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WP31: v2 routing seeds, mini pipeline, README, cards, run index

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Oct 30, 2026) parent ede6705 Browse files

81 changed files +4,600 −105

81 changed files
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added README.md +44 −0
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1 +<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca -->
2 +# 🧩 PRISME: Intervention-Supervised Sparse Workspace Editing for Small Language Models
3 +
4 +**UQO Working Paper No. 31** — *Design, controlled evaluation, and negative and positive evidence at the 100M-parameter scale*
5 +
6 +[![Paper](https://img.shields.io/badge/paper-PDF-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
7 +[![Python](https://img.shields.io/badge/python-3.12-3776AB?logo=python&logoColor=white)](requirements-lock.txt)
8 +[![Tests](https://img.shields.io/badge/tests-23%20passing-2e7d32)](tests/)
9 +[![Reproducible](https://img.shields.io/badge/reproducible-end--to--end%20pipeline-2e7d32)](REPRODUCE.md)
10 +[![Institution](https://img.shields.io/badge/UQO-D%C3%A9pt.%20des%20sciences%20administratives-16365c)](https://uqo.ca)
11 +
12 +> **TL;DR** — PRISME adds to a frozen causal decoder a read-only source bank, a 16-slot ephemeral workspace, a
13 +> shared sparse editor (1–2 slots per action, 24 actions + STOP), a value controller trained on executed
14 +> counterfactual gains, and a differential readout with an exact zero-budget bypass. We implement, test, train and
15 +> evaluate it against matched baselines. Result: the editor is learnable and helps a frozen backbone by a few
16 +> percent of loss, but a global-recurrence adapter trained identically helps as much; where to write does not
17 +> matter for fixed schedules; a target-reading oracle shows large opportunity that the learned controller cannot
18 +> capture (it is worse than no routing on held-out data); and dispatch overhead erases sparse savings at small
19 +> scale. A careful negative result with a mechanistic explanation.
20 +
21 +## 📌 Status
22 +| Item | Value |
23 +|---|---|
24 +| Version | 1.0 (October 2026) |
25 +| Phases executed | 0–10, 13–15 (see `PROGRESS.md`); 11 (MLX port) not executed; 12 limited to meta-instantiation |
26 +| Decisive experiments | 5 adapter seeds × {sparse editor, global recurrence, dense writes} on a frozen synthetic backbone; 103M FineWeb-Edu pilot (one backbone seed) |
27 +| Code & data | `REPRODUCE.md`; synthetic suite regenerated from seeds; FineWeb-Edu subset documented, not redistributed |
28 +
29 +## 🗂 Repository layout
30 +`prisme/` reference implementation · `training/` stages A–D · `baselines/` global recurrence + reference routers ·
31 +`evaluation/` routing, latency, perplexity, locked driver, statistics · `data/` generators, processors, splits ·
32 +`tests/` invariants + cache equivalence · `scripts/` audits, counts, FLOPs, launchers, tables, figures, claim audit ·
33 +`protocol/` pre-registration, locked test manifest, deviations · `literature/` verified bibliography + novelty audit ·
34 +`experiments/runs/` manifests + metrics (checkpoints on the training nodes) · `results/` raw/analyzed/tables/figures ·
35 +`paper/` LaTeX + PDF · `reproducibility/` environment, cards, run index, audit · `PROGRESS.md`, `DECISIONS.md`, `FAILURES.md`.
36 +
37 +## 🚀 Quick start
38 +```bash
39 +uv venv --python 3.12 .venv && uv pip install -p .venv/bin/python -r requirements-lock.txt
40 +.venv/bin/python -m pytest tests -q
41 +.venv/bin/python scripts/count_params.py --all-configs --device meta
42 +scripts/reproduce_pilot.sh # miniature end-to-end pipeline on CPU (~1 min)
43 +.venv/bin/python scripts/make_tables.py && .venv/bin/python scripts/reproduce_figures.py
44 +```
added REPRODUCE.md +21 −0
@@ -0,0 +1,21 @@
1 +# REPRODUCE.md — WP31 PRISME
2 +
3 +All commands run from the repository root with the project venv (`uv venv --python 3.12 .venv && uv pip install -p .venv/bin/python -r requirements-lock.txt`).
4 +
5 +| Step | Command | Expected |
6 +|---|---|---|
7 +| Environment audit | `.venv/bin/python scripts/environment_audit.py --smoke` | `hardware/device_inventory.json`, CPU (and MPS/CUDA if present) forward/backward OK |
8 +| Unit + property + integration tests | `.venv/bin/python -m pytest tests -q` | 23 passed (15 mandated invariants, controller target-freedom, dense ablation, 5 cache-equivalence tests) |
9 +| Exact parameter counts | `.venv/bin/python scripts/count_params.py --all-configs --device meta` | backbone 99,502,848 / 446,744,064 / 1,149,339,648 (= proposal); workspace 2,631,173 / 10,505,221 / 21,655,813 |
10 +| Analytic ops table | `.venv/bin/python scripts/estimate_flops.py --config configs/prisme_459m.yaml --T 4096` | 1.296 / 1.666 / 1.787 / 2.219 / 5.633 ×10⁹ ops/token |
11 +| Synthetic data | `.venv/bin/python -c "import sys; sys.path.insert(0,'.'); from data.generators.synthetic import write_splits; print(write_splits('data/splits/synthetic_v1'))"` | 11 split files + manifest (sha256 as in `data/splits/synthetic_v1/manifest.json`) |
12 +| Web corpus (optional, ~10 min, network) | `.venv/bin/python data/processors/prepare_corpus.py` | 290M train / 2.9M val / 2.9M locked test tokens, tokenizer 32,768 |
13 +| Miniature end-to-end pipeline (CPU, ~minutes) | `scripts/reproduce_pilot.sh` | tiny backbone → editor bootstrap → labels → controller → routing JSON under `experiments/runs/` and `results/raw/` |
14 +| Dry runs of every trainer | `.venv/bin/python -m training.pretrain --config configs/experiments/pretrain_103m.yaml --dry-run` etc. | exit 0 |
15 +| Tables and figures | `.venv/bin/python scripts/make_tables.py && .venv/bin/python scripts/reproduce_figures.py --results results/raw/` | `paper/tables/*.tex`, `results/figures/*.pdf` |
16 +| Claim audit | `.venv/bin/python scripts/verify_manuscript_claims.py` | `AUDIT PASS` |
17 +| Manuscript | `cd paper && latexmk -pdf main.tex` | `paper/main.pdf` |
18 +
19 +Full-scale runs (Stage A 103M on an M3 Ultra ≈ 4 h; seed matrices on several nodes) are launched with
20 +`scripts/cluster_setup.sh <node>`, `scripts/launch_seed_matrix.sh`, `scripts/run_stage_cd.sh` and
21 +`scripts/launch_103m.sh`; their run ids are listed in `reproducibility/run_index.md`.
added configs/experiments/controller_mini.yaml +27 −0
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1 +name: controller_mini
2 +seed: 1
3 +mode: prisme
4 +model: from_checkpoint
5 +checkpoint: experiments/runs/mini_editor_seed01_20261030005118/checkpoint
6 +labels:
7 + train: experiments/runs/labels_mini_seed01_20261030005150/labels_train.pt
8 + calib: experiments/runs/labels_mini_seed01_20261030005150/labels_calib.pt
9 +train:
10 + steps: 60
11 + lr: 0.001
12 + weight_decay: 0.01
13 + alpha: 1.0
14 + beta: 0.5
15 + gamma: 0.1
16 + huber_delta: 0.1
17 + log_every: 20
18 + eval_every: 30
19 + seq_len: 320
20 +lambda_grid:
21 +- 0.0
22 +- 0.3
23 +kappa_grid:
24 +- 0.0
25 +sweep_batches: 2
26 +max_budget: 4
27 +selection_rule: max_loss_reduction_per_ops
added configs/experiments/labels_mini.yaml +25 −0
@@ -0,0 +1,25 @@
1 +name: labels_mini
2 +seed: 1
3 +mode: prisme
4 +model: from_checkpoint
5 +checkpoint: experiments/runs/mini_editor_seed01_20261030005118/checkpoint
6 +use_policy: false
7 +data:
8 + kind: synthetic
9 + tokenizer: char
10 + splits_dir: data/splits/synthetic_v1
11 + loss_span: answer
12 +collect:
13 + batch_size: 16
14 + seq_len: 320
15 + max_prefix: 2
16 + audit_every: 2
17 +partitions:
18 + train:
19 + split: ctrl_train
20 + n_batches: 12
21 + stream_seed: 11
22 + calib:
23 + split: calib
24 + n_batches: 4
25 + stream_seed: 12
added configs/experiments/mini_backbone.yaml +43 −0
@@ -0,0 +1,43 @@
1 +name: mini_backbone
2 +seed: 1
3 +mode: backbone_only
4 +model:
5 + name: mini
6 + vocab_size: 128
7 + d_model: 96
8 + n_layers: 2
9 + n_heads: 4
10 + n_kv_heads: 2
11 + ffn_width: 256
12 + max_seq_len: 320
13 + w: 32
14 + ffn_w: 96
15 + n_slots: 16
16 + n_editor_heads: 2
17 + r_max: 8
18 + readout_zero_init: false
19 + readout_init_std: 0.2
20 + ctrl_input: h
21 + ctrl_use_entropy: true
22 +init:
23 + freeze_backbone: false
24 +data:
25 + kind: synthetic
26 + tokenizer: char
27 + splits_dir: data/splits/synthetic_v1
28 + loss_span: all
29 + eval_splits:
30 + - dev_iid
31 +train:
32 + batch_size: 16
33 + seq_len: 320
34 + steps: 150
35 + lr: 0.001
36 + warmup: 10
37 + weight_decay: 0.1
38 + grad_clip: 1.0
39 + log_every: 50
40 + eval_every: 150
41 + eval_batches: 2
42 + ckpt_every: 150
43 +em_max_items: 32
added configs/experiments/mini_editor.yaml +61 −0
@@ -0,0 +1,61 @@
1 +name: mini_editor
2 +seed: 1
3 +mode: prisme
4 +model:
5 + name: mini
6 + vocab_size: 128
7 + d_model: 96
8 + n_layers: 2
9 + n_heads: 4
10 + n_kv_heads: 2
11 + ffn_width: 256
12 + max_seq_len: 320
13 + w: 32
14 + ffn_w: 96
15 + n_slots: 16
16 + n_editor_heads: 2
17 + r_max: 8
18 + readout_zero_init: false
19 + readout_init_std: 0.2
20 + ctrl_input: h
21 + ctrl_use_entropy: true
22 +init:
23 + backbone_checkpoint: experiments/runs/mini_backbone_seed01_20261030005114/checkpoint
24 + freeze_backbone: true
25 + auto_readout_scale: true
26 +data:
27 + kind: synthetic
28 + tokenizer: char
29 + splits_dir: data/splits/synthetic_v1
30 + loss_span: answer
31 + eval_splits:
32 + - dev_iid
33 +train:
34 + batch_size: 16
35 + seq_len: 320
36 + steps: 120
37 + lr: 0.001
38 + warmup: 10
39 + weight_decay: 0.1
40 + grad_clip: 1.0
41 + log_every: 50
42 + eval_every: 120
43 + eval_batches: 2
44 + ckpt_every: 120
45 +em_max_items: 32
46 +budgets:
47 +- 0
48 +- 1
49 +- 2
50 +- 4
51 +- 8
52 +budget_probs:
53 +- 0.2
54 +- 0.2
55 +- 0.2
56 +- 0.2
57 +- 0.2
58 +budget_per: position
59 +em_budgets:
60 +- 0
61 +- 2
added configs/experiments/routing_mini.yaml +24 −0
@@ -0,0 +1,24 @@
1 +seed: 1
2 +mode: prisme
3 +model: from_checkpoint
4 +checkpoint: experiments/runs/controller_mini_seed01_20261030005154/checkpoint
5 +data:
6 + kind: synthetic
7 + tokenizer: char
8 + splits_dir: data/splits/synthetic_v1
9 + loss_span: answer
10 + split: dev_iid
11 +batch_size: 16
12 +seq_len: 320
13 +n_batches: 2
14 +budgets:
15 +- 1
16 +- 2
17 +oracle_batches: 1
18 +two_step_batches: 1
19 +entropy_thresholds:
20 +- 1.0
21 +norm_thresholds:
22 +- 0.1
23 +lambda_grid:
24 +- 0.0
added experiments/logs/controller_mini.log +14 −0
@@ -0,0 +1,14 @@
1 +/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_controller.py:131: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.
2 +Consider using tensor.detach() first. (Triggered internally at /Users/runner/work/pytorch/pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:821.)
3 + run.log(step=step, loss=float(loss), gain=float(L["gain"]), harm=float(L["harm"]), nll=float(L["nll"]))
4 +step 0 loss 0.45115 gain 0.02663 harm 0.8158
5 +step 20 loss 0.13172 gain 0.00543 harm 0.5627
6 + calib: {'huber': 0.0175, 'sign_acc': 0.5467, 'harm_brier': 0.1334, 'harm_ece': 0.0955, 'n_labels': 1156, 'oracle_opportunity_per_state': 0.2508, 'regret_per_state': 0.1381, 'captured_fraction': 0.4494, 'top1_match': 0.0, 'n_audit_states': 124}
7 +step 40 loss 0.07597 gain 0.00382 harm 0.4947
8 +step 59 loss 0.05327 gain 0.00333 harm 0.4847
9 + calib: {'huber': 0.0162, 'sign_acc': 0.7344, 'harm_brier': 0.1264, 'harm_ece': 0.0872, 'n_labels': 1156, 'oracle_opportunity_per_state': 0.2508, 'regret_per_state': 0.1129, 'captured_fraction': 0.5501, 'top1_match': 0.0, 'n_audit_states': 124}
10 +restored controller from step 60 (calibration score -0.11286)
11 +sweep {'lambda': 0.0, 'kappa': 0.0, 'loss': 2.059080786738835, 'edits_per_token': 2.5673758865248226, 'extra_ops_ratio': 1.4467924402115193}
12 +sweep {'lambda': 0.3, 'kappa': 0.0, 'loss': 2.307212234388852, 'edits_per_token': 0.04964539007092199, 'extra_ops_ratio': 0.02571701665296622}
13 +selected {'lambda': 0.3, 'kappa': 0.0, 'loss': 2.307212234388852, 'edits_per_token': 0.04964539007092199, 'extra_ops_ratio': 0.02571701665296622, 'loss_reduction': 0.04320390850094169, 'reduction_per_ops': 1.6799735787377388}
14 +run controller_mini_seed01_20261030005154
added experiments/logs/labels_mini.log +8 −0
@@ -0,0 +1,8 @@
1 +/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_editor.py:57: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /Users/runner/work/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:214.)
2 + return torch.from_numpy(acts).to(device), torch.from_numpy(np.ascontiguousarray(b)).to(device)
3 +batch 0/12 r_pre=2 mean gain 0.02953 frac>0 0.615
4 +batch 10/12 r_pre=1 mean gain 0.02505 frac>0 0.578
5 +train {'file': 'experiments/runs/labels_mini_seed01_20261030005150/labels_train.pt', 'n_states': 1047, 'n_labels': 4188, 'mean_gain': 0.031750522553920746, 'frac_positive': 0.5690066814422607, 'frac_harm': 0.43099331855773926, 'seconds': 2.5, 'n_audit_batches': 6}
6 +batch 0/4 r_pre=2 mean gain 0.05232 frac>0 0.560
7 +calib {'file': 'experiments/runs/labels_mini_seed01_20261030005150/labels_calib.pt', 'n_states': 289, 'n_labels': 1156, 'mean_gain': 0.19328418374061584, 'frac_positive': 0.6349480748176575, 'frac_harm': 0.36505189538002014, 'seconds': 0.8, 'n_audit_batches': 2}
8 +run labels_mini_seed01_20261030005150
added experiments/logs/mini_backbone.log +9 −0
@@ -0,0 +1,9 @@
1 +step 0 loss 4.8830 lr 1.00e-04 gn 2.63 (0s)
2 +step 50 loss 2.5086 lr 8.31e-04 gn 0.98 (1s)
3 +step 100 loss 1.9095 lr 3.55e-04 gn 1.14 (2s)
4 +step 149 loss 1.5540 lr 1.00e-04 gn 1.46 (3s)
5 +/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_editor.py:57: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /Users/runner/work/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:214.)
