arXiv CS
open access
Tiny Recursive Message Passing
Message passing neural networks have shown remarkable performance in algorithmic and constraint reasoning, due to their ability to encode the structure of the task directly in the architecture. Recent work has shown that better results can be achieved using recursive transformers trained with deep s
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Tiny Recursive Message Passing#1344149
- Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions#204836
- Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration#610072
- Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration#609010
- SCB: SpeechConversationBench for Evaluating Multi-Turn Reasoning in Speech-to-Speech Models#1154462
Record · ID 1319040
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive