1614479 results (page 10 of 64580)
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The Register Gap: A Meaning Intelligence Framework for Nigerian Public Discourse
We introduce the Meaning Intelligence Framework (MIF), a nine-dimension annotation and evaluation schema for Nigerian public discourse that separates surface sentiment from true communicative intent. Existing benchmarks for Nigerian languages, including NaijaSenti and AfriSenti, treat sentiment classification as a three-way polarity task (positive, negative, neutral). We argue that the dominant fa…
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Quantization as a Malicious Task: Removing Quantization-Conditioned Backdoors via Task Arithmetic
Model quantization is widely adopted to reduce memory usage and inference cost when deploying deep neural networks on resource-constrained devices. However, recent studies have revealed a new security threat known as Quantization-Conditioned Backdoors (QCBs), where a model behaves normally in full precision but activates malicious behavior only after quantization. Existing defenses typically modif…
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TrustMix: How to Mix Messages in a Mobile Ad-hoc Network
Mix networks are a highly effective way to achieve anonymity, defending against a wide range of traffic-analysis attacks. However, mix networks are usually designed for infrastructure networks and cannot be directly applied in the context of mobile ad hoc networks (MANETs). The few existing solutions for MANETs require advance knowledge of the topology or a trusted central party. In this paper, …
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Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentation
Existing weakly supervised semantic segmentation (WSSS) methods in computational pathology rely on a multi-stage paradigm: class activation map (CAM) generation, offline pseudo-mask refinement, and fully supervised retraining. While established, this decoupled approach presents fundamental limitations. The multi-stage process not only incurs high computational training costs but also suffers from …
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Geophysical and atmospheric implications of $f$O$_{2}$-dependent melting on rocky exoplanets
The geochemical evolution of long-lived magma oceans is strongly regulated by volatile exchange between the molten mantle and the atmosphere. For planets inside the runaway-greenhouse limit, this coupled evolution can persist for billions of years. However, most existing studies assume Earth-like (oxidized) conditions and neglect the influence of redox state on melt thermodynamics and volatile rel…
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The auxiliary-metric formulation of Born-Infeld New Massive Gravity
Born-Infeld New Massive Gravity (BINMG) completes New Massive Gravity to all orders in curvature through the determinant of the metric shifted by the Einstein tensor. We recast it with an independent auxiliary metric $q_{μν}$, whose algebraic equation of motion $q_{μν}=g_{μν}+\fracσ{m^2}G_{μν}(g)$ recovers the determinant action exactly on the regular branch and resums the infinite curvature serie…
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Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion parameter architectures impose prohibitive computational burdens during downstream fine-tuning and real-time inference. In this work, we reveal a highly non-trivial architectural characteristic of these continuous control foundation policies (e.g., pi_0,…
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Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt. However, the integration of external knowledge can introduce conflicts, not only between the model's internal parametric knowledge …
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SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs
Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evidence readout even when high-level reasoning is intact. Prior inference-time visual interventions can improve grounding without retraining, but they are largely open-loop and lack a mechanism to verify whether highlighte…
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Phoenix: Safe GitHub Issue Resolution via Multi-Agent LLMs
We present Phoenix, a multi-agent LLM system that resolves GitHub issues from triage through pull-request creation, combining seven layered safety controls with a baseline-aware test evaluation strategy. Phoenix decomposes the work across six specialized agents. Planner, reproducer, coder, tester, failure analyst and Pull Request (PR) agent, all coordinated by a label-based GitHub webhook state ma…
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Mitigating Trotter Errors via Post-Processed Symmetry Restoration
Quantum simulation is a powerful tool for exploring complex quantum many-body systems such as condensed matter physics and gauge theories. Trotterization, which approximates the ideal time evolution operator by decomposing it into a sequence of local gate operations, is one of the most widely used quantum simulation algorithms. However, such Trotterized implementations generally fail to preserve t…
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BAFIS: Dataset + Framework to assess occupational Bias and Human Preference in modern Text-to-image Models
Generative artificial intelligence has the potential to improve productivity and transform the production of creative content. However, existing research indicates that image generation models are significantly influenced by biases. This work investigates the inherent biases and language-induced biases present in text-to-image models within the context of occupation-related image generation, compl…
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Two-Sample IV: Efficient Two-Step Estimation and Tests for Overidentification and Weak-Instruments
Two-sample IV is a popular estimation method when the outcome and treatment variables are available in different samples, whereas instruments are available in both samples. The standard estimator is two-sample two-stage least squares estimator, which is efficient under homoskedasticity and homogeneity of the samples. We develop a robust two-step procedure for efficient estimation under general het…
