·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
What to expect from WWDC 2026: Siri’s highly anticipated revamp and Apple Intelligence updates1h◆Sriram Krishnan is leaving his role as White House AI advisor1h◆The Trump administration might take an equity stake in OpenAI3h◆Job Searcher4h◆The mayor of Shelbyville, Indiana, says only people who live in ‘shitty houses’ oppose data center4h◆Meta made its own AI-generated clickbait news feed5h◆Here comes new Siri again7h◆Persona Atlas: Mapping How Famous Minds Think7h◆Vision Hopfield Memory Networks15h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations15h◆Insurance of Agentic AI15h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety15h◆MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation15h◆Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics15h◆CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model15h◆Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads15h◆Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents15h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery15h◆TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management15h◆Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement15h◆What to expect from WWDC 2026: Siri’s highly anticipated revamp and Apple Intelligence updates1h◆Sriram Krishnan is leaving his role as White House AI advisor1h◆The Trump administration might take an equity stake in OpenAI3h◆Job Searcher4h◆The mayor of Shelbyville, Indiana, says only people who live in ‘shitty houses’ oppose data center4h◆Meta made its own AI-generated clickbait news feed5h◆Here comes new Siri again7h◆Persona Atlas: Mapping How Famous Minds Think7h◆Vision Hopfield Memory Networks15h◆Stable Deep Reinforcement Learning via Isotropic Gaussian Representations15h◆Insurance of Agentic AI15h◆Output Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety15h◆MUSE: Benchmarking Manufacturable, Functional, and Assemblable Text-to-CAD Generation15h◆Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics15h◆CogManip: Benchmarking Manipulative Behavior in Multi-Turn Interactions with Large Language Model15h◆Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads15h◆Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents15h◆MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery15h◆TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management15h◆Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement15h◆
News/Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement
arxiv
PublishedJune 6, 2026 at 4:00 AM
—neutral

Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

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

arXiv:2606.06468v1 Announce Type: new Abstract: We introduce Goedel-Architect, an agentic framework for formal theorem proving in Lean 4 centered on blueprint generation and refinement. A blueprint is a dependency graph of definitions and lemmas that builds up to the main theorem. First, Goedel-Arch

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

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
arxivVision Hopfield Memory Networks15harxivStable Deep Reinforcement Learning via Isotropic Gaussian Representations15harxivInsurance of Agentic AI15harxivOutput Type Before Quality: A Standards-Derived XAI Admissibility Rubric for Autonomous-Driving Safety15h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews