{ "schema_version": 1, "builder_plan": { "scenario": "Start with one repeated product task that has a measurable failure or cost.", "change": "Read the methods and identify one change you can isolate in that workflow.", "baseline": "Your current implementation on the same held-out tasks.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Successful outcomes, response time, human rework, and total cost per successful task.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.37604", "title": "GraphVQ: Structure-Aware Autoregressive Decoding over Context-Quantized Graph Tokens", "summary": "GraphVQ replaces one-pass autoregressive graph generation conditioned on a single global summary with a two-stage pipeline: a VQ-VAE tokenizes node contexts into a shared codebook, and a structure-aware decoder emits the adjacency from pair-level token features. The cost is training and maintaining two models (tokenizer plus pair decoder), which is feasible on a single 24 GB GPU for small molecular/protein-scale graphs but adds pipeline complexity, and the authors themselves report failure on MUTAG where the unweighted edge target under-generates. 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