False-science induction in autonomous scientific discovery
Authors: Hanbing Liang, Fujun Liu
Organizations: Nanophotonics and Biophotonics Key Laboratory of Jilin Province, School of Physics, Changchun University of Science and Technology, Changchun 130022, P.R. China
Closed-loop discovery systems increasingly execute experiments and update decisions autonomously, turning record integrity into part of the experimental apparatus. We show that false-science induction arises when legitimate physical objects and measurements are paired incorrectly, driving neural surrogates to faithfully learn record-induced associations that do not correspond to the true object-outcome relationship while marginal data distributions remain unchanged. Across green fluorescent protein fitness and materials band-gap prediction loops, coherent paired misbinding systematically redirects experimental budgets toward low-performing basins, whereas same-volume random swaps have negligible effects. These observations identify error coherence, rather than raw error frequency, as the primary variable controlling this budget misallocation in the tested loops. The resulting binding identifiability boundary supports monitored-axis quarantines and feedback-conflict triage, which intercept over-concentrated proposals before execution and isolate the corrupted hypothesis axis.
Figures & tables
Figure 1: False-science induction mechanism. Targeted paired misbinding preserves the label multiset but changes which object a value describes, effectively rewriting the conditional record function. A neural surrogate fits this induced association, and closed-loop acquisition subsequently allocates experimental budget toward a low-performing basin selected via the false association. Acquisition-trace concentration supports online quarantine, while feedback-conflict traces recover the implicated axis.
Figure 2: Cross-domain false pursuit. Targeted misbinding produces large final target or triggered-target acquisition counts in GFP and materials settings, while clean and random-swap controls remain near zero. Each primary setting uses 10 paired seeds. MLP denotes multilayer perceptron; TabM denotes the TabM tabular ensemble model.
Figure 3: Coherence-response relationship. a , With 25 swaps and an unchanged label multiset, final false-axis acquisitions increase with the fraction of coherent relinking in materials and GFP. b , False-association-strength lift follows the same monotone relation, with GFP showing score lift before large budget redirection. c , A scale-normalized coherence-risk score orders the fixed-swap target-capacity excess within both domains, whereas swap count is fixed.
Figure 4: Operating boundaries and blind action traces. a , Generic sorted joins preserve labels but do not induce Co pursuit, whereas coherent block relinking does. b , Dose sweeps show low tested onset and saturation or nonmonotonic acquisition. c , Threshold sweeps retain high target TPR at zero or near-zero control false-positive rate. d , All-axis proposed-trace monitoring recovers the implicated axis without pre-naming the target.