Kai9987kai/Supermix-Tether

Supermix Tether

A small CPU-local model for testing whether visual specialists change a receiver's answers in the right way, and whether a receiver can survive zero-shot majority specialist corruption using dark-knowledge forensics and perturbation-localized invariance.

Tether trains a compact receiver (2,762 parameters in v1, 2,786 parameters in v2) over frozen digit classifiers. It compares ordinary supervised learning (passive), donor dropout (dropout), an IIT-style intervention control (iit), single-loss ablations (effect_only, invariance_only), a row-shifted mechanistic pairing control (unpaired), and the dual-objective tether receiver across two separately recorded eight-seed confirmation studies:

• v0.1 (outputs/confirmation, docs/frozen-protocol.json): Tests output-level probability L2 residual matching and cross-expert majority consensus (protocol="v1"). Reports a transparent negative finding for naive L2 residual matching (-0.07 pp vs iit, -0.10 pp vs unpaired due to shared-bias cancellation) and shows that cross-expert consensus collapses to 1.39% under zero-shot 2-of-3 specialist corruption (damage_two).
• v0.2 (outputs/v2-confirmation, docs/v2-protocol.json): Replaces cross-expert consensus with intrinsic dark-knowledge specialist reliability (runner-up tail entropy $H(p_{-1})/\log 9$ and dispersion) and replaces naive output L2 differences with counterfactual co-variation & perturbation-localized invariance (protocol="v2"). Across seeds 101..108, v0.2 tether achieves 97.75% zero-shot accuracy on 2-of-3 specialist corruption (damage_two) — beating iit (80.76%) by +16.99 pp (95% CI [+12.43 pp, +21.44 pp], positive on all 8/8 seeds) and unpaired (11.18%) by +86.57 pp — while beating unpaired on heldout_independent by +4.15 pp (98.19% vs 94.04%) and matching the 98.19% frozen-donor oracle ceiling.
• v0.3 (Epistemic Trust Gating, Causal Attribution & Live Playground):
  • Epistemic Trust Gating & Safe Abstention: Detects total sensor/specialist spoofing (damage_common = 3/3 specialists corrupted) with AUROC = 1.0000 using tail dark-knowledge entropy $\tau = \max_e (1 - H(p_{s,e,-1})/\log 9)^+$. Setting threshold $\theta = 0.10$ safely abstains (selective_prediction = -1) on 100% of catastrophic failures with 0% false rejections on intact and partially damaged inputs.
  • Mechanistic Causal Attribution: Computes exact input $\times$ gradient saliency maps ($\mathcal{A}{s,e} = \sum_c P{s,e,c} |\partial \hat{z}/\partial P_{s,e,c}|$) revealing pathway-specific causal tethering.
  • Cross-Framework ONNX Export: Exports both Receiver and DonorBank to ONNX format with strict ONNX Runtime numerical verification within atol=1e-5: python -m tether export <checkpoint> --output <dir>.
  • Interactive Live Playground & Tamper Simulator: In-browser 8×8 drawing canvas, real digit presets, live adversarial tampering simulator, real-time epistemic trust gauge, and causal attribution bars served on http://127.0.0.1:8797.

Full checkpoints across all seeds and arms are released on Hugging Face: huggingface.co/Kai9987kai/Supermix-Tether. Full tables, mathematical proofs, and interpretation boundaries are recorded in results and paper.

Run on this PC

Requires Python 3.12+ and a CPU installation of PyTorch. From this folder:

python -m pip install -e ".[dev,export]"
python -m tether replay outputs/v2-confirmation --strict
python -m tether replay outputs/confirmation --strict
python -m tether export outputs/v2-confirmation/checkpoints/seed-101-tether.pt --output exports/seed-101
python -m tether serve outputs/v2-confirmation --port 8797

To train a fresh development study into a new directory:

python -m tether study --protocol v2 --output outputs/dev-run --seeds 11,12 --steps 400

Open the local lab. The viewer displays saved comparisons (with selectable evaluation conditions and metrics) and runs live checkpoint inference on frozen evaluation examples across seeds (101..108) and intervention modes (factual, relevant, irrelevant, damaged 1/3, damage_two 2/3, erased, context_shift), exposing both learned routing weights and per-specialist dark-knowledge trust scores. It does not train from browser inputs.

Training uses two CPU threads by default. No GPU, remote checkpoint, API key or network access is needed after dependencies are installed: the image data are bundled with scikit-learn. Runtime depends on the machine and configuration; no training-time estimate is promised here.

A study refuses to overwrite an existing output directory. Use a new path for a new run. python -m tether --help and python -m tether study --help show the supported controls. outputs/confirmation contains the recorded eight-seed study under docs/frozen-protocol.json, and docs/results.md reports its full findings.

