·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Figma announces new features to define how agents work on its platform49m◆Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs1h◆The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior1h◆Dual-Evolving Reinforcement Learning for Automated Reward Design1h◆SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning1h◆When Is Enough Not Enough? Illusory Completion in Search Agents1h◆Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis1h◆Learning Reusable Agent Subroutines from Experience1h◆AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions1h◆What Does a Discrete Diffusion Model Learn? One Reverse Process in Three Coordinates1h◆CausalDS: Benchmarking Causal Reasoning in Data-Science Agents1h◆SPARK: Learning from Verification in Speculative GRPO Rollouts1h◆Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL1h◆AVA-Encoder: Towards Agent-Native Video Representation Learning1h◆Mechanistic Circuit Identification for Controllable Data Generation1h◆The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations1h◆A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data1h◆SecJev: Bringing Security Expertise to System One Decision Models1h◆Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes1h◆Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data1h◆Figma announces new features to define how agents work on its platform49m◆Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs1h◆The Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior1h◆Dual-Evolving Reinforcement Learning for Automated Reward Design1h◆SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning1h◆When Is Enough Not Enough? Illusory Completion in Search Agents1h◆Semantic Communities and Boundary-Spanning Lyrics in K-pop: A Graph-Based Unsupervised Analysis1h◆Learning Reusable Agent Subroutines from Experience1h◆AgentWorld: A Data Factory for Training LLM Agents from Multi-Turn Interactions1h◆What Does a Discrete Diffusion Model Learn? One Reverse Process in Three Coordinates1h◆CausalDS: Benchmarking Causal Reasoning in Data-Science Agents1h◆SPARK: Learning from Verification in Speculative GRPO Rollouts1h◆Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL1h◆AVA-Encoder: Towards Agent-Native Video Representation Learning1h◆Mechanistic Circuit Identification for Controllable Data Generation1h◆The Dice Roll Method: A Standardized Protocol for Repeated-Query Auditing of Large Language Model Brand Recommendations1h◆A Computational Framework for Modelling Organisation-Level Semantic Identity from Longitudinal Textual Data1h◆SecJev: Bringing Security Expertise to System One Decision Models1h◆Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes1h◆Inverse Reasoning Models for Efficient Post-Training and Verification from Answer-Only Data1h◆
News/Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers
arxiv
PublishedJuly 29, 2026 at 4:00 AM

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers

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

arXiv:2512.17351v2 Announce Type: replace Abstract: Understanding architectural differences in language models is challenging, especially at academic-scale pretraining (e.g., 1.3B parameters, 100B tokens), where results are often dominated by noise and randomness. To overcome this, we introduce cont

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
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivGround-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs1harxivThe Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior1harxivDual-Evolving Reinforcement Learning for Automated Reward Design1harxivSWE-Spot: Building Small Repo-Experts with Repository-Centric Learning1h
The Bubble Brief
MON · WED · FRI

Read AI 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