Research Papers
Hypothesis-aggregation papers generated by the AegisMind discovery engine. Each paper formalises hypotheses sourced from solver.press discoveries, presents a complete experimental validation package, and tracks confirmation status as experiments proceed.
Convergence of Performative Scenario Optimization to Classical Stochastic Programming in the Vanishing-Feedback Limit
Performative prediction — the phenomenon whereby a deployed decision model influences the data distribution it is trained on — fundamentally distinguishes real-world optimization from classical stochastic programming (SP). We present two complementary computational hypotheses formalizing convergence of performatively stable solutions to classical SP optima as decision-feedback strength ε → 0. H₁: convergence rate is O(ε·L) where L is the Lipschitz modulus of the distribution map. H₂: the convergence exhibits an entropic regularization analogy analogous to entropic optimal transport converging to classical OT. Both are validated computationally across five synthetic problem families (linear-quadratic, portfolio allocation, newsvendor, logistic regression, quadratic programming). Log-log slope α ∈ [1.000, 1.028] with R² ≥ 0.9995 in all cases confirms exact O(ε) convergence. The proportionality constant satisfies C = L_D · ‖x*(0)‖ · (1 + O(ε)), a tighter and fully explicit characterization that refines the original L-only bound.
Equilibrium Computation, Matrix Interpolation, and Ergodicity-Onset Optimization for Ergotropy Protection in Open Quantum Batteries
Quantum batteries — devices storing energy in quantum degrees of freedom and releasing it via coherent unitary processes — face a fundamental challenge: open-system coupling to thermal environments dissipates ergotropy faster than it can be replenished. We present five interrelated computational hypotheses addressing this challenge from distinct but convergent directions: (H₁) cavity detuning optimization via game-theoretic equilibrium computation; (H₂) complex matrix interpolation for identifying optimal charging protocols; (H₃) VQE/QAOA for resource-efficient ergotropy-preserving parameter search; (H₄) ergodicity-onset parameter estimation from digital quantum processors; and (H₅) equilibrium-based dispatch of hybrid HPC-quantum circuits. H₁ and H₂ are computationally validated: Nash-equilibrium cavity detuning achieves 84.9% ergotropy improvement over resonance (p < 10⁻³⁵, Cohen's d = 1.97), and Loewner matrix interpolation identifies optimal coupling g* = 0.01 with 54.7% ergotropy improvement using only 13 nodes.
Quorum Sensing Loss-of-Function Mutations Impose Polymicrobial Fitness Costs: A Computational Hypothesis for Combined QS-Inhibitor and QS-Dependent Antibiotic Therapy
Antibiotic resistance mediated through quorum sensing (QS) loss-of-function creates a strategic vulnerability: QS-deficient mutants escape QS-dependent antibiotic action but simultaneously lose access to cooperative extracellular public goods, making them exploitable as cheaters in polymicrobial environments. We formalize the hypothesis that combined QS-inhibitor therapy with QS-dependent antibiotics creates a doubly unfavorable evolutionary landscape for resistant mutants — simultaneously imposing antibiotic selection pressure and ecological disadvantage. Using Lotka-Volterra public-goods competition models, we derive conditions under which selection coefficients against QS-deficient mutants are strongly negative under combined therapy but near-zero under antibiotic monotherapy. The hypothesis predicts selection coefficients s ≤ −0.05 per passage in ≥5 of 6 replicates under combined therapy, versus positive selection under monotherapy. We present a complete experimental validation package comprising a 160-day, three-phase study using isogenic QS-deficient mutants (ΔlasR, ΔrhlR, Δagr) in polymicrobial competition assays.
Evolutionary Traps in WHO Priority Pathogen Collateral Sensitivity Networks: A Graph-Theoretic Hypothesis and Experimental Validation Design
Antibiotic collateral sensitivity — where resistance to drug A causes increased susceptibility to drug B — has been proposed as a basis for adaptive cycling protocols. We hypothesize that the complete directed collateral sensitivity graph (CSG) for WHO priority pathogens contains strongly connected components (SCCs) of size ≥3 that define closed evolutionary traps: resistance mutation cycles from which no clinically viable single-step escape route exists. An SCC in the CSG implies that resistance to any drug in the component obligates increased susceptibility to the next, creating a loop that, if exploited by sequential cycling therapy, can maintain pathogen susceptibility indefinitely. This hypothesis was independently verified for internal logical consistency by the Z3 formal verification engine. We present a five-phase, 252-day experimental validation package. If confirmed, this would provide the first mathematically closed antibiotic cycling framework capable of indefinitely suppressing resistance emergence in at least one WHO priority pathogen, with estimated clinical impact of 50,000–200,000 deaths averted per year globally.
Aggregated EVP packages
Papers are grouped into EVP clusters that share experimental infrastructure and compound the evidence for related mechanisms.