Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 16 academic APIs.
Christos Smarlamakis, Efstratios Georgopoulos· Zenodo (CERN European Organi...· 0 citations
Reproducibility package accompanying the manuscript "Dynamic service composition reconfiguration in cloud manufacturing under limited shared recovery capacity: A reinforcement learning approach".
saberrrostami· Zenodo (CERN European Organi...· 0 citations
Artifacts accompanying the paper “Invited: EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring.” See the included README for contents and reproduction instructions.
Amur Ghose, Andrew B. Kahng, Yugesh Kothari et al.· Zenodo (CERN European Organi...· 0 citations
Abstract EN This document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes EPC Art. 54(2) (European Patent Convention), Fr...
Pillet Xavier· Zenodo (CERN European Organi...· 0 citations
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Artifacts accompanying the paper “Invited: EDA-env: A Reinforcement Learning Environment for RTL-to-GDS Design with Independent Scoring.” See the included README for contents and reproduction instructions.
Amur Ghose, Andrew B. Kahng, Yugesh Kothari et al.· Zenodo (CERN European Organi...· 0 citations
Developing a smart control system capable of accounting for the dynamics of industrial equipment state changes and promptly detecting features of developing failures is currently a pressing issue. The goal of this study is to develop an intelligent technology for industrial equipment state diagnostics based on a modifi...
G. Samigulina, Z. Samigulina, B. Dyussenkulova et al.· Discover Artificial Intellig...· 0 citations
Abstract ENThis document, produced with the assistance of ChatGPT o3 and GPT-5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes (art. L 611-11 CPI / art. 54(2) CBE), 35 U.S.C. §102...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
This record contains the replication package for the survey “The State of RL4SE: A Task-Oriented Review of Research on Reinforcement Learning for Software Engineering.” The package supports the study-identification, screening, classification, and artifact-availability results reported in the survey. It contains: • cand...
Yujia Fan· Zenodo (CERN European Organi...· 0 citations
We propose the Large Organismic Model (LOM), a developmental architecture for artificial intelligence. Rather than reporting the performance of an implemented system, this position paper formulates a testable computational hypothesis that integrates ideas from biology, developmental neuroscience, reinforcement learning...
T Hajime, T Masaki· Zenodo (CERN European Organi...· 0 citations
Reproducibility package accompanying the manuscript "Dynamic service composition reconfiguration in cloud manufacturing under limited shared recovery capacity: A reinforcement learning approach".
saberrrostami· Zenodo (CERN European Organi...· 0 citations
This archive accompanies the manuscript "Target-Scale Sensitivity in Adaptive Pessimism for Tabular Offline Reinforcement Learning: An EdNet-KT4 Study." It contains the analysis code, frozen experimental protocol, primary clean-200k results, and sensitivity-analysis results needed to reproduce the reported tables and f...
Muhammad Yusril Helmi Setyawan, Virdiandry Putratama, Ali Mohammad Rezza· Zenodo (CERN European Organi...· 0 citations
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.