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

Update / Clarification

I can no longer edit the original post, so I am adding a clearer technical summary here.

The main point of GALT is not that it replaces backpropagation today. A more precise framing is:

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.

The architecture is summarized in this flowchart:

What GALT is trying to solve

Modern 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.

GALT instead treats these objectives as explicit constraints in a graph:

model blocks / experts
+ forward consistency edges
+ task constraints
+ safety boundary constraints
+ memory / retention constraints
+ policy / action constraints

Training then alternates between local block updates and outer Augmented Lagrangian coordination.

Key architectural idea

GALT decomposes learning into responsibility channels:

• Task channel: goal achievement and performance optimization

• Safety channel: boundary conditions and feasible region

• Memory channel: retention and memory writes inside the safety scaffold

• Tool-action channel: execution and interaction policies

One 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.

In short:

safety boundary → memory scaffold → controllable retention

Why this may matter

If this direction holds at larger scale, GALT could provide a route toward:

• safer continual adaptation,

• reduced task/safety/memory interference,

• more controllable memory updates,

• responsibility-aware MoE routing,

• controllable NPC / agent systems,

• better post-training diagnostics through zero/scramble causal tests.

Current status

This is still early-stage research.

The current public snapshot includes:

• a Qwen-MLX real-carrier prototype,

• typed task/safety/memory routing experiments,

• route zeroing and scrambling probes,

• negative results showing that typed branches do not emerge automatically without appropriate learning signal,

• Stage D evidence suggesting route necessity under specific configurations.

The current evidence should be interpreted as prototype-level support, not as proof that GALT already replaces standard LLM training.

What I am asking for

I would appreciate feedback on three specific questions:

1. Is the AVBD / physics-solver → GALT constraint-graph mapping technically coherent?

2. Are the current Stage D experiments sufficient for a first arXiv preprint?

3. Which claims should be weakened or clarified before submission?

If someone qualified in the relevant arXiv category believes this is appropriate scientific content for arXiv, I would also be grateful for an endorsement.

Endorsement code: JV3V4P

GitHub paper/code/results: https://github.com/VigorFox/galt-paper

Thank you. I am especially interested in feedback from people working on constrained optimization, continual learning, MoE/routing, alignment, LLM systems, or agent safety.