A molecular clock for writing systems reveals the quantitative impact of imperial power on cultural evolution

• URL: http://arxiv.org/abs/2604.10957v1
• Date: Mon, 13 Apr 2026 03:54:01 GMT
• Title: A molecular clock for writing systems reveals the quantitative impact of imperial power on cultural evolution
• Abstract summary: We compile the Global Script Database (GSD): 300 writing and notation systems, 50 binary structural characters, and 259 phylogenetic edges spanning 5,400 years.<n>Applying four methods, we find that scripts exhibit a detectable molecular clock.<n>We identify 30 major script replacement events and rank their destructive impact.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Writing systems are cultural replicators whose evolution has never been studied quantitatively at global scale. We compile the Global Script Database (GSD): 300 writing and notation systems, 50 binary structural characters, and 259 phylogenetic edges spanning 5,400 years. Applying four methods -- phenetics, cladistics, Bayesian inference, and neural network clustering -- we find that scripts exhibit a detectable molecular clock. The best-fitting model (Mk+Gamma strict clock) yields a substitution rate of q = 0.226 substitutions/character/millennium (95% CI: 0.034-1.22; Delta BIC = -4.1 versus relaxed clock; Delta BIC = -1,364.7 versus Mk without rate variation). Political interventions break this clock: deviation from expected divergence times correlates with intervention intensity (Spearman rho = 0.556, p < 10^{-4}), and per-character rate analysis reveals that intervention selectively rewrites deep structural features rather than merely accelerating change (rate profile correlation rho = 0.320). We identify 30 major script replacement events and rank their destructive impact. A ceiling effect suppresses independent invention wherever writing already exists (Fisher's exact OR = 0.054, p < 10^{-6}), and colonial contact predicts script extinction (Cox HR = 5.25, p = 0.0006). The Spanish Empire extinguished the most scripts (6 of 12 contacted, 50%), followed by the Empire of Japan (3 of 9, 33.3%). Feature coding was validated by inter-rater reliability testing with two independent human coders (Cohen's kappa = 0.877; human-LLM kappa = 0.929; Fleiss' kappa = 0.911).

