880044 results (page 22 of 35202)
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Multi-LLM Token Filtering and Routing for Sequential Recommendation
Large language models (LLMs) have recently shown promise in recommendation by providing rich semantic knowledge. While most existing approaches rely on external textual corpora to align LLMs with recommender systems, we revisit a more fundamental yet underexplored question: Can recommendation benefit from LLM token embeddings alone without textual input? Through a systematic empirical study, we sh…
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Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models
Transformer-based embedding models suffer from quadratic computational and linear memory complexity, limiting their utility for long sequences. We propose recurrent architectures as an efficient alternative, introducing a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. …
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Continuous Focus Groups: A Longitudinal Method for Clinical HRI in Autism Care
Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static or one-off formats that cannot capture how stakeholder perspectives evolve over time. This limitation is especially evident in clinical contexts, where families and patients face heavy burdens and cannot easily participate in repeated research encou…
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Similarity-based Portfolio Construction for Black-box Optimization
In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks: inaccuracies in the selector may lead to poor choices, and even well-performing algorithms with high variance can yield unsatisfactory results in a single run. A natural remedy is to split the evaluation budget across m…
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Attraction, Repulsion, and Friction: Introducing DMF, a Friction-Augmented Drifting Model
Drifting Models [Deng et al., 2026] train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration at inference. The original analysis leaves two questions open. The drift-field iteration admits a locally repulsive regime in a two-particle surrogate, and vanishing of the drift ($V_{p,q}\equiv 0$) is not known to force the learned distribution $q$ to match…
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How Do People Accept Robot in Public Space? A Cross-Cultural Study in Germany and Japan
With the increasing deployment of robots in public spaces, encounters between robots and incidentally copresent persons (InCoPs) are becoming more frequent. However, InCoPs remain largely underexplored in the literature, particularly from a cross-cultural perspective. Therefore, the present study investigates cultural differences in InCoPs' existence acceptance (EA) of autonomous cleaning robots i…
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Local Convergence Results for Sequential Quadratic Programming with Complementarity Constraints
Mathematical programs with complementarity constraints (MPCCs) are a challenging class of nonlinear optimization problems, because their nonlinear programming reformulations violate standard constraint qualifications at every feasible point. This paper analyzes sequential quadratic programming with complementarity constraints (SQPCC). In this method, the complementarity constraints are retained in…
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Implementing CPSLint: A Data Validation and Sanitisation Tool for Industrial Cyber-Physical Systems
Raw datasets are often too large and unstructured to work with directly, and require a data preparation phase. The domain of industrial Cyber-Physical Systems (CPSs) is no exception, as raw data typically consists of large time-series data collections that log the system's status at regular time intervals. The processing of such raw data is often carried out using ad hoc, case-specific, one-off Py…
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Scalable Neighborhood-Based Multi-Agent Actor-Critic
We propose MADDPG-K, a scalable extension to Multi-Agent Deep Deterministic Policy Gradient (MADDPG) that addresses the computational limitations of centralized critic approaches. Centralized critics, which condition on the observations and actions of all agents, have demonstrated significant performance gains in cooperative and competitive multi-agent settings. However, their critic networks grow…
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Understanding the complex morphology of a CME II: how pre-eruptive conditions shape CME evolution
The morphology and heliospheric impact of coronal mass ejections (CMEs) are strongly shaped by their preeruptive magnetic configuration and surrounding coronal environment, yet these influences remain difficult to constrain observationally. We analyze a complex CME that erupted on 2024 October 26 using multiviewpoint remote sensing observations and in situ measurements. Using the physics based COR…
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Audio-DeepThinker: Progressive Reasoning-Aware Reinforcement Learning for High-Quality Chain-of-Thought Emergence in Audio Language Models
Large Audio-Language Models (LALMs) have made significant progress in audio understanding, yet they primarily operate as perception-and-answer systems without explicit reasoning processes. Existing methods for enhancing audio reasoning rely either on supervised chain-of-thought (CoT) fine-tuning, which is limited by training data quality, or on reinforcement learning (RL) with coarse rewards that …
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CanonSLR: Canonical-View Guided Multi-View Continuous Sign Language Recognition
Continuous Sign Language Recognition (CSLR) has achieved remarkable progress in recent years; however, most existing methods are developed under single-view settings and thus remain insufficiently robust to viewpoint variations in real-world scenarios. To address this limitation, we propose CanonSLR, a canonical-view guided framework for multi-view CSLR. Specifically, we introduce a frontal-view-a…
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The Metal Content of Resolved Galaxies
We present a homogeneous metallicity analysis of old stellar populations in Local Volume (LV) galaxies using data from the CMDs/TRGB catalog of the Extragalactic Distance Database (EDD; http://edd.ifa.hawaii.edu), which provides uniformly measured TRGB distances and PSF photometry for resolved stars in over 500 nearby galaxies observed with the Hubble Space Telescope. We apply the calibration of L…
