4581 papers
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.
This paper proposes a data-driven robust Markov decision process framework for Borel spaces with unknown disturbance distributions, utilizing ambiguity sets defined by distance functions to establish finite-sample performance guarantees, probabilistic convergence rates, and out-of-distribution bounds that empirical MDPs fail to provide.
MAPLE introduces a unified training paradigm that enhances medical large language models by integrating Test-Time Reinforcement Learning with expert-aligned Med-RPMs to replace unreliable majority voting with fine-grained process rewards, thereby significantly improving clinical reasoning accuracy and reliability across multiple benchmarks.
This book reinterprets generative AI, specifically through flow matching, as a statistical framework for nonparametric distribution learning that enables principled inference for tasks like missing-data imputation and causal analysis by integrating generative models with double/debiased machine learning techniques to ensure inferential validity.
This paper introduces Normalized Flow Matching (NFM), a novel method that distills quasi-deterministic couplings from pretrained auto-regressive normalizing flow models to train student flow models, achieving superior performance over both traditional flow matching approaches and the teacher models themselves.
This paper proposes an exact, parameterization-aware flatness measure tailored to the geometric structure of convolutional neural networks with global average pooling, demonstrating its effectiveness as a robust proxy for estimating and comparing generalization performance across various CNN architectures.
This paper introduces CALIPER, a data-only, detector-agnostic test that determines the sufficient post-drift data size for stable model retraining by analyzing the trend of a one-step proxy error against a locality parameter, thereby bridging the gap between drift detection and effective adaptation in streaming learning.
The paper introduces EPIC, a hardware- and physics-co-guided distributed scientific machine learning framework that significantly reduces communication latency and energy consumption while preserving physical fidelity by performing lightweight local encoding and physics-aware decoding with cross-attention for tasks like full-waveform inversion.
SCALAR is a bidirectional framework that couples LLM-guided symbolic planning with deep RL to iteratively refine skill specifications through execution feedback, significantly outperforming prior methods in complex environments like Craftax by correcting initial planning errors and improving sample efficiency.
This paper presents FlexServe, a high-performance and secure LLM serving system for mobile devices that leverages a novel Flexible Resource Isolation mechanism to overcome the significant overhead of ARM TrustZone, achieving up to 10.05× faster time-to-first-token and 24.30× faster multi-model workflow execution compared to baseline designs.
The paper introduces Sentinel, an autonomous AI agent that achieves reliable, scalable clinical triage for remote patient monitoring by outperforming individual clinicians in sensitivity and consistency while maintaining a clinically defensible overtriage profile at a negligible cost.
The paper proposes Sim2Act, a robust simulation-to-decision framework that enhances policy reliability in mission-critical domains by combining an adversarial calibration mechanism to align simulation fidelity with decision impact and a group-relative perturbation strategy to stabilize learning without overly conservative constraints.