What the model learns

Three source images are sampled for each task. Each image is evaluated by the same three heterogeneous visual specialists: hidden widths 32, 48 and 64, with ReLU, tanh and GELU respectively. Their probabilities form a [batch,3 sources,3 experts,10 classes] tensor. The specialists share training images and lineage; nine probability vectors are not nine independent evidence sources.

The query asks for identity, successor modulo ten, or 9 - digit on one source. A correlated context cue offers an easy shortcut during acquisition. Source routing, expert reliability weighting, task transformation matrices and the context branch are learned. Correct source selection and digit transformations are not hard-coded into receiver inference.

flowchart LR
    I[Three digit images] --> D[Frozen visual specialists]
    D --> R[Learned reliability weights]
    Q[Source and operation query] --> S[Learned source routing]
    R --> S
    S --> T[Learned task matrices]
    C[Proxy context cue] --> B[Learned shortcut]
    T --> O[Ten output logits]
    B --> O

The 1,797 images come from scikit-learn digits. Image identities are stratified into 1,078 training, 359 validation and 360 test images before tasks are built. This is a new within-dataset split, not the original UCI writer-disjoint protocol. The data card records the source attribution, transformations and sampling limits.

Seven matched receiver arms

Every step evaluates the same receiver architecture four times. Initialization, task draws and frozen donor bank match across arms within a seed. Tether and the IIT-style control receive exactly the same four labeled views.

Arm	Training objective
passive	Four repeated factual cross-entropies
dropout	Four factual cross-entropies with deterministic posterior-to-uniform donor masks
iit	Mean cross-entropy on factual, relevant, irrelevant and damaged views
effect_only	IIT-style cross-entropy plus relevant probability-effect matching
invariance_only	IIT-style cross-entropy plus irrelevant/damaged probability invariance
tether	IIT-style cross-entropy plus both additional losses
unpaired	Same cross-entropy/invariance as Tether; relevant residuals shifted across rows before effect matching

The unpaired control retains marginal squared-error terms while breaking the correct factual/relevant pairing. A benefit from pairing requires Tether to outperform this control, as well as an informative comparison with IIT-style training.

Relevant swaps replace all specialists at the queried source with another same-split image of a different digit. Irrelevant swaps change a different source. Damage fabricates a confident posterior by shifting a donor prediction without consulting the true label; it can accidentally correct an existing error. The original context cue is retained in all intervention views. See the paper for exact losses and evaluation conditions.

Equal view counts do not mean equal FLOPs: extra losses add arithmetic. Passive/dropout also see less intervention information, so they cannot isolate the effect of the additional losses.

Evidence and replay

Studies save configuration, dataset and split identity, source hashes, selected checkpoints, complete per-arm/per-seed metrics, inference traces and artifact hashes. Selection uses independent-cue validation examples from seen task combinations. Under the corrected phase policy, development conditions use validation images and confirmation conditions use test images. Fresh seeds still reuse the same underlying dataset and image partition.

An earlier pipeline pilot evaluated the test pool. It is a disclosed engineering pilot, not independent validation; no data, model or training hyperparameters were selected from its accuracies. See results for its exact source revision and the separate fixed study.

replay recomputes trace metrics and paired summaries. replay --strict also checks the recorded source fingerprint, restores the frozen donors and receiver, regenerates evaluation tasks and compares checkpoint inference within numerical tolerances. This establishes artifact and inference consistency. It does not rerun training or establish scientific truth.

Read research and prior art, the model card, the 27-repository review index, and the selected-file reports in docs/reviews. Upstream projects and papers were inspected as sources; their experiments were not reproduced here. Tether source was written for this project rather than copied from those repositories.

Verification

python -m pytest -q

Tests exercise data separation, intervention semantics, literal objective counterexamples, gradient flow, bounded learning, checkpoint replay, tampering rejection and local HTTP behavior. Current command output is the authority for whether verification passes.

Limits and licensing

Tasks are one-step source/operation recombinations on a small digit dataset. There is no language model, recurrent program execution, test-time adaptation, continual learning or guarantee of general compositional reasoning. Softmax values and learned weights are not calibrated confidence or causal explanations. Donor-erasure gaps must be read alongside accuracy and damage results: dependence can be brittle.

The objective builds on IIT and causal distillation. The dual-loss study is a hypothesis, not a claim of global novelty. No real-world safety, biological or consciousness claim is made.

No license has yet been assigned to the newly written project source. Dependencies and data retain their own terms; THIRD_PARTY.md records them. Citation of an upstream project does not import its license into Tether.