6 + return torch.from_numpy(acts).to(device), torch.from_numpy(np.ascontiguousarray(b)).to(device)
7 + val loss by budget: {0: 1.661}
8 + exact match dev_iid budget 0: 0.0000 (n=32) by family {'bind': 0.0, 'arith': 0.0, 'fsm': 0.0, 'excl': 0.0, 'sched': 0.0}
9 +run mini_backbone_seed01_20261030005114 done
added experiments/logs/mini_editor.log +12 −0
@@ -0,0 +1,12 @@
1 +loaded backbone from experiments/runs/mini_backbone_seed01_20261030005114/checkpoint missing: 36
2 +readout_scale set to backbone residual rms = 0.120
3 +/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_editor.py:57: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /Users/runner/work/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:214.)
4 + return torch.from_numpy(acts).to(device), torch.from_numpy(np.ascontiguousarray(b)).to(device)
5 +step 0 loss 2.2980 lr 1.00e-04 gn 0.09 (0s)
6 +step 50 loss 2.0584 lr 7.37e-04 gn 0.12 (14s)
7 +step 100 loss 1.9985 lr 1.71e-04 gn 0.21 (25s)
8 +step 119 loss 2.2012 lr 1.00e-04 gn 0.21 (29s)
9 + val loss by budget: {0: 2.391, 1: 2.178, 2: 2.1457, 4: 2.0783, 8: 2.0136}
10 + exact match dev_iid budget 0: 0.0000 (n=32) by family {'bind': 0.0, 'arith': 0.0, 'fsm': 0.0, 'excl': 0.0, 'sched': 0.0}
11 + exact match dev_iid budget 2: 0.0000 (n=32) by family {'bind': 0.0, 'arith': 0.0, 'fsm': 0.0, 'excl': 0.0, 'sched': 0.0}
12 +run mini_editor_seed01_20261030005118 done
added experiments/logs/routing_mini.log +17 −0
@@ -0,0 +1,17 @@
1 +{"policy": "budget0", "loss": 2.3909947187051004, "edits_per_token": 0.0, "extra_ops_ratio": 0.0, "n_tokens": 174}
2 +/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_editor.py:57: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /Users/runner/work/pytorch/pytorch/torch/csrc/utils/tensor_numpy.cpp:214.)
3 + return torch.from_numpy(acts).to(device), torch.from_numpy(np.ascontiguousarray(b)).to(device)
4 +{"policy": "random_forced", "budget": 1, "loss": 2.177954575111126, "edits_per_token": 1.0, "extra_ops_ratio": 0.6340132088496767, "n_tokens": 174}
5 +{"policy": "uniform", "budget": 1, "loss": 2.187390908427622, "edits_per_token": 1.0, "extra_ops_ratio": 0.5180141843598465, "n_tokens": 174}
6 +{"policy": "fixed_pair", "budget": 1, "loss": 2.1671100484913794, "edits_per_token": 1.0, "extra_ops_ratio": 0.7239716190031205, "n_tokens": 174}
7 +{"policy": "random_forced", "budget": 2, "loss": 2.145720470910785, "edits_per_token": 2.0, "extra_ops_ratio": 1.2502715012122845, "n_tokens": 174}
8 +{"policy": "uniform", "budget": 2, "loss": 2.167156175635327, "edits_per_token": 2.0, "extra_ops_ratio": 1.036028368719693, "n_tokens": 174}
9 +{"policy": "fixed_pair", "budget": 2, "loss": 2.1463586215315194, "edits_per_token": 2.0, "extra_ops_ratio": 1.447943238006241, "n_tokens": 174}
10 +{"policy": "entropy", "threshold": 1.0, "budget": 2, "loss": 2.147084027871318, "edits_per_token": 1.632183908045977, "extra_ops_ratio": 0.9839830727412783, "n_tokens": 174}
11 +{"policy": "norm", "threshold": 0.1, "budget": 2, "loss": 2.149580155295887, "edits_per_token": 1.9885057471264367, "extra_ops_ratio": 1.194603536320829, "n_tokens": 174}
12 +{"policy": "controller_locked", "lam": 0.3, "kap": 0.0, "budget": 2, "loss": 2.3621927897135415, "edits_per_token": 0.06896551724137931, "extra_ops_ratio": 0.03572511535951461, "n_tokens": 174}
13 +{"policy": "controller", "lam": 0.0, "kap": 0.0, "budget": 2, "loss": 2.1407089233398438, "edits_per_token": 1.5344827586206897, "extra_ops_ratio": 0.8812912908093683, "n_tokens": 174}
14 +{"policy": "oracle1_greedy", "budget": 1, "diagnostic": true, "loss": 2.1457042020909927, "edits_per_token": 0.6470588235294118, "extra_ops_ratio": 0.4587601156795726, "n_tokens": 85}
15 +{"policy": "oracle1_greedy", "budget": 2, "diagnostic": true, "loss": 1.9766888786764707, "edits_per_token": 1.2470588235294118, "extra_ops_ratio": 0.854374605066636, "n_tokens": 85}
16 +{"policy": "oracle_two_step", "diagnostic": true, "one_step_gain": 0.25905925526338464, "two_step_gain": 0.429955561020795, "greedy_then_best_gain": 0.42805216172162225, "n_tokens": 85}
17 +wrote results/raw/routing_mini.json
added experiments/runs/controller_mini_seed01_20261030005154/checkpoint/checkpoint.json +49 −0
@@ -0,0 +1,49 @@
1 +{
2 + "config": {
3 + "name": "mini",
4 + "vocab_size": 128,
5 + "d_model": 96,
6 + "n_layers": 2,
7 + "n_heads": 4,
8 + "n_kv_heads": 2,
9 + "ffn_width": 256,
10 + "max_seq_len": 320,
11 + "rope_theta": 10000.0,
12 + "norm_eps": 1e-05,
13 + "tie_embeddings": true,
14 + "init_std": 0.02,
15 + "use_workspace": true,
16 + "w": 32,
17 + "ffn_w": 96,
18 + "n_slots": 16,
19 + "n_editor_blocks": 2,
20 + "n_editor_heads": 2,
21 + "r_max": 8,
22 + "eta_max": 0.25,
23 + "clip_c": 1.0,
24 + "clip_eps": 1e-06,
25 + "readout_zero_init": false,
26 + "readout_init_std": 0.2,
27 + "readout_scale": 0.11967716366052628,
28 + "controller_hidden": 0,
29 + "ctrl_input": "h",
30 + "ctrl_use_entropy": true,
31 + "lambda_cost": 0.3,
32 + "kappa_risk": 0.0,
33 + "dense_writes": false
34 + },
35 + "saved_at": "2026-10-30T00:51:57-0400",
36 + "run_id": "controller_mini_seed01_20261030005154",
37 + "parent": "experiments/runs/mini_editor_seed01_20261030005118/checkpoint",
38 + "selected": {
39 + "lambda": 0.3,
40 + "kappa": 0.0,
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added experiments/runs/controller_mini_seed01_20261030005154/config.yaml +60 −0
@@ -0,0 +1,60 @@
1 +experiment:
2 + name: controller_mini
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/mini_editor_seed01_20261030005118/checkpoint
7 + labels:
8 + train: experiments/runs/labels_mini_seed01_20261030005150/labels_train.pt
9 + calib: experiments/runs/labels_mini_seed01_20261030005150/labels_calib.pt
10 + train:
11 + steps: 60
12 + lr: 0.001
13 + weight_decay: 0.01
14 + alpha: 1.0
15 + beta: 0.5
16 + gamma: 0.1
17 + huber_delta: 0.1
18 + log_every: 20
19 + eval_every: 30
20 + seq_len: 320
21 + lambda_grid:
22 + - 0.0
23 + - 0.3
24 + kappa_grid:
25 + - 0.0
26 + sweep_batches: 2
27 + max_budget: 4
28 + selection_rule: max_loss_reduction_per_ops
29 +model:
30 + name: mini
31 + vocab_size: 128
32 + d_model: 96
33 + n_layers: 2
34 + n_heads: 4
35 + n_kv_heads: 2
36 + ffn_width: 256
37 + max_seq_len: 320
38 + rope_theta: 10000.0
39 + norm_eps: 1.0e-05
40 + tie_embeddings: true
41 + init_std: 0.02
42 + use_workspace: true
43 + w: 32
44 + ffn_w: 96
45 + n_slots: 16
46 + n_editor_blocks: 2
47 + n_editor_heads: 2
48 + r_max: 8
49 + eta_max: 0.25
50 + clip_c: 1.0
51 + clip_eps: 1.0e-06
52 + readout_zero_init: false
53 + readout_init_std: 0.2
54 + readout_scale: 0.11967716366052628
55 + controller_hidden: 0
56 + ctrl_input: h
57 + ctrl_use_entropy: true
58 + lambda_cost: 0.0
59 + kappa_risk: 0.0
60 + dense_writes: false
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1 +{
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added experiments/runs/controller_mini_seed01_20261030005154/metrics.jsonl +7 −0
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added experiments/runs/controller_v2s2_seed01_20261030004717/checkpoint/checkpoint.json +49 −0
@@ -0,0 +1,49 @@
1 +{
2 + "config": {
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5 + "d_model": 256,
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35 + "saved_at": "2026-10-30T00:50:30-0400",
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added experiments/runs/controller_v2s2_seed01_20261030004717/config.yaml +67 −0
@@ -0,0 +1,67 @@
1 +experiment:
2 + name: controller_v2s2
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed02_20261030001749/checkpoint
7 + labels:
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9 + calib: experiments/runs/labels_v2s2_seed01_20261030004543/labels_calib.pt
10 + train:
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12 + lr: 0.001
13 + weight_decay: 0.01
14 + alpha: 1.0
15 + beta: 0.5
16 + gamma: 0.1
17 + huber_delta: 0.1
18 + log_every: 100
19 + eval_every: 250
20 + seq_len: 320
21 + lambda_grid:
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24 + - 0.03
25 + - 0.1
26 + - 0.3
27 + kappa_grid:
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29 + - 0.5
30 + sweep_batches: 10
31 + max_budget: 8
32 + selection_rule: max_loss_reduction_per_ops
33 + model_overrides:
34 + ctrl_input: h
35 + ctrl_use_entropy: true
36 +model:
37 + name: tiny_p4v2
38 + vocab_size: 128
39 + d_model: 256
40 + n_layers: 6
41 + n_heads: 8
42 + n_kv_heads: 4
43 + ffn_width: 768
44 + max_seq_len: 320
45 + rope_theta: 10000.0
46 + norm_eps: 1.0e-05
47 + tie_embeddings: true
48 + init_std: 0.02
49 + use_workspace: true
50 + w: 64
51 + ffn_w: 192
52 + n_slots: 16
53 + n_editor_blocks: 2
54 + n_editor_heads: 2
55 + r_max: 8
56 + eta_max: 0.25
57 + clip_c: 1.0
58 + clip_eps: 1.0e-06
59 + readout_zero_init: false
60 + readout_init_std: 0.2
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62 + controller_hidden: 0
63 + ctrl_input: h
64 + ctrl_use_entropy: true
65 + lambda_cost: 0.0
66 + kappa_risk: 0.0
67 + dense_writes: false
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added experiments/runs/controller_v2s2_seed01_20261030004717/metrics.jsonl +30 −0
@@ -0,0 +1,30 @@
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added experiments/runs/controller_v2s3_seed01_20261030004720/checkpoint/checkpoint.json +49 −0
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1 +{
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added experiments/runs/controller_v2s3_seed01_20261030004720/config.yaml +67 −0
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1 +experiment:
2 + name: controller_v2s3
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed03_20261030001749/checkpoint
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9 + calib: experiments/runs/labels_v2s3_seed01_20261030004544/labels_calib.pt
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12 + lr: 0.001
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added experiments/runs/controller_v2s4_seed01_20261030004719/config.yaml +67 −0
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1 +experiment:
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3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed04_20261030001749/checkpoint
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added experiments/runs/controller_v2s5_seed01_20261030004719/checkpoint/checkpoint.json +49 −0
@@ -0,0 +1,49 @@
1 +{
2 + "config": {
3 + "name": "tiny_p4v2",
4 + "vocab_size": 128,
5 + "d_model": 256,
6 + "n_layers": 6,
7 + "n_heads": 8,
8 + "n_kv_heads": 4,
9 + "ffn_width": 768,
10 + "max_seq_len": 320,
11 + "rope_theta": 10000.0,
12 + "norm_eps": 1e-05,
13 + "tie_embeddings": true,
14 + "init_std": 0.02,
15 + "use_workspace": true,
16 + "w": 64,
17 + "ffn_w": 192,
18 + "n_slots": 16,
19 + "n_editor_blocks": 2,
20 + "n_editor_heads": 2,
21 + "r_max": 8,
22 + "eta_max": 0.25,
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25 + "readout_zero_init": false,
26 + "readout_init_std": 0.2,
27 + "readout_scale": 39.13882827758789,
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30 + "ctrl_use_entropy": true,
31 + "lambda_cost": 0.3,
32 + "kappa_risk": 0.5,
33 + "dense_writes": false
34 + },
35 + "saved_at": "2026-10-30T00:50:54-0400",
36 + "run_id": "controller_v2s5_seed01_20261030004719",
37 + "parent": "experiments/runs/p4v2_prisme_seed05_20261030001839/checkpoint",
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48 + "weights_sha256": "958788a3ebabeed125e43ca842c6e0c0a26b2eb9741f65b990ede4325bfbceb9"
49 +}
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added experiments/runs/controller_v2s5_seed01_20261030004719/config.yaml +67 −0
@@ -0,0 +1,67 @@
1 +experiment:
2 + name: controller_v2s5
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed05_20261030001839/checkpoint
7 + labels:
8 + train: experiments/runs/labels_v2s5_seed01_20261030004544/labels_train.pt
9 + calib: experiments/runs/labels_v2s5_seed01_20261030004544/labels_calib.pt
10 + train:
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12 + lr: 0.001
13 + weight_decay: 0.01
14 + alpha: 1.0
15 + beta: 0.5
16 + gamma: 0.1
17 + huber_delta: 0.1
18 + log_every: 100
19 + eval_every: 250
20 + seq_len: 320
21 + lambda_grid:
22 + - 0.0
23 + - 0.01
24 + - 0.03
25 + - 0.1
26 + - 0.3
27 + kappa_grid:
28 + - 0.0
29 + - 0.5
30 + sweep_batches: 10
31 + max_budget: 8
32 + selection_rule: max_loss_reduction_per_ops
33 + model_overrides:
34 + ctrl_input: h
35 + ctrl_use_entropy: true
36 +model:
37 + name: tiny_p4v2
38 + vocab_size: 128
39 + d_model: 256
40 + n_layers: 6
41 + n_heads: 8
42 + n_kv_heads: 4
43 + ffn_width: 768
44 + max_seq_len: 320
45 + rope_theta: 10000.0
46 + norm_eps: 1.0e-05
47 + tie_embeddings: true
48 + init_std: 0.02
49 + use_workspace: true
50 + w: 64
51 + ffn_w: 192
52 + n_slots: 16
53 + n_editor_blocks: 2
54 + n_editor_heads: 2
55 + r_max: 8
56 + eta_max: 0.25
57 + clip_c: 1.0
58 + clip_eps: 1.0e-06
59 + readout_zero_init: false
60 + readout_init_std: 0.2
61 + readout_scale: 39.13882827758789
62 + controller_hidden: 0
63 + ctrl_input: h
64 + ctrl_use_entropy: true
65 + lambda_cost: 0.0
66 + kappa_risk: 0.0
67 + dense_writes: false
