2385 papers
This paper establishes that human preference for equally optimal combinatorial packing solutions is reliably driven by three quantifiable structural properties—alignment with greedy heuristics, simple within-bin composition, and ordered visual representation—thereby providing a concrete framework for designing interpretable algorithmic support systems.
This paper presents a controlled study using the Budget-Constrained Agentic Search (BCAS) framework to quantify how search depth, retrieval strategies, and completion budgets impact the accuracy and cost of Agentic RAG systems across six LLMs and three benchmarks, offering practical configuration guidelines for budget-constrained deployments.
This paper introduces FSbuHD, a novel feature selection model for hybrid information systems that addresses the computational and noise limitations of traditional fuzzy rough set theory by reformulating the problem as an optimization task based on combined object distances, demonstrating superior efficiency and effectiveness in both normal and optimistic states across UCI datasets.
This paper introduces NetDiffuser, a novel framework that leverages a feature categorization algorithm and diffusion models to generate natural adversarial examples that effectively deceive deep learning-based network intrusion detection systems while preserving traffic validity.
This paper introduces Transfer-Informed Betting (TIB), a novel method that combines betting-based confidence sequences with cross-domain transfer learning to achieve tighter, data-efficient risk guarantees for selective prediction, demonstrating significant coverage improvements over existing bounds across multiple benchmarks and applications.
FedLECC is a lightweight client selection strategy for federated learning under non-IID data that groups clients by label-distribution similarity and prioritizes those with higher local loss, thereby significantly improving test accuracy while reducing communication rounds and overhead.
This paper introduces a fully differentiable approach to discovering Strong Lottery Tickets by employing continuously relaxed Bernoulli gates to optimize sparsity via gradient descent on frozen weights, achieving significantly higher sparsity with minimal accuracy loss compared to existing non-differentiable methods like edge-popup.
This paper argues that citation visibility in generative search should be treated as a stochastic distribution requiring uncertainty estimates rather than a fixed value, demonstrating through empirical analysis of multiple AI platforms that single-run measurements are misleadingly precise and that robust statistical sampling is essential for accurate domain performance assessment.
This paper introduces a novel framework utilizing vision-language foundation models (Gemma 3 and Qwen3-VL) to automatically generate JSON simulation configurations for digital twin agriculture by interpreting drone imagery, demonstrating their potential to scale functional-structural plant modeling while highlighting current limitations in visual reasoning and reliance on contextual priors.
The paper presents "Guardian," an interpretable, three-layer decision-support system that combines Markov chains, reinforcement learning, and LLM-based validation to generate dynamic, probabilistic search plans for missing-child investigations within the critical first 72 hours.
PathoScribe is a unified retrieval-augmented large language model framework that transforms static pathology archives into an active, reasoning-enabled clinical intelligence platform, enabling natural language case retrieval, automated cohort construction, and real-time diagnostic support with high accuracy and efficiency.
The paper introduces VoxEmo, a comprehensive benchmark and toolkit for evaluating Speech Large Language Models on speech emotion recognition across 35 corpora and 15 languages, featuring a distribution-aware soft-label protocol that reveals how these models uniquely align with human subjective emotion distributions despite trailing supervised baselines in hard-label accuracy.
This paper proposes "AgentOS," a new paradigm that replaces traditional GUI-based operating systems with a natural language-driven ecosystem centered on an Agent Kernel, framing the realization of such a system as a Knowledge Discovery and Data Mining (KDD) challenge involving intent mining, workflow automation, and dynamic personal knowledge graphs.
The paper introduces BiCLIP, a simple and parameter-efficient framework that achieves state-of-the-art few-shot domain adaptation for vision-language models by applying a structured geometric transformation to align multimodal features across disparate domains using a small set of anchor samples.
This paper introduces Guardian, a consensus-driven, multi-LLM pipeline enhanced by QLoRA fine-tuning that coordinates specialized models and a consensus engine to perform auditable, structured information extraction for critical missing-person investigations while avoiding unconstrained decision-making.
This paper introduces \texttt{EinSum}, a tensor-relational extension of Einstein Summation Notation that automatically rewrites computations to leverage efficient numerical kernels for dense operations while utilizing relational systems to manage large-scale sparsity.
This paper introduces the FaBRIC strategy, which integrates new scalable backward reachability algorithms with existing forward analysis techniques to significantly improve the verification of reach-avoid specifications in nonlinear neural feedback systems.
This paper introduces Semantic Level of Detail (SLoD), a framework that utilizes heat kernel diffusion on hyperbolic manifolds to enable continuous, principled control over knowledge abstraction levels in AI memory systems, automatically detecting emergent semantic boundaries in both synthetic and real-world knowledge graphs without manual supervision.
This paper introduces Arbiter, a framework that combines formal rules with multi-model LLM analysis to detect interference patterns in coding agent system prompts, revealing that prompt architecture influences failure types and that multi-model evaluation uncovers distinct vulnerabilities missed by single-model approaches.
This study systematically characterizes the unique threat landscape of multi-agent AI systems and empirically evaluates 16 security frameworks, revealing that none achieve majority coverage of the identified risks, with Non-Determinism and Data Leakage being the most under-addressed domains.