WP31 PRISME: phases 0-3 implementation, tests, data pipeline, training scripts

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher committed 4 days ago (Oct 30, 2026) Browse files

30 changed files +20,001 −0

added .gitignore +24 −0

++.venv/
++__pycache__/
++*.pyc
++.pytest_cache/
++.hypothesis/
++experiments/runs/**/*.pt
++experiments/runs/**/*.safetensors
++experiments/runs/**/*.bin
++data/raw/
++data/cache/
++data/processed/*.bin
++data/processed/*.npy
++*.log.gz
++paper/*.aux
++paper/*.bbl
++paper/*.blg
++paper/*.fls
++paper/*.fdb_latexmk
++paper/*.out
++paper/*.toc
++paper/*.synctex.gz
++paper/*.lof
++paper/*.lot
++.DS_Store

added CLAUDE.md +15 −0

++# CLAUDE.md — WP31: PRISME-SLM (execution mandate)
++
++This repository executes UQO Working Paper 31, *PRISME: Intervention-Supervised Sparse Workspace Editing for
++Small Language Models* (author: Simon-Pierre Boucher, UQO). The full research mandate (phases 0–15, immutable
++rules, hypotheses H1–H7, baselines, ablations, paper structure) was supplied by the author on 2026-10-29 and is
++reproduced in `docs/MANDATE.md`. Working rules that bind every session in this repository:
++
++1. Scientific truth first: never fabricate runs, metrics, citations, hardware or proofs. Every number in the
++   manuscript traces to a run id in `experiments/runs/` and `results/claim_registry.csv`.
++2. Phases are executed in order with acceptance gates logged in `PROGRESS.md`; failures go to `FAILURES.md`,
++   design changes to `DECISIONS.md`. Unfavourable runs are never deleted.
++3. Minimal falsifiable version first (no persistent memory, no RAG, no MoE, no RL, no tool use).
++4. Resource boundaries: laptop (M5 Max) + existing MacLustr SSH access only; no purchases, no new credentials.
++5. Independent train / calibration / dev / locked-test splits; test splits are not read before
++   `protocol/preregistration.md` is frozen (see `protocol/locked_test_manifest.json`).

added DECISIONS.md +13 −0

++# DECISIONS.md — design decisions (dated)
++
++| id | date | decision | rationale |
++|---|---|---|---|
++| D1 | 2026-10-29 | Repository lives at `Recherche-UQO/ia/prisme-slm` (new `ia/` category of the UQO series); Python 3.12 venv via `uv`, PyTorch 2.15 (CPU/MPS), fp32 training. | Series convention; bf16 autocast gave no speed-up on MPS (15.3k vs 15.0k tok/s on M3 Ultra). |
++| D2 | 2026-10-29 | A dedicated `bank_norm` RMSNorm precedes the bank/z projections. | The pre-final-norm residual stream has growing norm; projecting it raw would couple editor dynamics to residual scale. Costs d parameters. |
++| D3 | 2026-10-29 | Workspace/controller parameter counts are NOT forced to the brief's figures. | The brief's figures are reproduced exactly by a closed form (`4dw+Mw+17w²+6wf_w+15.5w+4`); the implemented design needs 7 fewer w×w projections per config (bank is read directly; readout uses raw slot values; controller shares one state MLP across the 24 actions so that scoring costs O(A·w²) not O(A·w·f_w)). Reconciliation reported in `results/tables/param_counts.json`. |
++| D4 | 2026-10-29 | STOP is absorbing in forced-action training too. | Needed for incremental/full-prefix equivalence (test revealed the mismatch). |
++| D5 | 2026-10-29 | Cost proxy c(a) in the controller's Q is the analytic ops ratio of the action (not a learned ĉ); the ĉ head is kept as a diagnostic regressor. | Per-action cost is deterministic given k(a) and T; learning it adds noise without information. |
++| D6 | 2026-10-29 | Synthetic splits are deduplicated by exact text across splits (train written first); eval items colliding with train are dropped and counted. | Low-depth cells have few distinct items; 995 train dups, 256 dev_iid dups removed. |
++| D7 | 2026-10-29 | Phase 4 uses a character-level tokenizer and a 2.1M-parameter backbone trained from scratch on the synthetic train split. | Self-contained learnability test independent of the web corpus/tokenizer. |
++| D8 | 2026-10-29 | 103M pilot backbone: 196.6M tokens (12k steps × 32 × 512) of FineWeb-Edu, one seed; parameter-matched plain decoder baseline (ffn 2144, 102.16M) trained with the identical recipe on a second node. | ~3.7 h per run at 14.8k tok/s on an M3 Ultra; seeds are varied at the editor/controller stage where the pilot's decisive comparison lives. |
++| D9 | 2026-10-29 | `readout_scale`: Δ = readout_scale · U(r(S_f) − r(S0)), with readout_scale a fixed constant set once at bootstrap to the measured rms of the backbone residual stream (5.6 for the tiny backbone). | Scale mismatch blocked the U = 0 bootstrap (see FAILURES). The final RMSNorm makes logits invariant to the joint scale of h and Δ, so this is a reparametrisation of U's learning rate; identity properties are unchanged (tests pass). |

added FAILURES.md +7 −0

++# FAILURES.md — failed hypotheses, tests and attempts (never deleted)
++
++| date | what failed | evidence | resolution |
++|---|---|---|---|
++| 2026-10-29 | `test_incremental_equals_full_prefix_forced`: cached decoding ≠ full-prefix forward. | pytest, atol 1e-4 | Root cause: forced-action forward let edits resume after STOP; made STOP absorbing (D4). |
++| 2026-10-29 | rsync exclusion `experiments` also dropped `configs/experiments/`; baseline launch on M3U96b failed twice (`FileNotFoundError`). | `experiments/logs/pretrain_enlarged102m_seed1.log` (first attempts) | anchored pattern `/experiments`. |
++| 2026-10-29 | Stage B bootstrap degeneracy (brief §13.1 confirmed): with U = 0 and lr 1e-3, 700 steps of forced mixed-budget training left val loss identical at every budget (0.4144 / 0.4143 / 0.4143 / 0.4146 / 0.4164 for budgets 0/1/2/4/8); grad-norm 0.01–0.02. Diagnosis: scale mismatch — backbone residual rms 5.64 vs readout difference rms 0.06, so U must grow by ×100 before edits can affect logits. | `experiments/runs/p4_editor_v1_seed01_*/metrics.jsonl` (stopped at step ~750) | D9: fixed non-learned `readout_scale` = measured backbone residual rms at bootstrap (keeps U = 0 init and exact identity; equivalent to a scale-aware learning rate). Variants with nonzero U init (std 0.02) and lr 3e-3 kept running on M2U64 / M4M64c as evidence. |

added Makefile +10 −0

++PY=.venv/bin/python
++.PHONY: env test audit params flops paper figures smoke
++env:            ; uv venv --python 3.12 .venv && uv pip install -p .venv/bin/python -r requirements-lock.txt
++test:           ; $(PY) -m pytest tests -q
++audit:          ; $(PY) scripts/environment_audit.py --smoke
++params:         ; $(PY) scripts/count_params.py --all-configs --device meta
++flops:          ; $(PY) scripts/estimate_flops.py --config configs/prisme_459m.yaml --T 4096
++smoke:          ; $(PY) -m training.pretrain --config configs/experiments/pretrain_103m.yaml --dry-run
++figures:        ; $(PY) scripts/reproduce_figures.py --results results/raw/
++paper:          ; cd paper && latexmk -pdf -interaction=nonstopmode main.tex