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Committed SAE-Feature Traces for Audited-Session Substitution Detection in Hosted LLMs
Hosted-LLM providers have a silent-substitution incentive: advertise a stronger model while serving cheaper replies. Probe-after-return schemes such as SVIP leave a parallel-serve side-channel, since a dishonest provider can route the verifier's probe to the advertised model while serving ordinary users from a substitute. We propose a commit-open protocol that closes this gap. Before any opening r…
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Latest results from the IceCube Neutrino Observatory
The IceCube Neutrino Observatory has opened a new window into the high-energy Universe, providing measurements of neutrinos over a broad energy range. This contribution presents recent results, including a follow-up on the first identification of a steady neutrino source NGC 1068, measurements of the flavor composition of the diffuse astrophysical flux, limits on prompt atmospheric neutrinos, and …
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STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs
Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight into compositional skill gaps of LLMs and how to improve them. To make these weaknesses visible, we propose Scaffolded Task Design (STaD) framework. STaD generates controlled variations of benchmark tasks based on the concept of scaffolding, which…
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QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning
Large language models (LLMs) show strong capabilities in general reasoning but typically lack reliability in scientific domains like quantum mechanics, which demand strict adherence to physical constraints. This limitation arises from the scarcity of verifiable training resources and the inadequacy of coarse feedback signals in standard alignment paradigms. To address the data challenge, we introd…
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Trefftz methods with evanescent plane waves
Classical Trefftz methods approximate Helmholtz solutions using propagative plane waves and are subject to strong numerical instabilities. Evanescent plane wave bases can substantially mitigate this phenomenon. We propose a simple recipe to select such basis functions. We show that the numerical results obtained by the Ultraweak Variational Formulation (UWVF) greatly improve thanks to this choice.…
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Gravitational Waves from the Cosmic Dawn: Tracing Cosmic Black Hole Binaries with ET, LGWA and LISA
Next generation detectors, such as LISA, LGWA, and ET will, for the first time, probe the high redshift Universe, offering unique insight into the birth, growth, and dynamics of the first black holes (BHs) during their earliest stages formation. We aim to predict merger rates and gravitational wave (GW) signatures of "cosmic" binary BHs, forming as a result of galaxy mergers, at z>=4. We investiga…
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Alleviating Linguistic and Interactional Anxiety of Non-Native Speakers in Multilingual Communication
Non-native speakers (NNSs) frequently encounter speaking difficulties in multilingual communication, where existing approaches have shown promise in facilitating NNSs' comprehension and participation in real-time communication. However, they often overlook providing direct speaking support, where anxiety stemming from linguistic inadequacy and uncertain communication dynamics are core issues. To a…
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Copy-as-Decode: Grammar-Constrained Parallel Prefill for LLM Editing
LLMs edit text and code by autoregressively regenerating the full output, even when most tokens appear verbatim in the input. We study Copy-as-Decode, a decoding-layer mechanism that recasts edit generation as structured decoding over a two-primitive grammar: <copy lines="i-j"/> references an input line range, <gen>...</gen> emits new content. A token-level FSM guarantees syntactic validity, and a…
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Beyond Reproduction: A Paired-Task Framework for Assessing LLM Comprehension and Creativity in Literary Translation
Large language models (LLMs) are increasingly used for creative tasks such as literary translation. Yet translational creativity remains underexplored and is rarely evaluated at scale, while source-text comprehension is typically studied in isolation, despite the fact that, in professional translation, comprehension and creativity are tightly intertwined. We address these gaps with a paired-task f…
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Extending One-Step Image Generation from Class Labels to Text via Discriminative Text Representation
Few-step generation has been a long-standing goal, with recent one-step generation methods exemplified by MeanFlow achieving remarkable results. Existing research on MeanFlow primarily focuses on class-to-image generation. However, an intuitive yet unexplored direction is to extend the condition from fixed class labels to flexible text inputs, enabling richer content creation. Compared to the limi…
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Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models
Modern text-to-image (T2I) models amplify harmful societal biases, challenging their ethical deployment. We introduce an inference-time method that reliably mitigates social bias while keeping prompt semantics and visual context (background, layout, and style) intact. This ensures context persistency and provides a controllable parameter to adjust mitigation strength, giving practitioners fine-gra…
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Cramér-Rao Bound Optimization for Near-Field ISAC with Extended Targets
Near-field integrated sensing and communication (ISAC) requires target models beyond the point-target abstraction when the target has a non-negligible spatial extent. In this letter, a geometry-aware transmit design is developed for a parametric extended target (ET) described by its center, orientation, and size under spherical-wave propagation. The CRB for the geometric parameters is formulated a…