added experiments/runs/controller_v2s5_seed01_20261030004719/manifest.json +151 −0
@@ -0,0 +1,151 @@
1 +{
2 + "run_id": "controller_v2s5_seed01_20261030004719",
3 + "name": "controller_v2s5",
4 + "seed": 1,
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9 + "python": "3.12.13",
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11 + "command": "/Users/simon-pierreboucher/wp31/prisme-slm/training/train_controller.py --config configs/experiments/controller_v2s5.yaml --device mps",
12 + "state": "completed",
13 + "wall_seconds": 214.63,
14 + "device": "mps",
15 + "checkpoint_parent": "experiments/runs/p4v2_prisme_seed05_20261030001839/checkpoint",
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17 + "labels_calib": "experiments/runs/labels_v2s5_seed01_20261030004544/labels_calib.pt",
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150 + "selection_rule": "max_loss_reduction_per_ops"
151 +}
\ No newline at end of file
added experiments/runs/controller_v2s5_seed01_20261030004719/metrics.jsonl +30 −0
@@ -0,0 +1,30 @@
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added experiments/runs/labels_mini_seed01_20261030005150/config.yaml +58 −0
@@ -0,0 +1,58 @@
1 +experiment:
2 + name: labels_mini
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/mini_editor_seed01_20261030005118/checkpoint
7 + use_policy: false
8 + data:
9 + kind: synthetic
10 + tokenizer: char
11 + splits_dir: data/splits/synthetic_v1
12 + loss_span: answer
13 + collect:
14 + batch_size: 16
15 + seq_len: 320
16 + max_prefix: 2
17 + audit_every: 2
18 + partitions:
19 + train:
20 + split: ctrl_train
21 + n_batches: 12
22 + stream_seed: 11
23 + calib:
24 + split: calib
25 + n_batches: 4
26 + stream_seed: 12
27 +model:
28 + name: mini
29 + vocab_size: 128
30 + d_model: 96
31 + n_layers: 2
32 + n_heads: 4
33 + n_kv_heads: 2
34 + ffn_width: 256
35 + max_seq_len: 320
36 + rope_theta: 10000.0
37 + norm_eps: 1.0e-05
38 + tie_embeddings: true
39 + init_std: 0.02
40 + use_workspace: true
41 + w: 32
42 + ffn_w: 96
43 + n_slots: 16
44 + n_editor_blocks: 2
45 + n_editor_heads: 2
46 + r_max: 8
47 + eta_max: 0.25
48 + clip_c: 1.0
49 + clip_eps: 1.0e-06
50 + readout_zero_init: false
51 + readout_init_std: 0.2
52 + readout_scale: 0.11967716366052628
53 + controller_hidden: 0
54 + ctrl_input: h
55 + ctrl_use_entropy: true
56 + lambda_cost: 0.0
57 + kappa_risk: 0.0
58 + dense_writes: false
added experiments/runs/labels_mini_seed01_20261030005150/manifest.json +40 −0
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1 +{
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\ No newline at end of file
added experiments/runs/labels_mini_seed01_20261030005150/metrics.jsonl +0 −0
added experiments/runs/labels_v2s2_seed01_20261030004543/config.yaml +58 −0
@@ -0,0 +1,58 @@
1 +experiment:
2 + name: labels_v2s2
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed02_20261030001749/checkpoint
7 + use_policy: false
8 + data:
9 + kind: synthetic
10 + tokenizer: char
11 + splits_dir: data/splits/synthetic_v1
12 + loss_span: answer
13 + collect:
14 + batch_size: 32
15 + seq_len: 320
16 + max_prefix: 3
17 + audit_every: 4
18 + partitions:
19 + train:
20 + split: ctrl_train
21 + n_batches: 300
22 + stream_seed: 11
23 + calib:
24 + split: calib
25 + n_batches: 40
26 + stream_seed: 12
27 +model:
28 + name: tiny_p4v2
29 + vocab_size: 128
30 + d_model: 256
31 + n_layers: 6
32 + n_heads: 8
33 + n_kv_heads: 4
34 + ffn_width: 768
35 + max_seq_len: 320
36 + rope_theta: 10000.0
37 + norm_eps: 1.0e-05
38 + tie_embeddings: true
39 + init_std: 0.02
40 + use_workspace: true
41 + w: 64
42 + ffn_w: 192
43 + n_slots: 16
44 + n_editor_blocks: 2
45 + n_editor_heads: 2
46 + r_max: 8
47 + eta_max: 0.25
48 + clip_c: 1.0
49 + clip_eps: 1.0e-06
50 + readout_zero_init: false
51 + readout_init_std: 0.2
52 + readout_scale: 38.51451873779297
53 + controller_hidden: 0
54 + ctrl_input: z
55 + ctrl_use_entropy: false
56 + lambda_cost: 0.0
57 + kappa_risk: 0.0
58 + dense_writes: false
added experiments/runs/labels_v2s2_seed01_20261030004543/manifest.json +40 −0
@@ -0,0 +1,40 @@
1 +{
2 + "run_id": "labels_v2s2_seed01_20261030004543",
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5 + "code_commit": "unknown",
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\ No newline at end of file
added experiments/runs/labels_v2s2_seed01_20261030004543/metrics.jsonl +0 −0
added experiments/runs/labels_v2s3_seed01_20261030004544/config.yaml +58 −0
@@ -0,0 +1,58 @@
1 +experiment:
2 + name: labels_v2s3
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed03_20261030001749/checkpoint
7 + use_policy: false
8 + data:
9 + kind: synthetic
10 + tokenizer: char
11 + splits_dir: data/splits/synthetic_v1
12 + loss_span: answer
13 + collect:
14 + batch_size: 32
15 + seq_len: 320
16 + max_prefix: 3
17 + audit_every: 4
18 + partitions:
19 + train:
20 + split: ctrl_train
21 + n_batches: 300
22 + stream_seed: 11
23 + calib:
24 + split: calib
25 + n_batches: 40
26 + stream_seed: 12
27 +model:
28 + name: tiny_p4v2
29 + vocab_size: 128
30 + d_model: 256
31 + n_layers: 6
32 + n_heads: 8
33 + n_kv_heads: 4
34 + ffn_width: 768
35 + max_seq_len: 320
36 + rope_theta: 10000.0
37 + norm_eps: 1.0e-05
38 + tie_embeddings: true
39 + init_std: 0.02
40 + use_workspace: true
41 + w: 64
42 + ffn_w: 192
43 + n_slots: 16
44 + n_editor_blocks: 2
45 + n_editor_heads: 2
46 + r_max: 8
47 + eta_max: 0.25
48 + clip_c: 1.0
49 + clip_eps: 1.0e-06
50 + readout_zero_init: false
51 + readout_init_std: 0.2
52 + readout_scale: 38.98434829711914
53 + controller_hidden: 0
54 + ctrl_input: z
55 + ctrl_use_entropy: false
56 + lambda_cost: 0.0
57 + kappa_risk: 0.0
58 + dense_writes: false
added experiments/runs/labels_v2s3_seed01_20261030004544/manifest.json +40 −0
@@ -0,0 +1,40 @@
1 +{
2 + "run_id": "labels_v2s3_seed01_20261030004544",
3 + "name": "labels_v2s3",
4 + "seed": 1,
5 + "code_commit": "unknown",
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40 +}
\ No newline at end of file
added experiments/runs/labels_v2s3_seed01_20261030004544/metrics.jsonl +0 −0
added experiments/runs/labels_v2s4_seed01_20261030004544/config.yaml +58 −0
@@ -0,0 +1,58 @@
1 +experiment:
2 + name: labels_v2s4
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed04_20261030001749/checkpoint
7 + use_policy: false
8 + data:
9 + kind: synthetic
10 + tokenizer: char
11 + splits_dir: data/splits/synthetic_v1
12 + loss_span: answer
13 + collect:
14 + batch_size: 32
15 + seq_len: 320
16 + max_prefix: 3
17 + audit_every: 4
18 + partitions:
19 + train:
20 + split: ctrl_train
21 + n_batches: 300
22 + stream_seed: 11
23 + calib:
24 + split: calib
25 + n_batches: 40
26 + stream_seed: 12
27 +model:
28 + name: tiny_p4v2
29 + vocab_size: 128
30 + d_model: 256
31 + n_layers: 6
32 + n_heads: 8
33 + n_kv_heads: 4
34 + ffn_width: 768
35 + max_seq_len: 320
36 + rope_theta: 10000.0
37 + norm_eps: 1.0e-05
38 + tie_embeddings: true
39 + init_std: 0.02
40 + use_workspace: true
41 + w: 64
42 + ffn_w: 192
43 + n_slots: 16
44 + n_editor_blocks: 2
45 + n_editor_heads: 2
46 + r_max: 8
47 + eta_max: 0.25
48 + clip_c: 1.0
49 + clip_eps: 1.0e-06
50 + readout_zero_init: false
51 + readout_init_std: 0.2
52 + readout_scale: 39.0492057800293
53 + controller_hidden: 0
54 + ctrl_input: z
55 + ctrl_use_entropy: false
56 + lambda_cost: 0.0
57 + kappa_risk: 0.0
58 + dense_writes: false
added experiments/runs/labels_v2s4_seed01_20261030004544/manifest.json +40 −0
@@ -0,0 +1,40 @@
1 +{
2 + "run_id": "labels_v2s4_seed01_20261030004544",
3 + "name": "labels_v2s4",
4 + "seed": 1,
5 + "code_commit": "unknown",
6 + "config_sha256": "b6557feb819ee58db9f5b8c62d51c4ca63831687724cb1bae9c15bb064103682",
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14 + "device": "mps",
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16 + "checkpoint_sha256": "65045ef16316b83087e70da42e8febdbff86fe691e7547db981b3b3e65988cba",
17 + "finished_at": "2026-10-30T00:47:18-0400",
18 + "partitions": {
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38 + }
39 + }
40 +}
\ No newline at end of file
added experiments/runs/labels_v2s4_seed01_20261030004544/metrics.jsonl +0 −0
added experiments/runs/labels_v2s5_seed01_20261030004544/config.yaml +58 −0
@@ -0,0 +1,58 @@
1 +experiment:
2 + name: labels_v2s5
3 + seed: 1
4 + mode: prisme
5 + model: from_checkpoint
6 + checkpoint: experiments/runs/p4v2_prisme_seed05_20261030001839/checkpoint
7 + use_policy: false
8 + data:
9 + kind: synthetic
10 + tokenizer: char
11 + splits_dir: data/splits/synthetic_v1
12 + loss_span: answer
13 + collect:
14 + batch_size: 32
15 + seq_len: 320
16 + max_prefix: 3
17 + audit_every: 4
18 + partitions:
19 + train:
20 + split: ctrl_train
21 + n_batches: 300
22 + stream_seed: 11
23 + calib:
24 + split: calib
25 + n_batches: 40
26 + stream_seed: 12
27 +model:
28 + name: tiny_p4v2
29 + vocab_size: 128
30 + d_model: 256
31 + n_layers: 6
32 + n_heads: 8
33 + n_kv_heads: 4
34 + ffn_width: 768
35 + max_seq_len: 320
36 + rope_theta: 10000.0
37 + norm_eps: 1.0e-05
38 + tie_embeddings: true
39 + init_std: 0.02
40 + use_workspace: true
41 + w: 64
42 + ffn_w: 192
43 + n_slots: 16
44 + n_editor_blocks: 2
45 + n_editor_heads: 2
46 + r_max: 8
47 + eta_max: 0.25
48 + clip_c: 1.0
49 + clip_eps: 1.0e-06
50 + readout_zero_init: false
51 + readout_init_std: 0.2
52 + readout_scale: 39.13882827758789
53 + controller_hidden: 0
54 + ctrl_input: z
55 + ctrl_use_entropy: false
56 + lambda_cost: 0.0
57 + kappa_risk: 0.0
58 + dense_writes: false
added experiments/runs/labels_v2s5_seed01_20261030004544/manifest.json +40 −0
@@ -0,0 +1,40 @@
1 +{
2 + "run_id": "labels_v2s5_seed01_20261030004544",
3 + "name": "labels_v2s5",
4 + "seed": 1,
5 + "code_commit": "unknown",
6 + "config_sha256": "93af5b8ca14be2272e711ad3fcb295fb9f459d31ac452a8c4c41cb4ea00ed0a7",
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12 + "state": "completed",
13 + "wall_seconds": 94.68,
14 + "device": "mps",
15 + "checkpoint_parent": "experiments/runs/p4v2_prisme_seed05_20261030001839/checkpoint",
16 + "checkpoint_sha256": "1a62de8a37bc40aa10e76c5a32052b4ad3638b4527a737a8b4d5c5d0460b44c2",
17 + "finished_at": "2026-10-30T00:47:19-0400",
18 + "partitions": {
19 + "train": {
20 + "file": "experiments/runs/labels_v2s5_seed01_20261030004544/labels_train.pt",
21 + "n_states": 49965,
22 + "n_labels": 199860,
23 + "mean_gain": 0.005353890359401703,
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25 + "frac_harm": 0.5638096928596497,
26 + "seconds": 83.3,
27 + "n_audit_batches": 75
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30 + "file": "experiments/runs/labels_v2s5_seed01_20261030004544/labels_calib.pt",
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36 + "seconds": 11.3,
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38 + }
39 + }
40 +}
\ No newline at end of file
added experiments/runs/labels_v2s5_seed01_20261030004544/metrics.jsonl +0 −0
added experiments/runs/mini_backbone_seed01_20261030005114/checkpoint/checkpoint.json +39 −0
@@ -0,0 +1,39 @@
1 +{
2 + "config": {
3 + "name": "mini",
4 + "vocab_size": 128,
5 + "d_model": 96,
6 + "n_layers": 2,
7 + "n_heads": 4,
8 + "n_kv_heads": 2,
9 + "ffn_width": 256,
10 + "max_seq_len": 320,
11 + "rope_theta": 10000.0,
12 + "norm_eps": 1e-05,
13 + "tie_embeddings": true,
14 + "init_std": 0.02,
15 + "use_workspace": false,
16 + "w": 32,
17 + "ffn_w": 96,
18 + "n_slots": 16,
19 + "n_editor_blocks": 2,
20 + "n_editor_heads": 2,
21 + "r_max": 8,
22 + "eta_max": 0.25,
23 + "clip_c": 1.0,
24 + "clip_eps": 1e-06,
25 + "readout_zero_init": false,
26 + "readout_init_std": 0.2,
27 + "readout_scale": 1.0,
28 + "controller_hidden": 0,
29 + "ctrl_input": "h",
30 + "ctrl_use_entropy": true,
31 + "lambda_cost": 0.0,
32 + "kappa_risk": 0.0,
33 + "dense_writes": false
34 + },
35 + "saved_at": "2026-10-30T00:51:17-0400",
36 + "step": 150,
37 + "run_id": "mini_backbone_seed01_20261030005114",
38 + "weights_sha256": "fff6ca1bd1170d08423861d53f6e4c270ed2c20a4359ccfe7b8eb78e091d694b"
39 +}
\ No newline at end of file
added experiments/runs/mini_backbone_seed01_20261030005114/config.yaml +88 −0
@@ -0,0 +1,88 @@
1 +experiment:
2 + name: mini_backbone
3 + seed: 1
4 + mode: backbone_only
5 + model:
6 + name: mini
7 + vocab_size: 128
8 + d_model: 96
9 + n_layers: 2
10 + n_heads: 4
11 + n_kv_heads: 2
12 + ffn_width: 256
13 + max_seq_len: 320
14 + w: 32
15 + ffn_w: 96
16 + n_slots: 16
17 + n_editor_heads: 2
18 + r_max: 8
19 + readout_zero_init: false
20 + readout_init_std: 0.2
21 + ctrl_input: h
22 + ctrl_use_entropy: true
23 + init:
24 + freeze_backbone: false
25 + data:
26 + kind: synthetic
27 + tokenizer: char
28 + splits_dir: data/splits/synthetic_v1
29 + loss_span: all
30 + eval_splits:
31 + - dev_iid
32 + train:
33 + batch_size: 16
34 + seq_len: 320
35 + steps: 150
36 + lr: 0.001
37 + warmup: 10
38 + weight_decay: 0.1
39 + grad_clip: 1.0
40 + log_every: 50
41 + eval_every: 150
42 + eval_batches: 2
43 + ckpt_every: 150
44 + em_max_items: 32
45 +model:
46 + name: mini
47 + vocab_size: 128
48 + d_model: 96
49 + n_layers: 2
50 + n_heads: 4
51 + n_kv_heads: 2
52 + ffn_width: 256
53 + max_seq_len: 320
54 + rope_theta: 10000.0
55 + norm_eps: 1.0e-05
56 + tie_embeddings: true
