@@ -400,11 +400,11 @@ jobs:
         shell: bash
         run: |
           set +e
-          julia --project=. -t2 --compiled-modules=no test/runtests.jl numeric_policy exact_summaries high_precision derived_analyses validated_statistics 2>&1 | tee /tmp/validated-statistics-tests.log
+          julia --project=. -t2 --compiled-modules=no test/runtests.jl numeric_policy exact_summaries high_precision derived_analyses validated_statistics 2>&1 | tee /tmp/julia-validated-tests.log
           status="${PIPESTATUS[0]}"
           if [ "$status" -ne 0 ]; then
             enc() { head -c 3500 | sed -e 's/%/%25/g' -e 's/\r/%0D/g' | awk '{printf "%s%%0A", $0}'; }
-            echo "::error title=Validated-statistics tests failed::$(tail -n 120 /tmp/validated-statistics-tests.log | enc)"
+            echo "::error title=Validated statistics failed (tail)::$(tail -c 3500 /tmp/julia-validated-tests.log | enc)"
           fi
           exit "$status"
 
 
@@ -299,6 +299,7 @@ not exclude samples from this analysis.
   calibration caveat in its diagnostics, shown with the result and written
   into its CSV export. The caveat changes no p-value; the null-simulation
   evidence and full replication protocol live in `bench/null_calibration`.
+  The glmGamPoi path uses fixed-theta NB Wald fits, not a quasi-likelihood test.
 - `zinb`: a zero-inflated negative-binomial model with separate logistic
   zero and count components, a count-component library-size offset, a joint
   2-df likelihood-ratio test, and BH correction.
@@ -316,12 +317,15 @@ not exclude samples from this analysis.
   on `log(counts + 1)`, propagating bias-estimation variance into two-sided Wald
   tests. The effect is a bias-corrected log2 fold change; useful when asymmetric
   shifts or load changes would bias standard CLR centering (needs at least 3
-  tested taxa; assumes fewer than half are differential).
+  tested taxa). The implementation reports its mixture and bias diagnostics.
+  Its assumption that fewer than half of tested taxa are differential must be
+  checked rather than treated as a guarantee.
 - `aldex2`: Dirichlet(`count + 0.5`) Monte-Carlo CLR model (Fernandes et al.
   2013, 2014). Draws `mc_samples` (default 128, seeded by the study's `seed`)
   instances per sample and reports standardized CLR effect sizes with
   consistent-sign posterior predictive Welch p-values; suited to sparse or
   variable-depth cohorts where deterministic zero imputation is undesirable.
+  The reported estimate is a standardized CLR effect size, not an absolute fold change.
 - `songbird`: compositional baseline-category multinomial regression with
   reference-frame invariant CLR-centred differentials summing to zero across
   fitted taxa (Morton et al. 2019, *Nat. Commun.* 10:2719; Zhang et al. 2017).
@@ -336,7 +340,8 @@ not exclude samples from this analysis.
   composition (La Rosa et al. 2012), which absorbs between-sample
   overdispersion; `dm_overdispersion_method` picks `mle` (profiled) or `mom`.
   Both multinomial models bound their Newton fit with `max_iter`, `tolerance`
-  and `max_seconds`, and a fit that does not converge is refused.
+  and `max_seconds`, and a fit that does not converge is refused. Dirichlet-
+  multinomial diagnostics report the concentration, theta, and boundary status.
