2316 papers
MASEval introduces a framework-agnostic library that shifts multi-agent evaluation from a model-centric to a system-centric approach, demonstrating through extensive experiments that implementation decisions regarding topology and orchestration impact performance as significantly as model selection.
This paper theoretically and empirically demonstrates that hybrid sequence models, which combine Transformers and state-space models, can provably solve core synthetic tasks with significantly fewer parameters and less memory than non-hybrid models while also achieving superior length generalization and out-of-distribution robustness.
This paper proposes \textsc{applv}, a novel framework that leverages Vision-Language-Action models to dynamically predict and adapt classical planner parameters, thereby achieving superior navigation performance and generalization in highly constrained environments compared to existing methods.
This paper demonstrates that channel-centric models, including ray-tracing simulators, fail to accurately predict end-to-end throughput in private 5G networks due to systematic over-estimation of MIMO spatial layers, whereas data-driven Gaussian process models trained on direct measurements provide significantly more reliable predictions for communication-aware robot planning.
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 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 comprehensive multi-vector privacy evaluation framework that quantifies memorization risks in Genomic Language Models by integrating perplexity-based detection, canary sequence extraction, and membership inference, revealing that these models exhibit measurable data leakage dependent on architecture and training dynamics.
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.
The paper introduces MedCBR, a novel framework that integrates clinical guidelines with vision-language models to enhance the interpretability and accuracy of medical image diagnosis by transforming visual features into guideline-conformant concepts and structured clinical narratives.
This paper proposes a hierarchical reinforcement learning framework that jointly optimizes antenna tilt angles and the data collection ratio between a physical network and its digital twin to maximize user data rates while minimizing communication overhead and delay.
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.
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 ULFS-KDPE, a novel kernel-based estimator that achieves semiparametric efficiency for pathwise differentiable parameters in nonparametric models by constructing a data-adaptive debiasing flow via a universal least favorable submodel, thereby eliminating the need for explicit efficient influence function derivation while ensuring rigorous theoretical guarantees and computational tractability.
This thesis addresses the problem of overconfident conclusions in simulation-based inference by introducing a "balancing" regularization technique and a novel Bayesian neural network prior to ensure more reliable and calibrated statistical approximations.
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 survey introduces reinforcement learning to economists as a flexible, sample-based extension of dynamic programming capable of solving high-dimensional economic models, while critically examining its practical limitations such as sample inefficiency, sensitivity to hyperparameters, and reliance on accurate simulators.
This paper introduces the inequality to demonstrate that Mixture-of-Experts (MoE) models suffer from a structural "double penalty" of routing fragmentation and memory constraints during inference, often rendering them significantly less efficient than quality-matched dense models for long-context serving despite their training-time FLOP advantages.
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 proposes MAcPNN, a decentralized Mutual Assisted Learning paradigm inspired by Vygotsky's Sociocultural Theory that enables autonomous IoT devices to collaboratively address concept drifts and temporal dependence in data streams using Continuous Progressive Neural Networks while minimizing communication overhead compared to traditional Federated Learning.