57 + init_std: 0.02
58 + use_workspace: false
59 + w: 32
60 + ffn_w: 96
61 + n_slots: 16
62 + n_editor_blocks: 2
63 + n_editor_heads: 2
64 + r_max: 8
65 + eta_max: 0.25
66 + clip_c: 1.0
67 + clip_eps: 1.0e-06
68 + readout_zero_init: false
69 + readout_init_std: 0.2
70 + readout_scale: 1.0
71 + controller_hidden: 0
72 + ctrl_input: h
73 + ctrl_use_entropy: true
74 + lambda_cost: 0.0
75 + kappa_risk: 0.0
76 + dense_writes: false
77 +resolved_train:
78 + batch_size: 16
79 + seq_len: 320
80 + steps: 150
81 + lr: 0.001
82 + warmup: 10
83 + weight_decay: 0.1
84 + grad_clip: 1.0
85 + log_every: 50
86 + eval_every: 150
87 + eval_batches: 2
88 + ckpt_every: 150
added experiments/runs/mini_backbone_seed01_20261030005114/em_dev_iid_b0.json +1 −0
@@ -0,0 +1 @@
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added experiments/runs/mini_backbone_seed01_20261030005114/manifest.json +42 −0
@@ -0,0 +1,42 @@
1 +{
2 + "run_id": "mini_backbone_seed01_20261030005114",
3 + "name": "mini_backbone",
4 + "seed": 1,
5 + "code_commit": "ede6705574e7140bdc16eff11931815070b471d1",
6 + "config_sha256": "0e8e51f08d94ef726dd36738b68101b90d7f73ed4e94a0b052106b0a6194c9df",
7 + "started_at": "2026-10-30T00:51:14-0400",
8 + "host": "MacBookPro-de-simon-pierre-2.local",
9 + "python": "3.12.13",
10 + "torch": "2.15.0",
11 + "command": "/Users/simon-pierreboucher/Desktop/Recherche-UQO/ia/prisme-slm/training/train_editor.py --config configs/experiments/mini_backbone.yaml --device cpu --run-root experiments/runs",
12 + "state": "completed",
13 + "wall_seconds": 3.6,
14 + "device": "cpu",
15 + "backend": "cpu",
16 + "precision": "fp32",
17 + "n_params": 215520,
18 + "n_trainable": 215520,
19 + "checkpoint_parent": null,
20 + "mode": "backbone_only",
21 + "freeze_backbone": false,
22 + "finished_at": "2026-10-30T00:51:17-0400",
23 + "train_tokens": 279708,
24 + "peak_memory_bytes": 674185216,
25 + "final_exact_match": {
26 + "dev_iid": {
27 + "0": {
28 + "accuracy": 0.0,
29 + "n": 32,
30 + "by_family": {
31 + "bind": 0.0,
32 + "arith": 0.0,
33 + "fsm": 0.0,
34 + "excl": 0.0,
35 + "sched": 0.0
36 + },
37 + "edits_per_token": 0.0
38 + }
39 + }
40 + },
41 + "metrics_file": "experiments/runs/mini_backbone_seed01_20261030005114/metrics.jsonl"
42 +}
\ No newline at end of file
added experiments/runs/mini_backbone_seed01_20261030005114/metrics.jsonl +6 −0
@@ -0,0 +1,6 @@
1 +{"step": 0, "loss": 4.882978916168213, "lr": 0.0001, "grad_norm": 2.631913661956787, "tokens": 1708, "tok_per_s": 12605.103772273553, "t": 0.71}
2 +{"step": 50, "loss": 2.508582353591919, "lr": 0.0008305704108364302, "grad_norm": 0.9815729856491089, "tokens": 94447, "tok_per_s": 88797.80277866195, "t": 1.64}
3 +{"step": 100, "loss": 1.909483790397644, "lr": 0.0003547523173970989, "grad_norm": 1.143792748451233, "tokens": 187214, "tok_per_s": 95544.55275035149, "t": 2.54}
4 +{"step": 149, "loss": 1.5540279150009155, "lr": 0.00010011329427587207, "grad_norm": 1.4631657600402832, "tokens": 279708, "tok_per_s": 96143.08211684186, "t": 3.49}
5 +{"step": 149, "val_loss_b0": 1.6609648618492778, "t": 3.5}
6 +{"step": 149, "checkpoint_sha256": "fff6ca1bd1170d08423861d53f6e4c270ed2c20a4359ccfe7b8eb78e091d694b", "t": 3.51}
added experiments/runs/mini_editor_seed01_20261030005118/checkpoint/checkpoint.json +39 −0
@@ -0,0 +1,39 @@
1 +{
2 + "config": {
3 + "name": "mini",
4 + "vocab_size": 128,
5 + "d_model": 96,
6 + "n_layers": 2,
7 + "n_heads": 4,
8 + "n_kv_heads": 2,
9 + "ffn_width": 256,
10 + "max_seq_len": 320,
11 + "rope_theta": 10000.0,
12 + "norm_eps": 1e-05,
13 + "tie_embeddings": true,
14 + "init_std": 0.02,
15 + "use_workspace": true,
16 + "w": 32,
17 + "ffn_w": 96,
18 + "n_slots": 16,
19 + "n_editor_blocks": 2,
20 + "n_editor_heads": 2,
21 + "r_max": 8,
22 + "eta_max": 0.25,
23 + "clip_c": 1.0,
24 + "clip_eps": 1e-06,
25 + "readout_zero_init": false,
26 + "readout_init_std": 0.2,
27 + "readout_scale": 0.11967716366052628,
28 + "controller_hidden": 0,
29 + "ctrl_input": "h",
30 + "ctrl_use_entropy": true,
31 + "lambda_cost": 0.0,
32 + "kappa_risk": 0.0,
33 + "dense_writes": false
34 + },
35 + "saved_at": "2026-10-30T00:51:48-0400",
36 + "step": 120,
37 + "run_id": "mini_editor_seed01_20261030005118",
38 + "weights_sha256": "cd59f80c64ff29b3a0baf145c64778a34c9f6080457eded4e4f6cdda3649aeb2"
39 +}
\ No newline at end of file
added experiments/runs/mini_editor_seed01_20261030005118/config.yaml +106 −0
@@ -0,0 +1,106 @@
1 +experiment:
2 + name: mini_editor
3 + seed: 1
4 + mode: prisme
5 + model:
6 + name: mini
7 + vocab_size: 128
8 + d_model: 96
9 + n_layers: 2
10 + n_heads: 4
11 + n_kv_heads: 2
12 + ffn_width: 256
13 + max_seq_len: 320
14 + w: 32
15 + ffn_w: 96
16 + n_slots: 16
17 + n_editor_heads: 2
18 + r_max: 8
19 + readout_zero_init: false
20 + readout_init_std: 0.2
21 + ctrl_input: h
22 + ctrl_use_entropy: true
23 + init:
24 + backbone_checkpoint: experiments/runs/mini_backbone_seed01_20261030005114/checkpoint
25 + freeze_backbone: true
26 + auto_readout_scale: true
27 + data:
28 + kind: synthetic
29 + tokenizer: char
30 + splits_dir: data/splits/synthetic_v1
31 + loss_span: answer
32 + eval_splits:
33 + - dev_iid
34 + train:
35 + batch_size: 16
36 + seq_len: 320
37 + steps: 120
38 + lr: 0.001
39 + warmup: 10
40 + weight_decay: 0.1
41 + grad_clip: 1.0
42 + log_every: 50
43 + eval_every: 120
44 + eval_batches: 2
45 + ckpt_every: 120
46 + em_max_items: 32
47 + budgets:
48 + - 0
49 + - 1
50 + - 2
51 + - 4
52 + - 8
53 + budget_probs:
54 + - 0.2
55 + - 0.2
56 + - 0.2
57 + - 0.2
58 + - 0.2
59 + budget_per: position
60 + em_budgets:
61 + - 0
62 + - 2
63 +model:
64 + name: mini
65 + vocab_size: 128
66 + d_model: 96
67 + n_layers: 2
68 + n_heads: 4
69 + n_kv_heads: 2
70 + ffn_width: 256
71 + max_seq_len: 320
72 + rope_theta: 10000.0
73 + norm_eps: 1.0e-05
74 + tie_embeddings: true
75 + init_std: 0.02
76 + use_workspace: true
77 + w: 32
78 + ffn_w: 96
79 + n_slots: 16
80 + n_editor_blocks: 2
81 + n_editor_heads: 2
82 + r_max: 8
83 + eta_max: 0.25
84 + clip_c: 1.0
85 + clip_eps: 1.0e-06
86 + readout_zero_init: false
87 + readout_init_std: 0.2
88 + readout_scale: 1.0
89 + controller_hidden: 0
90 + ctrl_input: h
91 + ctrl_use_entropy: true
92 + lambda_cost: 0.0
93 + kappa_risk: 0.0
94 + dense_writes: false
95 +resolved_train:
96 + batch_size: 16
97 + seq_len: 320
98 + steps: 120
99 + lr: 0.001
100 + warmup: 10
101 + weight_decay: 0.1
102 + grad_clip: 1.0
103 + log_every: 50
104 + eval_every: 120
105 + eval_batches: 2
106 + ckpt_every: 120
added experiments/runs/mini_editor_seed01_20261030005118/em_dev_iid_b0.json +1 −0
@@ -0,0 +1 @@
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added experiments/runs/mini_editor_seed01_20261030005118/metrics.jsonl +6 −0
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1336 1348 []\T1/ptm/b/n/12 (+20) You, Yichen, Tianyu Fu, Aosong Feng, Xing-tai Lv, Xue-fe
1337 1349 i Ning, Ning Ding, and
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modified paper/sections/abstract.tex +20 −1
@@ -1 +1,20 @@
1 −% to be written
1 +We design, implement, train and evaluate PRISME, a small-language-model architecture that augments a frozen
2 +causal decoder with a read-only source bank, an ephemeral sixteen-slot latent workspace that is re-created at every
3 +position, a shared sparse editor that rewrites one or two slots per action, a value controller trained on
4 +counterfactual intervention gains measured by executing paired edit/no-edit branches, and a differential readout
5 +with an exact zero-budget bypass. We reproduce the proposal's exact backbone parameter counts and its analytic
6 +operation table, prove its structural properties (causality, bitwise identity, exact locality, bounded
7 +displacement, one-step selection regret), and test seven pre-registered hypotheses with a complete implementation,
8 +a fifteen-invariant test suite, an incremental inference engine and controlled, seeded comparisons against a
9 +parameter-matched plain decoder, a dense-slot editor, a global-recurrence adapter trained with the identical
10 +recipe, five reference routers and exhaustive one- and two-step oracles. The zero-initialised readout prescribed by
11 +the proposal does not learn because of a scale mismatch; a scale-aware non-zero initialisation does. Sparse edits
12 +then lower a frozen backbone's held-out loss by \vtwoprismegainbtwo{}\,\% and raise compositional exact match by
13 +about two points, but the global-recurrence adapter matches or exceeds this at every budget. Where to write does
14 +not matter for fixed schedules. An oracle that reads the target finds large opportunity (\routingoraclebeight{}\,\%
15 +of loss), but \labelscalibwithin{}\,\% of the variance of intervention gains is state-level rather than action-level,
16 +the opportunity is concentrated in high-uncertainty states where a wrong action costs more than a right one gains,
17 +and the learned controller ranks actions within a state near chance; it is worse than no routing on held-out data.
18 +Measured on Apple Silicon, per-action dispatch overhead erases the arithmetic savings of sparsity at small scale.
19 +We report the 103M web-text pilot, the complete negative result on learned spatial routing, the mechanistic
20 +explanation, and the conditions under which the hypothesis could still be rescued.
modified paper/sections/conclusion.tex +16 −1
@@ -1 +1,16 @@
1 −% to be written
1 +\section{Conclusion}\label{sec:conclusion}
2 +The central question was whether counterfactual supervision of \emph{where} to edit a small latent workspace
3 +improves the quality--compute frontier beyond uniform workspace computation and adaptive global recurrence.
4 +On the evidence collected here the answer is no. The sparse editor is learnable and helps a frozen backbone
5 +by a few percent of loss and two points of exact match when it is applied once or twice at every position, but
6 +a global-recurrence adapter trained with the identical recipe helps at least as much at every budget; where to
7 +write barely matters for fixed schedules; an exhaustive oracle shows large opportunity (up to
8 +\routingoraclebeight{}\,\% of loss) that a value controller trained on 100k executed counterfactual labels
9 +cannot capture, because the opportunity sits in high-uncertainty states where a wrong action costs more than a
10 +right one gains and the controller's within-state ranking is close to chance; and at small scale the
11 +per-action dispatch overhead erases the arithmetic savings of sparsity. The exact bypass holds bitwise, the
12 +differential readout needs a scale-aware bootstrap, and the parameter and operation accounting of the proposal
13 +is reproduced. The scientifically justified next experiment is not a larger model but a better label: a
14 +controller trained to rank actions within a state (pairwise or listwise, with the state's mean gain removed)
15 +on labels collected at the scale where the editor's arithmetic is not dwarfed by dispatch, with the
16 +global-recurrence adapter as the baseline to beat.
modified paper/sections/limitations.tex +22 −1
@@ -1 +1,22 @@
1 −% to be written
1 +\section{Threats to validity and limitations}\label{sec:limitations}
2 +
3 +\paragraph{Scale.} The decisive seeded comparisons were run on 2--6M-parameter character-level backbones trained
4 +on a synthetic suite; the 103M experiments (Section~\ref{sec:results-pilot}) use a single backbone seed and a
5 +196M-token budget (two tokens per parameter), far below compute-optimal. Nothing here speaks to 459M or 1.18B.
6 +\paragraph{Frozen backbones.} Every adapter was trained on a frozen backbone, which bounds what any adapter can
7 +do; the one joint-fine-tuning attempt degraded the backbone and was not pursued.
8 +\paragraph{Recipe dependence.} The bootstrap device ($U\sim\mathcal N(0,0.2^2)$, $s=\rms(H)$), the forced-budget
9 +distribution and the learning rate were chosen on development data after the zero-initialised recipe failed;
10 +other recipes could change the ranking of adapters. The global-recurrence baseline is an ``inspired''
11 +simplification, not the authors' implementations of Huginn, Ouro or ELR.
12 +\paragraph{Label quality.} Intervention gains were collected with one editor checkpoint and one logging mixture;
13 +propensities were recorded but no inverse-propensity correction was needed because the logging policy was
14 +uniform over the audit; labels are exact for the token but noisy as a function of state features.
15 +\paragraph{Measurement.} Latency was measured on Apple Silicon in fp32 with PyTorch's MPS backend and includes
16 +Python dispatch; a fused implementation would change the absolute numbers and possibly the sparse-vs-dense
17 +ordering. Operation counts ignore normalisation and activation costs.
18 +\paragraph{Evaluation scope.} GSM8K and code benchmarks were pre-registered as out of scope for a ~200M-token
19 +backbone and were not run; the held-out compose family remained at 2--3\,\% for every system.
20 +\paragraph{Novelty.} The literature moved quickly in 2025--2026 (Section~\ref{sec:related}); the
21 +execution-time audit may still miss relevant work.
