Add Colab-ready VLAv3 research notebook with 4 attention baselines and 3 experiments

[Vishal-sys-code]

Motivation

• Provide a single, Colab-T4-ready research notebook that implements VLAv3 in a Transformer scaffold and compares it against three strong baselines.
• Reproduce the three requested experiments (scaling, stability, behavior) with reproducible configs and publication-quality outputs.
• Produce CSV/PNG/JSON artifacts and logging suitable for tables/figures in a research paper.

Description

• Adds notebooks/06_VLAv3_Research_Engineer_Colab.ipynb which implements four pluggable attention modules: SoftmaxCausalAttention, LinearAttention (ELU+1), DeltaNetAttention, and VLAv3Attention.
• Adds a common Transformer scaffold (TransformerBlock, TinyTransformerLM) so each attention variant can be evaluated under identical model settings and a synthetic associative retrieval dataset + helpers (make_associative_batch, train_assoc_model, eval_assoc_accuracy, benchmark_forward).
• Implements the three end-to-end experiments and exports paper-ready artifacts to vlav3_paper_artifacts/ including CSV logs, PNG plots, and a paper_summary.json with experiment summaries and config metadata.
• Notebook contains Colab/T4 run notes and reasonable default ModelConfig/TrainConfig values for quick iteration and directions for scaling to publication-quality runs.

Testing

• Programmatically wrote the notebook file and validated notebook JSON/code-cell syntax by compiling every code cell with compile(...), and the check completed successfully.
• Verified that all code cells in notebooks/06_VLAv3_Research_Engineer_Colab.ipynb compile without syntax errors (automated compile/sanity check).

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Codex Task

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Project	Deployment	Actions	Updated (UTC)
variational-linear-attention	Ready	Preview, Comment	Apr 20, 2026 0:22am