added PROGRESS.md +10 −0

++# PROGRESS.md — WP31 PRISME phase log
++
++| date | phase | status | commands / evidence | measurements | gate | blockers / next |
++|---|---|---|---|---|---|---|
++| 2026-10-29 | 0 env audit | done | `scripts/environment_audit.py --smoke` → `hardware/device_inventory.json`; `scripts/cluster_audit.sh` (read-only SSH) | laptop M5 Max 18c/40 GPU/48 GB, torch 2.15 MPS+CPU smoke OK; 12 LAN nodes reachable incl. 2× M3 Ultra 96 GB, M2 Ultra 64 GB, 3× M4 Max 64 GB | PASS | — |
++| 2026-10-29 | 1 literature | done | `literature/papers.bib` (77 verified entries), `comparison_matrix.csv`, `novelty_audit.md` | all 12 mandated refs exist incl. RecurTrace 2609.03379 and ELR 2610.12582; C3 (differential zero-init bypass) judged not novel; C2 novel only in its multi-action+propensity form | PASS | preregistration frozen before any test split is read (see protocol/) |
++| 2026-10-29 | 2 backbone + counts | done | `scripts/count_params.py --all-configs --device meta`; `scripts/estimate_flops.py` | backbone counts match brief exactly (99,502,848 / 446,744,064 / 1,149,339,648); workspace 2,631,173 vs brief 3,088,260 (reconciled, D3); ops table reproduces brief within 1.3 % | PASS | — |
++| 2026-10-29 | 3 invariants | done | `python -m pytest tests -q` → 22 passed | 15 mandated invariants + controller target-freedom + dense ablation + 5 cache-equivalence tests | PASS | — |
++| 2026-10-29 | 4 learnability | running | `training.train_editor --config configs/experiments/p4_tiny_backbone.yaml --device mps` (laptop) | — | — | then editor bootstrap on frozen tiny backbone |
++| 2026-10-29 | 8 pilot (Stage A) | running | M3U96a: `pretrain_103m.yaml` (run `pretrain103m_seed01_*`); M3U96b: `pretrain_enlarged_102m.yaml` | 14.8k tok/s, 196.6M tokens planned (~3.7 h each) | — | editor/controller stages after completion |

added baselines/__init__.py +0 −0

added baselines/global_recurrence.py +78 −0

++"""Global latent-recurrence adapter (Huginn/Ouro/ELR-*inspired*, simplified): the whole top representation is
++refined r times by ONE shared block operating at workspace width w, with a zero-initialised residual back to d.
++
++Same frozen backbone, same forced mixed-budget training recipe and same budget semantics as PRISME; the only
++difference is that every "action" rewrites the entire latent (global, dense) instead of 1-2 of 16 slots, and
++there is no slot selection to learn. Disclosed simplifications: no prelude/coda retraining, no truncated
++backprop through time, adapter-level recurrence on a frozen backbone rather than from-scratch recurrent pretraining.
++Parameter count is matched to the PRISME workspace (ffn width chosen in the experiment config)."""
++from __future__ import annotations
++
++import torch
++import torch.nn as nn
++
++from prisme.attention import RMSNorm, SwiGLU
++from prisme.backbone import Backbone
++from prisme.config import PrismeConfig
++import torch.nn.functional as F
++
++
++class RecurrentBlock(nn.Module):
++    def __init__(self, w: int, heads: int, ffn: int, eps: float):
++        super().__init__()
++        self.h, self.hd = heads, w // heads
++        self.n1 = RMSNorm(w, eps); self.q = nn.Linear(w, w, bias=False); self.k = nn.Linear(w, w, bias=False)
++        self.v = nn.Linear(w, w, bias=False); self.o = nn.Linear(w, w, bias=False)
++        self.n2 = RMSNorm(w, eps); self.ffn = SwiGLU(w, ffn)
++
++    def forward(self, x, ctx_mask):
++        B, T, w = x.shape
++        xn = self.n1(x)
++        q = self.q(xn).view(B, T, self.h, self.hd).transpose(1, 2)
++        k = self.k(xn).view(B, T, self.h, self.hd).transpose(1, 2)
++        v = self.v(xn).view(B, T, self.h, self.hd).transpose(1, 2)
++        a = F.scaled_dot_product_attention(q, k, v, attn_mask=ctx_mask)
++        x = x + self.o(a.transpose(1, 2).reshape(B, T, w))
++        return x + self.ffn(self.n2(x))
++
++
++class GlobalRecurrenceModel(nn.Module):
++    """Interface-compatible with PrismeModel.forward(tokens, actions): any nonterminal action = one global
++    refinement step; STOP = no step. n_edits semantics identical."""
++
++    def __init__(self, cfg: PrismeConfig, ffn_w: int | None = None, heads: int | None = None):
++        super().__init__()
++        self.cfg = cfg
++        self.backbone = Backbone(cfg)
++        w = cfg.w
++        self.bank_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
++        self.down = nn.Linear(cfg.d_model, w, bias=False)
++        self.block = RecurrentBlock(w, heads or cfg.n_editor_heads, ffn_w or cfg.ffn_w, cfg.norm_eps)
++        self.ctx_proj = nn.Linear(2, w, bias=True)
++        self.up = nn.Linear(w, cfg.d_model, bias=False)
++        nn.init.zeros_(self.up.weight); nn.init.zeros_(self.ctx_proj.weight); nn.init.zeros_(self.ctx_proj.bias)
++        self.controller = nn.Identity()  # placeholder so parameter-group helpers work
++
++    def forward(self, tokens, actions=None, return_state=False):
++        cfg = self.cfg
++        H = self.backbone(tokens)
++        if actions is None or actions.shape[-1] == 0:
++            return {"logits": self.backbone.head(H), "H": H, "n_edits": None}
++        B, T, R = actions.shape
++        x0 = self.down(self.bank_norm(H))
++        x = x0
++        mask = torch.ones(T, T, dtype=torch.bool, device=H.device).tril().view(1, 1, T, T)
++        stopped = torch.zeros(B, T, dtype=torch.bool, device=H.device)
++        n_edits = torch.zeros(B, T, dtype=torch.long, device=H.device)
++        for r in range(R):
++            a = torch.where(stopped, torch.full_like(actions[:, :, r], cfg.stop_action), actions[:, :, r])
++            act = (a != cfg.stop_action)
++            stopped |= ~act
++            ctx = torch.stack([torch.full((B, T), r / cfg.r_max, device=H.device), torch.full((B, T), (R - r - 1) / cfg.r_max, device=H.device)], -1)
++            xn = self.block(x + self.ctx_proj(ctx), mask)
++            x = torch.where(act.unsqueeze(-1), xn, x)     # positions that stopped keep their state
++            n_edits += act.long()
++        delta = self.up(x - x0)
++        edited = n_edits > 0
++        delta = torch.where(edited.unsqueeze(-1), delta, torch.zeros_like(delta))
++        return {"logits": self.backbone.head(H + delta), "H": H, "n_edits": n_edits, "delta_out": delta}

added baselines/routers.py +54 −0

++"""Reference routing policies that share PRISME's editor (actions chosen without the learned value controller).
++All are target-free and deployable; they differ only in how the action is picked at each step.
++  random      uniform over the 24 nonterminal actions, fixed budget b
++  uniform     round-robin single-slot schedule SINGLE_0, SINGLE_1, ... (deterministic, fixed budget b)
++  fixed_pair  PAIR_0_1 repeated (fixed budget b)
++  entropy     continue (random action) while the current predictive entropy exceeds a threshold, else STOP
++  norm        continue while the last write moved the slot by more than a threshold (RMS), else STOP
++"""
++from __future__ import annotations
++
++import torch
++
++from prisme.model import PrismeModel
++
++
++@torch.no_grad()
++def rollout(model: PrismeModel, tokens, policy: str, budget: int, threshold: float = 0.0, seed: int = 0):
++    cfg = model.cfg
++    ctx = model.encode(tokens)
++    S = ctx.S0
++    B, T = tokens.shape
++    g = torch.Generator(device="cpu").manual_seed(seed)
++    stopped = torch.zeros(B, T, dtype=torch.bool, device=tokens.device)
++    n_edits = torch.zeros(B, T, dtype=torch.long, device=tokens.device)
++    trace = torch.full((B, T, budget), cfg.stop_action, dtype=torch.long, device=tokens.device)
++    last_rms = torch.full((B, T), float("inf"), device=tokens.device)
++    for r in range(budget):
++        if policy == "random":
++            a = torch.randint(0, cfg.n_actions, (B, T), generator=g).to(tokens.device)
++        elif policy == "uniform":
++            a = torch.full((B, T), r % cfg.n_slots, device=tokens.device)
++        elif policy == "fixed_pair":
++            a = torch.full((B, T), cfg.n_slots + (r % cfg.n_pairs), device=tokens.device)
++        elif policy == "entropy":
++            logits, _ = model.logits_from_state(S, ctx, n_edits > 0)
++            p = torch.softmax(logits.float(), -1)
++            ent = -(p * torch.log(p.clamp_min(1e-12))).sum(-1)
++            a = torch.randint(0, cfg.n_actions, (B, T), generator=g).to(tokens.device)
++            a = torch.where(ent > threshold, a, torch.full_like(a, cfg.stop_action))
++        elif policy == "norm":
++            a = torch.randint(0, cfg.n_actions, (B, T), generator=g).to(tokens.device)
++            a = torch.where(last_rms > threshold, a, torch.full_like(a, cfg.stop_action))
++        else:
++            raise ValueError(policy)
++        a = torch.where(stopped, torch.full_like(a, cfg.stop_action), a)
++        stopped |= a == cfg.stop_action
++        trace[:, :, r] = a
++        S_new, info = model.apply_action(S, ctx, a, r, budget - r - 1)
++        moved = (info["eta"].squeeze(-1) * info["delta_rms"] * info["scale"])  # [B,T,k] realised rms step per lane
++        last_rms = torch.where(a != cfg.stop_action, moved.max(-1).values, last_rms)
++        S = S_new
++        n_edits += (a != cfg.stop_action).long()
++    logits, _ = model.logits_from_state(S, ctx, n_edits > 0)
++    return {"logits": logits, "actions": trace, "n_edits": n_edits}

