SRT-Adapter v8a: Peer-Review Release

A peer-review distribution of the Semiotic-Reflexive Transformer Adapter (SRT-Adapter), v8a generation, trained on top of a frozen Qwen/Qwen2.5-7B. Includes the trained weights, an inference-only loader, evaluation data, benchmark artifacts, and the paper.

Custom-code model. This is not an AutoModel-loadable checkpoint. AutoModel.from_pretrained(...) will not work. Clone or download this repository and load the weights through the bundled SRTAdapter class. See How to get started with the model.

Training and research source code is held back during patent and publication review. This package ships the architecture as inference-only Python, sufficient to load the weights and read out all four semiotic channels. Training pipelines, loss code, dataset construction, and the wider SRT framework are not included.

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Model details

The SRT-Adapter bolts semiotic awareness onto a frozen 7B language model. It does not modify a single backbone parameter and does not degrade language modeling quality. It exposes four new readouts at every token position:

• a continuous 64-D community vector (which discourse community is speaking)
• per-layer divergence vectors (where meaning forks across communities)
• a continuous reflexivity estimate $\hat{r}$ (how contested is this token)
• a binary regime classification (subcritical / supercritical)

The v8a generation is the headline result of the paper: removing the discrete prototype basis used in v3–v7 leaves cross-entropy unchanged while substantially improving every encoder-geometry metric.

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Evaluation

All measured on Qwen/Qwen2.5-7B, with no backbone parameters touched.

Full reporting and version history (v3 → v8b): paper §5 and Appendix A.

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Lineage and validation history

This adapter is the production-scaling stage of a multi-year research program on computational semiotics. The architectural commitments and training objectives were validated in two prior stages on different backbones and datasets before this release. Treat the v8a numbers below as the latest checkpoint in a longer arc, not as a fresh proposal.

• Stage 1 (synthetic validation, 2026-03). Four core architectural claims (subspace specialization, community differentiation, divergence tracking, bifurcation detection) were tested on synthetic data with planted divergence signals. All four passed: linear-probe margin $\geq 0.15$ on each Peircean subspace, $3.28\times$ contested-vs-neutral cosine ratio, Spearman $\rho = 0.822$ on divergence tracking, 100% regime classification with $\Delta \hat{r} = 0.659$. Full record: VALIDATION_HISTORY.md, Stage 1.
• Stage 2 (natural-language validation, 2026-03). The full five-test suite was re-run on the Supabase semiotic news corpus (19K articles, 5 political communities, 141K Peircean sign annotations). All five tests passed at required thresholds: silhouette $1.45\times$, $2.29\times$ contested-vs-neutral divergence norm ratio, Pearson $r = 0.884$ correlation between $\hat{r}$ and external polarization, $1.31\times$ cross-topic transfer ratio, and 85% regime classification accuracy on held-out curated passages. Full record: VALIDATION_HISTORY.md, Stage 2.
• Stage 3 Phase 1 (frozen-backbone integration on TinyLlama-1.1B, 2026-03 to 2026-04). 105 training rounds (R21 through R105) on a frozen TinyLlama-1.1B backbone established that the semiotic modules transfer to production backbones. Community detection silhouette improved to $6.93\times$; $\hat{r}$ correlation with external polarization remained robust at 0.66; curated-passage regime classification reached 85%. Two tests plateaued on the sparse 2-community Supabase data (MAH divergence ratio at $1.05$ to $1.10\times$ vs. required $2.0\times$; cross-topic transfer at $1.03$ to $1.04\times$ vs. required $1.3\times$). The plateau triggered a data-first pivot to a denser corpus and a backbone capable of supporting it.
• Stage 3 Scalable Implementation (this release, 2026-04). v5 through v8a port the validated architecture onto Qwen 2.5-7B and the Reddit Discourse Corpus (35 communities, 1M training samples). v8a is the current best checkpoint. The headline gain is not the discovery of bifurcation detection (already established in Stages 1 and 2) but the demonstration that the framework scales to a 7B frozen backbone at 0.19% parameter overhead, with $\sim 325\times$ trajectory-anisotropy expansion and Reddit recall@1 at $16.7\times$ chance.

For the program-level theoretical foundation see Lancaster (2025), "The Treachery of Signs," SSRN 5987495. For the full prior architecture specification and Stage 1 + Stage 2 results see Lancaster (2026a), SSRN 6349978. The present paper reports Stage 3 Phase 1 plus the v5 through v8a Stage 3 Scalable progression.

