Search: “retrieval”

arXiv:2609.21257v1 Announce Type: cross Abstract: Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-s

arXiv:2609.20509v1 Announce Type: new Abstract: For art historians, goldsmith marks play a critical role in the identification and dating of artifacts. In practice, experts must manually compare a que

arXiv:2609.20303v1 Announce Type: new Abstract: Classical philosophical corpora pose three compounding challenges for language resources: they exist in several languages without parallel alignment, th

arXiv:2609.11123v1 Announce Type: new Abstract: Graph condensation aims to produce a small surrogate graph that preserves the downstream node-classification performance of a much larger original graph

arXiv:2609.07194v1 Announce Type: new Abstract: We present EmoMed - a multimodal medical consultation agent that adapts its responses based on users' emotional states while maintaining clinical accura

arXiv:2609.05039v1 Announce Type: cross Abstract: Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make experiment tracking, provenance, and reproduc

arXiv:2609.02343v1 Announce Type: cross Abstract: Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low

arXiv:2407.20371v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the

arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences.

arXiv:2608.21393v1 Announce Type: new Abstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepow

arXiv:2608.20397v1 Announce Type: new Abstract: Agentic large language models (LLMs) on the Model Context Protocol (MCP) re-encode verbose tool schemas every turn, so prefill - quadratic in sequence l

arXiv:2608.17836v1 Announce Type: new Abstract: As large language models (LLMs) are granted increasing autonomy, it is essential to investigate methods that can induce unsafe behavior. We propose a no

arXiv:2608.15857v1 Announce Type: new Abstract: Ethereum is now integral to mission-critical sectors, including finance, healthcare, and supply chain management. Execution fees, commonly referred to a

There’s a hands-on workshop on August 29 that builds and benchmarks this properly, end to end, using entirely open models, no API calls involved. Led by Ben Auffarth, AI Consultant and Founder of Chel

arXiv:2608.07913v1 Announce Type: cross Abstract: Selective-risk certificates promise that accepted outputs meet a declared error target. We develop Fed-SRC, a score-agnostic certificate for federated

arXiv:2608.08366v1 Announce Type: new Abstract: Spatial transcriptomics can resolve gene expression at single-cell resolution, but it is costly, limited to targeted panels of a few hundred to a few th

arXiv:2608.00544v1 Announce Type: new Abstract: Identifying the ``Inflamed'' immunophenotype in Gastric Adenocarcinoma predicts immunotherapy response but requires an expensive 10-gene RNA signature.

arXiv:2607.28627v1 Announce Type: new Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at

arXiv:2607.24776v1 Announce Type: cross Abstract: RAG pipelines return a ranked list of passages. We argue this is a mismatch: the downstream language model conditions on a set, and the selection prob

arXiv:2505.02271v2 Announce Type: replace Abstract: The proliferation of Generative Artificial Ingelligence (AI), especially Large Language Models, presents transformative opportunities for urban appl

arXiv:2607.20430v1 Announce Type: cross Abstract: We present LLM-INSTRUCT, the winning system for the UZH Shared Task at ArgMining 2026 on paragraph-level argument mining in UN and UNESCO resolutions.

arXiv:2607.19830v1 Announce Type: new Abstract: Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying sol

arXiv:2607.14046v1 Announce Type: new Abstract: This paper presents Earthquaker-AI, a hybrid educational framework building upon a previously implemented educational robotics project by integrating a

Apple Music serves listeners across 150+ storefronts in dozens of languages, with a catalog that grows by hundreds of thousands of new tracks daily. At this scale, search recall on misspelled, transli

arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the in

arXiv:2607.05217v1 Announce Type: cross Abstract: Public institutions increasingly use large language models (LLMs) to answer citizens' questions, often pairing a curated knowledge base with live web

arXiv:2607.02929v1 Announce Type: cross Abstract: Visual brand language is the set of visual properties that convey brand identity for a product. What is the impact of visual brand language on a perso

arXiv:2601.22652v2 Announce Type: replace-cross Abstract: Spectral gradient methods, such as the Muon optimizer, modify gradient updates by preserving directional information while discarding scale, a

arXiv:2607.02262v1 Announce Type: new Abstract: Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are pr

arXiv:2607.01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility. T

arXiv:2606.31517v1 Announce Type: cross Abstract: Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled ima

arXiv:2606.28379v1 Announce Type: cross Abstract: We introduce LEDGER to tackle the novel context engineering challenge of agentic document editing, where localized edits to long, structured documents

arXiv:2606.29467v1 Announce Type: new Abstract: Medical question-answering benchmarks rarely cover the maternal, neonatal, child, and reproductive-health questions a nurse-midwife asks, and, to our kn

arXiv:2511.03217v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel in generating fluent utterances but can lack reliable grounding in verified information. At the same time,

arXiv:2606.27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, effic

arXiv:2606.26916v1 Announce Type: new Abstract: Developing physically aware video generation models remains a significant challenge due to the difficulty in capturing diverse physical phenomena, such

arXiv:2606.24467v1 Announce Type: new Abstract: Long-context large language model (LLM) inference is increasingly constrained by the memory footprint and decoding cost of key-value (KV) caches, limiti

arXiv:2606.24543v1 Announce Type: new Abstract: Large Language Models (LLMs) are traditionally viewed as autoregressive generators. However, from the perspective of collective computation, they functi

arXiv:2606.10842v1 Announce Type: new Abstract: We describe ConvMemory v2, an opt-in token-evidence reranker that sits after the lightweight ConvMemory v1 reranker and reorders only v1's protected top

arXiv:2606.09160v1 Announce Type: cross Abstract: This paper presents a unified system designed to support precision agriculture by integrating advanced weather prediction, crop recommendation, and a

arXiv:2606.07843v1 Announce Type: cross Abstract: Schema matching, a critical task for integrating data from diverse sources, seeks to identify correspondences between columns across different schemas

arXiv:2606.07512v1 Announce Type: cross Abstract: Current Vision-Language Models struggle with hours-long videos because processing full-length visual sequences induces prohibitive token explosion and

arXiv:2606.05175v1 Announce Type: new Abstract: We study generic triple-latent sequence models that maintain a running token state and compressed pair-memory pathway to capture higher-order token inte

arXiv:2606.05901v1 Announce Type: new Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing. Despite these advances, LLMs and LLM-based sys

arXiv:2606.04437v1 Announce Type: new Abstract: Collaborative perception extends the perceptual range of autonomous vehicles by sharing information across agents, but heterogeneous sensors and percept

arXiv:2606.04302v1 Announce Type: new Abstract: Key-value (KV) caching accelerates inference of large language models (LLMs) by reusing past computations for generated tokens. Its importance becomes e