added configs/baselines/transformer_enlarged_102m.yaml +23 −0

++# PRISME reference configuration: prisme_103m (see docs/architecture_spec.md)
++model:
++  name: transformer_enlarged_102m
++  vocab_size: 32768
++  d_model: 768
++  n_layers: 12
++  n_heads: 12
++  n_kv_heads: 3
++  ffn_width: 2144   # parameter-matched plain decoder: +2.65M params ~= PRISME workspace+controller (2.63M)
++  max_seq_len: 4096
++  rope_theta: 10000.0
++  norm_eps: 1.0e-5
++  tie_embeddings: true
++  use_workspace: false
++  w: 256
++  ffn_w: 768
++  n_slots: 16
++  n_editor_blocks: 2
++  n_editor_heads: 4
++  r_max: 8
++  eta_max: 0.25
++  clip_c: 1.0
++  readout_zero_init: true

added configs/experiments/controller_p4.yaml +14 −0

++# Phase 6 (tiny scale): value controller fit on logged labels; (lambda, kappa) tuned on calibration rollouts.
++name: controller_p4
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++checkpoint: CHECKPOINT_DIR
++labels: {train: LABELS_TRAIN, calib: LABELS_CALIB}
++train: {steps: 3000, lr: 1.0e-3, weight_decay: 0.01, alpha: 1.0, beta: 0.5, gamma: 0.1, huber_delta: 0.1, log_every: 100, eval_every: 1000, seq_len: 320}
++lambda_grid: [0.0, 0.01, 0.03, 0.1, 0.3]
++kappa_grid: [0.0, 0.5]
++sweep_batches: 10
++max_budget: 8
++selection_rule: max_loss_reduction_per_ops

added configs/experiments/labels_p4.yaml +13 −0

++# Phase 5 (tiny scale): counterfactual label collection with the fixed Phase 4 editor checkpoint.
++name: labels_p4
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++checkpoint: CHECKPOINT_DIR
++use_policy: false
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: answer}
++collect: {batch_size: 32, seq_len: 320, max_prefix: 3, audit_every: 4}
++partitions:
++  train: {split: train, n_batches: 120, stream_seed: 11}
++  calib: {split: calib, n_batches: 30, stream_seed: 12}

added configs/experiments/p4_editor_bootstrap.yaml +16 −0

++# Phase 4 step 2: Stage B editor bootstrap on the FROZEN tiny backbone (v1), forced mixed budgets {0,1,2,4,8}.
++name: p4_editor_v1_scaled
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++init: {backbone_checkpoint: experiments/runs/p4_tiny_backbone_seed01_20261029232626/checkpoint, freeze_backbone: true, auto_readout_scale: true}
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: answer,
++       eval_splits: [dev_iid, dev_template, dev_depth, dev_compose]}
++budgets: [0, 1, 2, 4, 8]
++budget_probs: [0.2, 0.2, 0.2, 0.2, 0.2]
++budget_per: position
++train: {batch_size: 32, seq_len: 320, steps: 3000, lr: 1.0e-3, warmup: 100, weight_decay: 0.1, grad_clip: 1.0,
++        log_every: 50, eval_every: 500, eval_batches: 8, ckpt_every: 1500}
++em_budgets: [0, 2, 8]
++em_max_items: 400

added configs/experiments/p4_editor_bootstrap_lr3e3.yaml +16 −0

++# Phase 4 step 2: Stage B editor bootstrap on the FROZEN tiny backbone (v1), forced mixed budgets {0,1,2,4,8}.
++name: p4_editor_v1_lr3e3_b64
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++init: {backbone_checkpoint: experiments/runs/p4_tiny_backbone_seed01_20261029232626/checkpoint, freeze_backbone: true}
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: answer,
++       eval_splits: [dev_iid, dev_template, dev_depth, dev_compose]}
++budgets: [0, 1, 2, 4, 8]
++budget_probs: [0.2, 0.2, 0.2, 0.2, 0.2]
++budget_per: position
++train: {batch_size: 64, seq_len: 320, steps: 2000, lr: 3.0e-3, warmup: 100, weight_decay: 0.1, grad_clip: 1.0,
++        log_every: 50, eval_every: 500, eval_batches: 8, ckpt_every: 1500}
++em_budgets: [0, 2, 8]
++em_max_items: 400

added configs/experiments/p4_editor_bootstrap_nonzeroU.yaml +17 −0

++# Phase 4 step 2: Stage B editor bootstrap on the FROZEN tiny backbone (v1), forced mixed budgets {0,1,2,4,8}.
++name: p4_editor_v1_nonzeroU
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++init: {backbone_checkpoint: experiments/runs/p4_tiny_backbone_seed01_20261029232626/checkpoint, freeze_backbone: true}
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: answer,
++       eval_splits: [dev_iid, dev_template, dev_depth, dev_compose]}
++budgets: [0, 1, 2, 4, 8]
++budget_probs: [0.2, 0.2, 0.2, 0.2, 0.2]
++budget_per: position
++train: {batch_size: 32, seq_len: 320, steps: 2000, lr: 1.0e-3, warmup: 100, weight_decay: 0.1, grad_clip: 1.0,
++        log_every: 50, eval_every: 500, eval_batches: 8, ckpt_every: 1500}
++em_budgets: [0, 2, 8]
++em_max_items: 400
++model_overrides: {readout_zero_init: false}

added configs/experiments/p4_tiny_backbone.yaml +12 −0

++# Phase 4 step 1: tiny backbone pretrained from scratch on the synthetic TRAIN split (char-level), workspace off.
++name: p4_tiny_backbone
++seed: 1
++mode: backbone_only
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++init: {freeze_backbone: false}
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: all,
++       eval_splits: [dev_iid, dev_template, dev_depth, dev_compose]}
++train: {batch_size: 64, seq_len: 320, steps: 6000, lr: 1.0e-3, warmup: 200, weight_decay: 0.1, grad_clip: 1.0,
++        log_every: 50, eval_every: 500, eval_batches: 10, ckpt_every: 2000}
++em_max_items: 600

added configs/experiments/p4_tiny_backbone_v2.yaml +12 −0

++# Phase 4 step 1 (v2): stronger tiny backbone (≈5.6M params) from scratch on the synthetic TRAIN split, char-level.
++name: p4_tiny_backbone_v2
++seed: 1
++mode: backbone_only
++model: {name: tiny_p4v2, vocab_size: 128, d_model: 256, n_layers: 6, n_heads: 8, n_kv_heads: 4, ffn_width: 768,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++init: {freeze_backbone: false}
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: all,
++       eval_splits: [dev_iid, dev_template, dev_depth, dev_compose]}
++train: {batch_size: 64, seq_len: 320, steps: 15000, lr: 1.0e-3, warmup: 300, weight_decay: 0.1, grad_clip: 1.0,
++        log_every: 100, eval_every: 1000, eval_batches: 10, ckpt_every: 5000}
++em_max_items: 800

added configs/experiments/pretrain_103m.yaml +18 −0

++name: pretrain103m
++seed: 1
++model: configs/prisme_103m.yaml
++data:
++  train_bin: data/processed/fineweb_edu_300m/train.bin
++  val_bin: data/processed/fineweb_edu_300m/val.bin
++train:
++  batch_size: 32
++  seq_len: 512
++  steps: 12000         # 32*512*12000 = 196.6M tokens (pilot budget; see protocol/preregistration.md)
++  lr: 6.0e-4
++  warmup: 300
++  weight_decay: 0.1
++  grad_clip: 1.0
++  log_every: 20
++  eval_every: 500
++  eval_batches: 20
++  ckpt_every: 1000

