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arxiv: 2605.30977 · v1 · pith:P65YNZG3new · submitted 2026-05-29 · 🪐 quant-ph · cs.CR

Software Platform for Hybrid Pseudo-Random Sequence Generation and Predictability Analysis Based on LFSR and Mersenne Twister

Ali Abdolrahimi Zarnagh , Ali Motazedifard This is my paper

Pith reviewed 2026-06-28 21:58 UTC · model grok-4.3

classification 🪐 quant-ph cs.CR
keywords pseudo-random number generatorsLFSRMersenne Twisterhybrid generatorspredictability analysismachine learningquantum random sequencessoftware platform
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The pith

Hybrid LFSR-Mersenne Twister generators retain detectable predictability that machine learning can exploit.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a software platform that creates pseudo-random bit sequences from Linear Feedback Shift Registers and the Mersenne Twister, including hybrid combinations of the two. It then runs both statistical tests and machine-learning models on the output to measure how much structure remains. The work shows that increasing complexity through hybridization does not remove all patterns that allow partial prediction. This matters for systems that depend on these generators for communications, radar, and security. The platform also compares the classical outputs directly to quantum random sequences to illustrate the difference in resistance to prediction.

Core claim

The platform generates sequences with classical PRNGs and hybrids, then applies data-driven analysis to demonstrate that algorithmic generators carry inherent predictability limits even when their structure grows more complex; this supports preferring quantum random sequences for applications that need stronger resistance to prediction-based attacks.

What carries the argument

Hybrid LFSR-MT sequence generator paired with machine-learning and deep-learning predictability detectors that quantify residual structure.

If this is right

  • Hybrid LFSR-MT structures raise sequence complexity yet leave measurable predictability that classical methods cannot fully erase.
  • Machine-learning tools can serve as a practical test for when deterministic generators remain vulnerable.
  • Quantum random sequences exhibit higher unpredictability traceable to their physical source.
  • The platform supplies a concrete benchmark for comparing generators in sensing and communication systems.
  • Quantum sequences gain an edge in jamming scenarios where an adversary might attempt prediction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of cryptographic systems may need to treat hybrid classical generators as requiring extra safeguards or replacement by quantum sources.
  • The same analysis pipeline could be applied to other widely used PRNG families to map their predictability boundaries.
  • Routine use of data-driven predictability checks could become a standard step before deploying any new classical generator in security contexts.

Load-bearing premise

The machine-learning and deep-learning tools reliably identify real predictability in the generated sequences rather than fitting to artifacts of the data or model choices.

What would settle it

Applying the same machine-learning models to quantum random sequences and obtaining prediction accuracy no better than chance while the models succeed on the hybrid classical sequences would support the distinction; the reverse result would undermine it.

read the original abstract

Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces a software platform for generating pseudo-random bit sequences via LFSR, Mersenne Twister, and hybrid LFSR-MT combinations, together with statistical and data-driven (ML/DL) analysis tools to assess predictability. It claims that the platform's results demonstrate inherent limitations in the unpredictability of classical algorithmic generators, thereby supporting the use of quantum random sequences in security-critical applications such as jamming, and presents a comparative study showing higher unpredictability for quantum sequences due to their physical origin.

Significance. If the empirical ML/DL predictability results hold after proper validation, the platform could serve as a practical benchmarking tool for PRNG complexity in communications and sensing systems, and the comparison with quantum sequences would provide concrete support for preferring quantum RNGs where prediction resistance is required. No machine-checked proofs, reproducible code releases, or parameter-free derivations are described.

major comments (3)
  1. [Abstract] Abstract: the headline claim that 'the results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability' is unsupported by any quantitative outputs (prediction accuracies, ROC curves, statistical tests, or error bars), rendering the central empirical conclusion unverifiable from the manuscript text.
  2. [Abstract] Abstract: the assertion that ML/DL tools can detect 'partial predictability' in hybrid LFSR-MT sequences rests on the unexamined assumption that any detected structure is not an artifact of overfitting, architecture choice, or lack of held-out evaluation; no model architectures, training protocols, data splits, or regularization details are supplied to address this.
  3. [Abstract] Abstract: the comparative study between classical LFSR-MT and quantum sequences is invoked to conclude 'higher unpredictability' for quantum randomness, yet no metrics, sample sizes, or statistical significance tests for this comparison are reported, leaving the support for the security-application recommendation unsubstantiated.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'user-friendly software platform' is used without any description of the user interface, input/output formats, or availability of the code.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We agree that the abstract must be revised to include quantitative metrics, methodological details, and statistical support for the claims. We will update the abstract accordingly and ensure the main text provides the necessary backing. Below we respond to each major comment.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim that 'the results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability' is unsupported by any quantitative outputs (prediction accuracies, ROC curves, statistical tests, or error bars), rendering the central empirical conclusion unverifiable from the manuscript text.

    Authors: We accept the point. The abstract will be revised to report specific quantitative results from the platform, including ML prediction accuracies (e.g., for LFSR, MT, and hybrid cases), NIST statistical test outcomes, and error bars from repeated trials. This will make the claim directly verifiable. revision: yes

  2. Referee: [Abstract] Abstract: the assertion that ML/DL tools can detect 'partial predictability' in hybrid LFSR-MT sequences rests on the unexamined assumption that any detected structure is not an artifact of overfitting, architecture choice, or lack of held-out evaluation; no model architectures, training protocols, data splits, or regularization details are supplied to address this.

    Authors: We agree these details belong in the abstract. The revision will summarize the ML/DL setup, noting held-out test sets, regularization (dropout, L2), cross-validation, and model types (e.g., LSTM, CNN) to demonstrate that detected predictability is not an artifact of overfitting. revision: yes

  3. Referee: [Abstract] Abstract: the comparative study between classical LFSR-MT and quantum sequences is invoked to conclude 'higher unpredictability' for quantum randomness, yet no metrics, sample sizes, or statistical significance tests for this comparison are reported, leaving the support for the security-application recommendation unsubstantiated.

    Authors: We acknowledge the abstract lacks these specifics. It will be updated to include comparative metrics (prediction accuracy and entropy differences), sample sizes (e.g., sequences of 10^6 bits), and significance tests supporting higher unpredictability for quantum sequences, thereby grounding the recommendation for security applications. revision: yes

Circularity Check

0 steps flagged

No circularity: tool-description paper with empirical outputs only

full rationale

The paper introduces a software platform for PRNG generation (LFSR, MT, hybrids) and predictability analysis via statistical and ML/DL methods. Its central claim—that classical generators have inherent unpredictability limits—is presented as an output of running the described tools on sequences, not as a derivation from equations or fitted parameters. No self-definitional relations, predictions that reduce to fits by construction, or load-bearing self-citations appear in the provided text. The work is self-contained as a practical tool description whose results are externally falsifiable via the platform itself; no derivation chain reduces to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the effectiveness of the software platform and its ML analysis tools, but the abstract introduces no new free parameters, axioms, or invented entities; it relies on standard existing PRNG algorithms and ML methods.

pith-pipeline@v0.9.1-grok · 5818 in / 1036 out tokens · 26489 ms · 2026-06-28T21:58:42.499024+00:00 · methodology

discussion (0)

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