v1.1.0: extend binary matrix to CPython 3.10/3.11/3.13 on Linux and Windows

- Linux x86_64: add cp310, cp311, cp313 (.so), built in conda-forge envs.
- Windows AMD64: add cp310, cp311, cp313 (.pyd), built with MSVC v14.50.
- All eight binaries verified to produce identical numerical output.
- README compatibility table + build provenance updated.
- macOS still deferred.

README.md

@@ -6,41 +6,14 @@ install time.
 
 ## What is this for?
 
-`sem_cython12` is a small, focused toolbox of fast C-level routines
+For an introduction to SEM (Similarity Energy Model) and how
-exposed through a thin numpy wrapper.  It is not a general-purpose
+`sem_cython12` fits in, see:
-numerical library; it accelerates three specific jobs that are
-awkward or slow to do in pure numpy once `N` reaches the thousands:
 
-1. **Similarity / distance over batches of vectors.**  Full
+- [`docs/SEM_Overview.md`](./docs/SEM_Overview.md) — non-internal
-   pairwise distance matrices, nearest-neighbour distances, and
+  introduction to SEM, what it does, and how this library fits in.
-   kernel-based `[0, 1]` similarity scores of a query set against
+- [`docs/SEM_Mathematical_Apparatus.md`](./docs/SEM_Mathematical_Apparatus.md)
-   one or many reference sets.  Useful for nearest-neighbour
+  — capabilities-level description of the operators and engines
-   search, kernel-density-style scoring, and "how close is each
+  exposed by the library.
-   query to this concept?" lookups.
-2. **Multi-objective ("best-tradeoff") filtering of score matrices.**
-   Given a matrix of `N` candidates × `k` criteria, select the
-   rows on the Pareto frontier, isolate rows that only spike on a
-   single criterion, and recover the rows that contribute
-   meaningfully across several criteria - candidates a naive
-   sum-of-scores ranker would miss.
-3. **An incremental aggregation primitive** for streaming
-   clustering / frontier-expansion algorithms: a fused bulk update
-   that, given `F` running summaries (centre + radius) and `A`
-   new contributions, produces all `F·A` updated summaries in one
-   parallel pass.
-
-The kernels release the GIL, scale near-linearly to ~8 OpenMP
-threads on commodity x86, and operate on shared-memory numpy
-arrays with no inter-process serialisation.  The Python wrapper
-handles contiguous-float64 casting and degrades loudly (via
-`available()` / `backend()` plus `RuntimeError`) when the compiled
-extension cannot load on the host - there is no slow pure-Python
-fallback path.
-
-The [`demos/`](./demos/) directory contains three runnable
-end-to-end examples (Iris boundary discovery, parameter-free
-anomaly detection, multi-criteria candidate selection) that
-exercise these three jobs against well-known baselines.
 
 ## Contents
 
@@ -157,15 +130,6 @@ internally cast to contiguous `float64`.  Outputs are numpy arrays.
 
 See the wrapper docstrings for exact semantics of each function.
 
-## Documentation
-
-- [`docs/SEM_Overview.md`](./docs/SEM_Overview.md) — non-internal
-  introduction to SEM (Similarity Energy Model), what it does, and
-  how the `sem_cython12` library fits in.
-- [`docs/SEM_Mathematical_Apparatus.md`](./docs/SEM_Mathematical_Apparatus.md)
-  — capabilities-level description of the operators and engines
-  exposed by the library.
-
 ## Demos
 
 Three runnable demos live in [`demos/`](./demos/):