False-science induction in autonomous scientific discovery

arXiv:2609.27883 · cs.LG, cs.AI, physics.comp-ph · Submitted 2026-08-20 · Read on arXiv

cs.LG, cs.AI, physics.comp-ph

Submitted: 2026-08-20

Updated: 2026-08-20

Code: https://github.com/ToughClimb/false-science-induction

Terminology

• Trace — 44× in this paper · explained in 2 episodes
• Provenance — 35× in this paper · explained in 4 episodes
• Active Learning — 5× in this paper · explained in 6 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Data Provenance — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Neural Surrogates — 2× in this paper · explained in 2 episodes
• Acquisition Function — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Monte Carlo — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• A Closer Look at Memorization in Deep Networks
• Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
• Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
• Poisoning Attacks against Support Vector Machines
• BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
• Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

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