added configs/experiments/pretrain_enlarged_102m.yaml +18 −0

++name: pretrain_enlarged102m
++seed: 1
++model: configs/baselines/transformer_enlarged_102m.yaml
++data:
++  train_bin: data/processed/fineweb_edu_300m/train.bin
++  val_bin: data/processed/fineweb_edu_300m/val.bin
++train:
++  batch_size: 32
++  seq_len: 512
++  steps: 12000         # 32*512*12000 = 196.6M tokens (pilot budget; see protocol/preregistration.md)
++  lr: 6.0e-4
++  warmup: 300
++  weight_decay: 0.1
++  grad_clip: 1.0
++  log_every: 20
++  eval_every: 500
++  eval_batches: 20
++  ckpt_every: 1000

added configs/experiments/routing_p4.yaml +13 −0

++# Routing comparison (tiny scale) on dev splits with the LOCKED controller checkpoint.
++seed: 1
++mode: prisme
++model: {name: tiny_p4, vocab_size: 128, d_model: 192, n_layers: 4, n_heads: 6, n_kv_heads: 2, ffn_width: 512,
++        max_seq_len: 320, w: 64, ffn_w: 192, n_slots: 16, n_editor_heads: 2, r_max: 8}
++checkpoint: CHECKPOINT_DIR
++data: {kind: synthetic, tokenizer: char, splits_dir: data/splits/synthetic_v1, loss_span: answer, split: dev_iid}
++batch_size: 32
++seq_len: 320
++n_batches: 20
++budgets: [1, 2, 4, 8]
++oracle_batches: 4
++two_step_batches: 1

added configs/prisme_103m.yaml +23 −0

++# PRISME reference configuration: prisme_103m (see docs/architecture_spec.md)
++model:
++  name: prisme_103m
++  vocab_size: 32768
++  d_model: 768
++  n_layers: 12
++  n_heads: 12
++  n_kv_heads: 3
++  ffn_width: 2048
++  max_seq_len: 4096
++  rope_theta: 10000.0
++  norm_eps: 1.0e-5
++  tie_embeddings: true
++  use_workspace: true
++  w: 256
++  ffn_w: 768
++  n_slots: 16
++  n_editor_blocks: 2
++  n_editor_heads: 4
++  r_max: 8
++  eta_max: 0.25
++  clip_c: 1.0
++  readout_zero_init: true

added configs/prisme_1180m.yaml +23 −0

++# PRISME reference configuration: prisme_1180m (see docs/architecture_spec.md)
++model:
++  name: prisme_1180m
++  vocab_size: 32768
++  d_model: 2048
++  n_layers: 24
++  n_heads: 32
++  n_kv_heads: 8
++  ffn_width: 5632
++  max_seq_len: 4096
++  rope_theta: 10000.0
++  norm_eps: 1.0e-5
++  tie_embeddings: true
++  use_workspace: true
++  w: 768
++  ffn_w: 2048
++  n_slots: 16
++  n_editor_blocks: 2
++  n_editor_heads: 12
++  r_max: 8
++  eta_max: 0.25
++  clip_c: 1.0
++  readout_zero_init: true

added configs/prisme_459m.yaml +23 −0

++# PRISME reference configuration: prisme_459m (see docs/architecture_spec.md)
++model:
++  name: prisme_459m
++  vocab_size: 32768
++  d_model: 1536
++  n_layers: 16
++  n_heads: 24
++  n_kv_heads: 6
++  ffn_width: 4096
++  max_seq_len: 4096
++  rope_theta: 10000.0
++  norm_eps: 1.0e-5
++  tie_embeddings: true
++  use_workspace: true
++  w: 512
++  ffn_w: 1536
++  n_slots: 16
++  n_editor_blocks: 2
++  n_editor_heads: 8
++  r_max: 8
++  eta_max: 0.25
++  clip_c: 1.0
++  readout_zero_init: true

added data/__init__.py +0 −0

added data/char_tokenizer.py +29 −0

++"""Self-contained character-level tokenizer for the Phase 4 learnability experiments (no corpus dependency)."""
++from __future__ import annotations
++
++import json
++from pathlib import Path
++
++PRINTABLE = [chr(i) for i in range(32, 127)]
++
++
++class CharTokenizer:
++    def __init__(self):
++        self.specials = ["<eot>", "<pad>"]
++        self.itos = self.specials + PRINTABLE
++        self.stoi = {s: i for i, s in enumerate(self.itos)}
++        self.eot_id = 0
++        self.pad_id = 1
++
++    @property
++    def vocab_size(self) -> int:
++        return len(self.itos)
++
++    def encode(self, text: str) -> list[int]:
++        return [self.stoi.get(ch, self.stoi["?"]) for ch in text]
++
++    def decode(self, ids) -> str:
++        return "".join(self.itos[i] if i >= len(self.specials) else "" for i in ids)
++
++    def save(self, path):
++        Path(path).write_text(json.dumps({"itos": self.itos}))

