Knowledge catalogue
arXiv:2609.27606v1 Announce Type: new Abstract: We introduce State-Grounded Conditioning (SGC), a design principle for user-facing LLM agents that must condition on live user state (game state, sessio
arXiv:2512.11490v2 Announce Type: replace Abstract: Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both regio
arXiv:2609.27865v1 Announce Type: cross Abstract: Sharing a deep encoder does not, by itself, fix the central confound of task-comparison scores. We show that cross-evaluated heads on a frozen shared
arXiv:2609.26826v1 Announce Type: cross Abstract: Frontier benchmarks need tasks that current models cannot solve. But a task that no model solves is not automatically a hard task. The same zero pass
arXiv:2609.27560v1 Announce Type: cross Abstract: Avatar-streaming systems are commonly evaluated with image and video quality assessment (IQA/VQA) metrics, implicitly treating visual fidelity as a pr
arXiv:2609.26490v1 Announce Type: new Abstract: This study introduces an interdisciplinary framework for benchmarking robots deployed in public environments, addressing the gap between traditional lab
arXiv:2609.26567v1 Announce Type: new Abstract: Robots that store past experiences must select which one to reuse in a new scene. Most systems select by visual similarity, and most evaluations report
arXiv:2609.22696v1 Announce Type: new Abstract: Decentralized social media platforms create new opportunities and challenges for computational mental health research because data access, moderation, l
arXiv:2609.25606v1 Announce Type: new Abstract: Cable routing requires coordinated control of global cable topology and changing local contacts. We present CableVLA, an end-to-end multimodal vision-la
arXiv:2609.26460v1 Announce Type: new Abstract: Anomaly detection systems are often trained using normal data alone, while model selection and evaluation typically require labeled anomalies. We study
arXiv:2609.25340v1 Announce Type: new Abstract: Concept drift is a common phenomenon in real-world data streams, in which changes in the data-generating distribution can degrade predictive model perfo
arXiv:2609.25724v1 Announce Type: new Abstract: This paper present a spiral-cavity wheel for lunar regolith excavation and a sensor-light evaluation stack that jointly estimates fill ratio (vision), s
arXiv:2609.22851v2 Announce Type: cross Abstract: Large Audio Language Models (LALMs) utilize either continuous features or discrete tokens, yet the optimal representation paradigm for general audio u
arXiv:2609.23957v1 Announce Type: new Abstract: Scientific agents are mostly evaluated on whether they complete tasks or recover known results; we instead study variation across repeated open-ended ca
arXiv:2609.26792v1 Announce Type: new Abstract: Faithfully evaluating end-to-end driving policies in simulation requires observations that are not merely photo-realistic, but preserve the scene featur
arXiv:2609.24115v1 Announce Type: new Abstract: Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diver
arXiv:2609.22601v1 Announce Type: cross Abstract: The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized inf
arXiv:2609.25192v1 Announce Type: new Abstract: Financial search is a highly demanding task for LLM agents, requiring not only a correct final answer but also temporally valid information retrieval, a
arXiv:2602.14285v2 Announce Type: replace-cross Abstract: Automated scholarly paper review (ASPR) has entered the coexistence phase with traditional peer review, where artificial intelligence (AI) sys
arXiv:2609.25257v1 Announce Type: cross Abstract: Safety evaluation in sampling-based stochastic model predictive control often requires numerical estimation of exit functionals. The approximation of
SWE‑Serve evaluates AI coding agents on 53 inference‑engineering tasks derived from SGLang pull requests across six functional domains. Among 19 tasks that launch a live server, patches that pass loca
arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation u
arXiv:2609.25697v1 Announce Type: new Abstract: Purpose: To develop and evaluate an interpretable artificial intelligence (AI) framework for glaucoma screening from low-cost portable, handheld retinal
arXiv:2609.23925v1 Announce Type: cross Abstract: We study whether LLMs can produce executable workflow artifacts that remain consistent across graph structure, tool implementation, schema bindings, a
arXiv:2609.25813v1 Announce Type: new Abstract: This short report documents our entry to the COMFORT Localization Benchmark (IROS 2026), evaluated on the GrandTour dataset recorded with the Boxi paylo
arXiv:2609.26097v1 Announce Type: new Abstract: Remote-sensing (RS) multimodal large language models (MLLMs) are trained and evaluated only in English, while text-only instruction data covers over 100
arXiv:2609.23023v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) require coordinated choices over network representation, sampling, loss construction, and optimization, while e
arXiv:2609.25430v1 Announce Type: new Abstract: Neural surrogates can substantially accelerate computer-aided engineering (CAE) workflows, but their use in design requires uncertainty estimates that r
arXiv:2609.25009v1 Announce Type: new Abstract: Numerically equivalent word problems should yield the same canonical answer whether a quantity is written as a decimal, fraction, percentage, number wor
arXiv:2609.22792v1 Announce Type: cross Abstract: LLM agents are increasingly used for security tasks: vulnerability discovery, exploit reproduction, and patch generation. Improving them at the model
arXiv:2609.22408v1 Announce Type: new Abstract: AI systems are becoming participants in the evaluation and use of scientific research. They encounter citation counts, download statistics and lists of
arXiv:2609.22818v1 Announce Type: cross Abstract: Memory-poisoning defenses for LLM agents are typically evaluated by their ability to prevent attacks. However, the traffic they process is rarely adve
arXiv:2609.26035v1 Announce Type: new Abstract: Conversational agents often express answers in a uniformly confident register. We test whether expressed uncertainty, provenance-aware assertion, and ex
arXiv:2609.25900v1 Announce Type: new Abstract: When we evaluate the performance of our odometry, it is common practice to score the estimated track against a ground truth. Unfortunately, scoring uses
arXiv:2609.22376v1 Announce Type: cross Abstract: Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measure
arXiv:2609.23333v1 Announce Type: new Abstract: Digital direct-to-consumer (DTC) health campaigns are usually measured after the fact. In-flight forecasting commonly relies on a separate classifier fo
arXiv:2606.18193v2 Announce Type: replace-cross Abstract: We evaluate the adversarial robustness of three frontier large language models (LLMs) developed by Anthropic, Opus 4.8, Fable 5 and its succes
arXiv:2609.24467v1 Announce Type: new Abstract: Music festival lineups emerge from complex relationships among artists, genres, releases, labels, and past performances, making the prediction of future
arXiv:2609.24124v1 Announce Type: new Abstract: Active perception and manipulation are crucial for robots to interact with complex scenes. Existing benchmarks struggle to evaluate how robots effective
arXiv:2609.22096v1 Announce Type: new Abstract: Climate campaigns are often evaluated through attention and mobilization, but less is known about the well-being language that accompanies them. Whether
arXiv:2609.23048v1 Announce Type: cross Abstract: Compressed manipulation policies can pass offline evaluation while failing in closed-loop execution; this dissociation is established in prior work an
arXiv:2609.22291v1 Announce Type: new Abstract: This paper presents a comprehensive experimental evaluation and detailed analysis of state-of-the-art multi-object tracking algorithms, with an emphasis
arXiv:2609.23830v1 Announce Type: new Abstract: Comparing audio-visual deepfake detectors requires coordinating dataset adaptation, temporal input representation, model interfaces and experimental con
arXiv:2609.23659v1 Announce Type: new Abstract: Ethereum decentralized finance (DeFi) provides a public, time-stamped record of transaction-level event streams, but the same public symbols can create
arXiv:2609.23825v1 Announce Type: new Abstract: We present a comprehensive benchmark of Federated Learning (FL) for multilingual Automatic Speech Recognition (ASR), evaluating four Speech-LLM architec
arXiv:2609.22538v1 Announce Type: new Abstract: Large language model (LLM) planners can decompose natural-language instructions and select reusable robot skills, but choosing the correct skill does no
arXiv:2609.22103v1 Announce Type: cross Abstract: Electroencephalography (EEG) provides a noninvasive means of capturing emotion-related neural dynamics, yet reliable EEG emotion decoding lacks models
arXiv:2609.23714v1 Announce Type: new Abstract: Infectious bovine pinkeye is a contagious ocular disease that adversely affects cattle health, welfare, and agricultural productivity. Conventional diag
arXiv:2609.22262v1 Announce Type: cross Abstract: Medical time series (MedTS), including electrocardiograms (ECG), electroencephalograms (EEG), photoplethysmography (PPG), and vital-sign recordings, a
arXiv:2609.23003v1 Announce Type: new Abstract: We present M3GA-Wild, the first benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests. M3GA-Wild unifies and extends ex
arXiv:2609.22778v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require expert
arXiv:2609.22272v1 Announce Type: new Abstract: We formulate Artistic Intelligence as exploration driven world realization, leaving space for creative possibility while preserving the semantic, artist
arXiv:2609.22285v1 Announce Type: new Abstract: Adapting language models to new domains via continual pre-training raises a basic evaluation problem: if the training corpus overlaps with what the mode
arXiv:2609.23950v1 Announce Type: cross Abstract: Cardiovascular magnetic resonance (CMR) provides comprehensive cardiac assessment but remains underutilized because of the complexity of acquisition,
arXiv:2609.23125v1 Announce Type: new Abstract: Activation quantisation is usually evaluated with an aggregate metric, perplexity, averaged over every token a model predicts. We ask whether that avera
arXiv:2609.22200v1 Announce Type: new Abstract: LLM assistants and agentic systems log long multi-turn conversations. AI providers often scan these conversations for Personally Identifiable Informatio
arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that
arXiv:2609.22959v1 Announce Type: cross Abstract: The medications prescribed to a patient often change during a hospital admission as clinicians start, stop, or continue therapies. We study whether mo
arXiv:2603.26995v2 Announce Type: replace Abstract: Autonomous robots commonly aim to achieve a nominal objective while minimizing a cost. Without contingency preparation, this can leave them vulnerab
arXiv:2609.23425v1 Announce Type: new Abstract: Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is li