1. This result does not invalidate or constrain your work, but it theoretically extends your QUBO docking and machine learning surrogate models by providing a rigorous mathematical framework to predict and mitigate temperature-induced chaotic shifts in complex, disordered energy landscapes.

This discovery has a Claude-generated validation package with a full experimental design.

Precise Hypothesis

Spine & Adversarial ReadReady for validation

“This EVP tests whether a computable thermodynamic-chaos indicator derived from a QUBO docking system's energy landscape can predict, with r ≥ 0.6 cross-validated correlation, the magnitude of pose-ranking disruption caused by moderate temperature perturbations. ---”

• highThe hypothesis as stated ('theoretically extends... by providing a rigorous mathematical framework') is not itself falsifiable — it's a claim about applicability/relevance, not a concrete empirical prediction. The EVP had to manufacture a specific falsifiable version (r ≥ 0.6 correlation) that may not match what the original authors intended.
  This EVP explicitly restates the hypothesis into a testable form (Section 1), but this is an interpretive choice by the EVP author, not something confirmed by the original discovery text. Gap acknowledged: the original claim's vagueness ('theoretically extends') means any falsification result could be disputed as testing 'the wrong operationalization.'
• mediumWhy choose Hessian-based Lyapunov proxies, spectral gap, and Jacobian norms specifically, rather than other established chaos/sensitivity metrics from spin-glass theory (e.g., overlap distribution, replica correlation functions) which have decades of prior validation in disordered systems?
  These four indicators were chosen because they are computable directly from objects already present in a QUBO+surrogate pipeline (Hamiltonian matrix, surrogate Jacobian) without requiring new infrastructure, satisfying the low-overhead constraint in the hypothesis. However, this is a pragmatic/cost-driven justification, not a theoretical one — the EVP does not establish that these are the theoretically optimal indicators, only that they are testable cheaply. A more rigorous version would benchmark against spin-glass overlap-parameter methods as a comparison baseline; this is not currently in the protocol and should be added before treating a positive result as strong evidence.
• mediumNo external prior-art or expert search results were available, so claims of novelty and non-conflict with existing chaos-in-QUBO or annealing-temperature-sensitivity literature (which likely exists, e.g., quantum annealing benchmarking studies) are unverified.
  Explicitly flagged in EXTERNAL_CONFLICTS as an evidence gap. This EVP cannot certify novelty or non-redundancy without a follow-up literature search; results should be treated as provisional pending that search.

Experimental Protocol

ROI Projection

Implementation Sketch

for system in molecular_systems:
    qubo = build_qubo(system, pipeline=existing_pipeline)
    baseline_ranking = solve_qubo(qubo, T=T0, repeats=10)
    indicators = {
        "lyapunov_proxy": hessian_power_iteration(qubo.energy_fn, T0),
        "spectral_gap": compute_spectral_gap(qubo.transfer_matrix),
        "jacobian_norm": surrogate_model.jacobian_norm(system, T0),
        "curvature": second_derivative_partition_fn(qubo, T0)
    }
    shifts = {}
    for dT in [-0.5,-0.3,-0.1,0.1,0.3,0.5]:
        T_perturbed = T0 * (1+dT)
        ranking = solve_qubo(qubo, T=T_perturbed, repeats=10)
        shifts[dT] = spearman_shift(baseline_ranking, ranking)
    log(system, indicators, shifts)

# Regression phase
model = fit_regression(X=all_indicators, y=all_shifts, cv="leave_one_system_out")
report(model.r2, model.per_system_correlation)

# Mitigation test
adaptive_schedule = derive_schedule_from(indicators)
compare(adaptive_schedule vs naive_multi_temp_baseline, metric="ranking_variance")