Knowledge catalogue
arXiv:2609.27176v1 Announce Type: new Abstract: Evidence that evaluation material entered training does not reveal how much it affected evaluation. This distinction leaves a contaminated benchmark sco
arXiv:2609.28245v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistic
arXiv:2609.27299v1 Announce Type: cross Abstract: Information systems (IS) researchers increasingly use quasi-experimental methods such as difference-in-differences (DiD) and instrumental variables (I
arXiv:2609.27773v1 Announce Type: cross Abstract: As Large Language Models (LLMs) move from conversational assistants to advanced agentic systems, guardrail failures can convert adversarial intents in
arXiv:2609.27615v1 Announce Type: new Abstract: In multimodal emotion recognition (MER), human affective states are inferred by integrating complementary cues from multiple modalities. In audio-text M
arXiv:2603.19713v2 Announce Type: replace Abstract: Even when it is difficult to assign absolute class labels to individual instances, relational information may still be available, such as whether tw
arXiv:2609.27932v1 Announce Type: new Abstract: Ganian et al. (ICLR 2026) proved that quantized neural network training is fixed-parameter tractable when parameterized jointly by architecture treewidt
arXiv:2609.27342v1 Announce Type: new Abstract: Cutting changes both the shape and topology of deformable objects, making accurate simulation challenging for robotic manipulation. A simulator must tra
arXiv:2609.27871v1 Announce Type: cross Abstract: In mainstream agent frameworks, a step ends when the agent's own output says it has finished. Durable-execution platforms bound retries and time, but
arXiv:2609.27650v1 Announce Type: new Abstract: Brain-to-language decoding translates neural activity associated with language production, internal speech and perception into linguistic or expressive
arXiv:2609.27275v1 Announce Type: new Abstract: Most autonomous-driving datasets record only the action executed by a behavior policy and the single future that followed, providing limited supervision
arXiv:2609.27317v1 Announce Type: cross Abstract: Infrared (IR) imaging is crucial for autonomous driving, remote sensing, and other perception tasks. However, adverse weather may introduce fake struc
arXiv:2609.28281v1 Announce Type: new Abstract: Interlocking brick assembly provides a representative testbed for evaluating real-world robotic manipulation capabilities, where diverse structural desi
arXiv:2609.26828v1 Announce Type: cross Abstract: NAND-backed storage offers the capacity needed to scale LLM prefix caching, but its block I/O path incurs CPU cache contention and host-DRAM staging i
arXiv:2609.28328v1 Announce Type: new Abstract: Accurate localization of the bronchoscope within the bronchial tree is essential for clinicians to be able to reach target lesions, perform biopsies and
arXiv:2609.26927v1 Announce Type: new Abstract: The objective of this article is to provide design principles and a software architecture for enabling interaction between humans and multiple agents in
arXiv:2603.27597v2 Announce Type: replace Abstract: Research on artificial consciousness increasingly shifts evaluation from behaviour to internal architecture. Theory-based indicators are used to upd
arXiv:2609.27607v1 Announce Type: cross Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its pres
arXiv:2609.28090v1 Announce Type: cross Abstract: Backtest auditing is a calibration problem: high flaw recall is not useful when the model falsely flags matched clean strategies. We build a 96-item p
arXiv:2609.28449v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in coding tasks, but their ability to reason about code execution remains unclear. Existing reposit
arXiv:2609.27262v1 Announce Type: new Abstract: Production customer-support systems often require LLMs to support multiple skills, such as intent classification, question answering, summarization, or
arXiv:2609.27327v1 Announce Type: new Abstract: Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting
arXiv:2609.27336v1 Announce Type: new Abstract: Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn from failures found during testing. We present C
arXiv:2609.27681v1 Announce Type: new Abstract: Automated assessment of the Critical View of Safety (CVS) in laparoscopic cholecystectomy requires both recognition of the three CVS criteria and anatom
arXiv:2609.27825v1 Announce Type: new Abstract: Anomalous time series play a critical role in safety-critical domains, yet they are inherently scarce, heterogeneous, and costly to obtain. Existing tim
arXiv:2609.27745v1 Announce Type: new Abstract: This paper advocates category theory as a practical framework for structuring and improving rein- forcement learning in high-dimensional, partially obse
arXiv:2603.13768v2 Announce Type: replace-cross Abstract: Despite the strong performance of large audio language models (LALMs) in various tasks, exactly how and where they integrate acoustic features
arXiv:2609.27273v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly act on behalf of users online. What happens when the environments they operate in have incentives that do not al
arXiv:2609.27468v1 Announce Type: new Abstract: Action-chunked vision-language-action (VLA) policies improve inference efficiency, but limited feedback within committed action chunks can lead to accum
arXiv:2609.27067v1 Announce Type: cross Abstract: Large language model (LLM)-based agents use Electronic Design Automation (EDA) tools to generate and revise register-transfer-level (RTL) designs unde
arXiv:2609.27857v1 Announce Type: cross Abstract: Large language model (LLM) agents often process external tool responses as they arrive, making response timing part of the decision process. We introd
arXiv:2505.10083v2 Announce Type: replace Abstract: Conventional forecasting methods are trained end-to-end on unimodal time series, which limits their ability to exploit textual information and under
arXiv:2609.26945v1 Announce Type: new Abstract: Judicial reasoning remains challenging for large language models (LLMs) to analyze. This paper contributes a sentence-level benchmark for evaluating the
arXiv:2609.27469v1 Announce Type: new Abstract: Controlling the shape of a continuum soft robot typically requires an accurate dynamic model and actuation of all degrees of freedom. We show that regul
arXiv:2609.27242v1 Announce Type: cross Abstract: LLMs can generate programs for ARC-AGI-2 tasks, but the provided compute only allows a small number of attempts to generate, debug and validate soluti
arXiv:2609.26913v1 Announce Type: cross Abstract: No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or
arXiv:2609.26860v1 Announce Type: cross Abstract: Web applications are increasingly targeted by cyberattacks that exploit HTTP requests to evade security mechanisms. Traditional web application firewa
arXiv:2609.28250v1 Announce Type: new Abstract: At test time, large language models (LLMs) can encode historical information in activation memory (i.e., KV caches) and parametric memory (i.e., updated
arXiv:2609.27632v1 Announce Type: cross Abstract: We present a compliance-first architecture for AI in regulated finance that treats regulation as an orientation layer rather than a deterministic rule
arXiv:2609.27994v1 Announce Type: cross Abstract: Financial institutions are beginning to deploy agentic workflows in credit, fraud, collections, compliance, and operational control. Governance remain
arXiv:2609.27439v1 Announce Type: new Abstract: Delayed acoustic positioning packets constrain historical navigation states, but a current-time update evaluates them against a mismatched state, wherea
arXiv:2609.28290v1 Announce Type: new Abstract: Meaning identity (whether two sentences say the same thing after wording changes) is treated in retrieval and RAG as a geometric fact about independentl
arXiv:2609.28165v1 Announce Type: new Abstract: Multimodal learning (MML) falls into the optimization dilemma due to the modality imbalance phenomenon, leading to suboptimal overall performance in pra
arXiv:2609.27976v1 Announce Type: cross Abstract: Conformal Bayes combines Bayesian posterior predictive scores with conformal calibration, but under continuous label shift both the score and calibrat
arXiv:2609.27690v1 Announce Type: new Abstract: Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic
arXiv:2609.27354v1 Announce Type: cross Abstract: Generative AI systems are increasingly deployed to address domain problems. These systems operate under technical, regulatory, institutional, and norm
arXiv:2609.28299v1 Announce Type: new Abstract: Contact-implicit trajectory optimization formulates contact-rich manipulation as a single constrained program; however, that single program run collapse
arXiv:2609.28427v1 Announce Type: new Abstract: Personalizing exoskeleton assistance across operating conditions is constrained by the time and physical effort required to collect user feedback. We ex
arXiv:2609.28471v1 Announce Type: new Abstract: Our results show that contrastive learning outperforms a classification-based approach to authorship verification under the tested settings. We identify
arXiv:2609.27014v1 Announce Type: cross Abstract: Legal contracts are structurally complex documents in which contradictions may emerge across distant and interconnected provisions. Although large lan
arXiv:2609.28004v1 Announce Type: cross Abstract: Effective summarization of interrogative dialogues is a critical task in forensic and investigative settings, requiring high factual accuracy, coheren
arXiv:2609.28247v1 Announce Type: cross Abstract: Large Language Models (LLMs) introduce an exciting new paradigm for planning and navigation in robotics, but fail on even simple multi-robot tasks as
arXiv:2609.26853v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in
arXiv:2609.27381v1 Announce Type: new Abstract: Contact detection during robotic manipulation allows robots to recognize unexpected contact and adapt their motion accordingly. However, in low-cost rob
arXiv:2412.02575v3 Announce Type: replace Abstract: Driven by practical demands in land resource monitoring and national defense security, this paper introduces the Remote Sensing Copy-Move Question A
arXiv:2609.26905v1 Announce Type: cross Abstract: Stacking heterogeneous vision backbones (CNNs, ViTs, and hybrids) is the de facto recipe for accuracy, calibration, and robustness, yet two coupled pa
arXiv:2609.27314v1 Announce Type: new Abstract: Comparing current and past observations helps robots understand scene changes and select subsequent actions during manipulation. However, comparing visu
arXiv:2609.27720v1 Announce Type: new Abstract: Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surroun
arXiv:2609.27165v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to measure public value orientations from long social media posts, yet such posts often mix backgro
arXiv:2609.27421v1 Announce Type: new Abstract: Multi-constraint instruction following requires a model to respond to a query under many simultaneously active constraints. Even strong instruction-tune