Link the ICLR 2027 release

README.md

@@ -14,14 +14,30 @@ tags:
 
 # SparseWake
 
+SparseWake is a synthetic benchmark for sparse temporal hydrodynamic sensing.
+
+## ICLR 2027 release
+
+The expanded release adds controlled multi-source mixtures and common-prior nearest-source tasks, with complete core data banks, reference checkpoints, a small review supplement, and reproduction code with a frozen wake-library input.
+
+**[Download release iclr2027-v1.0rc2](versions/iclr2027-v1.0rc2/README.md)**
+
+The version page lists the three archives, exact sizes, checksums, extraction instructions, generation scope, and component licenses. Data and result tables are CC BY 4.0; new original code is MIT; historical and third-party components retain their supplied notices. The generation code supports sensing scenes based on the cited reduced-order model, not a complete free-swimming schooling simulator or DNS reproduction.
+
+For reproducibility, record the release identifier and final immutable repository commit. The historical root-level manifest and metadata describe the May 2026 files; the new release has its own archive checksums and package manifests.
+
+## Historical May 2026 release
+
+The files and instructions below describe the original May 2026 release only. They are retained for compatibility; their inclusion and license statements do not override the expanded release documentation linked above.
+
 SparseWake is an anonymous benchmark artifact for sparse temporal hydrodynamic sensing. It contains processed HDF5 datasets, metadata, summary CSV files, checksums, and lightweight Python scripts for inspecting the data and reproducing benchmark-level evaluation outputs.
-## Dataset Summary
+### Dataset Summary
 
 SparseWake evaluates whether sparse body-fixed temporal flow measurements can recover the relative state of a neighboring wake-producing fish in a controlled single-leader setting. The benchmark data are synthetic and were generated from a DNS-parameterized fish-schooling agent-based model. The artifact starts from processed HDF5 files and does not require the upstream simulator or high-fidelity solver.
 
 The released data support held-out-pose evaluation, temporal-history sweeps, sensor-count ablations, component-separated flow inputs, common raw-noise stress tests, sample-size convergence checks, architecture screening summaries, and paired self-signal controls.
 
-## What Is Included
+### What Is Included
 
 - Full processed HDF5 benchmark datasets in `data/processed/`.
 - A small sample HDF5 file in `data/sample/` for inspection and smoke tests.
@@ -33,7 +49,7 @@ The released data support held-out-pose evaluation, temporal-history sweeps, sen
 - Checksums in `data/checksums.sha256` and a full file manifest in `MANIFEST.json`.
 - Croissant-style metadata in `croissant_metadata.json`.
 
-## Main Files
+### Main Files
 
 | Path | Purpose |
 |---|---|
@@ -51,19 +67,19 @@ The released data support held-out-pose evaluation, temporal-history sweeps, sen
 | `scripts/reproduce_tables.py` | Copies stored summary CSVs into `tables/`. |
 | `scripts/make_main_figures.py`, `scripts/make_supp_figures.py` | Recreate lightweight figures from stored CSV summaries. |
 
-## What Is Not Included
+### What Is Not Included
 
 The upstream fish-schooling ABM source code, DNS solver files, MATLAB simulator-query code, and simulator internals are not redistributed. This release is intended to reproduce benchmark evaluations from processed HDF5 files, not to regenerate the DNS-parameterized wake library from first principles.
 
-## Intended Use
+### Intended Use
 
 SparseWake is intended for evaluating sparse temporal physical sensing, hydrodynamic neighbor-state estimation, robustness under controlled flow-component and noise protocols, and reproducibility of benchmark-level evaluation summaries.
 
-## Out-of-Scope Use
+### Out-of-Scope Use
 
 SparseWake should not be treated as a complete biological lateral-line model, a full natural fish-schooling simulator, a pressure/shear sensing dataset, a direct CFD replacement, or a validation dataset for multi-neighbor closed-loop schooling behavior.
 
-## Data Structure
+### Data Structure
 
 HDF5 arrays are stored in MATLAB-style or sample-first layouts depending on source export; `src/sparsewake/data.py` loads them sample-first. Core fields include:
 
@@ -78,7 +94,7 @@ HDF5 arrays are stored in MATLAB-style or sample-first layouts depending on sour
 
 See `docs/data_fields.md` for the full schema and `docs/benchmark_protocol.md` for the split and feature protocol.
 
-## Quick Start
+### Quick Start
 
 ```bash
 python -m venv .venv
@@ -90,7 +106,7 @@ python scripts/reproduce_tables.py --results data/results --out tables
 
 On Unix-like systems, use `.venv/bin/pip` and `.venv/bin/python`.
 
-## Reproduce Stored Tables and Diagnostic Plots
+### Reproduce Stored Tables and Diagnostic Plots
 
 ```bash
 python scripts/reproduce_tables.py --results data/results --out tables
@@ -99,11 +115,11 @@ python scripts/make_supp_figures.py --results data/results --out figures
 ```
 
 These commands use stored summary CSV files and are intended for fast numerical verification and diagnostic plotting. Final manuscript figures may include manual layout adjustments and are not part of the dataset artifact.
-## License
+### License
 
 The SparseWake processed datasets, metadata, result summaries, documentation, and release utilities are made available under the Creative Commons Attribution 4.0 International license (CC BY 4.0). Upstream simulator source code and solver assets are not redistributed and are not covered by this dataset release.
 
-## Citation
+### Citation
 
 During anonymous review, cite this dataset as: