Seeking arXiv cs.AI (cross-list cs.LG) Endorsement — GALT: Graph-Parallel Augmented-Lagrangian Training with Responsibility-Separated Channels

Hi everyone,

I’m an independent researcher and I’m preparing to submit my first preprint to arXiv in cs.AI. As a first-time submitter without institutional co-authors, I’m kindly seeking an endorsement from someone who has published in these categories in the past 5 years.

Paper: GALT — A New Training Paradigm Beyond Traditional Backpropagation

Modern large models still suffer from three fundamental limitations of backpropagation:

• strict depth-sequential dependence,
• constraints (safety, retention) treated as second-class soft penalties,
• complete entanglement of task, safety, and memory responsibilities in a single dense carrier.

GALT (Graph-Parallel Augmented-Lagrangian Training) reframes training as constraint satisfaction on an explicit graph. Each computational block is a node, forward consistency and external requirements (safety/memory) are edges in the same optimization object. Training alternates parallel local block solves (using Adam’s diagonal metric + low-rank constraint terms solved exactly via Sherman-Morrison/Woodbury) with outer Augmented-Lagrangian updates.

GALT is an operational superset of backpropagation: it reduces to standard first-order training when the graph collapses to a simple chain with no external constraints, but becomes strictly richer when graph structure or persistent constraints matter.

Key Result: Responsibility-Separated Channels + Safety as Memory Scaffold

On a real Transformer carrier (Qwen-MLX), we show that native routing variables + typed task/safety/memory channels become causally necessary (strong positive zero-gap and scramble-gap). Most excitingly, recent experiments reveal an asymmetric scaffold effect : safety-route supervision organizes and stabilizes memory (retain) behavior more reliably than memory-only routing. In pure counterfactual retain benchmarks, a strong safety boundary allows memory specialization to emerge naturally — even before a fully distinct memory route identity is learned.

This provides a concrete architectural path toward sustainable learning: update one channel while maintaining negotiated consistency across internal responsibilities.

Full paper, code, and experiments are available on GitHub: → GitHub - VigorFox/galt-paper: Paper and experiments for GALT, a graph-parallel augmented-Lagrangian training paradigm with typed task/safety/memory channels. · GitHub

I would be very grateful if any qualified researcher could help endorse the submission. My endorsement code: JV3V4P (You can endorse directly at: Log in to arXiv | arXiv e-print repository)

Happy to answer any questions, share the PDF, or provide more details about the implementation/results. Thank you in advance for your time and consideration — any help is greatly appreciated!