Related papers

• Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection [66.44527094471619]
  We present RECAL, an unsupervised framework using relation-balanced masked graph learning to better capture rare interaction patterns.<n>On three DARPA E3 datasets, RECAL achieves F1 scores of 99.99%, 99.93%, and 99.99%, outperforming the best baseline on each dataset by 0.88, 0.82, and 0.42 percentage points, respectively.
  arXiv  Detail & Related papers  (2026-09-15T00:31:01Z)
• Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging [41.62710134204231]
  We study the failure mode through psychological depth in short stories.<n>A judge's correlation with human ratings on its development set may not guarantee valid measurement when outputs are closely matched and human preferences are subjective.<n>These results suggest that development-set performance is insufficient evidence for deployment validity on a shifted distribution.
  arXiv  Detail & Related papers  (2026-09-12T07:34:54Z)
• Beyond Lexical Metrics: Sentence-Embedding Detection of Reviewer Habituation in AI Code Review [2.9152186617858304]
  We study 11,429 reviews paired with 10,104 human-authored inline comments from AI.<n>Approval rates rise from 30.5% in reviewers' early periods to 36.6% in late periods.<n>Four hand-crafted linguistic features - lexical diversity, Shannon entropy, technical specificity, and constructive actionability - show no monotonic decline across exposure deciles.
  arXiv  Detail & Related papers  (2026-09-05T18:15:18Z)
• Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk [0.0]
  Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters.<n>A common proposal closes that gap with a language model: compute feature attributions, hand them to an LLM, and let it write the rationale.<n>We build such a system end to end and test whether the second half of the promise holds.
  arXiv  Detail & Related papers  (2026-08-08T13:22:14Z)
• Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
  Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
  arXiv  Detail & Related papers  (2026-05-20T19:08:38Z)
• Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies [4.738949927143789]
  Successive self-training on a language model's own outputs is widely characterized as a process of flattening.<n>We provide evidence that this characterization is incomplete.<n>Across eleven generations of self-training on five models, language is not flattened uniformly -- it is restructured.
  arXiv  Detail & Related papers  (2026-05-20T01:44:47Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• The Digital Afterlife of Empires: Four Language Models Converge on the Same Imperial Cartography of Writing [0.0]
  Large language models process the world's writing systems with radical inequality.<n>Only 29 scripts (9.7%) are fully supported by contemporary digital infrastructure; among 158 living scripts, 60 (38.0%) lack complete support.<n> Tokenizer efficiency varies by a factor of 31.7 across 45 scripts measured with parallel text.
  arXiv  Detail & Related papers  (2026-04-30T07:02:45Z)
• The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious [35.76482964927589]
  We characterize the autocorrelation structure of 66 turn-level metrics across 202 multi-turn conversations.<n>We show that 42% of associations that appear significant under standard pooled testing fail to survive cluster-robust correction.<n>We present a two-stage correction framework combining Chelton (1983) effective degrees of freedom with conversation-level block bootstraps.
  arXiv  Detail & Related papers  (2026-04-15T20:54:39Z)
• Predicting Activity Cliffs for Autonomous Medicinal Chemistry [0.0]
  Activity cliff prediction has been a persistent challenge in computational medicinal chemistry.<n>This work focuses on a parsimonious definition: which small modifications, at which positions, confer the highest probability of an outcome change.<n> Position-level sensitivity is calculated using 25 million matched molecular pairs from 50 ChEMBL targets across six protein families.
  arXiv  Detail & Related papers  (2026-04-08T20:02:17Z)
• From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale [1.2328006781720982]
  Language models trained with contemporary pipelines exhibit severe reshaping of linguistic features, leading to extreme language re-distribution.<n>We analyze 17 models (410M-100B+ parameters) across 24 linguistically-motivated probes.<n>Our work reveals a structural limitation of current alignment pipelines, with implications for AI detection, training data contamination, and long-term linguistic evolution.
  arXiv  Detail & Related papers  (2026-04-08T02:13:46Z)
• DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects [6.107850985025956]
  We present DIA-HARM, the first benchmark for evaluating disinformation detection across 50 English dialects.<n>Human-written dialectal content degrades detection by 1.4-3.6% F1, while AI-generated content remains stable.<n>We release the DIA-HARM framework, D3 corpus, and evaluation tools.
  arXiv  Detail & Related papers  (2026-04-07T01:43:48Z)
• Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News [47.03825808787752]
  Can humans tell whether a news article was written by a person or a large language model (LLM)?<n>We investigate this question using JudgeGPT, a study platform that measures source attribution (human vs. machine) and authenticity judgment (legitimate vs. fake) on continuous scales.<n>We report five findings: (1) participants cannot reliably distinguish machine-generated from human-written text; (2) this inability holds across all tested models, including open-weight models with as few as 7B parameters; and (3) accuracy degrades after approximately 30 sequential evaluations due to cognitive fatigue.
  arXiv  Detail & Related papers  (2026-04-04T15:03:42Z)
• Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1 [0.0]
  This paper presents a controlled experiment in which GPT4.1 was assigned one of three socioeconomic personas.<n>We find that the model reproduces key behavioral signatures predicted by Kahneman and Tversky's Prospect Theory.
  arXiv  Detail & Related papers  (2026-03-16T19:03:19Z)
• On the Insecurity of Keystroke-Based AI Authorship Detection: Timing-Forgery Attacks Against Motor-Signal Verification [0.0]
  Recent proposals advocate using keystroke timing signals to distinguish human-composed text from AI-generated content.<n>We show that this class of defenses is insecure against two practical attack classes.<n>We formalize a non-identifiability result: when the detector observes only timing, the mutual information between features and content provenance is zero for copy-type attacks.
  arXiv  Detail & Related papers  (2026-01-24T03:39:38Z)
• Intention Collapse: Intention-Level Metrics for Reasoning in Language Models [0.0]
  We call this process intention collapse: a many-to-one projection from a high dimensional intention space I into an external language space L.<n>We define three model agnostic intention metrics (intention entropy, effective dimensionality dimeff, and latent knowledge recoverability Recov)<n>Using a 4 bit Mistral 7B model on 200 GSM8K problems, we compare a direct answer baseline, a chain of thought (CoT) regime, and a babble control.
  arXiv  Detail & Related papers  (2026-01-03T00:19:53Z)
• LLMs on Drugs: Language Models Are Few-Shot Consumers [51.736723807086385]
  We present the first controlled study of psychoactive framings on GPT-5-mini using ARC-Challenge.<n>Four single-sentence prompts -- LSD, cocaine, alcohol, and cannabis -- are compared against a sober control.<n> Persona text behaves like a "few-shot consumable" that can destroy reliability without touching model weights.
  arXiv  Detail & Related papers  (2025-12-21T00:19:02Z)
• Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection [58.419940585826744]
  We introduce FairOPT, an algorithm for group-specific threshold optimization for probabilistic AI-text detectors.<n>We partitioned data into subgroups based on attributes (e.g., text length and writing style) and implemented FairOPT to learn decision thresholds for each group to reduce discrepancy.<n>Our framework paves the way for more robust classification in AI-generated content detection via post-processing.
  arXiv  Detail & Related papers  (2025-02-06T21:58:48Z)
• CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense [61.78357530675446]
  Humans are difficult to be cheated by subtle manipulations, since we make judgments only based on essential factors.<n>Inspired by this observation, we attempt to model label generation with essential label-causative factors and incorporate label-non-causative factors to assist data generation.<n>For an adversarial example, we aim to discriminate perturbations as non-causative factors and make predictions only based on the label-causative factors.
  arXiv  Detail & Related papers  (2024-10-30T15:06:44Z)

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.