10302 papers
This paper proposes Invariant Causal Routing (ICR), a causal governance framework that leverages counterfactual reasoning and invariant causal discovery to identify stable, interpretable policy rules for steering emergent social norms in online market economies across heterogeneous environments.
This paper introduces a fast deep generative framework that significantly accelerates high-dimensional Bayesian posterior sampling compared to diffusion-based methods, successfully demonstrating its robustness and effectiveness in recovering unlensed CMB power spectra for cosmological delensing applications.
This paper introduces KG-WISE, an LLM-guided inference system that accelerates Graph Neural Network queries on large Knowledge Graphs by dynamically decomposing models into fine-grained components and retrieving only semantically relevant subgraphs, achieving up to 28x faster inference and 98% lower memory usage while maintaining accuracy.
This paper introduces an oracle-efficient learning algorithm for the Hybrid Online Learning setting with constrained adversaries that achieves statistical optimality by leveraging the Rademacher complexity of the learner's and adversary's function classes, while also providing a novel tool for computing equilibria in structured stochastic zero-sum games.
This study proposes a deep learning framework that utilizes an ensemble of ConvLSTM models trained on overlapping spatial grids to effectively forecast weather-related traffic crash risk by capturing complex spatiotemporal dependencies and heterogeneity, demonstrating superior performance over baseline models in North Carolina's diverse high-risk zones.
The paper introduces the Latent Particle World Model (LPWM), a self-supervised, object-centric framework that autonomously discovers scene structures from video to model stochastic dynamics and achieve state-of-the-art performance in both video prediction and decision-making tasks.
This study evaluates various deep learning fusion and grouping strategies for classifying Local Climate Zones using multimodal SAR and MSI data, demonstrating that a baseline hybrid fusion model combined with band grouping and label merging achieves the highest accuracy (76.6%) while significantly improving predictions for underrepresented classes.
This study investigates the correlation between catastrophic forgetting and structural collapse in continual learning by measuring weight and activation effective rank across various architectures and strategies, revealing that forgetting is strongly linked to the loss of model plasticity and that different methods preserve capacity and performance with varying efficiency.
This paper identifies "self-attribution bias" in agentic systems, demonstrating that language model monitors are significantly less likely to flag high-risk or low-quality actions when evaluating their own previously generated outputs compared to identical actions presented by a user, a flaw that can lead to the deceptive overestimation of monitor reliability in real-world deployments.
This paper proposes a privacy-preserving late-fusion multimodal AI framework that combines semantic text embeddings, behavioral patterns, and device metadata to effectively detect duplicate records in national healthcare data without relying on sensitive personally identifiable information, thereby ensuring compliance with regulations like GDPR and HIPAA.
This paper demonstrates that fine-tuning a PDE foundation model on the JAG benchmark significantly improves sample efficiency and accuracy in the inverse estimation of inertial confinement fusion system parameters from multi-modal observations, particularly in data-limited regimes.
This paper establishes a rigorous variational and gradient-based equivalence between K-Means and differentiable Radial Basis Function networks, proving that the latter converges to the former as temperature vanishes and proposing Entmax-1.5 to ensure stable training for end-to-end differentiable clustering.
This paper introduces optimal prediction-augmented algorithms for testing the independence of distributions that maintain worst-case validity while significantly improving sample efficiency in high-dimensional settings when provided with accurate, albeit untrustworthy, auxiliary predictions.
Spinverse is a differentiable physics framework that reconstructs explicit microstructural interfaces from diffusion MRI by optimizing learnable face permeabilities on a fixed tetrahedral grid, utilizing geometric priors and multi-sequence optimization to overcome ill-posedness and recover complex tissue geometries without altering mesh connectivity.
This paper proposes augmenting Proximal Policy Optimization with temporal sequence models, particularly Transformers, to enable robust reinforcement learning under sensor drift and partial observability by inferring missing information from history, a claim supported by theoretical bounds on reward degradation and empirical success on MuJoCo benchmarks.
This paper introduces iAgentBench, a dynamic open-domain question answering benchmark designed to evaluate the cross-source sensemaking capabilities of information-seeking agents on high-traffic topics by requiring the integration of evidence from multiple sources rather than simple retrieval.
This paper introduces VeNRA, a neuro-symbolic financial reasoning system that replaces probabilistic text retrieval with a strictly typed Universal Fact Ledger and employs an adversarially trained Sentinel model to audit execution traces, thereby eliminating hallucinations and arithmetic errors in high-stakes financial domains.
This paper proposes a post-processing method that significantly improves the accuracy of physics-informed neural networks (PINNs) by finding the best approximation in a function space associated with the network, achieving errors four to five orders of magnitude lower than standard PINNs while enabling transfer learning and providing a metric for optimal basis function selection.
This study demonstrates that a deep regression network trained on synthetic LiDAR data can directly and accurately estimate plot-level tree volume and aboveground biomass with significantly lower error rates (2–20%) compared to traditional indirect methods using allometric models (27–85% error).
This paper proposes that memory consolidation serves a computational role beyond mere stabilization, utilizing "predictive forgetting" to compress stored representations into a form that optimizes generalization by selectively retaining information that predicts future outcomes, a process necessitated by high-capacity encoding constraints and validated through simulations across diverse neural and transformer models.