reinforcement learning

Statistical Rigidity of Wigner-Dyson Eigenvalue Distributions in Gaussian Unitary Ensembles: The Spectral Integrity Operator and Selberg Automorphic Stabilization

Definition and exact evaluation of the Spectral Integrity Operator for Gaussian Unitary Ensemble (GUE) random matrices. Proves the identity T(E/I) = 1.000000 by combining the Wigner surmise normalization with the Dyson-Mehta spectral rigidity statistic at the James Arthur truncation scale. Establishes Selberg automorph...

QTrust-PPO: Confidence-Aware Finite-Shot Policy Optimization for Variational Quantum Reinforcement Learning

Initial public release of QTrust-PPO. This release contains the implementation notebooks, processed statistical results, figures, and table data for the QTrust-PPO experiments. Included components: Primary three-environment benchmark Confidence and rollback ablations Output-noise robustness experiments VQC depth analys...

Why Transformers Cannot Invent: The Manifold Confinement Problem and the Thermodynamic Engine of Scientific Discovery

The contemporary artificial intelligence research consensus posits that scaling test-time compute, Monte Carlo tree search, and Reinforcement Learning with Verifiable Rewards (RLVR / GRPO) will inevitably culminate in automated, superhuman scientific discovery. In this paper, we demonstrate the structural impossibility...

Poor Environments Make People More Prosocial to Humans but Not to AI: A Social Boundary of Foraging Theory

The richness of an environment can influence when individuals choose to act to help others. Recent research grounded in a foraging-theoretic framework has shown that, compared with rich environments, people are more willing to act to obtain rewards in poor foraging environments, and that this environmental influence is...

Exploring Hamiltonian Control of a Quantum Walk Environment via Reinforcement Learning: A Case Study on the Traveling Salesman Problem

The role of school culture in fostering responsibility in elementary school students

The purpose of this study is to analyze the effectiveness of school culture in shaping students’ attitudes toward responsibility at Seroja State Elementary School, Serang City. This research was conducted using a qualitative method, employing a case study approach. The research subjects were 35 sixth-grade students and...

Sparse Neural Code Representations for Reinforcement Learning with Linear Action-Value Function Approximation

Jev at the Agent Authorization Boundary: Evaluating TypeSafe's Decision Model and Its Reported RLCD Approach

TypeSafe describes Jev as a typed decision model trained using Reinforcement Learning for Calibrated Decisions (RLCD). We evaluate its behavior at an agent authorization boundary, where software must allow, hold, or deny a proposed action. A historical audit of 150 proposals motivates two separate paired comparisons: J...

HoomanAI/Decentralized-Dispatch-Agents: V1.2

Multi-agent reinforcement learning for autonomous emergency medical dispatch on wildfire-degraded road networks. Each vehicle agent keeps a private belief over road passability, reconciles evidence with peers when communication allows, and learns route-level exploration under triage-specific survival-risk budgets.

Nonlinear Inversion of Dissipative Trajectories in Hilbert Spaces via Riccati Differential Matrix Control and Saddle-Node Bifurcation Stabilization

Rigorous formulation and solution of the deterministic nonlinear trajectory inversion problem in four-dimensional Hilbert spaces via the Riccati Differential Equation (DRE) and Continuous Algebraic Riccati Equation (CARE). Derives the optimal LQR feedback law, proves entropy neutralization, and establishes bifurcation...

Dolgun Model: From Willful Choice to Stable States - Dolgun Modeli: İradî Seçimden Kararlı Durumlara

AbstractThis study proposes an original conceptual framework called the Dolgun Model to explain how human lifetransforms over time into specific behavioral orientations and relatively stable states through willful choices andactions. The primary starting point of the model is the idea that human life does not consist o...

HoomanAI/Decentralized-Dispatch-Agents: V1.2

Multi-agent reinforcement learning for autonomous emergency medical dispatch on wildfire-degraded road networks. Each vehicle agent keeps a private belief over road passability, reconciles evidence with peers when communication allows, and learns route-level exploration under triage-specific survival-risk budgets.