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

@@ -6,14 +6,41 @@ install time.
 
 ## What is this for?
 
-For an introduction to SEM (Similarity Energy Model) and how
+`sem_cython12` is a small, focused toolbox of fast C-level routines
-`sem_cython12` fits in, see:
+exposed through a thin numpy wrapper.  It is not a general-purpose
+numerical library; it accelerates three specific jobs that are
+awkward or slow to do in pure numpy once `N` reaches the thousands:
 
-- [`docs/SEM_Overview.md`](./docs/SEM_Overview.md) — non-internal
+1. **Similarity / distance over batches of vectors.**  Full
-  introduction to SEM, what it does, and how this library fits in.
+   pairwise distance matrices, nearest-neighbour distances, and
-- [`docs/SEM_Mathematical_Apparatus.md`](./docs/SEM_Mathematical_Apparatus.md)
+   kernel-based `[0, 1]` similarity scores of a query set against
-  — capabilities-level description of the operators and engines
+   one or many reference sets.  Useful for nearest-neighbour
-  exposed by the library.
+   search, kernel-density-style scoring, and "how close is each
+   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
 
@@ -130,6 +157,15 @@ 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/):