Knowledge catalogue
arXiv:2609.03681v1 Announce Type: new Abstract: Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinforcement learning with expensive and poten
arXiv:2605.25784v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have advanced rapidly in remote-sensing analysis, yet existing evaluations remain predominantly 2D-centric.
arXiv:2609.01833v1 Announce Type: new Abstract: Sentence-level recognition of depression symptoms is challenging because similar expressions can differ in symptom relevance, and language-model inferen
arXiv:2609.01846v1 Announce Type: new Abstract: Recorded lecture videos, often enhanced with search and summarization features, are a standard study resource. However, students cannot easily ask cours
arXiv:2609.02730v1 Announce Type: new Abstract: Production recommender systems shape what billions of people see, and sustaining their performance requires continual optimization: as content, user beh
arXiv:2609.02270v1 Announce Type: cross Abstract: Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing generators primarily focus on inducing
arXiv:2609.02771v1 Announce Type: cross Abstract: Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which example
arXiv:2609.02138v1 Announce Type: cross Abstract: Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their performance depends strongly on hyperparam
Meet E-Commerce Bench, a new benchmark for long-horizon autonomous business operations. 🚀 Agents start with ¥100,000 to run online stores for 365 days, handling sourcing, negotiation, pricing, promoti
arXiv:2609.01947v1 Announce Type: cross Abstract: Compact instruction-following rerankers are attractive for deployment, but conventional distillation pipelines typically train students by offline imi
arXiv:2609.02510v1 Announce Type: new Abstract: We study body-only, 12-class acted-emotion classification from skeleton motion under leave-performer-out (LPO) evaluation, a hard, underdetermined setti
arXiv:2609.02231v1 Announce Type: new Abstract: Interview assessment requires per-criterion judgments grounded in behavioral evidence, yet surging applicant volumes have made human-only evaluation cos
arXiv:2603.16749v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode.
arXiv:2504.03626v2 Announce Type: replace-cross Abstract: We present quantum speedups for sampling from distributions of the form pipropto e^{-f} on R^d. We consider two stochastic oracle models: a st
arXiv:2609.02476v1 Announce Type: new Abstract: Wireless Capsule Endoscopy (WCE) captures and streams video while passing through a patient's Gastrointestinal (GI) tract and is used to examine its irr
arXiv:2609.02652v1 Announce Type: new Abstract: Leech-lattice vector quantization holds the strongest reported 2-bit quality under its own evaluation protocol. Its kernel decodes one shell; we found n
arXiv:2507.08468v2 Announce Type: replace Abstract: This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the frame
arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting
arXiv:2508.17635v2 Announce Type: replace Abstract: Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documen
arXiv:2609.00769v1 Announce Type: new Abstract: This letter presents a compact two-degree-of-freedom (DoF) robotic finger with a flexion-selective passive continuously variable transmission (CVT) to a
arXiv:2601.04243v2 Announce Type: replace-cross Abstract: This paper presents a hybrid insider threat detection framework for enterprise environments, integrating multi-agent simulation, layered SIEM
arXiv:2609.00076v1 Announce Type: new Abstract: Clinical artificial intelligence is increasingly embedded in real-world care, yet existing safety mechanisms are poorly suited to reconstructing and lea
arXiv:2609.00211v1 Announce Type: new Abstract: Conversational artificial intelligence is increasingly embedded in everyday social environments, where it functions as both an informational tool and a
arXiv:2609.00232v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on OCR-centric document understanding and text-rich visual reasoning benchmark
arXiv:2609.01194v1 Announce Type: new Abstract: Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine on
arXiv:2609.01554v1 Announce Type: cross Abstract: Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appearan
arXiv:2609.01352v1 Announce Type: cross Abstract: Role-playing large language models (LLMs) are expected to adopt a character's style while also respecting that character's knowledge boundaries. Prior
arXiv:2609.01111v1 Announce Type: new Abstract: Clinical LLM assistants must reason over multi-visit patient trajectories, yet whether the compact history representations used to scale them---retrieva
arXiv:2609.01600v1 Announce Type: cross Abstract: Dynamic agent harnesses let language models change the software that shapes their own execution. This flexibility brings a new reasoning burden: a loc
