Add stage-5 unknown-format discovery: header clustering + calibration

Implements phase A of the DESIGN_clustering.md design: a Dirichlet-process
mixture of per-position categoricals over the first 32 header bytes (257-symbol
alphabet) that clusters the binary/ pile by file format, plus signature
extraction and promotion nomination. All base-Julia (a Lanczos loggamma keeps
the Dirichlet-multinomial marginal dependency-free).

- src/cluster.jl: header_symbols feature extraction, collapsed Gibbs sampler
  (phase A), sequential CRP-predictive assignment (phase B core), signatures/
  promotion, and ARI/V-measure calibration metrics.
- bin/cluster_calibrate.jl: grid-tunes hyperparameters against magic-collapsed
  ground truth and cross-checks a model-free NCD (gzip) baseline.
- FS_CLUSTER_*/FS_PROMOTE_* config knobs; wire cluster.jl into the module.
- Tests for the three DESIGN §10 assertions plus the model primitives.

Calibrated defaults (n=32, alpha=1.0, beta=0.1) recover known formats at
ARI 0.77 (0.885 excl. tar); docx+zip and the ELF family merge correctly and the
NCD baseline agrees. DESIGN §11 records the results and three assumptions the
data corrected (tar/ELF header-zero merge, the cold-start seeding deadlock, and
the Bernoulli signature / Occam-penalized restart scoring).

src/FileServer.jl

@@ -1,6 +1,7 @@
 module FileServer
 
 using Logging
+using Random
 using UUIDs
 using HTTP
 using JSON3
@@ -18,6 +19,7 @@ include("classify.jl")  # Classifier + load_classifier/classify (needs model.jl)
 include("metadata.jl")  # exiftool extraction + sidecar enrichment (stage 2)
 include("content.jl")   # binary-vs-text triage for unknown files (stage 3)
 include("language.jl")  # natural + programming language enrichment for text (stage 4)
+include("cluster.jl")   # unknown-format discovery by header clustering (stage 5)
 include("worker.jl")
 
 # Globals the HTTP handlers read at request time. Set once in `run`, before the