1177010 results (page 87 of 47081)
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OLLM: Options-based Large Language Models
We introduce Options LLM (OLLM), a simple, general method that replaces the single next-token prediction of standard LLMs with a \textit{set of learned options} for the next token, indexed by a discrete latent variable. Instead of relying on temperature or sampling heuristics to induce diversity, OLLM models variation explicitly: a small latent space parametrizes multiple plausible next-token opti…
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MUCOCO: Automated Consistency Testing of Code LLMs
Code LLMs often portray inconsistent program behaviors. Developers typically employ benchmarks to assess Code LLMs, but most benchmarks are hand-crafted, static and do not target consistency property. In this work, we pose the scientific question: how can we automatically discover inconsistent program behaviors in Code LLMs? To address this challenge, we propose an automated consistency testing me…
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PROMETHEE-based Modeling of Endogenous Behavioral Uncertainty of EV Owners
The electric vehicle (EV) charging demands (CD) are jointly determined by the EV owners' behavior (i.e., human factor) and the electricity prices (i.e., decisions of distribution system operators (DSO)). However, most existing studies either neglect the decision-dependent nature of EVCD uncertainty or idealistically treat EV owners as perfect decision-makers. This paper formulates the optimal oper…
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DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation
Synthetic aperture radar tomography (TomoSAR) enables 3-D imaging by exploiting multibaseline acquisitions and has become an important tool for urban mapping. To achieve super-resolution inversion, sparse reconstruction methods based on compressive sensing (CS) are widely adopted. However, most CS-based TomoSAR methods rely on grid-based formulations and therefore suffer from off-grid bias. Gridle…
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ProjLens: Unveiling the Role of Projectors in Multimodal Model Safety
Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threatened by critical safety vulnerabilities. While prior works have demonstrated the feasibility of backdoors in MLLMs via fine-tuning data poisoning to manipulate inference, the underlying mechanisms of backdoor attacks remain opaque, complicating the un…
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Proactive Detection of GUI Defects in Multi-Window Scenarios via Multimodal Reasoning
Multi-window mobile scenarios, such as split-screen and foldable modes, make GUI display defects more likely by forcing applications to adapt to changing window sizes and dynamic layout reflow. Existing detection techniques are limited in two ways: they are largely passive, analyzing screenshots only after problematic states have been reached, and they are mainly designed for conventional full-scr…
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Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization
Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in both offline and low-latency streaming settings remains challenging. We present a Unified ASR framework for Transducer (RNNT) training that supports both offline and streaming decoding within a single model, using chunk-limited attention with right con…
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High-Order Multi-Scale Method and Its Convergence Analysis for Nonlinear Thermo-Electro-Mechanical Coupling Problems of Composite Structures
This study proposes a high-order multi-scale method tailored for time-dependent nonlinear thermo-electro-mechanical coupling problems of composite structures with highly spatial heterogeneity, which incorporate temperature-dependent material properties and Joule heating effect. By employing the multi-scale asymptotic approach and the Taylor series technique, a high-accuracy multi-scale asymptotic …
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What is Powering the Enigmatic He II Emitter Hebe: The First Stars or Black Holes?
Recent high-resolution spectroscopy with the James Webb Space Telescope (JWST) has confirmed the presence of a strong He II, $\lambda1640$ emitting clump in the vicinity of GN-z11, with only upper limits on its metallicity. To explain the peculiar properties of this source, now termed Hebe, a cluster of metal-free, Population III (Pop III) stars has been invoked. A less likely source for the hard …
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A comprehensive framework for phase-coherent mapping of the gravitational-wave sky with pulsar timing arrays
We present a practical implementation of a phase-coherent mapping technique for pulsar timing arrays that resolves the full complex polarisation state of the gravitational-wave sky as a function of direction and frequency. Unlike standard cross-correlation methods, this approach preserves the amplitude, phase, and polarisation of the signal in every sky pixel. The resulting maps constitute a compa…
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S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection
Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the key requirement is that the support of the unknown marginal distribution has the geometric structure of a Riemannian manifold. Typically, the Laplace-Beltrami operator-based manifold regularization can be approximated empirically by the Laplacian regul…
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HoWToBench: Holistic Evaluation for LLM's Capability in Human-level Writing using Tree of Writing
Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills and the limitations of existing metrics. LLM's performance in thousand-words level and open-ended writing is inadequately assessed by traditional reference-based metrics or modern LLM-as-a-judge methods. We propose Tree-of-Writing (ToW), to resolv…
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TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only
