6 Commits

Include renames

Author	SHA1	Message	Date
lars	ee29b9a303	router: seed EnergyRouter centers from data quantiles instead of a fixed linspace ... 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.	2026-07-27 13:11:16 +02:00
larsandClaude Sonnet 5	68fb99bed8	Condition on material/particle physical properties instead of learned embeddings ... 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>	2026-07-17 15:12:54 +02:00
larsandClaude Opus 4.8	f3fec8bcb3	Add ComposedRouter for multi-axis MoE gating ... 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>	2026-07-15 13:53:21 +02:00
larsandClaude Sonnet 5	ac5fbd14b4	Add PdgRouter for particle-type-based expert gating ... 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>	2026-07-15 10:13:07 +02:00
lars	0b3ece52ed	Add ProcessRouter for physics-process-based expert gating ... 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.	2026-07-08 16:15:09 +02:00
larsandClaude Sonnet 5	bac541240f	Add mixture-of-experts routing prototype for Stage 1 and Stage 2 ... 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>	2026-07-08 15:17:19 +02:00