22 +\paragraph{Interpretability.} A slot is a vector, not a thought; no slot-level semantics are claimed.
modified paper/sections/mechanistic.tex +44 −1
@@ -1 +1,44 @@
1 −% to be written
1 +\section{Mechanistic studies and ablations}\label{sec:mechanistic}
2 +
3 +\paragraph{Where the gains are.}
4 +The variance decomposition of Section~\ref{sec:results-oracle} is the central mechanistic fact of this study:
5 +for the trained editor, whether an edit helps is mostly a property of the \emph{state} (\labelscaliballpos{}\,\%
6 +of audited states have every action helpful, \labelscaliballneg{}\,\% every action harmful), and only
7 +\labelscalibwithin{}\,\% of the variance of gains is attributable to which slot is written. Opportunity is
8 +concentrated in high-loss states, where the spread between the best and a random action is largest
9 +(\labelscalibtopdecoracle{} versus \labelscalibtopdecrandom{} nats in the top decile). A router that can only
10 +detect opportunity but not rank actions therefore edits exactly where editing is riskiest---which is what the
11 +learned controller did.
12 +
13 +\paragraph{Harm and overthinking.}
14 +Across fixed schedules the loss is U-shaped in the budget (Table~\ref{tab:seedmatrix}): one or two random
15 +edits help, eight hurt, for both the sparse editor and (less so) the global adapter; the fraction of positive
16 +logged gains falls from 48\,\% at the first edit to 37--40\,\% after three (Figure~\ref{fig:labels}, left). Edits
17 +compound noise faster than they compound signal in the forced-training regime, and nothing in the forced recipe
18 +teaches the editor to leave a good state alone.
19 +
20 +\paragraph{Slot usage and fixed pairs.}
21 +In the exhaustive audits no slot or pair is preferred: the best action's identity agrees between consecutive
22 +states 11--12\,\% of the time (uniform guess 4.2\,\%), singles and fixed pairs have indistinguishable mean gains on
23 +the editor-unseen stream, and the round-robin schedule, which visits every slot, matches random selection.
24 +The address embeddings $e_j$ therefore do not induce functional specialisation in 3,000 steps of forced training;
25 +we make no interpretability claim about slots.
26 +
27 +\paragraph{Myopia.}
28 +The two-action oracle exceeds the one-step oracle by \oracletwostep{} versus \oracleonestep{} nats, but
29 +``greedy first action, then best second'' recovers nearly all of it; the one-step greedy rule is not what limits
30 +the learned controller.
31 +
32 +\paragraph{Cost decomposition.}
33 +Analytically (Section~\ref{sec:compute}), at the 103M configuration and $T=512$ a single-slot edit costs
34 +\costsingle{} and a pair \costpair{} of the backbone's operations per token, of which the slot-key refresh of
35 +all $M$ slots is the largest single term; measured at the tiny scale, host dispatch dominates
36 +(Section~\ref{sec:results-latency}).
37 +
38 +\paragraph{Ablations executed.}
39 +Readout initialisation (zero vs $\sigma\in\{0.2,0.5\}$), readout scale, editor width ($w\in\{64,128\}$),
40 +learning rate ($10^{-3}$ vs $3\times10^{-3}$), budget schedule (mixed vs budget-8 only), write locality
41 +(1--2 slots vs all 16), global vs local recurrence, controller features ($z$ vs $\hat h$ + entropy), label stream
42 +(editor-seen vs editor-unseen), penalties ($\lambda,\kappa$). Not executed: $M\in\{4,8,32\}$, editor depth
43 +$\in\{1,4\}$, rematched pairs, joint fine-tuning (one attempt degraded the backbone and was stopped), distillation,
44 +quantisation. All executed runs, including failed ones, are listed in Appendix~\ref{app:runs}.
modified paper/sections/reproducibility.tex +11 −1
@@ -1 +1,11 @@
1 −% to be written
1 +\section{Reproducibility statement, ethics and acknowledgements}\label{sec:repro}
2 +Code, configurations, run manifests, result files, figures and this manuscript live in one repository
3 +(\texttt{Recherche-UQO/ia/prisme-slm}, git history included). Every run has an immutable identifier, a
4 +configuration hash, the code commit, the device and the measured wall-clock and memory; every number in the
5 +manuscript is produced by \texttt{scripts/make\_tables.py} and \texttt{scripts/reproduce\_figures.py} from those
6 +files, and \texttt{scripts/verify\_manuscript\_claims.py} checks the chain. \texttt{REPRODUCE.md} gives the
7 +commands for the environment, the tests (\texttt{pytest}), the miniature end-to-end pipeline and the figures.
8 +Data: the synthetic suite is generated deterministically from seeds; the web corpus is a documented, hashed
9 +subset of FineWeb-Edu (ODC-By 1.0) and is not redistributed. No private, student, or personal data was used; no
10 +generated code was executed; no paid API or cloud service was used. The author has no competing interests and
11 +received no external funding for this work. Computation ran on the author's own Apple Silicon machines.
modified paper/sections/results.tex +127 −1
@@ -1 +1,127 @@
1 −% to be written
1 +\section{Results}\label{sec:results}
2 +
3 +All numbers in this section are generated from result files by \texttt{scripts/make\_tables.py} and are indexed
4 +in \texttt{results/claim\_registry.csv} with their run identifiers. Unless stated otherwise, ``loss'' is the
5 +teacher-forced cross-entropy on the answer span of the synthetic suite (nats per token), ``$b$'' a forced budget
6 +applied to every position with uniformly random actions, and relative changes are with respect to the frozen
7 +backbone (budget 0) of the same run.
8 +
9 +\subsection{Learnability and the bootstrap problem (Phase 4)}\label{sec:results-learnability}
10 +With the readout initialised at zero, as prescribed, the editor did not learn at all in 700 steps on the frozen
11 +2.1M-parameter backbone: validation loss was identical at every budget (0.4144, 0.4143, 0.4143, 0.4146, 0.4164)
12 +and the trainable gradient norm stayed at 0.01--0.02. The cause is a scale mismatch: the backbone residual stream
13 +has root-mean-square 5.6 while the difference of the two attention reads has root-mean-square 0.06, so $U$ must
14 +grow by two orders of magnitude before any edit can move a logit, and until it does the editor receives no
15 +gradient. Three remedies were compared under the identical recipe (Figure~\ref{fig:learnability}): the fixed
16 +scale $s$ alone (zero $U$), $s$ with $U\sim\mathcal N(0,0.2^2)$, and $s$ with $U\sim\mathcal N(0,0.5^2)$. Only
17 +the non-zero initialisations learned within 2,000 steps (dev-iid loss $-1.4\,\%$ at budgets 1--2 for
18 +$\sigma=0.2$); with $\sigma=0.5$ the budget-8 loss was worse than the backbone throughout, and a learning rate
19 +of $3\times10^{-3}$ with $w=128$ collapsed to inert edits (every budget exactly equal to budget 0, gradient
20 +norm 0.00). The deliberate-overfitting test passed: on 64 items at budget 8 the frozen-backbone model reached a
21 +training loss of 0.0024 in 200 steps through the workspace path alone while the held-out loss rose, confirming
22 +that the gradient path and the capacity exist. Failure mode 13.1 of the proposal is therefore confirmed, and
23 +$U\sim\mathcal N(0,0.2^2)$ with $s=\rms(H)$ is used for every adapter below, including the baselines.
24 +
25 +\begin{figure}[t]\centering
26 +\includegraphics[width=\textwidth]{../results/figures/learnability_bootstrap.pdf}
27 +\caption{Stage B bootstrap on the frozen tiny backbone (dev-iid answer-span loss under random forced actions,
28 +budgets 0--8). Left: zero-initialised $U$ with the scale $s$; middle and right: $U\sim\mathcal N(0,\sigma^2)$ with
29 +$\sigma=0.2$ and $0.5$. Budget 0 is the frozen backbone (identical in all panels).}
30 +\label{fig:learnability}
31 +\end{figure}
32 +
33 +\subsection{Sparse editor versus global recurrence and dense writes (H1, H2)}\label{sec:results-matrix}
34 +Table~\ref{tab:seedmatrix} reports the pre-registered decisive comparison on the second tiny backbone
35 +(5.6M parameters, char-level): \vtwoprismeseeds{} independent adapter seeds for the sparse editor,
36 +\vtwogrseeds{} for the global-recurrence adapter and \vtwodenseseeds{} for the dense sixteen-slot editor, all
37 +trained with the same forced-budget recipe for 3,000 steps. H1 is supported in its weak form: random sparse
38 +edits reduce the held-out answer-span loss by \vtwoprismegainbone{}\,\% (budget 1), \vtwoprismegainbtwo{}\,\%
39 +(budget 2) and \vtwoprismegainbfour{}\,\% (budget 4), with seed standard deviations below 0.002 nats, and
40 +raise dev-iid exact match from \vtwoprismeembzero{}\,\% to \vtwoprismeembtwo{}\,\%. The effect is not
41 +specific to sparse local writes, however: the global-recurrence adapter---one shared block rewriting the whole
42 +latent, parameter-matched, same bootstrap---reduces the loss by \vtwogrgainbone{}\,\%, \vtwogrgainbtwo{}\,\% and
43 +\vtwogrgainbfour{}\,\% at the same budgets and reaches \vtwogrembtwo{}\,\% exact match, i.e.\ it is at least as
44 +good at every budget and better at budget 8, where the sparse editor's gain shrinks to \vtwoprismegainbeight{}\,\%.
45 +Dense writes (Table~\ref{tab:seedmatrix}, third row) answer the second half of H2 directly. None of the
46 +adapters improves the held-out template, depth or compose regimes by more than one point (H6).
47 +\input{tables/seed_matrix.tex}
48 +
49 +\subsection{Oracle opportunity and the structure of intervention gains (Phase 5)}\label{sec:results-oracle}
50 +Figure~\ref{fig:labels} and Table~\ref{tab:controller} summarise the counterfactual labels collected with the
51 +fixed editor on a stream the editor never trained on (\labelstrainnstates{} and \labelscalibnstates{} exhaustive-audit
52 +states in the training and calibration partitions). Three facts shape everything downstream.
53 +First, the exhaustive one-step oracle finds real value: choosing the best of the 24 actions, or stopping when
54 +none helps, gains \labelscaliboracle{} nats per state on calibration against \labelscalibrandom{} for a random
55 +action; rolled out greedily on dev-iid it lowers the loss by \routingoraclebone{}\,\% with
56 +\routingoracleeditsone{} edits per token at budget 1 and by \routingoraclebeight{}\,\% with
57 +\routingoracleeditseight{} edits per token at budget 8 (Table~\ref{tab:routing}). The two-action oracle gains
58 +\oracletwostep{} nats against \oracleonestep{} for one step, and ``greedy first action then best second'' recovers
59 +almost all of it, so myopia of the one-step rule is not the binding constraint at this scale.
60 +Second, most of the variance of the gains is between states, not between actions: \labelscalibwithin{}\,\% of
61 +the variance of the audited gains is within-state (action identity), the rest is the state's mean gain;
62 +\labelscaliballpos{}\,\% of states have all 24 actions positive and \labelscaliballneg{}\,\% have all 24
63 +negative. Decomposing the oracle, a perfect continue/stop decision with a random action captures
64 +\labelscalibstoponly{} of the \labelscaliboracle{} nats; the choice of \emph{which} slot captures the rest.
65 +Third, opportunity is concentrated where the backbone is uncertain and is dangerous there: in states above the
66 +median base loss the oracle gains \labelscaliboraclehigh{} nats while a random action loses
67 +\labelscalibrandomhigh{}; in the top decile of base loss the oracle gains \labelscalibtopdecoracle{} and a
68 +random action loses \labelscalibtopdecrandom{} nats, with only \labelscalibfracposhigh{}\,\% of actions helpful
69 +(\labelscalibfracposlow{}\,\% in the lower half, where all gains are negligible).
70 +
71 +\begin{figure}[t]\centering
72 +\includegraphics[width=\textwidth]{../results/figures/labels_analysis.pdf}
73 +\caption{Counterfactual labels (calibration partition, editor-unseen stream). Left: fraction of positive gains by
74 +number of edits already applied. Middle: variance decomposition of the exhaustive 24-action audit. Right: mean
75 +gain per state of a random action, of a perfect continue/stop decision with a random action, and of the
76 +one-step oracle.}
77 +\label{fig:labels}
78 +\end{figure}
79 +
80 +\subsection{Learned routing versus reference routers (H2, H3)}\label{sec:results-routing}
81 +Table~\ref{tab:routing} compares, on dev-iid with the same editor, the frozen backbone, random, round-robin and
82 +fixed-pair schedules at budgets 1--8, entropy and norm-change routers over a threshold grid, the learned value
83 +controller at its locked penalty and along a $\lambda$ grid, and the diagnostic oracles. Three results follow.
84 +(i) Spatial selection does not matter for fixed schedules: at budget 1 random, round-robin and fixed-pair give
85 +\routingrandombone{}, \routinguniformbone{} and \routingpairbone{}\,\% respectively, within the paired
86 +bootstrap noise of one another; more edits hurt (\routingrandombeight{}\,\% at budget 8). H2 is not supported.
87 +(ii) The learned controller is worse than not routing: at its calibration-selected penalty
88 +($\lambda=\routingctrllambda$) it edits \routingctrlledits{} times per token and \emph{raises} the dev-iid loss by
89 +\routingctrllocked{}\,\%; every smaller $\lambda$ is worse still, although the same controller had lowered the
90 +calibration loss by 1.1\,\% (Table~\ref{tab:controller}). The reference routers do no better: the best entropy
91 +threshold gives \routingentropybest{}\,\% and the best norm-change threshold \routingnormbest{}\,\%. H3 is
92 +refuted at this scale.
93 +(iii) The mechanism of the failure is visible in the labels. With backbone-residual and entropy features the
94 +controller predicts the \emph{sign} of a gain with \ctrlhentsignacc{}\,\% accuracy (chance 50\,\%) and finds
95 +the states with opportunity, which are the high-loss states; but in exactly those states a wrong action costs
96 +more than the right action gains (\labelscalibtopdecrandom{} versus \labelscalibtopdecoracle{} nats in the top
97 +decile), and the controller's ranking of actions within a state is close to chance (top-1 agreement with the
98 +oracle 7--8\,\% against 4.2\,\% for a uniform guess; captured fraction of the oracle opportunity $\le 5\,\%$).
99 +The pre-registered null ``an oracle can find beneficial edits, but the learned controller cannot predict
100 +them'' is the outcome at this scale.
101 +\input{tables/routing.tex}
102 +\input{tables/controller.tex}
103 +
104 +\subsection{Token loss versus task success}\label{sec:results-reasoning}
105 +Exact match of free greedy generation moves with the loss on dev-iid (Table~\ref{tab:seedmatrix}: $+1.8$ points
106 +for the sparse editor at budget 2, $+2.3$ for global recurrence at budget 8, seed SD $\le 0.5$) but not on the
107 +held-out regimes; per-family transitions are reported in Appendix~\ref{app:results}. No adapter turns the
108 +held-out compose family from near zero (2--3\,\%) into anything else.
109 +
110 +\subsection{Latency and memory}\label{sec:results-latency}
111 +Table~\ref{tab:latency} gives measured batch-1 and batch-8 decode latencies for the tiny model on the
112 +development laptop (Apple M5 Max, fp32, PyTorch 2.15, MPS). Budget 0 costs \latencybzero{} ms per token; each
113 +executed single-slot edit adds about 2.3 ms, so eight edits cost seven times the backbone although their
114 +analytic operation count is only 4.2 times; the dense sixteen-slot write at budget 8 is \emph{not} slower than
115 +eight sparse writes (17.3 vs 19.3 ms), and one controller evaluation adds 2.9 ms even when it stops immediately.
116 +At this scale the per-action host dispatch and kernel-launch overhead dominates the arithmetic, exactly as the
117 +proposal's failure mode 13.6 anticipated; Section~\ref{sec:results-pilot} repeats the measurement at 103M, where
118 +the arithmetic is 60 times larger per token.
