Knowledge catalogue
arXiv:2605.29971v1 Announce Type: new Abstract: Causal interventions in language model representations have largely targeted discrete features, like grammatical number. However, language models must a
arXiv:2602.11389v2 Announce Type: replace Abstract: World models require robust relational understanding to support prediction, reasoning, and control. While object-centric representations provide a u
arXiv:2605.29272v1 Announce Type: cross Abstract: Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates:
arXiv:2605.29836v1 Announce Type: cross Abstract: Despite strong average-case performance, deep learning models often exhibit systematic errors on specific population groups, known as error slices. Id
arXiv:2605.30131v1 Announce Type: new Abstract: Radiology report generation (RRG) is commonly formulated as a single-path generation task, where a multimodal large language model (MLLM) produces one d
arXiv:2605.29809v1 Announce Type: cross Abstract: Large-scale text-to-image (T2I) diffusion models have enabled unprecedented creative applications, but their unauthorized use has raised serious intel
arXiv:2408.15451v3 Announce Type: replace Abstract: While machine learning models have proven effective across various scenarios, it is widely acknowledged that many models are vulnerable to adversari
arXiv:2605.29788v1 Announce Type: new Abstract: Critical sequential decisions are rarely single-timescale: a strategic decision causally shapes the context in which every subsequent tactical choice is
ChatGPT diagnosed 40 million people with a disease that was invented as a joke. Not a real disease. Not a misunderstood disease. A completely fictional condition with a fake name, fake papers, and fak
The Grok Build changelog provides daily updates on new features, improvements, and fixes for Grok, xAI's AI assistant. Users can track the development progress and changes to the platform's capabiliti
arXiv:2605.30100v1 Announce Type: new Abstract: World models require state tracking, which is the ability to maintain a correct latent state across action sequences. Existing benchmarks are often synt
arXiv:2605.29868v1 Announce Type: cross Abstract: Centralised biometric identity systems expose users to single points of failure, opaque verification processes, and irreversible biometric compromise.
arXiv:2605.29742v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) for regulatory compliance demands rigorous traceability via comprehensive citations across multi-tiered authority
arXiv:2605.30310v1 Announce Type: cross Abstract: City-scale 3D surface reconstruction from multiview images for downstream 3D simulation, poses highly challenging problems due to the scale and comple
arXiv:2605.29935v1 Announce Type: cross Abstract: Autonomous driving systems are commonly trained and evaluated within limited geographic regions, which hinders their scalability when deployed in new
arXiv:2605.29638v1 Announce Type: new Abstract: E-learning systems should deliver contents that reflect various phenomena of the language as it is used. In addition to formal Korean, e-learning system
Claude really can roleplay an economist. I love this little comment Claude made after some robustness checks on the paper it wrote: 'On a 1–10 identification scale, I'd now put the paper at about 4.5
Welcome to the second Cloud CISO Perspectives for May 2026. Today, Usman Chaudhary, Field CISO, Google Public Sector, offers a guide for CISOs protecting government agencies and critical infrastructur
arXiv:2605.29933v1 Announce Type: new Abstract: Clustering is a fundamental problem in data science with a long-standing research history, yielding numerous insightful algorithms. Despite this progres
arXiv:2605.29607v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) enable parallel decoding by predicting all masked positions at each denoising step, yet existing training-free
CNN today opened a lawsuit against the AI firm Perplexity AI Inc., alleging the company has violated copyright protections by using artificial intelligence to rewrite its reporting, thereby underminin
arXiv:2602.10637v2 Announce Type: replace Abstract: Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this b
arXiv:2605.29277v1 Announce Type: cross Abstract: We present Code-QA-Bench, a fully automated framework for synthesizing repository-level code understanding benchmarks that separates genuine code comp
arXiv:2510.14150v5 Announce Type: replace Abstract: We introduce CodeEvolve, an open-source framework that couples large language models with island-based evolutionary search for end-to-end algorithmi
arXiv:2604.06805v2 Announce Type: replace Abstract: Multi-step Chain-of-Thought (CoT) has significantly advanced the mathematical reasoning capabilities of LLMs by leveraging explicit reasoning steps.