Zero-shot reasoning on text-rich networks (TRNs) remains a challenging frontier, as models must integrate textual semantics with relational structure without task-specific supervision. While graph neural networks rely on fixed label spaces and supervised objectives, recent large language model (LLM)-based approaches often overlook graph context or depend on distillation from larger models, limitin…
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Product-of-Experts Training Reduces Dataset Artifacts in Natural Language Inference
Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlations, and 38.6% of the baseline errors are the result of these artifacts. We propose Product-of-Experts (PoE) training, which downweights examples where biased models are overconfident. PoE nearly preserves accuracy (89.10% vs. 89.30%) while cutting bi…
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Taylor Tube Method for Validated IVP
We recently introduced a novel architecture for the design of validated IVP algorithms. This architecture forms the basis of our complete validated algorithm for IVP. A key subroutine in our algorithm is the \textbf{Euler Tube}: it gave a technique for refining end- and full-enclosures and is also key to deriving a complexity bound of our IVP solver. In this paper, we generalize it…
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Age-Dependent Heterogeneity in the Association Between Physical Activity and Mental Distress: A Causal Machine Learning Analysis of 3.2 Million U.S. Adults
Physical activity (PA) is widely recognized as protective against mental distress, yet whether this benefit varies systematically across population subgroups remains poorly understood. Using pooled data from ten consecutive annual waves of the U.S. Behavioral Risk Factor Surveillance System (2015-2024; n = 3,242,218), we investigate heterogeneity in the association between leisure-time PA and freq…
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Last-Iterate Guarantees for Learning in Co-coercive Games
We establish finite-time last-iterate guarantees for vanilla stochastic gradient descent in co-coercive games under noisy feedback. This is a broad class of games that is more general than strongly monotone games, allows for multiple Nash equilibria, and includes examples such as quadratic games with negative semidefinite interaction matrices and potential games with smooth concave potentials. Pri…
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The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation
In vision-and-language navigation (VLN), self-improvement from policy-induced experience, using only standard VLN action supervision, critically depends on balancing behavioral diversity and learning stability, which governs whether the agent can extract a reliable learning signal for improvement. Increasing behavioral diversity is necessary to expose alternative action hypotheses but can destabil…
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Differentiable Satellite Constellation Configuration via Relaxed Coverage and Revisit Objectives
Satellite constellation design requires optimizing orbital parameters across multiple satellites to maximize mission specific metrics. For many types of mission, it is desirable to maximize coverage and minimize revisit gaps over ground targets. Existing approaches to constellation design either restrict the design space to symmetric parametric families such as Walker constellations, or rely on me…
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Three-Module SC-VAMP for LDPC-Coded Nonlinear Channels
We propose a three-module extension of score-based VAMP (SC-VAMP) for signal recovery in nonlinear channels, where the received signal is obtained by applying a nonlinearity to a linear mixture of the transmitted signal, followed by additive Gaussian noise. The key idea is to introduce a latent variable representing the output of the linear mixing stage, which decomposes the inference problem into…
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Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports
Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format without reasoning supervision. Across three …
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AeroBridge-TTA: Test-Time Adaptive Language-Conditioned Control for UAVs
Language-guided unmanned aerial vehicles (UAVs) often fail not from bad reasoning or perception, but from execution mismatch: the gap between a planned trajectory and the controller's ability to track it when the real dynamics differ from training (mass changes, drag shifts, actuator delay, wind). We propose AeroBridge-TTA, a language-conditioned control pipeline that targets this gap with t…
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Heuristic Search Space Partitioning for Low-Latency Multi-Tenant Cloud Queries
Large-scale cloud security platforms must continuously query millions of structured cloud resource records distributed across thousands of tenant accounts. Broad, account-spanning queries saturate database infrastructure, producing P95 latencies exceeding 60 seconds. We identify buffer cache pressure as the dominant latency driver: in a controlled experiment, the same query executing with the same…
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QoS-Constrained Scheduling in Multi-Cell Multi-User MIMO Networks
In 5G and beyond networks, efficient scheduling is essential to exploit the gains of multi-user MIMO (MU-MIMO) equipped with carrier aggregation and joint transmission (JT). However, cross-cell and cross-carrier scheduling under QoS constraints is challenging due to the strong coupling across users, base stations, and carriers. In this work, we address this problem in multi-cell MU-MIMO networks t…
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ATRIE: Adaptive Tuning for Robust Inference and Emotion in Persona-Driven Speech Synthesis
High-fidelity character voice synthesis is a cornerstone of immersive multimedia applications, particularly for interacting with anime avatars and digital humans. However, existing systems struggle to maintain consistent persona traits across diverse emotional contexts. To bridge this gap, we present ATRIE, a unified framework utilizing a Persona-Prosody Dual-Track (P2-DT) architecture. Our system…