Search: “models”

arXiv:2605.18991v1 Announce Type: cross Abstract: We take the position that agent security must be approached as a systems problem: the AI model powering the agent must be treated as an untrusted comp

arXiv:2605.19337v1 Announce Type: new Abstract: A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve

arXiv:2605.19010v1 Announce Type: new Abstract: Natural language to SQL (NL2SQL) conversion is an important problem for researchers and enterprises due to the ubiquitous importance of relational datab

arXiv:2512.03869v4 Announce Type: replace Abstract: We present CaravelMetrics, a computational framework for automated cerebrovascular analysis that models vessel morphology through skeletonization-de

arXiv:2605.19325v1 Announce Type: new Abstract: Nonnegative matrix factorization (NMF) seeks a low-rank approximation X approx UV^T with nonnegative factors and is commonly solved using interior metho

Samantha Kelly / Bloomberg: An interview with Match Group CEO Spencer Rascoff about plans for Tinder, including a redesign, AI features, live events, and group dating to win over Gen Z — The dating ap

Anyone understand what Google mean by 'Gemini Spark runs on Gemini 3.5 and uses the Antigravity harness' - is 'Antigravity' a generic term they're using for their agent harnesses now or is their Claw-

arXiv:2605.19767v1 Announce Type: cross Abstract: Zeroth-order (ZO) optimization enables large-language-model fine-tuning without storing backpropagation activations, while LoRA supplies compact train

arXiv:2510.05746v2 Announce Type: replace Abstract: Large Language Model (LLM)-powered Multi-agent systems (MAS) have achieved state-of-the-art results on various complex reasoning tasks. Recent works

arXiv:2605.20084v1 Announce Type: cross Abstract: Large language models (LLMs) can enhance factuality via retrieval-augmented generation (RAG), but applying RAG to every query is unnecessary when the

arXiv:2603.14918v2 Announce Type: replace-cross Abstract: Model-based approaches for (bio)process systems often suffer from incomplete knowledge of the underlying physical, chemical, or biological law

arXiv:2605.19147v1 Announce Type: cross Abstract: Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samples are poisoned using trigger-based harmful conte

arXiv:2605.19069v1 Announce Type: cross Abstract: Code-switching -- the natural alternation between two languages within a single utterance -- represents one of the most challenging and under-studied

arXiv:2510.25348v2 Announce Type: replace Abstract: Information cascade popularity prediction is a key problem in analyzing content diffusion in social networks. However, current related works suffer

arXiv:2510.01499v2 Announce Type: replace-cross Abstract: With the rapid progress of multi-agent large language model (LLM) reasoning, how to effectively aggregate answers from multiple LLMs has emerg

arXiv:2605.19674v1 Announce Type: new Abstract: Strategic classification(SC) studies the interaction between decision models and agents who strategically manipulate their features for favorable outcom

arXiv:2510.16559v5 Announce Type: replace Abstract: Engineering construction automation aims to transform natural language specifications into physically viable structures, requiring complex integrate

arXiv:2605.19538v1 Announce Type: cross Abstract: CAPTCHAs are widely deployed as human verification mechanisms and frequently block intelligent agents from completing end-to-end automation in real-wo

arXiv:2605.19806v1 Announce Type: cross Abstract: This paper investigates chunking strategies for retrieval-augmented generation on German statutory law, using the German Civil Code as a structured be

arXiv:2509.14787v2 Announce Type: replace Abstract: Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. E

arXiv:2605.19374v1 Announce Type: cross Abstract: Vision-language alignment using chest X-rays and radiology reports has emerged as an advanced paradigm for zero-shot classification and grounding of c

arXiv:2605.19075v1 Announce Type: cross Abstract: Grounded multi-video question answering over real-world news events requires systems to surface query-relevant evidence across heterogeneous video arc

arXiv:2605.18810v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffus

arXiv:2605.19270v1 Announce Type: new Abstract: Large language models can deceive by subtly manipulating truthful information -- omitting key facts, shifting focus, or obscuring meaning -- making such

arXiv:2603.29382v2 Announce Type: replace-cross Abstract: Lightweight cryptographic primitives are widely deployed in resource-constrained environments, particularly in Internet of Things (IoT) device

arXiv:2105.00933v3 Announce Type: replace-cross Abstract: The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the

arXiv:2605.19621v1 Announce Type: cross Abstract: Deep generative models have emerged as state-of-the-art for solving inverse problems, but applying them to inverse problems for PDEs, like electrical

arXiv:2605.19779v1 Announce Type: new Abstract: We adapt split conformal prediction and adaptive conformal inference (ACI) to continuous AI agent evaluation, providing distribution-free coverage guara

arXiv:2605.20104v1 Announce Type: cross Abstract: Speculative decoding (SD) accelerates large language model inference by leveraging a draft-then-verify paradigm. To maximize the acceptance rate, rece

arXiv:2510.12773v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) process every token through all layers of a transformer stack, causing wasted computation on simple queries and i

arXiv:2601.05391v2 Announce Type: replace Abstract: Accurate multistep forecasting of node-level attributes on dynamic graphs is critical for applications ranging from financial trust networks to biol

arXiv:2605.19155v1 Announce Type: new Abstract: Biological visual systems learn from limited experience, unlike deep learning models that rely on millions of training images. What learning principles

arXiv:2605.19004v1 Announce Type: new Abstract: Accurately forecasting human trajectories from an egocentric perspective plays a central role in applications such as humanoid robotics, wearable sensin

arXiv:2605.19093v1 Announce Type: new Abstract: System prompts are a central control mechanism in modern AI systems, shaping behavior across conversations, tasks, and user populations. Yet they are di

arXiv:2605.18820v1 Announce Type: cross Abstract: Superposition allows Transformers to reason in depth, carrying an entire reasoning frontier in parallel through a bounded-depth forward pass instead o

arXiv:2605.19283v1 Announce Type: cross Abstract: Sequential prediction is challenging in regimes of delayed disambiguation, where early observations are ambiguous and multiple latent explanations rem

arXiv:2605.19478v1 Announce Type: cross Abstract: Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has fo

arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node

arXiv:2512.08237v3 Announce Type: replace Abstract: The advancement of vision-only Bird's-Eye-View (BEV) perception, a core paradigm for cost-effective autonomous driving, is hindered by the long-stan

arXiv:2605.18789v1 Announce Type: cross Abstract: Features in language models have life history: they emerge, persist, and die during training, yet the importance of that history remains largely unexp

arXiv:2601.01901v2 Announce Type: replace Abstract: Data-free knowledge distillation-based one-shot federated learning (OSFL) trains a model in a single communication round without sharing raw data, m

arXiv:2602.04555v2 Announce Type: replace Abstract: Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past info

for anyone curious, this was the result of many experiments bouncing around but this version uses... initial ideation: chatgpt for ideas/@replit for quick build final repo build: opus 4.7 prompting cl

For centuries, the scientific method has been our best tool for progress. But today, there’s so much data out there that it’s impossible for any one researcher to connect all the dots. We want to fix

arXiv:2605.18764v1 Announce Type: cross Abstract: Artificial Intelligence (AI) pipelines have become integral to modern research, supporting fields such as Medical Sciences, Agriculture, and Social Sc

fun read on real tradeoffs & design decisions we debated when designing Engine for the data scale that customers produce one common thread is that we’re pretty strong supporters of just giving the age