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  "path": "/t/seeking-arxiv-cs-ai-cross-list-cs-lg-endorsement-galt-graph-parallel-augmented-lagrangian-training-with-responsibility-separated-channels/175521#post_1",
  "publishedAt": "2026-04-24T08:57:36.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "GitHub - VigorFox/galt-paper: Paper and experiments for GALT, a graph-parallel augmented-Lagrangian training paradigm with typed task/safety/memory channels. · GitHub",
    "Log in to arXiv | arXiv e-print repository"
  ],
  "textContent": "Hi everyone,\n\nI’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.\n\n### Paper: GALT — A New Training Paradigm Beyond Traditional Backpropagation\n\nModern large models still suffer from three fundamental limitations of backpropagation:\n\n  * strict depth-sequential dependence,\n  * constraints (safety, retention) treated as second-class soft penalties,\n  * complete entanglement of task, safety, and memory responsibilities in a single dense carrier.\n\n\n\n**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.\n\nGALT 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.\n\n### Key Result: Responsibility-Separated Channels + Safety as Memory Scaffold\n\nOn 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.\n\nThis provides a concrete architectural path toward sustainable learning: update one channel while maintaining negotiated consistency across internal responsibilities.\n\n**Full paper, code, and experiments are available on GitHub:**\n→ GitHub - VigorFox/galt-paper: Paper and experiments for GALT, a graph-parallel augmented-Lagrangian training paradigm with typed task/safety/memory channels. · GitHub\n\nI would be very grateful if any qualified researcher could help endorse the submission.\n**My endorsement code:** `JV3V4P`\n(You can endorse directly at: Log in to arXiv | arXiv e-print repository)\n\nHappy 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!",
  "title": "Seeking arXiv cs.AI (cross-list cs.LG) Endorsement — GALT: Graph-Parallel Augmented-Lagrangian Training with Responsibility-Separated Channels"
}