a given predicate. We investigate i
arXiv:2509.23573v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to help security analysts manage the surge of cyber threats, automating tasks from vulnerab
arXiv:2511.19065v2 Announce Type: replace-cross Abstract: MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the under
arXiv:2605.24642v1 Announce Type: cross Abstract: Recent work explores new opportunities at the intersection of vision-language-action models (VLAs) and geometric foundation models (GFMs) for 3D recon
arXiv:2605.25903v1 Announce Type: new Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each mo
arXiv:2212.07944v3 Announce Type: replace Abstract: We study a multi-factor block model for variable clustering and connect it to regularized subspace clustering through a distributionally robust vers
arXiv:2605.24084v1 Announce Type: cross Abstract: Shapley additive explanations (SHAP) are widely recognised as computationally intractable for neural networks, since they induce an exponential search
arXiv:2605.24031v1 Announce Type: cross Abstract: We study the reconstruction of implied volatility surfaces from sparse and noisy option quotes using deep learning models under no-arbitrage constrain
arXiv:2510.15514v3 Announce Type: replace Abstract: Reinforcement Learning from AI Feedback (RLAIF) relies on LLM judges as preference measurement instruments, yet these instruments are fundamentally
arXiv:2510.16435v2 Announce Type: replace-cross Abstract: With the growing use of large language models and conversational interfaces in human-robot interaction, robots' ability to answer user questio
arXiv:2605.24296v1 Announce Type: new Abstract: We study when LLM-generated synthetic data helps low-resource multi-label patent classification, separating true synthetic value from the confound that
arXiv:2605.26046v1 Announce Type: cross Abstract: Customizing an LLM judge to a specific task or domain often involves optimizing its prompt across multiple evaluation criteria simultaneously. Textual
arXiv:2605.25629v1 Announce Type: new Abstract: Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched train--t
arXiv:2605.25304v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) have emerged as a cornerstone approach for interpretable machine learning, providing human-understandable intermediate
arXiv:2510.08609v3 Announce Type: replace-cross Abstract: Developers consistently use version constraints to specify acceptable versions of the dependencies for their project. Pinning dependencies can
arXiv:2605.24651v1 Announce Type: cross Abstract: We propose a Weak-form Physics-Informed Neural Operator (WINO), a data-free framework that combines the efficiency of neural operators with the geomet