 
 ZINB and hurdle models can overfit sparse data; do not select ZINB solely
 because counts are sparse. Compare against NB diagnostics. The methods report
@@ -818,23 +823,22 @@ analysis:
     normalisation: hellinger     # hellinger | none | rarefy | srs
     depth: 0                     # rarefy/srs reads per sample; 0 = smallest non-empty sample
   differential:
-    method: nb_glm               # nb_glm | clr_welch | zinb | hurdle_nb | ancom_bc
-                                 #   | aldex2 | songbird | multinomial | dirichlet_multinomial
+    method: nb_glm               # nb_glm | clr_welch | multinomial | dirichlet_multinomial
+                                 # ancom_bc | aldex2 | songbird | zinb | hurdle_nb
     offset: tss                  # count models: tss | rle
     dispersion: ml               # nb_glm: ml | glmGamPoi
     min_prevalence: 0.0          # fraction of samples a taxon must be seen in
     replacement_method: bayesian_multiplicative
                                  # clr_welch: bayesian_multiplicative | multiplicative_replacement
     replacement_delta: 0.65      # clr_welch: strictly between 0 and 1
-    bayesian_alpha: ~            # clr_welch: null (GBM estimate) or a number > 0
-    mc_samples: 128              # aldex2: Dirichlet Monte-Carlo instances in [16, 4096]
-    mn_reference_taxon: ~        # multinomial models: null picks the most abundant taxon
-    mn_penalty: 0.0              # multinomial models: L2 ridge penalty, >= 0
-    dm_overdispersion_method: mle
-                                 # dirichlet_multinomial: mle | mom
+    bayesian_alpha: ~            # Bayesian replacement: null (GBM estimate) or a number > 0
+    mn_reference_taxon: ~        # multinomial models: null selects the most abundant fitted taxon
+    mn_penalty: 0.0              # multinomial models: non-negative L2 penalty
+    dm_overdispersion_method: mle # dirichlet_multinomial: mle | mom
     max_iter: 500                # multinomial models: Newton iteration cap
-    tolerance: 1.0e-8            # multinomial models: convergence tolerance
+    tolerance: 0.00000001        # multinomial models: convergence tolerance
     max_seconds: 300.0           # multinomial models: wall-clock budget
+    mc_samples: 128              # aldex2: Dirichlet Monte-Carlo instances in [16, 4096]
   nmds:
     max_stress: 0.2              # warn if NMDS stress exceeds this value
 ```
 
@@ -6,7 +6,7 @@ occupancy work. The GitHub Wiki roadmap still needs a synchronized update; see
 
 ## Available now
 
-- The differential-abundance config offers four per-taxon count models
+- The differential-abundance config offers four per-taxon methods
   (`nb_glm`, `clr_welch`, `zinb`, `hurdle_nb`) and five compositional models
   (`ancom_bc`, `aldex2`, `songbird`, `multinomial`, `dirichlet_multinomial`);
   see [the differential-abundance section](README.md#differential-abundance).
 
@@ -6,9 +6,9 @@ not an AnalysisConfig method or the standard endpoint. The full workflow is
 tracked in [issue #205](https://github.com/hyperpolymath/MetaManifold-WebUI/issues/205),
 a follow-up to #18.
 
-The differential-abundance configuration supports `zinb` and `hurdle_nb` in
-addition to the existing `nb_glm` and `clr_welch` models. The two new methods
-are fixed-effect, per-taxon models with a two-part negative-binomial likelihood.
+The differential-abundance configuration supports `zinb` and `hurdle_nb`
+alongside the count, CLR, and compositional methods. These two methods are
+fixed-effect, per-taxon models with a two-part negative-binomial likelihood.
 They use the configured `tss` or `rle` library-size offset in the count
 component and apply Benjamini–Hochberg correction to the joint tests.
 
 
@@ -21,9 +21,9 @@ import type {
   TreeFileInfo, TreeFile, RunRef, ReadFunnelData, ReportItem, ReportKind,
   PlacementDoc, PlacementSummary, PlacementDetail, PlacementQueries, PhyloStep,
   ReferenceTreeDoc, ReferenceTreeSummary, ReferenceTreeDetail, AlignmentQC, TrimSettings,
+  CladisticsCreateRequest, CladisticsSummary, CladisticsDetail,
   CladeCumulusSourcesResponse, CladeCumulusRequest, CladeCumulusResponse,
   CladeMoveValidationRequest, CladeMoveValidationResponse,