added data/generators/__init__.py +0 −0

added data/generators/synthetic.py +236 −0

++"""Verifiable synthetic task generators with programmatic ground truth.
++
++Families (each item is a dict: family, template, depth, prompt, answer, text, meta):
++  arith   multi-step arithmetic chains            (depth = number of operations)
++  bind    variable binding + dependent substitution (depth = chain length)
++  fsm     register / finite-state updates          (depth = number of updates)
++  excl    conjunctive constraints with distractors (depth = number of constraints)
++  sched   rule-based selection under a budget and exclusions (depth = number of candidates)
++  compose held-out composition: bind chain feeding an arithmetic expression (depth = total steps)
++
++Splitting is done BEFORE sampling: by (family, template id, depth regime, seed stream). Train uses templates
++T0-T2 and depths <= max_train_depth; held-out "template" items use templates T3-T4; held-out "depth" items use
++depths > max_train_depth; the compose family is entirely held out from training unless explicitly requested.
++Text format: "<prompt> Answer: <answer>" ; the answer is the only supervised span for exact-match evaluation.
++"""
++from __future__ import annotations
++
++import hashlib
++import json
++import random
++from dataclasses import dataclass, asdict
++from typing import Callable
++
++OPS = {"+": lambda a, b: a + b, "-": lambda a, b: a - b, "*": lambda a, b: a * b}
++
++# Phrasing templates: index 0..2 = train, 3..4 = held-out paraphrases.
++TEMPLATES = {
++    "arith": ["Compute {expr}.", "What is {expr}?", "Evaluate the expression {expr}.", "Calculate {expr} step by step and give the result.", "Find the value of {expr}."],
++    "bind": ["Let {chain}. What is {var}?", "Given {chain}, compute {var}.", "{chain}. Find {var}.", "Suppose {chain}. Then {var} equals what?", "With {chain}, the value of {var} is?"],
++    "fsm": ["Start with register {init}. {ops}. What is the register?", "The counter begins at {init}. {ops}. Final counter?", "Initial value {init}. {ops}. Result?", "A register holds {init}. Apply: {ops}. What does it hold now?", "Begin at {init}; {ops}; final value?"],
++    "excl": ["Items: {items}. Keep the items that are {rules}. Which items remain?", "Consider {items}. Select those that are {rules}. List them.", "{items}. Filter: {rules}. Remaining items?", "Given the list {items}, keep only the ones {rules}. Answer with the names.", "From {items}, which satisfy: {rules}?"],
++    "sched": ["Budget {budget}. Tasks: {tasks}. Rule: {rule}. Choose the affordable task with the highest value.", "You have {budget} units. Options: {tasks}. Constraint: {rule}. Which affordable option has the largest value?", "Capacity {budget}. Candidates: {tasks}. Exclusion: {rule}. Pick the best affordable candidate.", "With a budget of {budget}, among {tasks}, under the rule {rule}, which task maximises value while staying affordable?", "Budget is {budget}. {tasks}. {rule}. Best affordable task by value?"],
++    "compose": ["Let {chain}. Compute {expr}.", "Given {chain}, what is {expr}?", "{chain}. Evaluate {expr}.", "Suppose {chain}. Then find {expr}.", "With {chain}, calculate {expr}."],
++}
++
++
++def _rng(family: str, template: int, depth: int, seed: int, idx: int) -> random.Random:
++    key = f"{family}|{template}|{depth}|{seed}|{idx}"
++    return random.Random(int(hashlib.sha256(key.encode()).hexdigest()[:16], 16))
++
++
++def gen_arith(r: random.Random, depth: int, template: int):
++    vals = [r.randint(1, 20)]
++    ops = []
++    expr = str(vals[0])
++    cur = vals[0]
++    for _ in range(depth):
++        op = r.choice(["+", "-", "*"])
++        b = r.randint(1, 9) if op == "*" else r.randint(1, 30)
++        cur = OPS[op](cur, b)  # left-to-right evaluation (stated below)
++        expr += f" {op} {b}"
++        ops.append(op)
++    prompt = TEMPLATES["arith"][template].format(expr=expr) + " Evaluate left to right."
++    return prompt, str(cur), {"ops": "".join(ops)}
++
++
++def gen_bind(r: random.Random, depth: int, template: int):
++    names = r.sample(list("abcdefghjkmnpqrstuvwxyz"), depth + 1)
++    vals = {names[0]: r.randint(1, 15)}
++    parts = [f"{names[0]} = {vals[names[0]]}"]
++    for i in range(1, depth + 1):
++        op = r.choice(["+", "-", "*"])
++        b = r.randint(1, 5) if op == "*" else r.randint(1, 12)
++        src = names[i - 1]
++        vals[names[i]] = OPS[op](vals[src], b)
++        parts.append(f"{names[i]} = {src} {op} {b}")
++    # distractor: an unused variable
++    d = r.choice([n for n in "ABCDEFG"])
++    parts.insert(r.randint(1, len(parts)), f"{d} = {r.randint(1, 40)}")
++    chain = ", ".join(parts)
++    target = names[depth]
++    prompt = TEMPLATES["bind"][template].format(chain=chain, var=target)
++    return prompt, str(vals[target]), {"target": target}
++
++
++def gen_fsm(r: random.Random, depth: int, template: int):
++    init = r.randint(0, 10)
++    cur = init
++    steps = []
++    for _ in range(depth):
++        op = r.choice(["add", "sub", "double", "reset", "halve"])
++        if op == "add":
++            b = r.randint(1, 9); cur += b; steps.append(f"add {b}")
++        elif op == "sub":
++            b = r.randint(1, 9); cur -= b; steps.append(f"subtract {b}")
++        elif op == "double":
++            cur *= 2; steps.append("double it")
++        elif op == "halve":
++            cur = cur // 2; steps.append("halve it (round down)")
++        else:
++            b = r.randint(0, 9); cur = b; steps.append(f"reset to {b}")
++    prompt = TEMPLATES["fsm"][template].format(init=init, ops=", then ".join(steps))
++    return prompt, str(cur), {}
++
++
++def gen_excl(r: random.Random, depth: int, template: int):
++    n = r.randint(4, 7)
++    names = r.sample(["apple", "pear", "plum", "fig", "kiwi", "lime", "date", "yam", "nut", "bean"], n)
++    items = {nm: (r.randint(1, 30), r.choice(["red", "green", "blue"])) for nm in names}
++    rules = []
++    def keep(v, c):
++        return True
++    preds: list[Callable] = []
++    for _ in range(depth):
++        kind = r.choice(["gt", "lt", "color", "even"])
++        if kind == "gt":
++            t = r.randint(5, 25); rules.append(f"greater than {t}"); preds.append(lambda v, c, t=t: v > t)
++        elif kind == "lt":
++            t = r.randint(5, 25); rules.append(f"smaller than {t}"); preds.append(lambda v, c, t=t: v < t)
++        elif kind == "color":
++            col = r.choice(["red", "green", "blue"]); rules.append(f"not {col}"); preds.append(lambda v, c, col=col: c != col)
++        else:
++            rules.append("even"); preds.append(lambda v, c: v % 2 == 0)
++    remaining = [nm for nm in names if all(p(*items[nm]) for p in preds)]
++    items_s = ", ".join(f"{nm} ({v}, {c})" for nm, (v, c) in items.items())
++    prompt = TEMPLATES["excl"][template].format(items=items_s, rules=" and ".join(rules))
++    ans = " ".join(remaining) if remaining else "none"
++    return prompt, ans, {"n_items": n, "n_remaining": len(remaining)}
++
++
++def gen_sched(r: random.Random, depth: int, template: int):
++    names = [chr(ord("A") + i) for i in range(depth)]
++    tasks = {nm: (r.randint(1, 12), r.randint(1, 20)) for nm in names}
++    budget = r.randint(4, 12)
++    kind = r.choice(["cost_gt", "value_lt", "none"])
++    if kind == "cost_gt":
++        t = r.randint(3, 10); rule = f"exclude tasks with cost greater than {t}"; ok = lambda c, v: c <= t
++    elif kind == "value_lt":
++        t = r.randint(3, 10); rule = f"exclude tasks with value below {t}"; ok = lambda c, v: v >= t
++    else:
++        rule = "no exclusion"; ok = lambda c, v: True
++    feas = [(v, nm) for nm, (c, v) in tasks.items() if c <= budget and ok(c, v)]
++    if not feas:
++        ans = "none"
++    else:
++        best = max(v for v, _ in feas)
++        ans = sorted(nm for v, nm in feas if v == best)[0]
++    tasks_s = ", ".join(f"{nm} (cost {c}, value {v})" for nm, (c, v) in tasks.items())
++    prompt = TEMPLATES["sched"][template].format(budget=budget, tasks=tasks_s, rule=rule)
++    return prompt, ans, {"n_feasible": len(feas)}
++
++
++def gen_compose(r: random.Random, depth: int, template: int):
++    # bind chain of length depth-2 feeding a 2-op arithmetic expression over two chain variables
++    k = max(1, depth - 2)
++    names = r.sample(list("abcdefghjkmnpqrstuvwxyz"), k + 1)
++    vals = {names[0]: r.randint(1, 10)}
++    parts = [f"{names[0]} = {vals[names[0]]}"]
++    for i in range(1, k + 1):
++        op = r.choice(["+", "-"]); b = r.randint(1, 9); src = names[i - 1]
++        vals[names[i]] = OPS[op](vals[src], b); parts.append(f"{names[i]} = {src} {op} {b}")
++    v1, v2 = r.sample(names, 2) if len(names) > 1 else (names[0], names[0])
++    op1, op2 = r.choice(["+", "-", "*"]), r.choice(["+", "-"])
++    c = r.randint(1, 9)
++    val = OPS[op2](OPS[op1](vals[v1], vals[v2]), c)
++    expr = f"{v1} {op1} {v2} {op2} {c} (left to right)"
++    prompt = TEMPLATES["compose"][template].format(chain=", ".join(parts), expr=expr)
++    return prompt, str(val), {}
++
++
++GENERATORS = {"arith": gen_arith, "bind": gen_bind, "fsm": gen_fsm, "excl": gen_excl, "sched": gen_sched, "compose": gen_compose}
++
++
++def make_item(family: str, template: int, depth: int, seed: int, idx: int) -> dict:
++    r = _rng(family, template, depth, seed, idx)
++    prompt, answer, meta = GENERATORS[family](r, depth, template)
++    text = f"{prompt} Answer: {answer}"
++    return {"family": family, "template": template, "depth": depth, "seed": seed, "idx": idx,
++            "prompt": prompt + " Answer:", "answer": " " + answer, "text": text, "meta": meta,
++            "uid": hashlib.sha256(text.encode()).hexdigest()[:16]}
++
++
++@dataclass
++class SplitSpec:
++    name: str
++    families: tuple[str, ...]
++    templates: tuple[int, ...]
++    depths: tuple[int, ...]
++    seed: int
++    n_per_cell: int
++
++
++def generate_split(spec: SplitSpec) -> list[dict]:
++    items = []
++    for fam in spec.families:
++        for tpl in spec.templates:
++            for dep in spec.depths:
++                for i in range(spec.n_per_cell):
++                    items.append(make_item(fam, tpl, dep, spec.seed, i))
++    return items
++
++
++def default_splits(n_train_per_cell: int = 400, n_eval_per_cell: int = 60) -> dict[str, SplitSpec]:
++    """Train: 5 families, templates 0-2, depths 1-4, seed 1. Dev: seed 2 same cells (IID generalisation).
++    Held-out template: templates 3-4. Held-out depth: depths 5-6. Compose: fully held-out family.
++    Final test: seed 9 across all eval regimes (LOCKED until preregistration is frozen)."""
++    fams = ("arith", "bind", "fsm", "excl", "sched")
++    return {
++        "train": SplitSpec("train", fams, (0, 1, 2), (1, 2, 3, 4), 1, n_train_per_cell),
++        "calib": SplitSpec("calib", fams, (0, 1, 2), (1, 2, 3, 4), 3, n_eval_per_cell // 2),
++        "dev_iid": SplitSpec("dev_iid", fams, (0, 1, 2), (1, 2, 3, 4), 2, n_eval_per_cell),
++        "dev_template": SplitSpec("dev_template", fams, (3, 4), (1, 2, 3, 4), 2, n_eval_per_cell),
++        "dev_depth": SplitSpec("dev_depth", fams, (0, 1, 2), (5, 6), 2, n_eval_per_cell),
++        "dev_compose": SplitSpec("dev_compose", ("compose",), (0, 1, 2), (3, 4, 5), 2, n_eval_per_cell),
++        "test_iid": SplitSpec("test_iid", fams, (0, 1, 2), (1, 2, 3, 4), 9, n_eval_per_cell),
++        "test_template": SplitSpec("test_template", fams, (3, 4), (1, 2, 3, 4), 9, n_eval_per_cell),
++        "test_depth": SplitSpec("test_depth", fams, (0, 1, 2), (5, 6), 9, n_eval_per_cell),
++        "test_compose": SplitSpec("test_compose", ("compose",), (0, 1, 2), (3, 4, 5), 9, n_eval_per_cell),
++    }
++
++
++def write_splits(out_dir: str, n_train_per_cell: int = 400, n_eval_per_cell: int = 60) -> dict:
++    from pathlib import Path
++    out = Path(out_dir); out.mkdir(parents=True, exist_ok=True)
++    manifest = {}
++    all_uids: dict[str, str] = {}
++    for name, spec in default_splits(n_train_per_cell, n_eval_per_cell).items():
++        items = generate_split(spec)
++        # cross-split exact-duplicate removal: an item whose full text already appeared in an earlier split
++        # (train is written first) is DROPPED from the later split; within-split duplicates are dropped too.
++        kept, dups = [], 0
++        for it in items:
++            if it["uid"] in all_uids:
++                dups += 1
++                continue
++            all_uids[it["uid"]] = name
++            kept.append(it)
++        items = kept
++        p = out / f"{name}.jsonl"
++        with open(p, "w") as f:
++            for it in items:
++                f.write(json.dumps(it) + "\n")
++        manifest[name] = {"spec": asdict(spec), "n": len(items), "duplicates_dropped": dups,
++                          "sha256": hashlib.sha256(p.read_bytes()).hexdigest(), "locked": name.startswith("test")}
++    (out / "manifest.json").write_text(json.dumps(manifest, indent=2))
++    return manifest

