·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
AI researchers stranded Claude on a virtual desert island. It started to “play.”1h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods6h◆Can Graph Learning Learn Circuits?6h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery6h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification6h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence6h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions6h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6h◆Functional Welfare for AI Safety6h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving6h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies6h◆AI researchers stranded Claude on a virtual desert island. It started to “play.”1h◆RCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods6h◆Can Graph Learning Learn Circuits?6h◆Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery6h◆Trajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification6h◆Sentence-Level Topic Modeling with Lexical and Contextual Evidence6h◆Referring Multi-Object Tracking with Diverse Dynamic Expressions6h◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6h◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6h◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6h◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6h◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6h◆Functional Welfare for AI Safety6h◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6h◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6h◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6h◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6h◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6h◆Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving6h◆Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies6h◆
News/Turbulence-like 5/3 spectral scaling in contextual representations of language as a complex system
arxiv
PublishedApril 8, 2026 at 4:00 AM
—neutral

Turbulence-like 5/3 spectral scaling in contextual representations of language as a complex system

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2604.05536v1 Announce Type: cross Abstract: Natural language is a complex system that exhibits robust statistical regularities. Here, we represent text as a trajectory in a high-dimensional embedding space generated by transformer-based language models, and quantify scale-dependent fluctuation

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    transformer-based language models
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#language-models#natural-language-processing#complex-systems#self-similarity

No replies yet. Be first.

Mentioned models
01
  • 01
    transformer-based language models
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#language-models#natural-language-processing#complex-systems#self-similarity

Related coverage

More from ARXIV
arxivRCPN: towards robust pseudo-labels in unsupervised CLIP adaptation via consistent prototypes and neighborhoods6harxivCan Graph Learning Learn Circuits?6harxivBeyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery6harxivTrajectory-Guided Steering: Mitigating Spurious Feature Bias in Clinical Classification6h
The Bubble Brief
MON · WED · FRI

Read language-models insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews