---
license: apache-2.0
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
  - meta-llama/Llama-3.2-1B-Instruct
  - google/gemma-2-2b-it
tags:
  - introspection
  - reflexion
  - meta-attention
  - self-correction
  - calibration
  - cross-attention
language:
  - en
library_name: pytorch
pipeline_tag: text-generation
---

# Meta-transformers — Architectural Introspection for LLMs

This repository hosts the **weights, data, and results** of the architectural-introspection
experiments ("meta-transformers"). The idea: instead of text-based reflection, give a model
direct access to its own activations through a learnable feedback loop.

The base model stays **frozen**. Only a thin introspection pathway is trained (~188M params
on Llama-3.1-8B, ~2.3% of the base): an activation encoder + 32 BottleneckCrossAttention
modules. At inference the model runs two passes — it reads its own per-layer activations,
encodes them into *cognitive tokens*, and injects them back via gated cross-attention. This
yields calibrated refusal and self-correction.

**Code, training and evaluation scripts:** https://codeberg.org/imperius/meta-transformers-ENG.git

---

## Repository layout

| Folder | Contents |
|--------|----------|
| `checkpoints/` | Trained introspection weights (encoder + cross-attention). The base model is **not** included — download it separately from Hugging Face. |
| `data/` | Pre-collected activations (training datasets for the introspection pathway). |
| `results/` | Metrics, training logs and histories for every experiment (JSON / txt / log). |

> ⚠️ This is **not a drop-in HF model**. The weights load into a `ReflexionModel*` wrapper
> from the code repository and require two-pass generation. Without the code the weights
> are not usable on their own.

---

## Key results

| Experiment | Selective accuracy | Refusal precision | Checkpoint |
|------------|:------------------:|:-----------------:|------------|
| **Phase 5 Multi-Position (Variant B)** — record | **90.1%** | 98.7% | `checkpoints/phase5_multipos/` |
| **Phase 2 Selective MMLU** — calibration record | 89.1% | **99.84%** | `checkpoints/phase2_selective_best_model.pt` |
| **Cross-domain (MMLU→TriviaQA, zero-shot)** | **91.1%** | **100%** | `checkpoints/selective_mmlu_best_model.pt` |
| Phase 4 Dynamic Gates (7/7 checks) | 88.9% | 99.0% | `checkpoints/phase4_dynamic_gates/` |
| Phase 1 Selective (basis of the records) | 71.4% | 84.9% | `checkpoints/selective/` |

Baseline (no introspection) on full MMLU: ~83% selective accuracy, 0% refusal.

**Cross-domain is the strongest evidence of generalization:** a checkpoint trained only on
MMLU keeps **100% refusal precision** zero-shot on TriviaQA. The encoder reads the *model's
own internal uncertainty signal*, not benchmark-specific patterns.

---

## Architecture (brief)

```
Pass 1 (read):     text → frozen LLM → hooks capture per-layer activations
                   → SelectiveIntrospectionEncoder → cognitive tokens (one per layer)
Pass 2 (generate): text + cognitive tokens injected via
                   32× BottleneckCrossAttention (tanh gates) → answer
```

Five components: **ActivationCollector** (hooks) → **Cognitive Encoder** →
**cognitive tokens** → **meta-attention (BottleneckCrossAttention)** → **gates**.
Details in the code repository's `docs/`.

---

## How to use

1. Get access to the base model on Hugging Face (for Llama, accept the Llama 3.1/3.2
   Community License).
2. Download the checkpoint you want from `checkpoints/`.
3. Load it into the matching wrapper from the code repository, e.g. the Phase 2 Selective
   record:

```python
# from the meta-transformers repository
# see src/phase2_selective_llama8b/04_evaluate.py for the full loading + two-pass example
from src.phase2_selective_llama8b.reflexion_model_selective import ReflexionModelSelective
# build frozen Llama-3.1-8B-Instruct, wrap it, load the encoder + cross-attention weights
```

Checkpoint → code mapping:

| Checkpoint | Code module |
|------------|-------------|
| `phase5_multipos/` | `src/phase5_multipos_llama8b/` |
| `phase2_selective_best_model.pt` | `src/phase2_selective_llama8b/` |
| `selective_mmlu_best_model.pt`, `selective/` | `src/phase1_selective_llama8b/`, `src/cross_domain_llama8b/` |
| `phase4_dynamic_gates/` | `src/phase4_dynamic_gates_llama8b/` |
| `allca/`, `allca_tg/` | `src/phase1_allca_llama8b/`, `src/phase1_allca_tg_llama8b/` |
| `allheads/` | `src/phase1_allheads_llama8b/` |
| `phase7_llama1b_*` | `src/phase7_recursive_introspection_llama1b/` |
| `phase8_*` | `src/phase8_transformer_encoder_llama1b/` |

---

## Training details

- **Base:** fully frozen (Llama-3.1-8B / Llama-3.2-1B / Gemma-2-2B depending on the phase).
- **Trained:** SelectiveIntrospectionEncoder (~51.7M) + 32× BottleneckCrossAttention (~136.5M).
- **Objective:** standard LM cross-entropy on confirm / correct / refuse targets (Phase 2+).
- **Gate init:** 0.3 (linear region of tanh), gate lr ×5.
- **Data:** full MMLU (12,042 questions, 57 subjects) for the records; TriviaQA / MMLU Hard / GSM8K in other phases.

---

## Limitations

- **Over-refusal:** the record checkpoints refuse often (~63% on MMLU). Refusal precision is
  near-perfect but the model is conservative — it prefers a calibrated refusal over a risky answer.
- **Self-correction rarely fires** at 8B (the model prefers refusing to correcting). Richer
  tokenization (Phase 5) improves accuracy but not correction.
- Requires the two-pass generation wrapper from the code repository; not a drop-in HF model.

---

## Citation

```bibtex
@software{meta_transformers_core_2026,
  title = {Meta-transformers: Architectural Introspection for Large Language Models},
  year  = {2026}
}
```

## License

Apache 2.0. Base models are subject to their own licenses: Llama 3.1 / 3.2 Community License,
Gemma Terms of Use — obtain their weights from Hugging Face separately.