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2505.22644

On the Intractability of Chaotic Symbolic Walks: Toward a Non-Algebraic Post-Quantum Hardness Assumption

Mohamed Aly Bouke

incompletehigh confidence
Category
Not specified
Journal tier
Specialist/Solid
Processed
Sep 28, 2025, 12:56 AM

Audit review

The paper’s PSPACE- and #P-hardness “proofs” are high-level sketches that omit the crucial isolation and no-return invariants needed for a valid many-one (and parsimonious) reduction. For PSPACE-hardness, the paper merely maps states to lattice points and edges to contractive affine maps with noise, then asserts an inversion equivalence without excluding spurious trajectories or proving layer-respecting behavior; see its Step 2–5 under 4.1 PSPACE-Hardness and the reliance on “≈” and noise to force rounding, with no quantitative margins or persistence of separation . For #P-hardness, it similarly claims a one-to-one mapping from DAG paths to SPIP trajectories, again without a proof that trajectories can neither merge nor spuriously arise from wrong choices under noise . The paper also uses an internally inconsistent rounding description (“floor rounding to the nearest integer”), further undermining soundness of the reductions . By contrast, the candidate model supplies a complete, quantified construction: a layered encoding with a single contractive matrix, carefully spaced grids, explicit isolation lemmas for wrong-layer and wrong-source transitions, and a no-return lemma with concrete parameter choices—together yielding a proper many-one reduction for reachability and a parsimonious reduction for counting.

Referee report (LaTeX)

\textbf{Recommendation:} major revisions

\textbf{Journal Tier:} specialist/solid

\textbf{Justification:}

The work promotes a non-algebraic hardness viewpoint and frames an appealing inversion problem, but the central reductions are not yet rigorous. The PSPACE- and #P-hardness arguments lack the separation/no-return machinery and precise rounding semantics required for a valid many-one or parsimonious reduction. With these details added—preferably in a layered, parameterized construction—the results could meet the bar.