We introduce the official open-access repository of the Amanollahi Geodesic Curvature Flow Solver, a breakthrough continuous optimization paradigm for Boolean Satisfiability (SAT) that definitively bridges a 40-year theoretical gap in pure continuous constraint satisfaction. Historically, continuous optimization framew...
Sarvamanollahi, Mohammadreza· Zenodo (CERN European Organi...· 0 citations
Efficiently implementing Clifford circuits is crucial for quantum error correction and quantum algorithms. Linear reversible circuits, equivalent to circuits composed of CNOT\xspace gates, have important applications in classical computing. In this work we present methods for CNOT\xspace and general Clifford circuit sy...
Mark Webster, Stergios Koutsioumpas, Dan E. Browne· Quantum· 0 citations
Abstract Distributed acoustic sensing (DAS) offers advantages such as low cost, high sampling density, and minor geological structures identification. However, while highly sensitive fiber-optic sensors effectively capture valuable data, they also introduce complex noise fields containing simultaneous interference from...
Chao Liang, Hongbo Lin, Haitao Ma et al.· Geophysics· 0 citations
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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...
Dimitar Stavrev Prodromov· Zenodo (CERN European Organi...· 0 citations
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...
Rakib Hossain· Zenodo (CERN European Organi...· 0 citations
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...
Lina Bessonova· Zenodo (CERN European Organi...· 0 citations
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...
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...
Laksmi Evasufi Widi Fajari, Anggi Rahmani, Salwa Nur Fadhilah et al.· Mediterranean Journal of Soc...· 0 citations
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...
Junior Williams· Zenodo (CERN European Organi...· 0 citations
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 7, 2026
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026