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Versions and roadmap

• v8a (this release). Headline result. Removing the discrete prototype basis used in v3 through v7 leaves cross-entropy unchanged while substantially improving every encoder-geometry metric. All paper §5 numbers are measured on this checkpoint.
• v8b. A falsification run included in the paper. Pushing the supervised-contrastive objective harder partially undoes v8a's gains. Documented as a negative result.
• v9 (in training). An experimental generation that adds a target-norm penalty on the inject-back arm to attack the central open problem from §6.3 (the inject-back arm carries no measurable signal). Will be released as a follow-up revision on this repo only if it improves on v8a across multiple metrics. Otherwise it will be documented as an additional ablation in a future paper revision and v8a will remain canonical.

If you are reviewing the paper, use v8a. The model card will be updated with a revision tag if v9 ships as an upgrade.

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Package contents

srt-adapter-v8a/
├── README.md                   ← you are here
├── LICENSE                     ← Apache-2.0
├── paper.pdf                   ← preprint with full architecture spec (§3 + Appendix A)
├── VALIDATION_HISTORY.md       ← Stage 1 + Stage 2 + Stage 3 Phase 1 evidence summary
├── config.json                 ← v8a hyperparameters and module dimensions
├── adapter.safetensors         ← v8a weights (~28 MB, safetensors, preferred)
├── adapter.pt                  ← v8a weights (~28 MB, PyTorch state-dict, legacy)
├── requirements.txt            ← torch + transformers + numpy + safetensors
├── src/
│   └── srt/                    ← inference-only model code
│       ├── config.py           ← config dataclasses
│       ├── adapter.py          ← SRTAdapter (frozen-backbone wrapper)
│       └── modules/            ← CDH, MAH, RRM, BEN
├── examples/
│   ├── README.md
│   └── load_and_score.py       ← end-to-end demo, prints all 4 readouts
├── data/
│   ├── DATA.md                 ← schema + reproduction instructions for the full corpus
│   ├── NOTICE                  ← copyright notice for bundled Reddit comments
│   ├── val_200.jsonl           ← 200 held-out samples with per-token r_true labels
│   └── archetypes.json         ← 33-class out-of-distribution archetype probe
└── benchmarks/
    ├── curated_metrics.json    ← reference metrics from paper §5
    └── curated_traces.json     ← per-token trace dumps used in plots

Note on benchmarks/curated_metrics.json. This file reports v8a numbers on a 100-passage curated probe (regime accuracy, per-layer divergence norms, community-protocol activations). The near-zero $\hat{r}$ vs $r_{\text{true}}$ Pearson on this slice is expected and is discussed in paper §5.7 ($\hat{r}$ tracks information density as much as contestedness on short curated passages). The headline Pearson and recall numbers in the Evaluation table above come from the full Reddit validation split, not this curated probe.

What's NOT in this package

• Training pipelines, loss functions, and the dataset construction code. Held back during patent and publication review.
• The wider SRT research framework (annotation pipeline, ablation harness, sweep tooling, instrumentation scripts).
• The full 1M-sample training corpus. Reddit's redistribution terms preclude bundling it; see data/DATA.md for schema and reproduction.
• The Qwen 2.5-7B backbone weights. Pulled from HuggingFace under the Tongyi Qianwen License.

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How to get started with the model

# 1. set up a venv (Python ≥ 3.10) and install deps
pip install -r requirements.txt

# 2. score a passage end-to-end
cd examples
python load_and_score.py --text "Vaccine mandates are an obvious public health win."

First run downloads Qwen/Qwen2.5-7B (~15 GB) from HuggingFace. The example loads adapter.safetensors by default and falls back to adapter.pt if the safetensors file is absent.

For a programmatic-use snippet, see examples/README.md.

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Uses

The adapter is most useful as a diagnostic instrument for what a frozen language model already encodes about discourse structure. Some concrete patterns:

1. Per-token contestedness scoring

Use BEN's regime logits to flag which token positions in a passage sit in a contested-meaning regime. Useful for:

• highlighting ideologically loaded spans in user-generated text
• routing inputs to human review when supercritical regime probability exceeds a threshold
• annotating debate transcripts, news comment threads, or policy documents with per-token tension scores

2. Unsupervised discourse-community clustering

The 64-D community vector from CDH supports nearest-neighbor retrieval and clustering without ever needing community labels at inference. Useful for:

• segmenting a corpus by latent discourse community (recall@1 = 16.7× chance on 35 known communities)
• retrieving thematically aligned passages for downstream modeling
• detecting coordinated-inauthentic-behavior signatures via tight community clustering of supposedly independent accounts

3. Counterfactual community-conditioned decoding

By steering the community vector at decode time, you can ask "how would this community complete this sentence?" Useful for:

• cross-community simulation studies (the paper measures 0.95 mean disagreement on contested prompts vs 0.00 on factual ones)
• synthetic-disagreement generation for training argument-mining or stance-detection systems
• audit / red-team probes that surface latent assumptions across reader communities

4. Hallucination signal for retrieval and generation

$\hat{r}$ correlates with epistemic instability and gives a usable zero-shot AUROC of 0.573 on TruthfulQA. Useful as:

