mild-rgb/ouro-1.4b-thinking-evals

Ouro-1.4B-Thinking: small eval banks + activation accounting

Model: ByteDance/Ouro-1.4B-Thinking on a Tesla T4, fp16, transformers 4.54.1, 2026-09-20.

bank	n	score	hit token cap	generated tokens
GSM8K (test, seed 0)	24	16/24 (67%)	8	15310
MBPP sanitized (test, seed 0)	16	9/16 (56%)	7 (no code block)	16652

Sampling: temperature 1.0, top_p 0.7; math capped at 1024 new tokens, code at 1536. MBPP is scored by executing the hidden asserts on the last ```python block in the response. Every MBPP failure here is a response that was still reasoning when it hit the token cap, so no code block was produced.

Space needed to save all activations (fp16, all 96 layer-loop passes, per token)

tier	KB/token
A residual stream only (layer output)	384
B + attention q,k,v and attn output	1536
C + MLP gate, up, act(gate)*up	4704
D + the two RMSNorm outputs	5472

The model ran all 4 loops on every token (no early exit at threshold 1.0). Attention probability maps scale with sequence length squared and are not included. Across the 38,162 tokens processed in these two runs, the residual-stream tier alone would be 14 GB and the fullest tier 199 GB.

Files

• gsm8k_results.jsonl, mbpp_results.jsonl: one row per question with the full model response and the score.
• summary.json: scores, token counts, timings, config and the activation size table.
• ouro_eval.ipynb: the Colab notebook, including the transformers-4.54 cache patch needed to run the model.

Loop-detection linear probes (probe/)

Residual stream recorded after 0, 6, 18 and 24 layers in every loop for 31,112 tokens (40 prompt+response sequences from the banks above, teacher-forced, capped at 1024 tokens). "After 0 layers" is the raw embedding in loop 1 and RMSNorm(previous loop output) in loops 2-4, because Ouro applies its final norm between loops. One 4-way logistic-regression probe per point, split by sequence (every 4th sequence held out). "Norm-only" is a control probe that sees only log ||h||.

point	linear probe (test)	train	norm-only control	per-loop recall (loops 1-4)
after 0 layers	98.5%	99.7%	57.1%	100% / 100% / 96% / 99%
after 6 layers	97.3%	99.0%	65.4%	100% / 99% / 94% / 96%
after 18 layers	86.8%	92.7%	47.3%	97% / 88% / 70% / 92%
after 24 layers	95.5%	98.3%	31.3%	99% / 93% / 95% / 95%

Chance is 25%. Loop 1 is always easy to spot; confusions are almost all between adjacent later loops.

Files: probe/after{0,6,18,24}.npy are fp16 arrays of shape [tokens, loop, hidden] = [31112, 4, 2048]; probe/meta.npz gives seq_id, position, token_id and bank (0 = math, 1 = code) per token; probe/loop_probes.pt holds per-point weight, bias, mu, sd (apply as W @ ((h - mu) / sd) + b, argmax = loop index 0-3); probe/probe_results.json has the accuracies and confusion matrices.

Per-loop logit lens on one chain of thought (logit_lens_example.json)

lm_head(norm(h)) read at the end of each loop for every generated token of one correct GSM8K answer, i.e. what the model would have said had it exited after that loop. Top-1 accuracy vs the actual next token by loop: loop 1: 78% / loop 2: 87% / loop 3: 89% / loop 4: 88%. Loop 1 already matches the final loop on 80% of tokens; later loops mostly repair function words and clause openers, not digits.

Contrastive Activation Addition (caa/)

Steering vectors (mean residual difference at the final contrast token, after 6/12/18 layers, per loop) for sentiment and five more concepts (tense, number, animal/vehicle, yes/no answer bias, digit magnitude). Injection is alpha x local residual norm x unit vector, every configuration paired with a random direction of the same norm, damage measured as NLL on a neutral paragraph. Files: caa_sentiment.json (raw-multiplier sweep, too strong), caa_sentiment_v2.json (calibrated sweep), caa_sentiment_vectors.pt, caa_perturbation_decay.json (a loop-1 injection tracked through loops 2-4), caa_concepts.json (five concepts: sweep, controls, damage, decay, generations).

Findings: steering works; the effect grows monotonically with the injection loop (loop 4 >> loop 1) and vectors from different loops share a direction (cosine 0.8-1.0). The loop is only weakly contractive (a random perturbation keeps ~half its norm after three loops); it carries and even amplifies syntactic pushes (tense, number), rotates lexical pushes (sentiment, category) off their direction at ~1/3 per loop, and digit magnitude barely steers at all.

Steering self-detection (steer_detect/)

48 prompts x 7 conditions (natural, tone-prompted negative/positive, CAA-steered sentiment negative/positive, steered past tense, steered animals; loop-4 injection after 12 layers at 20% of residual norm), 60 sampled tokens each, teacher-forced through the unsteered model. Probes on the residual stream (after 12 layers per loop, end of loop 4), prompt-level split. A sentiment-steered-vs-unsteered probe reaches text AUC ~0.8 and is not a sentiment probe, but it does not transfer to other steered concepts (tense ~0.5, animals ~0.65; leave-one-concept-out 0.5-0.7), and the model's own surprise tracks content typicality rather than steering. No concept-general "I did not write this" signal. Files: steer_detect_texts.json, steer_detect_results.json, steer_detect.ipynb.