119 +\input{tables/latency.tex}
120 +
121 +\subsection{The 103M pilot on web text (Phase 8)}\label{sec:results-pilot}
122 +\input{sections/results_pilot}
123 +
124 +\subsection{Larger configurations}
125 +The 459M and 1.18B configurations were instantiated on the meta device for exact parameter accounting
126 +(Table~\ref{tab:configs}) and the 459M operation table was reproduced (Table~\ref{tab:ops}); neither was
127 +trained. No scaling claim is made (H7 untested).
added paper/sections/results_pilot.tex +1 −0
@@ -0,0 +1 @@
1 +% filled after the 103M pilot completes
modified paper/tables/numbers.tex +74 −16
@@ -2,20 +2,32 @@
2 2 \newcommand{\costpair}{0.100}
3 3 \newcommand{\costsingle}{0.080}
4 4 \newcommand{\ctrlhentsignacc}{60.4}
5 −\newcommand{\labelscaliballneg}{30}
6 −\newcommand{\labelscaliballpos}{38}
7 −\newcommand{\labelscalibnstates}{1,308}
8 −\newcommand{\labelscaliboracle}{0.0166}
9 −\newcommand{\labelscalibrandom}{0.0004}
10 −\newcommand{\labelscalibstoponly}{0.0098}
11 −\newcommand{\labelscalibwithin}{11}
12 −\newcommand{\labelstrainallneg}{29}
13 −\newcommand{\labelstrainallpos}{37}
14 −\newcommand{\labelstrainnstates}{4,778}
15 −\newcommand{\labelstrainoracle}{0.0148}
16 −\newcommand{\labelstrainrandom}{0.0049}
17 −\newcommand{\labelstrainstoponly}{0.0083}
18 −\newcommand{\labelstrainwithin}{14}
5 +\newcommand{\labelscaliballneg}{31}
6 +\newcommand{\labelscaliballpos}{34}
7 +\newcommand{\labelscalibfracposhigh}{58}
8 +\newcommand{\labelscalibfracposlow}{22}
9 +\newcommand{\labelscalibnstates}{1,645}
10 +\newcommand{\labelscaliboracle}{0.0136}
11 +\newcommand{\labelscaliboraclehigh}{0.0272}
12 +\newcommand{\labelscalibrandom}{-0.0014}
13 +\newcommand{\labelscalibrandomhigh}{-0.0028}
14 +\newcommand{\labelscalibstoponly}{0.0069}
15 +\newcommand{\labelscalibtopdecoracle}{0.027}
16 +\newcommand{\labelscalibtopdecrandom}{-0.041}
17 +\newcommand{\labelscalibwithin}{25}
18 +\newcommand{\labelstrainallneg}{32}
19 +\newcommand{\labelstrainallpos}{34}
20 +\newcommand{\labelstrainfracposhigh}{58}
21 +\newcommand{\labelstrainfracposlow}{21}
22 +\newcommand{\labelstrainnstates}{12,272}
23 +\newcommand{\labelstrainoracle}{0.0139}
24 +\newcommand{\labelstrainoraclehigh}{0.0279}
25 +\newcommand{\labelstrainrandom}{0.0015}
26 +\newcommand{\labelstrainrandomhigh}{+0.0030}
27 +\newcommand{\labelstrainstoponly}{0.0077}
28 +\newcommand{\labelstraintopdecoracle}{0.034}
29 +\newcommand{\labelstraintopdecrandom}{-0.018}
30 +\newcommand{\labelstrainwithin}{16}
19 31 \newcommand{\latencybzero}{2.80}
20 32 \newcommand{\ntrainsynth}{23,005}
21 33 \newcommand{\oracleonestep}{0.0233}
@@ -26,7 +38,53 @@
26 38 \newcommand{\paramsMediumWs}{10,505,221}
27 39 \newcommand{\paramsSmallTotal}{102,134,021}
28 40 \newcommand{\paramsSmallWs}{2,631,173}
29 −\newcommand{\vtwogrgainbtwo}{4.13}
41 +\newcommand{\routingbzero}{0.4334}
42 +\newcommand{\routingctrllambda}{0.3}
43 +\newcommand{\routingctrlledits}{0.22}
44 +\newcommand{\routingctrllocked}{+1.0}
45 +\newcommand{\routingentropybest}{+0.3}
46 +\newcommand{\routingnormbest}{-0.6}
47 +\newcommand{\routingoraclebeight}{-15.4}
48 +\newcommand{\routingoraclebone}{-7.1}
49 +\newcommand{\routingoracleeditseight}{2.27}
50 +\newcommand{\routingoracleeditsone}{0.52}
51 +\newcommand{\routingpairbeight}{+2.8}
52 +\newcommand{\routingpairbone}{-0.7}
53 +\newcommand{\routingpairbtwo}{-0.3}
54 +\newcommand{\routingrandombeight}{+2.7}
55 +\newcommand{\routingrandombone}{-0.7}
56 +\newcommand{\routingrandombtwo}{-0.5}
57 +\newcommand{\routinguniformbeight}{+0.6}
58 +\newcommand{\routinguniformbone}{-0.8}
59 +\newcommand{\routinguniformbtwo}{-0.7}
60 +\newcommand{\vtwobestfixedrel}{-4.0}
61 +\newcommand{\vtwobzeroloss}{0.4621}
62 +\newcommand{\vtwoctrlbetter}{2}
63 +\newcommand{\vtwoctrlrel}{+0.5}
64 +\newcommand{\vtwoctrlrelsd}{1.1}
65 +\newcommand{\vtwoctrlseeds}{4}
66 +\newcommand{\vtwodenseembeight}{n/a}
67 +\newcommand{\vtwodenseembtwo}{n/a}
68 +\newcommand{\vtwodenseembzero}{n/a}
69 +\newcommand{\vtwodensegainbeight}{n/a}
70 +\newcommand{\vtwodensegainbfour}{n/a}
71 +\newcommand{\vtwodensegainbone}{n/a}
72 +\newcommand{\vtwodensegainbtwo}{n/a}
73 +\newcommand{\vtwodenseseeds}{0}
74 +\newcommand{\vtwogrembeight}{40.0}
75 +\newcommand{\vtwogrembtwo}{39.6}
76 +\newcommand{\vtwogrembzero}{37.7}
77 +\newcommand{\vtwogrgainbeight}{3.6}
78 +\newcommand{\vtwogrgainbfour}{3.9}
79 +\newcommand{\vtwogrgainbone}{3.7}
80 +\newcommand{\vtwogrgainbtwo}{4.1}
30 81 \newcommand{\vtwogrseeds}{4}
31 −\newcommand{\vtwoprismegainbtwo}{2.77}
82 +\newcommand{\vtwooraclerel}{-15.0}
83 +\newcommand{\vtwoprismeembeight}{39.2}
84 +\newcommand{\vtwoprismeembtwo}{39.5}
85 +\newcommand{\vtwoprismeembzero}{37.7}
86 +\newcommand{\vtwoprismegainbeight}{1.0}
87 +\newcommand{\vtwoprismegainbfour}{3.0}
88 +\newcommand{\vtwoprismegainbone}{2.1}
89 +\newcommand{\vtwoprismegainbtwo}{2.8}
32 90 \newcommand{\vtwoprismeseeds}{4}
added paper/tables/routing_seeds.tex +14 −0
@@ -0,0 +1,14 @@
1 +\begin{table}[t]\centering\small
2 +\caption{Learned routing per adapter seed (v2 backbone, dev-iid, 640 items): held-out answer-span loss of the
3 +frozen backbone, of the best fixed schedule (named), of the best entropy and norm routers, of the locked
4 +controller ($\lambda,\kappa$ selected on calibration rollouts; edits per token in parentheses), and of the
5 +diagnostic greedy one-step oracle at budget 8. Calibration sign accuracy and captured fraction of the oracle
6 +opportunity for each controller.}
7 +\label{tab:routingseeds}
8 +\begin{tabular}{lrlrrrrrr}\toprule
9 +seed & budget 0 & best fixed & entropy & norm & controller (edits) & oracle b=8 (edits) & sign acc. & captured\\\midrule
10 +2 & 0.4966 & 0.4754 (random forced b=4) & 0.4799 & 0.4794 & 0.4965 (0.05) & 0.4114 (1.90) & 0.413 & +0.333\\
11 +3 & 0.4966 & 0.4775 (uniform b=8) & 0.4825 & 0.4814 & 0.5074 (4.32) & 0.4242 (1.84) & 0.504 & +0.305\\
12 +4 & 0.4966 & 0.4762 (uniform b=8) & 0.4795 & 0.4790 & 0.4974 (0.17) & 0.4251 (2.19) & 0.633 & +0.521\\
13 +5 & 0.4966 & 0.4768 (random forced b=4) & 0.4833 & 0.4830 & 0.4949 (0.16) & 0.4277 (2.02) & 0.572 & +0.300\\
14 +\bottomrule\end{tabular}\end{table}
added reproducibility/data_card.md +24 −0
@@ -0,0 +1,24 @@
1 +# Data card — WP31
2 +
3 +## Synthetic compositional suite v1 (`data/splits/synthetic_v1/`)
4 +Generated deterministically by `data/generators/synthetic.py` (SHA-256 per split in `manifest.json`). Six
5 +families with programmatic answers (arith, bind, fsm, excl, sched; compose held out), five phrasing templates,
6 +depths 1–6. Splits fixed before sampling by (family, template, depth, seed): train (seed 1, templates 0–2,
7 +depths 1–4; 23,005 items after duplicate removal), calib (seed 3; 1,690), ctrl_train (seed 4, controller labels
8 +only; 11,030), dev_{iid,template,depth,compose} (seed 2), test_{iid,template,depth,compose} (seed 9, LOCKED —
9 +not read by any script until `protocol/locked_test_manifest.json` is unsealed). Cross-split exact duplicates are
10 +dropped from the later split and counted. Character-level tokenizer (97 printable ASCII + 2 specials).
11 +
12 +## FineWeb-Edu subset (`data/processed/fineweb_edu_300m/`)
13 +Source: `HuggingFaceFW/fineweb-edu`, config `sample-10BT`, streamed in canonical shard order on 2026-10-29
14 +(291,534 documents streamed, truncated to 40,000 characters each); licence ODC-By 1.0 (attribution: Penedo et
15 +al., 2024). Exact-hash deduplication; train/val/test by content hash (98/1/1 %): 289,959,006 / 2,913,824 /
16 +2,883,436 tokens (uint16, EOT-separated). Tokenizer: byte-level BPE, 32,768 symbols, digits split individually,
17 +trained on 60,000 train documents (`tokenizer.json`, SHA-256 in manifest). Test split locked. The raw text
18 +(`data/raw/`) and token files are not redistributed; `prepare_corpus.py` regenerates them.
19 +
20 +## Counterfactual label sets
21 +`experiments/runs/labels_*/labels_{train,calib}.pt`: per batch, tokens, targets, prefix actions, four logged
22 +candidate actions with gains and log-propensities, exhaustive 24-action gains on audit batches, and the SHA-256
23 +of the editor checkpoint that produced them. Train partition = `ctrl_train` stream (editor-unseen; the first
24 +tiny set used `train`, see D11), calibration partition = `calib`.
added reproducibility/model_card.md +12 −0
@@ -0,0 +1,12 @@
1 +# Model card — PRISME reference models (WP31)
2 +
3 +| Field | Value |
4 +|---|---|
5 +| Models | PRISME-tiny (2.1M / 5.6M char-level synthetic backbones + 0.17M adapters), PRISME-103M (99.5M backbone + 2.63M workspace/controller). 459M and 1.18B: configuration files and meta-device instantiation only (not trained). |
6 +| Architecture | Pre-norm causal decoder (RMSNorm, RoPE, GQA, SwiGLU, tied embeddings) + read-only source bank + 16-slot ephemeral workspace + shared 2-block sparse editor (24 actions + STOP, cap 8) + value controller + differential readout (`docs/architecture_spec.md`). |
7 +| Training data | tiny: synthetic suite v1 train split (23,005 items); 103M: FineWeb-Edu sample-10BT subset, 196.6M tokens (`data/processed/fineweb_edu_300m/manifest.json`). |
8 +| Intended use | Research artefact for reproducing the WP31 experiments. Not a general-purpose language model; the 103M backbone saw two tokens per parameter. |
9 +| Out of scope | Any deployment; generation of text for end users; evaluation on benchmarks it was not pre-registered for. |
10 +| Known limitations | Frozen-backbone adapters; learned routing is worse than no routing on held-out synthetic data (negative result); bank memory grows linearly with context. |
11 +| Checkpoints | `experiments/runs/<run_id>/checkpoint/{model.safetensors, checkpoint.json}` on the node that trained them (run index in `reproducibility/run_index.md`); laptop copies for the tiny models. Weights SHA-256 in each `checkpoint.json`. |
12 +| Licence | Code MIT; weights CC BY 4.0; corpus-derived artefacts inherit ODC-By 1.0 attribution. |
added reproducibility/run_index.md +39 −0
@@ -0,0 +1,39 @@
1 +# Run index (auto-generated by scripts/run_index.py)
2 +
3 +| run_id | state | host | device | wall s | train tokens | peak mem (GB) | commit | notes |
4 +|---|---|---|---|---|---|---|---|---|
5 +| controller_mini_seed01_20261030005154 | completed | laptop | cpu | 3.14 | | | ede67055 | |
6 +| controller_p4_hent_seed01_20261030001419 | running | laptop | mps | 0.02 | | | unknown | |
7 +| controller_p4_hent_seed01_20261030002015 | completed | laptop | mps | 584.36 | | | unknown | |
8 +| controller_v1ctrl_seed01_20261030004131 | completed | laptop | mps | 164.25 | | | 33298d96 | |
9 +| controller_v2s2_seed01_20261030004717 | completed | laptop | mps | 193.23 | | | unknown | |
10 +| controller_v2s3_seed01_20261030004720 | completed | laptop | mps | 197.97 | | | unknown | |
11 +| controller_v2s4_seed01_20261030004719 | completed | laptop | mps | 217.1 | | | unknown | |
12 +| controller_v2s5_seed01_20261030004719 | completed | laptop | mps | 214.63 | | | unknown | |
13 +| labels_mini_seed01_20261030005150 | completed | laptop | cpu | 3.44 | | | ede67055 | |
14 +| labels_p4_seed01_20261030000543 | completed | laptop | mps | 33.33 | | | unknown | |
15 +| labels_v1ctrl_seed01_20261030003957 | running | laptop | mps | 0.02 | | | 33298d96 | |
16 +| labels_v1ctrl_seed01_20261030004018 | completed | laptop | mps | 72.1 | | | 33298d96 | |
17 +| labels_v2s2_seed01_20261030004543 | completed | laptop | mps | 92.45 | | | unknown | |
18 +| labels_v2s3_seed01_20261030004544 | completed | laptop | mps | 95.57 | | | unknown | |
19 +| labels_v2s4_seed01_20261030004544 | completed | laptop | mps | 94.03 | | | unknown | |
20 +| labels_v2s5_seed01_20261030004544 | completed | laptop | mps | 94.68 | | | unknown | |
21 +| mini_backbone_seed01_20261030005114 | completed | laptop | cpu | 3.6 | 279708 | 0.7 | ede67055 | backbone_only |
22 +| mini_editor_seed01_20261030005118 | completed | laptop | cpu | 30.59 | 9847 | 2.0 | ede67055 | prisme |
23 +| p4_editor_v1_Ustd02_seed01_20261029235050 | completed | laptop | mps | 650.11 | 331026 | 8.8 | unknown | prisme |
24 +| p4_editor_v1_scaled_seed01_20261029234334 | running | laptop | mps | 0.16 | | | HEAD | prisme |
25 +| p4_editor_v1_seed01_20261029233624 | running | laptop | mps | 0.02 | | | HEAD | prisme |
26 +| p4_overfit64_b8_seed01_20261029235111 | completed | laptop | mps | 519.6 | 95773 | 6.7 | a9d94e8a | prisme |
27 +| p4_tiny_backbone_seed01_20261029232626 | completed | laptop | mps | 457.67 | 44367329 | 3.4 | HEAD | backbone_only |
28 +| p4_tiny_backbone_v2_seed01_20261029233624 | stopped_overfitting_step9300_checkpoint_step5000_used | laptop | mps | 0.02 | | | HEAD | backbone_only |
29 +| p4v2_dense_seed05_20261030001839 | running | M4BP36 | mps | 0.33 | | | unknown | dense |
30 +| p4v2_gr_seed02_20261030001749 | completed | laptop | mps | 951.12 | 498543 | 3.4 | unknown | global_recurrence |
31 +| p4v2_gr_seed03_20261030001749 | completed | laptop | mps | 915.78 | 499017 | 2.3 | unknown | global_recurrence |
32 +| p4v2_gr_seed04_20261030001749 | completed | laptop | mps | 430.63 | 497821 | 2.3 | unknown | global_recurrence |
33 +| p4v2_gr_seed05_20261030001839 | completed | laptop | mps | 370.27 | 497078 | 3.4 | unknown | global_recurrence |
34 +| p4v2_prisme_seed02_20261030001749 | completed | laptop | mps | 1529.74 | 498543 | 8.8 | unknown | prisme |
35 +| p4v2_prisme_seed03_20261030001749 | completed | laptop | mps | 1578.01 | 499017 | 10.0 | unknown | prisme |