arXiv:2605.29602v1 Announce Type: new Abstract: Multi-modal Retrieval-Augmented Generation (MMRAG) has emerged as a powerful paradigm for enhancing Multimodal Large Language Models in knowledge-intens
arXiv:2605.29271v1 Announce Type: new Abstract: Tool retrieval over large API catalogs is a core bottleneck for LLM agents: user queries arrive in colloquial, often underspecified language, while the
Pritam Biswas / Reuters: Coinbase and Kalshi introduce perpetual crypto futures, marking the first time the contracts will be available to US investors via domestic, regulated exchanges — Cryptocurren
arXiv:2602.10765v2 Announce Type: replace Abstract: In federated learning (FL), K clients jointly train a model without sharing raw data. Because each participant invests data and compute, clients nee
arXiv:2605.30332v1 Announce Type: new Abstract: Diffusion models achieve state-of-the-art image synthesis, with their generative trajectories fundamentally exhibiting a spectral bias, resolving low-fr
arXiv:2604.01904v2 Announce Type: replace-cross Abstract: Data rights owners can detect unauthorized data use in large language model (LLM) training by querying with proprietary samples. Often, superi
arXiv:2605.29628v1 Announce Type: cross Abstract: Contrastive Language-Audio Pretraining (CLAP) models are widely used for audio understanding and support modality-agnostic condition swapping in many
ComfyUI just added @OpenRouter support. Instead of being locked into a single LLM, you can now access 20+ models directly inside Comfy. More flexibility, less friction, same workflow. Links to the wor
Command A+ sets a new high for Cohere's machine translation capabilities. Opening a clear gap over open source peers Mistral Medium 3.5, DeepSeek, & OpenAI's gpt-oss, as well as Claude Opus 4.6. A+ al
arXiv:2605.28861v1 Announce Type: cross Abstract: A recent article [Phys. Rev. X 15, 011047 (2025)] utilizes group-equivariant convolutional neural networks to study the ground state of the kagome Hei
arXiv:2605.30241v1 Announce Type: new Abstract: Misinformation verification increasingly occurs in public, fast-moving, and multilingual online settings, where static benchmarks provide an incomplete
arXiv:2605.29476v1 Announce Type: new Abstract: This work presents a comparative evaluation of machine translation systems applied to images containing textual information, a task that lies at the int
arXiv:2605.29452v1 Announce Type: new Abstract: Image-based 3D reconstruction offers a low-cost alternative to traditional sensor-based techniques for road surface assessment. This study compares four
arXiv:2605.28977v1 Announce Type: cross Abstract: Recent advances in deep learning have enabled increasingly accurate electroencephalography (EEG)-based classification of Major Depressive Disorder (MD
arXiv:2605.29966v1 Announce Type: new Abstract: Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and spa
arXiv:2404.16077v4 Announce Type: replace-cross Abstract: Effective code optimization in compilers is crucial for computer and software engineering. The success of these optimizations primarily depend
arXiv:2605.30333v1 Announce Type: new Abstract: A plausible future mathematical claim must satisfy two constraints: it should follow the direction of prior work and respect the formal dependencies tha
arXiv:2605.29467v1 Announce Type: cross Abstract: Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be pre
This post demonstrates a comprehensive observability solution using Amazon Managed Grafana dashboards that provides a holistic view of both quality and quantity for LLMs served on Amazon SageMaker AI
arXiv:2605.28886v1 Announce Type: cross Abstract: Antibodies play a central role in the immune response by specifically recognizing and neutralizing antigens, and therapeutic antibodies have become ma
arXiv:2602.09499v2 Announce Type: replace Abstract: We study the computational relationship between replicability (Impagliazzo et al. [STOC `22], Ghazi et al. [NeurIPS `21]) and other stability notion
arXiv:2605.29268v1 Announce Type: cross Abstract: LLM-guided evolutionary search (Evolve systems) has reached state-of-the-art results on mathematical and combinatorial tasks, yet most existing system
arXiv:2605.29612v1 Announce Type: cross Abstract: Although large language model (LLM) based multi-agent systems (MAS) show their capability to solve complex tasks and achieve higher performance over s
arXiv:2605.28920v1 Announce Type: cross Abstract: Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machin
arXiv:2605.30085v1 Announce Type: new Abstract: Language model reasoning traces are rarely all-or-nothing; they frequently contain valid intermediate steps before a critical error occurs. Existing unc
arXiv:2605.29350v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intens
arXiv:2505.02604v5 Announce Type: replace Abstract: Empirical studies have shown that continuous low-loss paths can be constructed between independently trained neural network models. This phenomenon,
arXiv:2605.29415v1 Announce Type: cross Abstract: Task-based assessment of image quality (IQ) is critically important for the design and optimization of medical imaging systems. Ideal observers, inclu
arXiv:2605.28889v1 Announce Type: cross Abstract: Context distillation compresses contextual information into model parameters, yet existing methods often ignore how multiple distilled latent memories
arXiv:2605.28866v1 Announce Type: cross Abstract: Token-based time series large language models (TS-LLMs) have emerged as a promising direction for time series analysis and reasoning. However, prior s
arXiv:2510.01711v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained V
arXiv:2510.02480v3 Announce Type: replace Abstract: Large language models (LLMs) can be influenced by harmful or irrelevant context, which can significantly harm model performance on downstream tasks.
arXiv:2605.30103v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS), yet no formal convergence theory exists
arXiv:2605.29054v1 Announce Type: cross Abstract: Coding agents increasingly act as codebase-scale collaborators that can assist with codebase conversion, but this progress has exposed a critical weak
arXiv:2605.29497v1 Announce Type: new Abstract: We study the problem of robustly learning Gaussian Single Index Models (SIMs) in the presence of heavy-tailed noise and a constant fraction of adversari