-  CladisticsSummary, CladisticsDetail, CladisticsCreateRequest,
 } from './types'
 
 import type { FigureDoc } from '../figure/types'
 
@@ -220,6 +220,16 @@ export function DifferentialPanel({ study, runs, option, aggregate }: {
                 `replaced values hold ${(100 * z.median_imputed_fraction).toPrecision(2)}% of the median sample ` +
                 `and at most ${(100 * z.max_imputed_fraction).toPrecision(2)}% of any, and are not measurements.`
             })()}
+            {result.diagnostics.model && (() => {
+              const m = result.diagnostics.model
+              return ` Reference taxon: ${m.reference_taxon}; ${m.n_parameters} coefficients, ` +
+                `penalty ${m.penalty}, ${m.iterations} Newton steps in ${m.runtime_seconds.toPrecision(2)} s.`
+            })()}
+            {result.diagnostics.overdispersion && (() => {
+              const o = result.diagnostics.overdispersion
+              return ` Concentration ${o.concentration.toPrecision(3)} by ${o.method}, ` +
+                `theta = ${o.theta.toPrecision(3)}${o.at_bound ? ' (at a bound: see the row notes)' : ''}.`
+            })()}
             {result.diagnostics.ancom_bc && (() => {
               const b = result.diagnostics.ancom_bc
               const warn = b.warnings.length > 0 ? ` Warning: ${b.warnings.join('; ')}.` : ''
 
@@ -2,6 +2,11 @@
 import { expect, test } from 'bun:test'
 import { CONFIG_DESCRIPTIONS, CONFIG_TYPES } from './PipelineStages'
 
+const ALL_DIFFERENTIAL_METHODS = [
+  'nb_glm', 'clr_welch', 'zinb', 'hurdle_nb', 'ancom_bc', 'aldex2', 'songbird',
+  'multinomial', 'dirichlet_multinomial',
+]
+
 test('dispersion uses the enum editor with both supported models', () => {
   expect(CONFIG_TYPES['analysis.differential.dispersion']).toEqual({
     kind: 'enum', options: ['ml', 'glmGamPoi'],
@@ -10,14 +15,23 @@ test('dispersion uses the enum editor with both supported models', () => {
 
 test('differential model editor exposes all nine supported methods', () => {
   expect(CONFIG_TYPES['analysis.differential.method']).toEqual({
-    kind: 'enum',
-    options: [
-      'nb_glm', 'clr_welch', 'zinb', 'hurdle_nb', 'ancom_bc', 'aldex2', 'songbird',
-      'multinomial', 'dirichlet_multinomial',
-    ],
+    kind: 'enum', options: ALL_DIFFERENTIAL_METHODS,
   })
 })
 
+test('multinomial settings retain their enums and optimiser budgets', () => {
+  expect(CONFIG_TYPES['analysis.differential.dm_overdispersion_method']).toEqual({
+    kind: 'enum', options: ['mle', 'mom'],
+  })
+  expect(CONFIG_TYPES['analysis.differential.mn_penalty']).toEqual({
+    kind: 'float', min: 0, step: 0.05,
+  })
+  expect(CONFIG_TYPES['analysis.differential.max_seconds']).toEqual({
+    kind: 'float', min: 1,
+  })
+  expect(CONFIG_TYPES['analysis.differential.max_iter']).toEqual({ kind: 'int' })
+})
+
 test('advanced zero handling exposes both methods, bounded delta and nullable alpha', () => {
   expect(CONFIG_TYPES['analysis.differential.replacement_method']).toEqual({
     kind: 'enum', options: ['bayesian_multiplicative', 'multiplicative_replacement'],
 
@@ -270,12 +270,12 @@ export const CONFIG_TYPES: Record<string, ConfigType> = {
   'analysis.differential.replacement_method': { kind: 'enum', options: ['bayesian_multiplicative', 'multiplicative_replacement'] },
   'analysis.differential.replacement_delta': { kind: 'float', min: 0, max: 1, step: 0.05 },
   'analysis.differential.bayesian_alpha': { kind: 'float', nullable: true, min: 0, step: 0.1 },
-  'analysis.differential.mc_samples': { kind: 'int' },
   'analysis.differential.mn_penalty': { kind: 'float', min: 0, step: 0.05 },
   'analysis.differential.dm_overdispersion_method': { kind: 'enum', options: ['mle', 'mom'] },
   'analysis.differential.max_iter': { kind: 'int' },
   'analysis.differential.tolerance': { kind: 'float', min: 0 },
   'analysis.differential.max_seconds': { kind: 'float', min: 1 },
+  'analysis.differential.mc_samples': { kind: 'int' },
   'analysis.alpha.show_points':   { kind: 'boolean' },
   'analysis.alpha.annotate_significance': { kind: 'boolean' },
   'analysis.alpha.pairwise_brackets': { kind: 'boolean' },