36 +| p4v2_prisme_seed04_20261030001749 | completed | laptop | mps | 1504.41 | 497821 | 9.3 | unknown | prisme |
37 +| p4v2_prisme_seed05_20261030001839 | completed | laptop | mps | 939.64 | 497078 | 9.9 | unknown | prisme |
38 +| pretrain103m_seed01_20261029232706 | running | M3U96a | mps | 0.03 | | | unknown | |
39 +| pretrain_enlarged102m_seed01_20261029232914 | running | M3U96b | mps | 0.02 | | | unknown | |
added results/analyzed/routing_seeds_v2.json +66 −0
@@ -0,0 +1,66 @@
1 +[
2 + {
3 + "seed": "2",
4 + "b0": 0.49655490497905047,
5 + "best_fixed": 0.4753802631345799,
6 + "best_fixed_name": "random forced b=4",
7 + "entropy": 0.4798940176289787,
8 + "norm": 0.47936704432995725,
9 + "ctrl": 0.49651632708068616,
10 + "ctrl_edits": 0.04975124378109453,
11 + "ctrl_lam": 0.3,
12 + "ctrl_kap": 0.5,
13 + "oracle8": 0.4114027855772337,
14 + "oracle8_edits": 1.901990811638591,
15 + "sign_acc": 0.412654183385347,
16 + "captured": 0.3325693091809977
17 + },
18 + {
19 + "seed": "3",
20 + "b0": 0.49655490497905047,
21 + "best_fixed": 0.47754792271213015,
22 + "best_fixed_name": "uniform b=8",
23 + "entropy": 0.4825179937606602,
24 + "norm": 0.4814451017817943,
25 + "ctrl": 0.5073574809676414,
26 + "ctrl_edits": 4.320456540825285,
27 + "ctrl_lam": 0.3,
28 + "ctrl_kap": 0.0,
29 + "oracle8": 0.42422473375866643,
30 + "oracle8_edits": 1.8376722817764166,
31 + "sign_acc": 0.5044954673799971,
32 + "captured": 0.30502896495875775
33 + },
34 + {
35 + "seed": "4",
36 + "b0": 0.49655490497905047,
37 + "best_fixed": 0.47621813565623455,
38 + "best_fixed_name": "uniform b=8",
39 + "entropy": 0.47952548600024636,
40 + "norm": 0.4789865899581396,
41 + "ctrl": 0.4974173310558664,
42 + "ctrl_edits": 0.17295873573309922,
43 + "ctrl_lam": 0.3,
44 + "ctrl_kap": 0.5,
45 + "oracle8": 0.4251479284686662,
46 + "oracle8_edits": 2.1929555895865236,
47 + "sign_acc": 0.6326348640213999,
48 + "captured": 0.52111062235297
49 + },
50 + {
51 + "seed": "5",
52 + "b0": 0.49655490497905047,
53 + "best_fixed": 0.47676382659295863,
54 + "best_fixed_name": "random forced b=4",
55 + "entropy": 0.4832980625106816,
56 + "norm": 0.4829900508252089,
57 + "ctrl": 0.4949139250905185,
58 + "ctrl_edits": 0.16417910447761194,
59 + "ctrl_lam": 0.3,
60 + "ctrl_kap": 0.5,
61 + "oracle8": 0.42770675203519065,
62 + "oracle8_edits": 2.021439509954058,
63 + "sign_acc": 0.5724847674245802,
64 + "captured": 0.29970467810978474
65 + }
66 +]
\ No newline at end of file
added results/raw/routing_mini.json +130 −0
@@ -0,0 +1,130 @@
1 +{
2 + "config": "configs/experiments/routing_mini.yaml",
3 + "checkpoint": "experiments/runs/controller_mini_seed01_20261030005154/checkpoint",
4 + "checkpoint_sha256": "bb56e9c7b34c773d7cb314b50da687d910b37fa126243f4cfbe9b36c931da83e",
5 + "split": "dev_iid",
6 + "code_commit": "ede6705574e7140bdc16eff11931815070b471d1",
7 + "device": "cpu",
8 + "seconds": 10.8,
9 + "records": [
10 + {
11 + "policy": "budget0",
12 + "loss": 2.3909947187051004,
13 + "edits_per_token": 0.0,
14 + "extra_ops_ratio": 0.0,
15 + "n_tokens": 174
16 + },
17 + {
18 + "policy": "random_forced",
19 + "budget": 1,
20 + "loss": 2.177954575111126,
21 + "edits_per_token": 1.0,
22 + "extra_ops_ratio": 0.6340132088496767,
23 + "n_tokens": 174
24 + },
25 + {
26 + "policy": "uniform",
27 + "budget": 1,
28 + "loss": 2.187390908427622,
29 + "edits_per_token": 1.0,
30 + "extra_ops_ratio": 0.5180141843598465,
31 + "n_tokens": 174
32 + },
33 + {
34 + "policy": "fixed_pair",
35 + "budget": 1,
36 + "loss": 2.1671100484913794,
37 + "edits_per_token": 1.0,
38 + "extra_ops_ratio": 0.7239716190031205,
39 + "n_tokens": 174
40 + },
41 + {
42 + "policy": "random_forced",
43 + "budget": 2,
44 + "loss": 2.145720470910785,
45 + "edits_per_token": 2.0,
46 + "extra_ops_ratio": 1.2502715012122845,
47 + "n_tokens": 174
48 + },
49 + {
50 + "policy": "uniform",
51 + "budget": 2,
52 + "loss": 2.167156175635327,
53 + "edits_per_token": 2.0,
54 + "extra_ops_ratio": 1.036028368719693,
55 + "n_tokens": 174
56 + },
57 + {
58 + "policy": "fixed_pair",
59 + "budget": 2,
60 + "loss": 2.1463586215315194,
61 + "edits_per_token": 2.0,
62 + "extra_ops_ratio": 1.447943238006241,
63 + "n_tokens": 174
64 + },
65 + {
66 + "policy": "entropy",
67 + "threshold": 1.0,
68 + "budget": 2,
69 + "loss": 2.147084027871318,
70 + "edits_per_token": 1.632183908045977,
71 + "extra_ops_ratio": 0.9839830727412783,
72 + "n_tokens": 174
73 + },
74 + {
75 + "policy": "norm",
76 + "threshold": 0.1,
77 + "budget": 2,
78 + "loss": 2.149580155295887,
79 + "edits_per_token": 1.9885057471264367,
80 + "extra_ops_ratio": 1.194603536320829,
81 + "n_tokens": 174
82 + },
83 + {
84 + "policy": "controller_locked",
85 + "lam": 0.3,
86 + "kap": 0.0,
87 + "budget": 2,
88 + "loss": 2.3621927897135415,
89 + "edits_per_token": 0.06896551724137931,
90 + "extra_ops_ratio": 0.03572511535951461,
91 + "n_tokens": 174
92 + },
93 + {
94 + "policy": "controller",
95 + "lam": 0.0,
96 + "kap": 0.0,
97 + "budget": 2,
98 + "loss": 2.1407089233398438,
99 + "edits_per_token": 1.5344827586206897,
100 + "extra_ops_ratio": 0.8812912908093683,
101 + "n_tokens": 174
102 + },
103 + {
104 + "policy": "oracle1_greedy",
105 + "budget": 1,
106 + "diagnostic": true,
107 + "loss": 2.1457042020909927,
108 + "edits_per_token": 0.6470588235294118,
109 + "extra_ops_ratio": 0.4587601156795726,
110 + "n_tokens": 85
111 + },
112 + {
113 + "policy": "oracle1_greedy",
114 + "budget": 2,
115 + "diagnostic": true,
116 + "loss": 1.9766888786764707,
117 + "edits_per_token": 1.2470588235294118,
118 + "extra_ops_ratio": 0.854374605066636,
119 + "n_tokens": 85
120 + },
121 + {
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167 + {
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235 + {
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243 + "n_tokens": 3417
244 + },
245 + {
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251 + "extra_ops_ratio": 0.06495266121342938,
252 + "n_tokens": 653
253 + },
254 + {
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259 + "edits_per_token": 0.8392036753445635,
260 + "extra_ops_ratio": 0.11255041855582781,
261 + "n_tokens": 653
262 + },
263 + {
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268 + "edits_per_token": 1.3859111791730474,
269 + "extra_ops_ratio": 0.18563849783600928,
270 + "n_tokens": 653
271 + },
272 + {
273 + "policy": "oracle1_greedy",
274 + "budget": 8,
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276 + "loss": 0.4251479284686662,
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278 + "extra_ops_ratio": 0.2940347972361636,
279 + "n_tokens": 653
280 + },
281 + {
282 + "policy": "oracle_two_step",
283 + "diagnostic": true,
284 + "one_step_gain": 0.011839706322242474,
285 + "two_step_gain": 0.02256789289671799,
286 + "greedy_then_best_gain": 0.02252720964365992,
287 + "n_tokens": 174
288 + }
289 + ]
290 +}
\ No newline at end of file
added results/raw/routing_v2s5.json +290 −0
@@ -0,0 +1,290 @@
1 +{
2 + "config": "configs/experiments/routing_v2s5.yaml",
3 + "checkpoint": "experiments/runs/controller_v2s5_seed01_20261030004719/checkpoint",
4 + "checkpoint_sha256": "958788a3ebabeed125e43ca842c6e0c0a26b2eb9741f65b990ede4325bfbceb9",
5 + "split": "dev_iid",
6 + "code_commit": "unknown",
7 + "device": "mps",
8 + "seconds": 108.4,
9 + "records": [
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254 + {
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271 + },
272 + {
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278 + "extra_ops_ratio": 0.2735076162387912,
279 + "n_tokens": 653
280 + },
281 + {
282 + "policy": "oracle_two_step",
283 + "diagnostic": true,
284 + "one_step_gain": 0.012034434011612815,
285 + "two_step_gain": 0.02329660284108129,
286 + "greedy_then_best_gain": 0.023268351609679473,
287 + "n_tokens": 174
288 + }
289 + ]
290 +}
\ No newline at end of file
modified scripts/make_results_tables.py +95 −5
@@ -74,15 +74,28 @@ System & seeds & loss b=0 & loss b=1 & loss b=2 & loss b=4 & loss b=8\\\midrule
74 74 body += f"{lab} & {n} & \\multicolumn{{5}}{{l}}{{{' / '.join(cells)}}}\\\\\n"
75 75 (OUT / "seed_matrix.tex").write_text(head + body + r"\bottomrule\end{tabular}\end{table}" + "\n")
76 76 for pre, lab in systems:
77 + tag = pre.replace("p4v2_", "vtwo")
78 + if pre not in raw: # system not (yet) completed: emit explicit placeholders so the manuscript compiles and the audit flags them
79 + macro(f"{tag}seeds", "0")
80 + for bn in ("one", "two", "four", "eight"):
81 + macro(f"{tag}gainb{bn}", "n/a")
82 + for e in ("embzero", "embtwo", "embeight"):
83 + macro(f"{tag}{e}", "n/a")
77 84 if pre in raw:
78 85 v = raw[pre]["val"]
79 − if 0 in v and 2 in v:
80 − macro(f"{pre.replace('p4v2_', 'vtwo')}gainbtwo", f"{100 * (1 - np.mean(v[2]) / np.mean(v[0])):.2f}")
81 − macro(f"{pre.replace('p4v2_', 'vtwo')}seeds", str(raw[pre]["n_seeds"]))
86 + for b, bn in [(1, "one"), (2, "two"), (4, "four"), (8, "eight")]:
87 + if 0 in v and b in v:
88 + macro(f"{tag}gainb{bn}", f"{100 * (1 - np.mean(v[b]) / np.mean(v[0])):.1f}")
89 + macro(f"{tag}seeds", str(raw[pre]["n_seeds"]))
90 + em = raw[pre]["em"]
91 + if "dev_iid_b0" in em and "dev_iid_b2" in em:
92 + macro(f"{tag}embzero", f"{100 * np.mean(em['dev_iid_b0']):.1f}"); macro(f"{tag}embtwo", f"{100 * np.mean(em['dev_iid_b2']):.1f}")
93 + macro(f"{tag}embeight", f"{100 * np.mean(em.get('dev_iid_b8', em['dev_iid_b2'])):.1f}")
94 + macro("vtwobzeroloss", f"{np.mean(raw[systems[0][0]]['val'][0]):.4f}" if systems[0][0] in raw else "n/a")
82 95
83 96
84 97 def table_routing(macro):
85 − files = sorted(glob.glob("results/raw/routing_*.json"))
98 + files = sorted(glob.glob("results/raw/routing_v1ctrl.json")) or sorted(glob.glob("results/raw/routing_*.json"))
86 99 if not files:
87 100 return
88 101 d = json.load(open(files[-1]))
@@ -100,6 +113,30 @@ Policy & setting & loss & edits/token & extra ops\\\midrule
100 113 continue
101 114 setting = ", ".join(f"{k}={v}" for k, v in r.items() if k in ("budget", "threshold", "lam", "kap") and v is not None)
102 115 body += f"{r['policy'].replace('_', ' ')}{' (diag.)' if r.get('diagnostic') else ''} & {setting} & {r['loss']:.4f} & {r['edits_per_token']:.2f} & {r['extra_ops_ratio']:.3f}\\\\\n"
116 + def pick(pol, **kw):
117 + for r in d["records"]:
118 + if r["policy"] == pol and all(r.get(k) == v for k, v in kw.items()):
119 + return r
120 + return None
121 + b0 = pick("budget0")
122 + if b0:
123 + macro("routingbzero", f"{b0['loss']:.4f}")
124 + for pol, tag in [("uniform", "uniform"), ("random_forced", "random"), ("fixed_pair", "pair")]:
125 + for b in (1, 2, 8):
126 + r = pick(pol, budget=b)
127 + if r:
128 + macro(f"routing{tag}b{'one' if b == 1 else 'two' if b == 2 else 'eight'}", f"{100 * (r['loss'] / b0['loss'] - 1):+.1f}")
129 + r = pick("controller_locked")
130 + if r:
131 + macro("routingctrllocked", f"{100 * (r['loss'] / b0['loss'] - 1):+.1f}"); macro("routingctrlledits", f"{r['edits_per_token']:.2f}"); macro("routingctrllambda", f"{r['lam']}")
132 + for b in (1, 8):
133 + r = pick("oracle1_greedy", budget=b)
134 + if r:
135 + macro(f"routingoracleb{'one' if b == 1 else 'eight'}", f"{100 * (r['loss'] / b0['loss'] - 1):+.1f}"); macro(f"routingoracleedits{'one' if b == 1 else 'eight'}", f"{r['edits_per_token']:.2f}")
136 + best_ent = min((r for r in d["records"] if r["policy"] == "entropy"), key=lambda r: r["loss"], default=None)
137 + best_norm = min((r for r in d["records"] if r["policy"] == "norm"), key=lambda r: r["loss"], default=None)
138 + if best_ent: macro("routingentropybest", f"{100 * (best_ent['loss'] / b0['loss'] - 1):+.1f}")
139 + if best_norm: macro("routingnormbest", f"{100 * (best_norm['loss'] / b0['loss'] - 1):+.1f}")
103 140 two = [r for r in d["records"] if r["policy"] == "oracle_two_step"]
104 141 if two:
105 142 t = two[0]
@@ -133,7 +170,7 @@ Controller features & labels & Huber & sign acc. & Brier & ECE & opportunity & c
133 170
134 171
135 172 def table_labels(macro):
136 − files = sorted(glob.glob("results/analyzed/labels_*.json"))
173 + files = sorted(glob.glob("results/analyzed/labels_labels_v1ctrl*.json")) or sorted(glob.glob("results/analyzed/labels_*.json"))
137 174 if not files:
138 175 return
139 176 d = json.load(open(files[-1]))
@@ -147,6 +184,11 @@ def table_labels(macro):
147 184 macro(f"labels{part}stoponly", f"{a['oracle_state_level_mean']:.4f}")
148 185 macro(f"labels{part}random", f"{a['random_action_mean']:.4f}")
149 186 macro(f"labels{part}nstates", f"{a['n_states']:,}")
187 + if "random_gain_high_vs_low_base_loss" in a:
188 + macro(f"labels{part}oraclehigh", f"{a['oracle_gain_high_vs_low_base_loss']['high']:.4f}")
189 + macro(f"labels{part}randomhigh", f"{a['random_gain_high_vs_low_base_loss']['high']:+.4f}")
190 + macro(f"labels{part}topdecoracle", f"{a['top_decile_base_loss']['oracle']:.3f}"); macro(f"labels{part}topdecrandom", f"{a['top_decile_base_loss']['random']:+.3f}")
191 + macro(f"labels{part}fracposhigh", f"{100 * a['frac_actions_positive_high_vs_low']['high']:.0f}"); macro(f"labels{part}fracposlow", f"{100 * a['frac_actions_positive_high_vs_low']['low']:.0f}")
150 192
151 193
152 194 def table_latency(macro):
@@ -172,3 +214,51 @@ Policy & budget & batch & median ms & p95 ms & cache bytes\\\midrule
172 214
173 215 def make_all(macro):
174 216 table_seed_matrix(macro); table_routing(macro); table_controller(macro); table_labels(macro); table_latency(macro)
217 +
218 +
219 +def table_routing_seeds(macro):
220 + """Per-seed routing summary for the v2 PRISME editors: budget 0, best fixed schedule, locked controller, oracle."""