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Optimizing Agricultural Drone Operations: From Launch and Recovery Siting to Tiered Routing Strategies
Drones are increasingly used in agriculture, where tight margins demand efficient planning. Current optimization tools suffer from exponential runtimes as problem sizes grow, necessitating practical heuristics for daily operations. This paper presents an operational framework and benchmarking analysis for drone spraying operations. We evaluate the trade-offs between facility siting methods and tie…
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Random Projections for Multi-Copy Quantum Algorithms
Estimating nonlinear properties of quantum states is a central task in quantum information science. Multivariate traces, $\mathrm{tr}(ρ_1 \cdots ρ_K)$, and nonlinear observables such as $\mathrm{tr}(ρ^K)$, for integer $K$, can be accessed through collective measurements on multiple state copies, but standard protocols based on swap tests require coherent operations on the full Hilbert space and be…
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A Multi-Agent system for Multi-Objective constrained optimization
Many decision-making problems in computing and networking systems can be naturally formulated as cost-minimization problems under performance constraints. In dynamic environments, reinforcement learning (RL) is often used to solve such problems at runtime by embedding both costs and constraint violations into a single scalar reward through weighted penalty terms, following a Lagrangian-inspired fo…
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ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments
Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration. However, existing benchmarks are insufficient for systematically evaluating agentic academic search under realistic open literature environments. We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agent…
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A conservative adaptive rank method for the Wigner-Poisson system
We propose a conservative adaptive rank method for the 1D1V Wigner-Poisson system. The method targets a central challenge in deterministic quantum kinetic simulations: reducing the cost of phase-space evolution while preserving the macroscopic invariants needed for physical fidelity. The scheme combines a sampling-based adaptive rank Wigner-Poisson update [7] with a conservative macroscopic correc…
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Mobile Target Search with Imperfect Perception: A Partially Observable Stochastic Game Theoretical Approach
This paper investigates mobile target search under imperfect perceptions caused by sensor limitations, malicious jamming, or communication noise. Searchers and targets operate in a grid-shaped area with bounded mobility, leading to a dynamic interplay between search and evasion. To capture this adversarial interaction under imperfect perceptions, we adopt the partially observable stochastic game (…
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Thermodynamic Measure of Intelligence
Can intelligence be measured? We propose that intelligence can be defined as the lawful amplification of rare but valid futures: a system increases the probability of outcomes that would be unlikely under passive dynamics but remain admissible under the constraints of the domain. We start with the premise that an intelligent system must model the world and its own place within it. Because the syst…
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SysML Modeling of Digital Twins for Renewable Energy Communities
Renewable Energy Communities (RECs) are emerging as a key organizational model for local and global sharing of renewable generation, storage, and flexible loads. Engineering Digital Twins of RECs is made difficult by the heterogeneity of devices, contracts, and runtime data involved. In this paper, we take a first step toward a Model-Based Systems Engineering (MBSE) workflow for REC's Digital Twin…
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QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation
Large Language Models (LLMs) have made significant progress in reasoning, particularly in deductive reasoning, which is crucial for high-stakes decision-making. As models improve, evaluation benchmarks should evolve to keep pace. However, existing benchmarks lack fine-grained control over logical complexity and struggle to balance semantic diversity with logical consistency. To address these iss…
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Analysis of uncertain fixed-effects model for Latin square designs
Uncertain data without frequency stability often arises in experimental design. Classical fixed-effects models can only analyze precise experimental data. Based on an uncertain measure, this paper establishes uncertain fixed-effect models for Latin-square designs. First, we propose three methods with uncertainty to estimate the treatment and blocked effects and construct their confidence intervals…
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Actionable Activation Directions for Detecting and Mitigating Emergent Misalignment Across Language Model Families
Fine-tuning language models on insecure code induces emergent misalignment with poorly understood internal structure. We investigate whether this misalignment corresponds to a causally actionable activation-space direction shared across architectures. Across four instruction-tuned model families (Qwen2.5-1.5B, Gemma-2-2B, Llama-3.2-1B, Ministral-3-3B) finetuned identically, a difference-in-means d…
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TOI-2147 b and TOI-6019 b: Two eccentric warm Jupiters detected and characterized with TESS and MaHPS
The population of Jupiter-sized exoplanets with orbital periods between 10 and 200 days (WJs) exhibits a broad range of orbital eccentricities and system architectures, suggesting a diversity of formation and migration pathways. In this work, we report the detection and characterization of two new eccentric WJs, TOI-2147 b and TOI-6019 b, initially identified as planet candidates by the Transiting…