Trains the routed trunk's forward combination as a hard one-hot sample
(matching eval-time top-1 dispatch exactly) while keeping a smooth gradient
on the backward pass, targeting the train/eval mismatch identified as a
likely contributor to experts overlapping instead of partitioning in the
first energy-router rollout benchmark. Off by default (model.router.gumbel);
existing routed configs/checkpoints are unaffected.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
EnergyRouter's gate sharpness was a single fixed temperature shared by
every expert, with no way for an expert to independently learn how much
of the energy axis it covers. Adds two mutually exclusive, default-off
modes: learn_width (per-expert learnable width) and learn_temperature
(single learnable shared scalar), both bounded via a sigmoid
interpolation warm-started to reproduce today's fixed-temperature gate
exactly at init, to compare against each other without risking the
unbounded-width collapse failure mode. Also promotes gate_stats's
entropy into a generic, optional Router.entropy_loss (lambda_entropy) as
a secondary guard against all experts' widths co-inflating together.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
The 2026-07-22 rollout benchmark's router_gating diagnostic showed the
10-expert EnergyRouter's default linspace(-2, 2, n_experts) init assumes a
roughly uniform z-normalized energy distribution, leaving experts heavily
overlapping instead of partitioning the range. Add an optional
centers_init kwarg (backward compatible, defaults to the old linspace) and
have giant train estimate it from a reservoir sample of the real energy
column, collected during the existing normalizer-fitting pass.
Adds model.conditioning = "physical" | "embedding": physical mode routes
particle mass/charge and material Z_eff/A_eff/density/X0/lambda_int through
small MLPs to replace the learned PDG/material embedding tables, so the
surrogate generalizes to PDG codes/materials outside the training vocab
instead of memorizing it. "embedding" stays available as the comparison
baseline (old checkpoints without the key default to it).
Stage 2 now regresses a secondary's mass/charge directly against a fixed
physics-derived target instead of a learned/snapped embedding, and uses no
snapping at inference — the model's raw predicted (mass, charge) is the
secondary's physical identity, including for its own further rollout steps.
A separate reporting-only nearest-known-PDG lookup (never fed back into the
model) populates output pdg columns / the embedding-mode rollout fallback.
giant/materials.py's table is populated with Geant4's own built-in NIST
constants (Z_eff, A_eff, density, X0, lambda_int), extracted directly from
the Geant4 11.4.1 build vendored in minicalosim via G4NistManager rather
than hand-typed literature values. G4_LYSO is left unfilled: confirmed (both
by runtime lookup and by searching minicalosim's history) that it's never
actually a constructed Geant4 material there, only documentation/UI color-map
text.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Route on several independent axes at once (e.g. energy x pdg), each with
its own expert count and hyperparameters. The joint gate is the outer
product of per-axis softmax gates, so it stays a partition of unity and
top1/balance_loss factor per-axis. Config uses flat axis{i}_{field} keys
in model.router (TOML/CLI friendly), also settable via repeatable
--router-axis flags.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Routes on the pre-step PDG code, which — unlike ProcessRouter's process
label — is already known at gate time (a conditioning input), so no
supervision is needed and classify_loss falls back to the zero default.
Generalizes EnergyRouter's soft-turn-on-then-Voronoi trick from a 1-D
distance to a small learned PDG embedding space: its own embedding table
maps each PDG code to a point, and n_experts learnable centers partition
that space.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Routes on the physics process (Compton, phot, brems, ...) that ends a
step, supervised by a small classifier since process is a post-step
outcome unobservable at gate time. Threads a process label end-to-end
through the data pipeline (loader, build_features, dataset batches,
training loss/checkpointing) alongside the existing EnergyRouter.
Both stages can now route through a pluggable Router (EnergyRouter as the
first implementation, a soft turn-on gate over pre-step log-energy) into
several small ExpertTrunks instead of one monolithic trunk. Trains as a
differentiable soft mixture and dispatches to a single expert per row at
eval time, which is the source of the per-call speedup this prototype is
after (issue #5's ~10x native-Geant4 budget). Disabled by default, so
existing configs/checkpoints are unaffected; build_models() centralizes
routed-vs-monolith construction across train/predict/rollout.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
lars