added data/processed/fineweb_edu_300m/manifest.json +48 −0

++{
++  "source": "HuggingFaceFW/fineweb-edu sample-10BT (streaming, canonical shard order)",
++  "license": "ODC-By 1.0 (https://opendatacommons.org/licenses/by/1-0/)",
++  "prepared_at": "2026-10-29T23:19:53-0400",
++  "args": {
++    "out": "data/processed/fineweb_edu_300m",
++    "target_train_tokens": 300000000,
++    "vocab_size": 32768,
++    "tokenizer_docs": 60000,
++    "val_pct": 0.01,
++    "test_pct": 0.01,
++    "max_doc_chars": 40000
++  },
++  "docs_streamed": 291647,
++  "docs": {
++    "train": 285604,
++    "val": 2957,
++    "test": 2973
++  },
++  "chars": {
++    "train": 1290001347,
++    "val": 12988848,
++    "test": 12933032
++  },
++  "splits": {
++    "train": {
++      "tokens": 289959006,
++      "docs": 285604,
++      "sha256": "e931e83c4ba6e62a2125e50a5390e8df78913dbed31af805d8af17fafad95123",
++      "locked": false
++    },
++    "val": {
++      "tokens": 2913824,
++      "docs": 2957,
++      "sha256": "0565e3f4a4028b241f40ef07f20bc68b24e45d99e147860be066e3667d1532be",
++      "locked": false
++    },
++    "test": {
++      "tokens": 2883436,
++      "docs": 2973,
++      "sha256": "1ce9ca3ab6d659fe08042844fc0589951e09386ee79d2ef76504ffaaebb0947b",
++      "locked": true
++    }
++  },
++  "tokenizer_sha256": "5a78df618767945743623d5ff238d035839d02388b4c53c548a437b3d8a206ee",
++  "eot_id": 0,
++  "dtype": "uint16"
++}
+ \ No newline at end of file

added data/processed/fineweb_edu_300m/test.bin +0 −0

Binary file not shown.

