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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_2",
  "publishedAt": "2026-04-27T06:17:33.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "https://github.com/VigorFox/galt-paper"
  ],
  "textContent": "Update / Clarification\n\nI can no longer edit the original post, so I am adding a clearer technical summary here.\n\nThe main point of GALT is not that it replaces backpropagation today. A more precise framing is:\n\n> GALT extends the training object beyond single-loss backpropagation by representing forward consistency, safety, memory, and routing identity as explicit constraint edges in a graph-structured optimization process.\n\nThe architecture is summarized in this flowchart:\n\n## What GALT is trying to solve\n\nModern LLM post-training often mixes task performance, safety behavior, and memory/retention into a single dense carrier through weighted loss terms. This can lead to interference: improving one objective may degrade another.\n\nGALT instead treats these objectives as explicit constraints in a graph:\n\n\n    model blocks / experts\n    + forward consistency edges\n    + task constraints\n    + safety boundary constraints\n    + memory / retention constraints\n    + policy / action constraints\n\n\nTraining then alternates between local block updates and outer Augmented Lagrangian coordination.\n\n## Key architectural idea\n\nGALT decomposes learning into responsibility channels:\n\n  * Task channel: goal achievement and performance optimization\n\n  * Safety channel: boundary conditions and feasible region\n\n  * Memory channel: retention and memory writes inside the safety scaffold\n\n  * Tool-action channel: execution and interaction policies\n\n\n\n\nOne important hypothesis from the current results is that memory should not be modeled as a fully independent parallel constraint. Instead, memory appears to grow more stably when scaffolded by a safety boundary.\n\nIn short:\n\n\n    safety boundary → memory scaffold → controllable retention\n\n\n## Why this may matter\n\nIf this direction holds at larger scale, GALT could provide a route toward:\n\n  * safer continual adaptation,\n\n  * reduced task/safety/memory interference,\n\n  * more controllable memory updates,\n\n  * responsibility-aware MoE routing,\n\n  * controllable NPC / agent systems,\n\n  * better post-training diagnostics through zero/scramble causal tests.\n\n\n\n\n## Current status\n\nThis is still early-stage research.\n\nThe current public snapshot includes:\n\n  * a Qwen-MLX real-carrier prototype,\n\n  * typed task/safety/memory routing experiments,\n\n  * route zeroing and scrambling probes,\n\n  * negative results showing that typed branches do not emerge automatically without appropriate learning signal,\n\n  * Stage D evidence suggesting route necessity under specific configurations.\n\n\n\n\nThe current evidence should be interpreted as prototype-level support, not as proof that GALT already replaces standard LLM training.\n\n## What I am asking for\n\nI would appreciate feedback on three specific questions:\n\n  1. Is the AVBD / physics-solver → GALT constraint-graph mapping technically coherent?\n\n  2. Are the current Stage D experiments sufficient for a first arXiv preprint?\n\n  3. Which claims should be weakened or clarified before submission?\n\n\n\n\nIf someone qualified in the relevant arXiv category believes this is appropriate scientific content for arXiv, I would also be grateful for an endorsement.\n\nEndorsement code: `JV3V4P`\n\nGitHub paper/code/results:\nhttps://github.com/VigorFox/galt-paper\n\nThank you. I am especially interested in feedback from people working on constrained optimization, continual learning, MoE/routing, alignment, LLM systems, or agent safety.",
  "title": "Seeking arXiv cs.AI (cross-list cs.LG) Endorsement — GALT: Graph-Parallel Augmented-Lagrangian Training with Responsibility-Separated Channels"
}