Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction
Conformal prediction framework for building coverage-guaranteed prediction sets in counterfactual decision-making with uncertainty quantification.
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Conformal prediction framework for building coverage-guaranteed prediction sets in counterfactual decision-making with uncertainty quantification.
Self-explainable operator learning framework reformulates neural architectures as interpretable linear combinations of functional models for physics systems.
Eticas AI Risk Taxonomy operationalizes AI audits by bridging 74+ competing risk taxonomies with executable tests, measurements, and defensible severity grades.
H-Score objective enables mutual information-inspired feature learning with proof of invariance to invertible transformations under constrained approximation.
Taxonomy of 53 human-AI team studies reveals five clusters (AI Assistant, Ad-hoc Dependency, Paired Equanimity, Group Equanimity) with distinct characteristics.
Overview of AI risk assessment and management methodologies across regulatory frameworks including EU AI Act with techniques for technical and ethical risks.
Online resource allocation algorithm bounds regret for sequential requests with continuous reward/consumption distributions and degenerate fluid relaxation.
DSGNAR optimization framework solves ill-conditioning in physics-informed neural networks via doubly-sketched Gauss-Newton with adaptive ratio.
Model-agnostic framework for privacy-preserving distributed computing unifies federated and decentralized learning defenses against adversarial manipulation.
UA-ChatDev mitigates hallucination propagation in LLM-based multi-agent software development by tracking uncertainty across collaboration stages.
Mistral announces Leanstral 1.5 (details unavailable).
RadiomicNet combines handcrafted radiomics features with deep learning for interpretable medical image segmentation.
Microsoft follows Amazon, OpenAI and Anthropic with its new AI deployment group.
DALorRA applies Bayesian sparse low-rank adaptation to quantify LLM uncertainty and reduce overconfidence in fine-tuned models.
Rubric-based clinical reasoning benchmark shows frontier LLMs achieve ~32% on hard expert-authored medical cases.
Dynamic Neural Graph Encoding uses temporal graphs to analyze neural network weight spaces and inference sequences.
Proves multi-secretary problem requires Ω((log T)²) regret for bounded-density distributions with support gaps.
Ensemble machine learning methods detect early-stage Alzheimer's disease biomarkers from neural network analysis.
A²utoLPBench auto-generates infinite linear programming word problems via inverse-KKT for LLM-agent evaluation.
Probes chemical language models across eight pre-trained variants to identify which molecular substructures are encoded.
ART learns adaptive timestep schedules for diffusion sampling via continuous-time control and actor-critic optimization.
Paper-replication workflow enables coding agents to verify computational claims in scientific ML papers with recorded evidence and automated verification.
AbsoluteDegradation introduces physics-based synthetic film degradation pipeline and benchmark for archival film restoration without paired training data.
Deep learning model for automated population-scale penile tissue segmentation in MRI to enable quantitative male reproductive health phenotyping.
Rolling Split Conformal Prediction framework for pre-incident traction loss detection via tire slip monitoring in vehicle safety systems.
Behavioral monitoring technique to distinguish guardrail blocks from LLM rejections in black-box adversarial evaluation of production systems.
vLLM-based inference pipeline for unified audio understanding and generation with native support for multi-token prediction and delay-pattern interleaving.
FitOne domain-specialized LLM (8B/32B) improves scientific fitness coaching reliability through domain-specific post-training.
Artificial intelligence may have captured the public imagination through chatbots and image generators, but some of its most consequential use cases are unfolding far from consumer-facing tools. In industries where physical infrastructure, operational continuity, and safety are…
ContextNest open specification for verifiable context governance in autonomous AI agents with provenance, version control, and integrity guarantees.