added data/processed/fineweb_edu_300m/tokenizer.json +19236 −0

++{
++  "version": "1.0",
++  "truncation": null,
++  "padding": null,
++  "added_tokens": [
++    {
++      "id": 0,
++      "content": "<|endoftext|>",
++      "single_word": false,
++      "lstrip": false,
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++      "Ġregulators": 18590,
++      "Ġskeleton": 18591,
++      "Ġcongregation": 18592,
++      "ĠAstronomy": 18593,
++      "Ġgambling": 18594,
++      "BE": 18595,
++      "Paul": 18596,
++      "bee": 18597,
++      "every": 18598,
++      "ãĥ": 18599,
++      "orpor": 18600,
++      "immer": 18601,
++      "ĠIvan": 18602,
++      "Ġdelete": 18603,
++      "ĠEN": 18604,
++      "Ġchords": 18605,
++      "ĠRick": 18606,
++      "ĠRAM": 18607,
++      "ĠGray": 18608,
++      "ĠLun": 18609,
++      "Ġabused": 18610,
++      "Ġinteger": 18611,
++      "Ġjunk": 18612,
++      "Ġoutlets": 18613,
++      "Ġscored": 18614,
++      "Ġhadn": 18615,
++      "Ġcolder": 18616,
++      "ussia": 18617,
++      "ĠExodus": 18618,
++      "Ġgrounded": 18619,
++      "Ġbride": 18620,
++      "ocrine": 18621,
++      "Ġvisualize": 18622,
++      "content": 18623,
++      "Ġimplied": 18624,
++      "Ġsplend": 18625,
++      "ĠKnowing": 18626,
++      "Ġambient": 18627,
++      "Ġhypotheses": 18628,
++      "Ġglacial": 18629,
++      "Ġrefrigerator": 18630,
++      "ongevity": 18631,
++      "ĠArabs": 18632,
++      "wax": 18633,
++      "Ġnatives": 18634,
++      "ĠAwareness": 18635,
++      "usable": 18636,
++      "Ġleap": 18637,
++      "ĠInn": 18638,
++      "Ġunrelated": 18639,
++      "ĠStories": 18640,
++      "Ġscaling": 18641,
++      "ĠChart": 18642,
++      "Ġcoloured": 18643,
++      "racted": 18644,
++      "Ġserotonin": 18645,
++      "Ġretin": 18646,
++      "Ġdirectors": 18647,
++      "Ġredist": 18648,
++      "Ġexpectation": 18649,
++      "ĠRegiment": 18650,
++      "Ġoutputs": 18651,
++      "Ġtrout": 18652,
++      "without": 18653,
++      "ĠGraham": 18654,
++      "ĠSoviets": 18655,
++      "Ġawa": 18656,
++      "ĠIdaho": 18657,
++      "Ġethnicity": 18658,
++      "Ġadolescence": 18659,
++      "ĠHomo": 18660,
++      "Ġmultiplication": 18661,
++      "ĠCoalition": 18662,
++      "ĠBronze": 18663,
++      "Ġtransgender": 18664,
++      "chestra": 18665,
++      "ĠScandinav": 18666,
++      "bishop": 18667,
++      "ea": 18668,
++      "ucc": 18669,
++      "Äĵ": 18670,
++      "Ġ).": 18671,
++      "inctions": 18672,
++      "Ġbaked": 18673,
++      "ctal": 18674,
++      "rais": 18675,
++      "ulence": 18676,
++      "amas": 18677,
++      "ĠHarm": 18678,
++      "Ġcanals": 18679,
++      "ĠFitz": 18680,
++      "ĠLect": 18681,
++      "ĠLibr": 18682,
++      "izards": 18683,
++      "ĠYoga": 18684,
++      "Ġsystematically": 18685,
++      "Ġsubspecies": 18686,
++      "Ġinsisted": 18687,
++      "Ġsolids": 18688,
++      "Ġrealities": 18689,
++      "scar": 18690,
++      "Ġproposition": 18691,
++      "ĠAmsterdam": 18692,
++      "ylogen": 18693,
++      "Ġconcise": 18694,
++      "Ã©e": 18695,
++      "ĠGlass": 18696,
++      "containing": 18697,
++      "Ġdynam": 18698,
++      "ÃŃs": 18699,
++      "Ġcorporation": 18700,
++      "Ġamplifier": 18701,
++      "ĠSigns": 18702,
++      "Ġrecruited": 18703,
++      "Ġacoustic": 18704,
++      "Ġgrateful": 18705,
++      "SV": 18706,
++      "Tr": 18707,
++      "Ter": 18708,
++      "Washington": 18709,
++      "looking": 18710,
++      "Ġtect": 18711,
++      "invasive": 18712,
++      "atra": 18713,
++      "Ġpoured": 18714,
++      "Ġmph": 18715,
++      "leen": 18716,
++      "Ġreun": 18717,
++      "Ġreper": 18718,
++      "Ġbean": 18719,
++      "ĠSue": 18720,
++      "ĠPositive": 18721,
++      "opot": 18722,
++      "ĠBranch": 18723,
++      "ĠWA": 18724,
++      "ĠEgg": 18725,
++      "ĠGent": 18726,
++      "Ġshake": 18727,
++      "agos": 18728,
++      "Ġdiscrete": 18729,
++      "ĠProced": 18730,
++      "Ġinsomnia": 18731,
++      "ĠArmed": 18732,
++      "Ġideological": 18733,
++      "Ġguy": 18734,
++      "ĠResolution": 18735,
++      "Ġillusion": 18736,
++      "ĠQuick": 18737,
++      "Ġremembering": 18738,
++      "Ġtoothp": 18739,
++      "Ġseedlings": 18740,
++      "Ġlabeling": 18741,
++      "Environmental": 18742,
++      "Ġunusually": 18743,
++      "ĠMadagascar": 18744,
++      "ĠâĨĴ": 18745,
++      "Ġfishermen": 18746,
++      "Ġrabbit": 18747,
++      "Ġthreads": 18748,
++      "ĠPearl": 18749,
++      "Ġspontaneous": 18750,
++      "Ġsubmarine": 18751,
++      "ĠBengal": 18752,
++      "clerosis": 18753,
++      "Ġfinancially": 18754,
++      "Air": 18755,
++      "Year": 18756,
++      "âĨ": 18757,
++      "igraph": 18758,
++      "ĠSah": 18759,
++      "ĠPel": 18760,
++      "Ġvap": 18761,
++      "Ġnoteb": 18762,
++      "ĠKel": 18763,
++      "Ġblamed": 18764,
++      "Ġneeding": 18765,
++      "Ġnowadays": 18766,
++      "ĠButter": 18767,
++      "Ġpolluted": 18768,
++      "ulsive": 18769,
++      "Ġfigured": 18770,
++      "essori": 18771,
++      "Ġnursery": 18772,
++      "Ġprevailing": 18773,
++      "Ġaccidentally": 18774,
++      "ĠWorkshe": 18775,
++      "ĠInterestingly": 18776,
++      "dad": 18777,
++      "lon": 18778,
++      "solving": 18779,
++      "ĠTang": 18780,
++      "chin": 18781,
++      "Ġstagn": 18782,
++      "Ġanomal": 18783,
++      "ĠPrad": 18784,
++      "ĠHipp": 18785,
++      "Ġpled": 18786,
++      "Ġmanages": 18787,
++      "Ġcommunist": 18788,
++      "them": 18789,
++      "Ġglance": 18790,
++      "Ġsentiment": 18791,
++      "Ġdenomin": 18792,
++      "Ġcereals": 18793,
++      "Ġwetland": 18794,
++      "Moreover": 18795,
++      "Ġexceeded": 18796,
++      "Ġencompasses": 18797,
++      "Ġcommodities": 18798,
++      "established": 18799,
++      "Tur": 18800,
++      "effic": 18801,
++      "folk": 18802,
++      "nom": 18803,
++      "pend": 18804,
++      "uer": 18805,
++      "Ñĥ": 18806,
++      "Ġowing": 18807,
++      "icated": 18808,
++      "Ġgamma": 18809,
++      "Ġstal": 18810,
++      "Ġprose": 18811,
++      "ifers": 18812,
++      "ifest": 18813,
++      "umption": 18814,
++      "ĠHD": 18815,
++      "ĠLane": 18816,
++      "Ġenvision": 18817,
++      "Ġworsen": 18818,
++      "aryn": 18819,
++      "Ġscary": 18820,
++      "ĠVIII": 18821,
++      "ificate": 18822,
++      "derived": 18823,
++      "Answer": 18824,
++      "Ġoptional": 18825,
++      "Ġoriginate": 18826,
++      "borough": 18827,
++      "Ġjournalism": 18828,
++      "ĠPublications": 18829,
++      "Ġlibert": 18830,
++      "ĠInfantry": 18831,
++      "Ġoptimistic": 18832,
++      "Ġregulates": 18833,
++      "Ġarchaeology": 18834,
++      "izzes": 18835,
++      "Ġreservoirs": 18836,
++      "Ġexpenditures": 18837,
++      "Ġmantle": 18838,
++      "ĠNATO": 18839,
++      "cool": 18840,
++      "named": 18841,
++      "squ": 18842,
++      "uced": 18843,
++      "atl": 18844,
++      "Ġmos": 18845,
++      "Ġonion": 18846,
++      "amph": 18847,
++      "ĠEra": 18848,
++      "Ġnotions": 18849,
++      "Ġspices": 18850,
++      "Ġaddict": 18851,
++      "Ġsimulate": 18852,
++      "Ġcurb": 18853,
++      "ĠSpeed": 18854,
++      "Ġretard": 18855,
++      "Ġposters": 18856,
++      "Ġbiases": 18857,
++      "Ġcrashes": 18858,
++      "Ġversatile": 18859,
++      "ĠApproach": 18860,
++      "Ġinstitute": 18861,
++      "Ġrebels": 18862,
++      "Ġstormwater": 18863,
++      "ĠSalmon": 18864,
++      "ĠMontreal": 18865,
++      "Ġsticky": 18866,
++      "Ġpelvic": 18867,
++      "ĠHumans": 18868,
++      "Ġhedge": 18869,
++      "Ġinjustice": 18870,
++      "Cs": 18871,
++      "Total": 18872,
++      "USA": 18873,
++      "Ult": 18874,
++      "great": 18875,
++      "rational": 18876,
++      "rators": 18877,
++      "Ġtribute": 18878,
++      "Ġtheological": 18879,
++      "Ġsaves": 18880,
++      "esters": 18881,
++      "Ġlime": 18882,