• a feature in hallucination classifiers (alongside other signals)
• a per-token routing signal for retrieval-augmented generation: high $\hat{r}$ tokens warrant a retrieval round
• a calibration probe in evaluation pipelines

5. Feature extraction for downstream classifiers

The MAH divergence vectors (3 × 256-D per token) are usable as semiotic features in any downstream classifier without retraining the backbone or the adapter. Useful for:

• stance and frame classification on small labeled sets
• author / community attribution
• topical drift detection across long documents

6. Reviewer probes against the paper's claims

data/val_200.jsonl ships with per-token r_true labels. Reviewers can validate claims about $\hat{r}$ correlation, regime accuracy, and divergence norms against the bundled benchmarks/curated_metrics.json without rerunning training.

Out-of-scope uses

• Treating $\hat{r}$ as a calibrated truth score. It correlates with information density as much as contestedness; see paper §5.7.
• Expecting the inject-back arm to noticeably change generation. The observation half is well-formed; the intervention half does not yet carry signal. See paper §6.3 and §6.5.
• Safety-critical decisions without independent validation. The adapter is a research instrument, not a deployed safety system.
• Generation models. The adapter does not improve text generation quality; it adds structured side-channel readouts.

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Training details

Training data

• Corpus: ~1M Reddit comments × 35 discourse communities, with per-token reflexivity labels and chain-of-interpretants annotations.
• The full corpus is not redistributed; see data/DATA.md for schema and reproduction.
• A 200-sample held-out evaluation subset is bundled in data/val_200.jsonl under the terms in data/NOTICE.

Training procedure

• Optimizer: AdamW, learning rate 3e-4, batch 16, max sequence length 512.
• Schedule: 3 epochs over 1M samples, early-stopped at ~10K steps based on validation cross-entropy.
• Backbone: frozen, bf16. No backbone parameter is updated.
• Hardware: single NVIDIA A6000 (48 GB).

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Architecture summary

Full module specifications and loss decomposition: paper §3, §4, and Appendix A.

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Bias, risks, and limitations

The paper publishes its failures in full. In short:

1. The inject-back arm currently carries no measurable signal. Ablating it changes nothing on validation. Central open problem; v9 targets this.
2. $\hat{r}$ tracks information density as much as contestedness. Useful signal, not a clean reading of "is this token contested."
3. The 33 archetypes collapse to ~4 functional clusters. Read as a finding (the geometry resists fine-grained quantization), not a classification bug.
4. v8b is a falsification. Pushing the supervised-contrastive objective harder partially undoes v8a's gains.
5. Backbone dependence. Only validated on Qwen 2.5-7B. Module dimensions are tied to the backbone's hidden size (3584).
6. Training corpus bias. Reddit comments skew English, US-centric, and over-represent argumentative and politically charged communities. The community vector geometry inherits those biases. Treat community recall and counterfactual-decoding numbers as descriptive, not normative.

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Citation

@article{lancaster2026srtadapter,
  title   = {Semiotic Taps: Lightweight Adapter Modules for Bifurcation
             Detection in Frozen Language Models},
  author  = {Lancaster, James Burton},
  year    = {2026},
  note    = {Preprint, peer-review distribution}
}

@article{lancaster2026srtpreprint,
  title   = {Semiotic-Reflexive Language Model Training: Bridging
             Interpretive Bifurcations through Metapragmatic Chain
             Architectures and Embodied Grounding},
  author  = {Lancaster, James Burton},
  year    = {2026},
  journal = {SSRN},
  url     = {https://papers.ssrn.com/abstract=6349978}
}

@article{lancaster2025treachery,
  title   = {The Treachery of Signs: Semiotic Mediation, Pitchfork
             Bifurcation, and Political Polarization in Algorithmically
             Curated Societies},
  author  = {Lancaster, James Burton},
  year    = {2025},
  journal = {SSRN},
  url     = {https://papers.ssrn.com/abstract=5987495}
}

Full reference list (Peirce, Wildgen, Anderson, Silverstein, Kockelman, Evans, von Foerster, Maturana & Varela, Leighton, VanSaders, Bennett, Landauer, Parrondo, and others) in paper.pdf.

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License

• Adapter weights, inference code, config, benchmark artifacts, archetype data, and this package: Apache-2.0 (LICENSE).
• Validation samples in data/val_200.jsonl: included for research reproduction; comments remain the intellectual property of their original Reddit authors. See data/NOTICE.
• Training pipelines, dataset construction, and the wider SRT framework: held back during patent and publication review. Not included.
• Qwen 2.5-7B backbone: governed separately by the Tongyi Qianwen License.

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Contact

For training-code access, reproduction questions, or follow-up: see paper PDF for current author contact details.