Evidence of Layered Positional and Directional Constraints in the Voynich Manuscript: Implications for Cipher-Like Structure

• URL: http://arxiv.org/abs/2604.19762v1
• Date: Thu, 26 Mar 2026 14:01:19 GMT
• Title: Evidence of Layered Positional and Directional Constraints in the Voynich Manuscript: Implications for Cipher-Like Structure
• Abstract summary: We present a systematic analysis of the Voynich Manuscript (VMS)<n>The VMS exhibits cipher-like structural constraints that are difficult to reproduce from simple positional or frequency-based mechanisms alone.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Voynich Manuscript (VMS) exhibits a script of uncertain origin whose grapheme sequences have resisted linguistic analysis. We present a systematic analysis of its grapheme sequences, revealing two complementary structural layers: a character-level right-to-left optimization in word-internal sequences and a left-to-right dependency at word boundaries, a directional dissociation not observed in any of our four comparison languages (English, French, Hebrew, Arabic). We further evaluate two classes of structured generator against a four-signature joint criterion: a parametric slot-based generator and a Cardan grille implementing Rugg's (2004) gibberish hypothesis. Across their full tested parameter spaces, neither class reproduces all four signatures simultaneously. While these results do not rule out generator classes we have not tested, they provide the first quantitative benchmarks against which any future generative or cryptanalytic model of the VMS can be evaluated, and they suggest that the VMS exhibits cipher-like structural constraints that are difficult to reproduce from simple positional or frequency-based mechanisms alone.

Related papers

• LoRC: Detecting AI-Generated Images via Low-Rank Collapse in Semantic Residuals [50.157719466558696]
  We show that modern generators exhibit low-rank collapse in the semantic-residual subspace while preserving the dominant semantic direction.<n>We propose textbfLoRC, a framework that decouples semantic dominance to capture the collapsed residual geometry.<n>Our method improves accuracy by an average of 7.0% across multiple benchmarks and achieves 97.0% accuracy on 39 unseen generators.
  arXiv  Detail & Related papers  (2026-08-21T08:58:47Z)
• Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models [5.237172334460829]
  We introduce a framework that combines sparse dictionary learning with causal intervention to extract, validate, and causally test interpretable features in genomic foundation models.<n>We show that the naive validation of such features against position weight matrices is severely confounded by GC composition and repetitive elements.<n>We establish that specific features are emphcausally used to represent cell-type-specific TF binding, not merely motif presence.
  arXiv  Detail & Related papers  (2026-07-21T22:54:46Z)
• Weave of Formal Thought [51.56484100374058]
  We introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations.<n>We present a latent-variable fine-tuning method training the language model to interleave non-terminal grammar symbols directly into generation.<n>For Python, fine-tuning StarCoder2-3B with our RWS objective reduces per-token cross-entropy by 14.3% relative to a text-only SFT baseline.
  arXiv  Detail & Related papers  (2026-06-24T15:58:11Z)
• Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics [49.443264461057645]
  Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling.<n>Progress in both paradigms is overwhelmingly tracked by generative perplexity (gen-PPL): the per-token negative log-likelihood of samples under a frozen autoregressive (AR) scorer.<n>We argue that this metric is unsound. By construction, gen-PPL measures only predictability under the scoring AR, not grammaticality or semantic coherence.
  arXiv  Detail & Related papers  (2026-06-07T02:35:56Z)
• Check Your LLM's Secret Dictionary! Five Lines of Code Reveal What Your LLM Learned (Including What It Shouldn't Have) [0.0]
  We show that singular value decomposition of the lm_head weight matrix of a transformer-based large language model reveals interpretable semantic subspaces directly from the model weights.<n>Analysing GPT-OSS-120B, Gemma-2-2B, and Qwen2.5-1.5B, we find that singular value spectra and vocabulary cluster structures differ systematically across models.
  arXiv  Detail & Related papers  (2026-05-21T05:02:51Z)
• HAAF: Hierarchical Adaptation and Alignment of Foundation Models for Few-Shot Pathology Anomaly Detection [10.649984141835189]
  We propose the Hierarchical Adaptation and Alignment Framework (HAAF)<n>At its core is a novel Cross-Level Scaled Alignment mechanism that enforces a sequential calibration order.<n>A dual-branch inference strategy integrates semantic scores with geometric prototypes to ensure stability in few-shot settings.
  arXiv  Detail & Related papers  (2026-01-24T10:31:21Z)
• Spectral Archaeology: The Causal Topology of Model Evolution [0.0]
  Behavioral benchmarks tell us textitwhat a model does, but not textithow.<n>We introduce a training-free mechanistic probe using attention-graph spectra.<n>Across 12 models and 10 languages, these measures yield stable fingerprints'' that expose discontinuities missed by standard evaluation.
  arXiv  Detail & Related papers  (2026-01-06T21:26:54Z)
• Hierarchical Geometry of Cognitive States in Transformer Embedding Spaces [0.0]
  We investigate whether sentence embeddings encode a graded, hierarchical structure aligned with human-interpretable cognitive or psychological attributes.<n>We construct a dataset of 480 natural-language sentences annotated with continuous ordinal energy scores and discrete tier labels.<n>Across models, both continuous scores and tier labels are reliably decodable, with shallow nonlinear probes providing consistent performance gains.
  arXiv  Detail & Related papers  (2025-12-23T03:37:34Z)
• Beyond cognacy [0.21756081703275998]
  Two fully automated methods are compared to extract phylogenetic signal directly from lexical data.<n>Results show that MSA-based inference yields trees more consistent with linguistic classifications, better predicts typological variation, and provides a clearer phylogenetic signal.
  arXiv  Detail & Related papers  (2025-07-02T06:47:34Z)
• Localizing Factual Inconsistencies in Attributable Text Generation [74.11403803488643]
  We introduce QASemConsistency, a new formalism for localizing factual inconsistencies in attributable text generation.<n>We show that QASemConsistency yields factual consistency scores that correlate well with human judgments.
  arXiv  Detail & Related papers  (2024-10-09T22:53:48Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Zero-Shot Logit Adjustment [89.68803484284408]
  Generalized Zero-Shot Learning (GZSL) is a semantic-descriptor-based learning technique. In this paper, we propose a new generation-based technique to enhance the generator's effect while neglecting the improvement of the classifier. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative zero-shot learning frameworks.
  arXiv  Detail & Related papers  (2022-04-25T17:54:55Z)
• Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Task [70.29624135819884]
  We study the extent to which BERT is able to perform lexically-independent subject-verb number agreement (NA) on targeted syntactic templates. Our results on nonce sentences suggest that the model generalizes well for simple templates, but fails to perform lexically-independent syntactic generalization when as little as one attractor is present.
  arXiv  Detail & Related papers  (2022-04-14T11:33:15Z)
• Aspect Sentiment Quad Prediction as Paraphrase Generation [53.33072918744124]
  We introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence. We propose a novel textscParaphrase modeling paradigm to cast the ASQP task to a paraphrase generation process. On the other hand, the semantics of the sentiment elements can be fully exploited by learning to generate them in the natural language form.
  arXiv  Detail & Related papers  (2021-10-02T12:57:27Z)
• A Non-Linear Structural Probe [43.50268085775569]
  We study the case of a structural probe, which aims to investigate the encoding of syntactic structure in contextual representations. By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant. We test on 6 languages and find that the radial-basis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement.
  arXiv  Detail & Related papers  (2021-05-21T07:53:10Z)
• Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models [63.808843089941405]
  Large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. We present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions.
  arXiv  Detail & Related papers  (2020-10-16T18:02:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.