++      "Ġbeads": 18883,
++      "Ġgaming": 18884,
++      "idate": 18885,
++      "ĠApr": 18886,
++      "Ġanonymous": 18887,
++      "illation": 18888,
++      "Ġshrine": 18889,
++      "ĠKaz": 18890,
++      "Ġdetention": 18891,
++      "ĠAlpha": 18892,
++      "ĠReich": 18893,
++      "Ġretro": 18894,
++      "ĠGuy": 18895,
++      "ĠHerod": 18896,
++      "Ġmisleading": 18897,
++      "ĠDeveloping": 18898,
++      "Ġapproximate": 18899,
++      "Ġphysicists": 18900,
++      "ĠClassical": 18901,
++      "ĠDefinition": 18902,
++      "Ġfellows": 18903,
++      "Ġfulfilled": 18904,
++      "Ġbathroom": 18905,
++      "Ġintentional": 18906,
++      "Ġculp": 18907,
++      "Ġstereotypes": 18908,
++      "ĠGhana": 18909,
++      "Ġconsecutive": 18910,
++      "Continue": 18911,
++      "Ġcriminals": 18912,
++      "ĠJoshua": 18913,
++      "Different": 18914,
++      "Fran": 18915,
++      "Leg": 18916,
++      "tz": 18917,
++      "etz": 18918,
++      "racle": 18919,
++      "abe": 18920,
++      "umerous": 18921,
++      "Ġvague": 18922,
++      "ivial": 18923,
++      "Ġlever": 18924,
++      "Ġlethal": 18925,
++      "ĠNathan": 18926,
++      "ĠLDL": 18927,
++      "astro": 18928,
++      "Ġscream": 18929,
++      "Ġaccent": 18930,
++      "full": 18931,
++      "Ġbuses": 18932,
++      "scribe": 18933,
++      "Ġdrains": 18934,
++      "Ġhighlighting": 18935,
++      "Ġteenage": 18936,
++      "topl": 18937,
++      "Ġinherently": 18938,
++      "Rober": 18939,
++      "enium": 18940,
++      "Ġbump": 18941,
++      "lett": 18942,
++      "igator": 18943,
++      "Ġstup": 18944,
++      "ĠCemetery": 18945,
++      "Ġcanvas": 18946,
++      "ĠEagle": 18947,
++      "rik": 18948,
++      "plain": 18949,
++      "Ġrecount": 18950,
++      "Ġtwist": 18951,
++      "Ġhumor": 18952,
++      "Ġsecuring": 18953,
++      "ĠReason": 18954,
++      "Ġlocalized": 18955,
++      "Ġresponsive": 18956,
++      "Ġcopied": 18957,
++      "Ġseparating": 18958,
++      "Ġanswering": 18959,
++      "Ġbrass": 18960,
++      "Ġconstituted": 18961,
++      "ĠSalv": 18962,
++      "ĠStein": 18963,
++      "ĠTownship": 18964,
++      "Ġpockets": 18965,
++      "Ġunsuccessful": 18966,
++      "British": 18967,
++      "Ident": 18968,
++      "hored": 18969,
++      "Ġbachelor": 18970,
++      "ingles": 18971,
++      "Ġthigh": 18972,
++      "Ġlined": 18973,
++      "raits": 18974,
++      "ĠMine": 18975,
++      "aba": 18976,
++      "ĠHalf": 18977,
++      "Ġjelly": 18978,
++      "Ġunve": 18979,
++      "Ġprehistoric": 18980,
++      "ĠYOU": 18981,
++      "Ġspeculation": 18982,
++      "ĠScreen": 18983,
++      "risoned": 18984,
++      "Ġsuperf": 18985,
++      "nsic": 18986,
++      "Ġlogo": 18987,
++      "ĠAntib": 18988,
++      "Ġtruths": 18989,
++      "Ġconsulting": 18990,
++      "ĠBreak": 18991,
++      "ĠPractices": 18992,
++      "Ġbombing": 18993,
++      "Ġcybersecurity": 18994,
++      "Ġcompositions": 18995,
++      "Ġdistributions": 18996,
++      "Ġprospects": 18997,
++      "sensitive": 18998,
++      "Ġreluctant": 18999,
++      "Sah": 19000,
++      "cra": 19001,
++      "click": 19002,
++      "dale": 19003,
++      "jar": 19004,
++      "lined": 19005,
++      "Ġdizziness": 19006,
++      "umatic": 19007,
++      "ĠHousing": 19008,
++      "ĠNit": 19009,
++      "ĠLam": 19010,
++      "Ġunre": 19011,
++      "Ġadditive": 19012,
++      "ructose": 19013,
++      "Ġcorals": 19014,
++      "Ġpredicting": 19015,
++      "Ġphotographer": 19016,
++      "Ġsunset": 19017,
++      "hetical": 19018,
++      "Ġenjoyment": 19019,
++      "blue": 19020,
++      "ĠSurv": 19021,
++      "Ġflooded": 19022,
++      "ĠSubject": 19023,
++      "Ġpulp": 19024,
++      "Ġrecipients": 19025,
++      "Ġawaken": 19026,
++      "Ġyielded": 19027,
++      "ĠSingle": 19028,
++      "ĠPopular": 19029,
++      "ĠCreator": 19030,
++      "Ġintestines": 19031,
++      "Ġcapsule": 19032,
++      "Ġenrollment": 19033,
++      "Ġelectrolyte": 19034,
++      "computer": 19035,
++      "DE": 19036,
++      "Mac": 19037,
++      "Taking": 19038,
++      "nih": 19039,
++      "hell": 19040,
++      "Ġbored": 19041,
++      "Ġlubric": 19042,
++      "ĠTru": 19043,
++      "ĠTrees": 19044,
++      "Ġgroom": 19045,
++      "ĠSAT": 19046,
++      "ĠSinai": 19047,
++      "ĠCeltic": 19048,
++      "ĠPred": 19049,
++      "opia": 19050,
++      "ĠRic": 19051,
++      "ĠRural": 19052,
++      "ĠLooking": 19053,
++      "Ġapprent": 19054,
++      "ĠKon": 19055,
++      "rama": 19056,
++      "owner": 19057,
++      "Ġdiscs": 19058,
++      "armed": 19059,
++      "Ġtestosterone": 19060,
++      "anguages": 19061,
++      "Ġwarn": 19062,
++      "Ġresearched": 19063,
++      "ĠGener": 19064,
++      "Ġbarley": 19065,
++      "Ġrevolt": 19066,
++      "Ġanalyse": 19067,
++      "ĠArtificial": 19068,
++      "Ġhairs": 19069,
++      "Ġbattlefield": 19070,
++      "Individual": 19071,
++      "Select": 19072,
++      "Ġsailing": 19073,
++      "Ġadvisory": 19074,
++      "ĠFactors": 19075,
++      "ĠVarious": 19076,
++      "Special": 19077,
++      "Ġbiochemical": 19078,
++      "ĠLieutenant": 19079,
++      "associated": 19080,
++      "Video": 19081,
++      "uf": 19082,
++      "ÙĲ": 19083,
++      "Ġsands": 19084,
++      "Ġstarch": 19085,
++      "ĠConditions": 19086,
++      "ĠFamilies": 19087,
++      "ĠLore": 19088,
++      "sole": 19089,
++      "Ġrecomb": 19090,
++      "Ġneedles": 19091,
++      "Ġreliance": 19092,
++      "Ġindef": 19093,
++      "tlement": 19094,
++      "Ġsheer": 19095,
++      "Ġdepressive": 19096,
++      "Ġenduring": 19097,
++      "Ġfinishing": 19098,
++      "oidal": 19099,
++      "Ġfilt": 19100,
++      "aneously": 19101,
++      "ĠHealthcare": 19102,
++      "Ġavoidance": 19103,
++      "Ġpositioning": 19104,
++      "Ġcustody": 19105,
++      "Ġcontributors": 19106,
++      "ĠReduce": 19107,
++      "Ġrebuild": 19108,
++      "Ġmotivate": 19109,
++      "products": 19110,
++      "Ġcocaine": 19111,
++      "ĠColonial": 19112,
++      "ĠParticipants": 19113,
++      "Ġcohort": 19114,
++      "Ġelegant": 19115,
++      "ĠConstantinople": 19116,
++      "ISA": 19117,
++      "cribed": 19118,
++      "Ïĥ": 19119,
++      "ãĢ": 19120,
++      "Ġpains": 19121,
++      "eder": 19122,
++      "arthritis": 19123,
++      "Ġmills": 19124,
++      "Ġdread": 19125,
++      "ilian": 19126,
++      "Ġlaund": 19127,
++      "Ġstool": 19128,
++      "ĠMaced": 19129,
++      "ĠMobile": 19130,
++      "ĠHend": 19131,
++      "Ġchol": 19132,
++      "ĠFresh": 19133,
++      "unal": 19134,
++      "river": 19135,
++      "rencies": 19136,
++      "Ġassurance": 19137,
++      "Ġpresidency": 19138,
++      "Ġfounders": 19139,
++      "ĠAli": 19140,
++      "ĠScale": 19141,
++      "dehy": 19142,
++      "Ġlightweight": 19143,
++      "Ġperformances": 19144,
++      "Ġcalf": 19145,
++      "ĠMedieval": 19146,
++      "Ġheavens": 19147,
++      "Ġprogresses": 19148,
++      "Ġalternating": 19149,
++      "ĠPressure": 19150,
++      "Ġoptimize": 19151,
++      "Ġoptimization": 19152,
++      "Ġroofs": 19153,
++      "Recently": 19154,
++      "carbon": 19155,
++      "Everyone": 19156,
++      "Ġmirrors": 19157,
++      "Ġslee": 19158,
++      "Ġdeterioration": 19159,
++      "IES": 19160,
++      "degree": 19161,
++      "Ġreapp": 19162,
++      "ĠSN": 19163,
++      "ĠMaterial": 19164,
++      "Ġweighed": 19165,
++      "ĠHus": 19166,
++      "email": 19167,
++      "Ġseaw": 19168,
++      "ĠNIH": 19169,
++      "Ġenjo": 19170,
++      "Ġintend": 19171,
++      "Ġreside": 19172,
++      "ĠChamber": 19173,
++      "Ġtrafficking": 19174,
++      "Ġnoon": 19175,
++      "Ġflip": 19176,
++      "gger": 19177,
++      "scient": 19178,

Diff truncated — file too large.

spb/wp31_uqo

WP31 PRISME: phases 0-3 implementation, tests, data pipeline, training scripts

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