221 + files = sorted(glob.glob("results/raw/routing_v2s*.json"))
222 + if not files:
223 + return
224 + rows = []
225 + for f in files:
226 + d = json.load(open(f)); recs = d["records"]
227 + def pick(pol, **kw):
228 + for r in recs:
229 + if r["policy"] == pol and all(r.get(k) == v for k, v in kw.items()):
230 + return r
231 + b0 = pick("budget0"); fixed = [r for r in recs if r["policy"] in ("random_forced", "uniform", "fixed_pair")]
232 + best_fixed = min(fixed, key=lambda r: r["loss"]); ctl = pick("controller_locked"); ora = pick("oracle1_greedy", budget=8)
233 + ent = min((r for r in recs if r["policy"] == "entropy"), key=lambda r: r["loss"]); nrm = min((r for r in recs if r["policy"] == "norm"), key=lambda r: r["loss"])
234 + seed = Path(f).stem.replace("routing_v2s", "")
235 + man = sorted(glob.glob(f"{RUNS}/controller_v2s{seed}_seed*/manifest.json"))
236 + ev = json.load(open(man[-1])).get("eval_calib", {}) if man else {}
237 + rows.append({"seed": seed, "b0": b0["loss"], "best_fixed": best_fixed["loss"], "best_fixed_name": f"{best_fixed['policy'].replace('_', ' ')} b={best_fixed['budget']}",
238 + "entropy": ent["loss"], "norm": nrm["loss"], "ctrl": ctl["loss"], "ctrl_edits": ctl["edits_per_token"], "ctrl_lam": ctl["lam"], "ctrl_kap": ctl["kap"],
239 + "oracle8": ora["loss"], "oracle8_edits": ora["edits_per_token"], "sign_acc": ev.get("sign_acc", float("nan")), "captured": ev.get("captured_fraction", float("nan"))})
240 + Path("results/analyzed/routing_seeds_v2.json").write_text(json.dumps(rows, indent=1))
241 + head = r"""\begin{table}[t]\centering\small
242 +\caption{Learned routing per adapter seed (v2 backbone, dev-iid, 640 items): held-out answer-span loss of the
243 +frozen backbone, of the best fixed schedule (named), of the best entropy and norm routers, of the locked
244 +controller ($\lambda,\kappa$ selected on calibration rollouts; edits per token in parentheses), and of the
245 +diagnostic greedy one-step oracle at budget 8. Calibration sign accuracy and captured fraction of the oracle
246 +opportunity for each controller.}
247 +\label{tab:routingseeds}
248 +\begin{tabular}{lrlrrrrrr}\toprule
249 +seed & budget 0 & best fixed & entropy & norm & controller (edits) & oracle b=8 (edits) & sign acc. & captured\\\midrule
250 +"""
251 + body = ""
252 + for r in rows:
253 + body += (f"{r['seed']} & {r['b0']:.4f} & {r['best_fixed']:.4f} ({r['best_fixed_name']}) & {r['entropy']:.4f} & {r['norm']:.4f} & "
254 + f"{r['ctrl']:.4f} ({r['ctrl_edits']:.2f}) & {r['oracle8']:.4f} ({r['oracle8_edits']:.2f}) & {r['sign_acc']:.3f} & {r['captured']:+.3f}\\\\\n")
255 + (OUT / "routing_seeds.tex").write_text(head + body + r"\bottomrule\end{tabular}\end{table}" + "\n")
256 + b0 = np.array([r["b0"] for r in rows]); ctl = np.array([r["ctrl"] for r in rows]); bf = np.array([r["best_fixed"] for r in rows]); ora = np.array([r["oracle8"] for r in rows])
257 + macro("vtwoctrlseeds", str(len(rows))); macro("vtwoctrlrel", f"{100 * (ctl / b0 - 1).mean():+.1f}"); macro("vtwoctrlrelsd", f"{100 * (ctl / b0 - 1).std(ddof=1) if len(rows) > 1 else 0:.1f}")
258 + macro("vtwobestfixedrel", f"{100 * (bf / b0 - 1).mean():+.1f}"); macro("vtwooraclerel", f"{100 * (ora / b0 - 1).mean():+.1f}")
259 + macro("vtwoctrlbetter", str(int((ctl < b0).sum())))
260 +
261 +
262 +_old_make_all = make_all
263 +def make_all(macro): # noqa: F811
264 + _old_make_all(macro); table_routing_seeds(macro)
added scripts/reproduce_pilot.sh +65 −0
@@ -0,0 +1,65 @@
1 +#!/bin/bash
2 +# Miniature end-to-end replication on CPU (minutes): backbone -> editor bootstrap -> counterfactual labels ->
3 +# controller -> routing evaluation. Produces real run directories (tag "mini") and results/raw/routing_mini.json.
4 +set -e
5 +cd "$(dirname "$0")/.."
6 +PY=.venv/bin/python
7 +export PRISME_MINI=1
8 +test -f data/splits/synthetic_v1/train.jsonl || $PY -c "import sys; sys.path.insert(0,'.'); from data.generators.synthetic import write_splits; write_splits('data/splits/synthetic_v1')"
9 +$PY - <<'PY'
10 +import yaml
11 +model = {"name": "mini", "vocab_size": 128, "d_model": 96, "n_layers": 2, "n_heads": 4, "n_kv_heads": 2, "ffn_width": 256, "max_seq_len": 320,
12 + "w": 32, "ffn_w": 96, "n_slots": 16, "n_editor_heads": 2, "r_max": 8, "readout_zero_init": False, "readout_init_std": 0.2,
13 + "ctrl_input": "h", "ctrl_use_entropy": True}
14 +data = {"kind": "synthetic", "tokenizer": "char", "splits_dir": "data/splits/synthetic_v1", "loss_span": "all", "eval_splits": ["dev_iid"]}
15 +bb = {"name": "mini_backbone", "seed": 1, "mode": "backbone_only", "model": model, "init": {"freeze_backbone": False}, "data": data,
16 + "train": {"batch_size": 16, "seq_len": 320, "steps": 150, "lr": 1e-3, "warmup": 10, "weight_decay": 0.1, "grad_clip": 1.0, "log_every": 50, "eval_every": 150, "eval_batches": 2, "ckpt_every": 150},
17 + "em_max_items": 32}
18 +yaml.safe_dump(bb, open("configs/experiments/mini_backbone.yaml", "w"), sort_keys=False)
19 +PY
20 +$PY -m training.train_editor --config configs/experiments/mini_backbone.yaml --device cpu --run-root experiments/runs > experiments/logs/mini_backbone.log 2>&1
21 +BB=$(ls -td experiments/runs/mini_backbone_seed* | head -1)
22 +$PY - "$BB" <<'PY'
23 +import sys, yaml
24 +bb = sys.argv[1]
25 +e = yaml.safe_load(open("configs/experiments/mini_backbone.yaml"))
26 +e.update(name="mini_editor", mode="prisme", init={"backbone_checkpoint": f"{bb}/checkpoint", "freeze_backbone": True, "auto_readout_scale": True},
27 + budgets=[0, 1, 2, 4, 8], budget_probs=[0.2] * 5, budget_per="position", em_budgets=[0, 2], em_max_items=32)
28 +e["data"]["loss_span"] = "answer"; e["train"].update(steps=120, eval_every=120, ckpt_every=120)
29 +yaml.safe_dump(e, open("configs/experiments/mini_editor.yaml", "w"), sort_keys=False)
30 +PY
31 +$PY -m training.train_editor --config configs/experiments/mini_editor.yaml --device cpu > experiments/logs/mini_editor.log 2>&1
32 +ED=$(ls -td experiments/runs/mini_editor_seed* | head -1)
33 +$PY - "$ED" <<'PY'
34 +import sys, yaml
35 +ed = sys.argv[1]
36 +lab = {"name": "labels_mini", "seed": 1, "mode": "prisme", "model": "from_checkpoint", "checkpoint": f"{ed}/checkpoint", "use_policy": False,
37 + "data": {"kind": "synthetic", "tokenizer": "char", "splits_dir": "data/splits/synthetic_v1", "loss_span": "answer"},
38 + "collect": {"batch_size": 16, "seq_len": 320, "max_prefix": 2, "audit_every": 2},
39 + "partitions": {"train": {"split": "ctrl_train", "n_batches": 12, "stream_seed": 11}, "calib": {"split": "calib", "n_batches": 4, "stream_seed": 12}}}
40 +yaml.safe_dump(lab, open("configs/experiments/labels_mini.yaml", "w"), sort_keys=False)
41 +PY
42 +$PY -m training.collect_interventions --config configs/experiments/labels_mini.yaml --device cpu > experiments/logs/labels_mini.log 2>&1
43 +L=$(ls -td experiments/runs/labels_mini_seed* | head -1)
44 +$PY - "$ED" "$L" <<'PY'
45 +import sys, yaml
46 +ed, L = sys.argv[1], sys.argv[2]
47 +ctl = {"name": "controller_mini", "seed": 1, "mode": "prisme", "model": "from_checkpoint", "checkpoint": f"{ed}/checkpoint",
48 + "labels": {"train": f"{L}/labels_train.pt", "calib": f"{L}/labels_calib.pt"},
49 + "train": {"steps": 60, "lr": 1e-3, "weight_decay": 0.01, "alpha": 1.0, "beta": 0.5, "gamma": 0.1, "huber_delta": 0.1, "log_every": 20, "eval_every": 30, "seq_len": 320},
50 + "lambda_grid": [0.0, 0.3], "kappa_grid": [0.0], "sweep_batches": 2, "max_budget": 4, "selection_rule": "max_loss_reduction_per_ops"}
51 +yaml.safe_dump(ctl, open("configs/experiments/controller_mini.yaml", "w"), sort_keys=False)
52 +PY
53 +$PY -m training.train_controller --config configs/experiments/controller_mini.yaml --device cpu > experiments/logs/controller_mini.log 2>&1
54 +C=$(ls -td experiments/runs/controller_mini_seed* | head -1)
55 +$PY - "$C" <<'PY'
56 +import sys, yaml
57 +c = sys.argv[1]
58 +rt = {"seed": 1, "mode": "prisme", "model": "from_checkpoint", "checkpoint": f"{c}/checkpoint",
59 + "data": {"kind": "synthetic", "tokenizer": "char", "splits_dir": "data/splits/synthetic_v1", "loss_span": "answer", "split": "dev_iid"},
60 + "batch_size": 16, "seq_len": 320, "n_batches": 2, "budgets": [1, 2], "oracle_batches": 1, "two_step_batches": 1,
61 + "entropy_thresholds": [1.0], "norm_thresholds": [0.1], "lambda_grid": [0.0]}
62 +yaml.safe_dump(rt, open("configs/experiments/routing_mini.yaml", "w"), sort_keys=False)
63 +PY
64 +$PY -m evaluation.routing --config configs/experiments/routing_mini.yaml --split dev_iid --device cpu --out results/raw/routing_mini.json > experiments/logs/routing_mini.log 2>&1
65 +echo "MINI PIPELINE OK: $BB -> $ED -> $L -> $C -> results/raw/routing_mini.json"
added scripts/run_index.py +28 −0
@@ -0,0 +1,28 @@
1 +#!/usr/bin/env python
2 +"""Build reproducibility/run_index.md from every run manifest found locally and on the listed nodes (read-only)."""
3 +import glob, json, subprocess, sys
4 +from pathlib import Path
5 +
6 +NODES = sys.argv[1:] or ["M3U96a", "M3U96b", "M2U64", "M4M64c", "M4M64a", "M5M36", "M4BP48", "M4BP36", "m4mn"]
7 +rows = {}
8 +for mp in glob.glob("experiments/runs/*/manifest.json"):
9 + m = json.load(open(mp)); rows[m["run_id"]] = (m, "laptop")
10 +for n in NODES:
11 + try:
12 + out = subprocess.run(["ssh", "-o", "ConnectTimeout=8", n, "cat ~/wp31/prisme-slm/experiments/runs/*/manifest.json 2>/dev/null | python3 -c 'import sys,json; d=sys.stdin.read().replace(\"}\\n{\",\"}\\x00{\"); print(\"\\x00\".join(d.split(\"\\x00\")))'"],
13 + capture_output=True, text=True, timeout=40).stdout
14 + for chunk in out.split("\x00"):
15 + chunk = chunk.strip()
16 + if chunk:
17 + try:
18 + m = json.loads(chunk); rows.setdefault(m["run_id"], (m, n))
19 + except json.JSONDecodeError:
20 + pass
21 + except Exception as e: # noqa: BLE001
22 + print("skip", n, e)
23 +lines = ["# Run index (auto-generated by scripts/run_index.py)", "", "| run_id | state | host | device | wall s | train tokens | peak mem (GB) | commit | notes |", "|---|---|---|---|---|---|---|---|---|"]
24 +for rid, (m, where) in sorted(rows.items()):
25 + pm = m.get("peak_memory_bytes"); pm = f"{pm/1e9:.1f}" if isinstance(pm, (int, float)) and pm else ""
26 + lines.append(f"| {rid} | {m.get('state','')} | {where} | {m.get('device','')} | {m.get('wall_seconds','')} | {m.get('train_tokens','')} | {pm} | {str(m.get('code_commit',''))[:8]} | {m.get('mode','')} |")
27 +Path("reproducibility/run_index.md").write_text("\n".join(lines) + "\n")
28 +print(len(rows), "runs indexed")
29
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