arXiv:2609.00366v1 Announce Type: cross Abstract: High test accuracy and good aggregate calibration do not show whether an individual prediction is structurally supported by its evidence. In tabular d
arXiv:2609.00508v1 Announce Type: new Abstract: Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative new
arXiv:2603.15604v2 Announce Type: replace Abstract: Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy
arXiv:2609.01409v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in generating scientific figures from text or images. However, producing publication-ready f
arXiv:2609.01526v1 Announce Type: new Abstract: Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in f
arXiv:2609.00089v1 Announce Type: new Abstract: Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for ele
arXiv:2609.00344v1 Announce Type: new Abstract: Translation students need to learn both how to use translation technologies and how to judge the choices those technologies make available. This article
arXiv:2510.15144v4 Announce Type: replace Abstract: Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now appro
arXiv:2609.00282v1 Announce Type: cross Abstract: Motor imagery (MI) electroencephalography (EEG) decoding could support post-stroke rehabilitation, but models developed on healthy cohorts may not tra
arXiv:2609.00012v1 Announce Type: new Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy tha
arXiv:2602.03053v2 Announce Type: replace Abstract: Multi-Agent Systems (MAS) built on Large Language Models (LLMs) often exhibit high variance in their reasoning trajectories. Process verification, w
arXiv:2609.00491v1 Announce Type: new Abstract: Communicating across cultures is inherently challenging, especially through culturally dense and ambiguous formats like memes. While people expect large
arXiv:2609.00389v1 Announce Type: new Abstract: We combine neural network means with exact Matern kernel regressions of their residuals and of their learned features, and evaluate the pairing on two p
arXiv:2609.01135v1 Announce Type: new Abstract: Minimum Bayes Risk (MBR) decoding enables high-quality text generation by selecting the hypothesis that maximizes a utility metric over sampled pseudo-r
arXiv:2609.00906v1 Announce Type: new Abstract: High-precision insertion remains a fundamental challenge in robotic manipulation due to the strict alignment requirements and contact-rich interactions
arXiv:2609.01427v1 Announce Type: new Abstract: Dense self-supervised learning (SSL) is a powerful paradigm for learning without annotations the local descriptors required to solve dense medical imagi
arXiv:2609.00555v1 Announce Type: cross Abstract: Language-driven dexterous grasp models, such as DextER, perform well when instructions specify where to grasp, but we find they fail systematically wh
arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key lim
arXiv:2609.00384v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and i
arXiv:2609.01276v1 Announce Type: new Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exist
arXiv:2609.00663v1 Announce Type: new Abstract: Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating
arXiv:2609.01343v1 Announce Type: new Abstract: Looped Transformers increase effective depth by iterating a shared block of layers, but most evaluations compare at fixed model size, conflating archite
arXiv:2609.00595v1 Announce Type: cross Abstract: Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating f
arXiv:2609.00304v1 Announce Type: new Abstract: Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus in
arXiv:2609.00519v1 Announce Type: cross Abstract: Safeguards in deployed LLM services are evaluated by refusal, attack success, and policy violation rates. Those rates characterize how a control perfo
arXiv:2609.00868v1 Announce Type: cross Abstract: Vision-language models are evaluated by aggregate accuracy on multimodal benchmarks, a practice that implicitly assumes the model uses its visual inpu
arXiv:2510.01030v2 Announce Type: replace Abstract: The human ability to translate diverse perceptual and linguistic inputs into structured behavior has been thought to rest on learning robust represe
arXiv:2609.00858v1 Announce Type: new Abstract: Effective humanitarian response depends on the rapid synthesis of heterogeneous, high-volume information sources - a task that routinely exceeds human a
arXiv:2601.12449v2 Announce Type: replace-cross Abstract: AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper to
arXiv:2608.29133v1 Announce Type: new Abstract: History is not preserved in complete, continuous form. Accounts of a person's activities, relationships and historical contexts are scattered across tex
arXiv:2608.30506v1 Announce Type: new Abstract: Automated inspection of small industrial components, including sub-centimetre-scale parts where defects are geometry-driven and poorly resolved by stand