π§ SCLM: Stateful Coherent Language Model
SCLM adds persistent latent memory to transformer language models, enabling better coherence across long conversations and multi-turn generation.
π― Key Features
- Persistent State: Memory that evolves across conversation turns
- Entity Coherence: Maintains context about characters, places, and objects
- Edit Mode: Make local changes without affecting global memory
- Lightweight: Only 91.7M additional parameters (2.44% overhead)
π Architecture: EARCP
EARCP = Encapsulation + Alignment + Revision + Coherence + Propagation
| Component | Function |
|---|---|
| Encapsulation | GRU-style state update from hidden states |
| Alignment | Cross-attention between state and hidden layers |
| Revision | Drift detection and correction |
| Coherence | Mixture-of-Experts for consistency |
| Propagation | State injection into transformer layers |
π§ Model Details
| Parameter | Value |
|---|---|
| Base Model | mistralai/Mistral-7B-v0.1 |
| EARCP Parameters | 91.7M |
| Latent State Dim | 256 |
| Injection Layers | [8, 16] |
| Alpha (injection strength) | 0.02 |
| Experts | 2 |
π Quick Start
# Note: Full SCLM requires custom loading (see below)
# The inference widget uses the base model only
from transformers import AutoTokenizer
import torch
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("amewebstudio/ananke-sclm")
# For full SCLM functionality, load weights separately:
# 1. Load base Mistral-7B
# 2. Load EARCP weights from earcp_weights.pt
# 3. Apply SCLM wrapper
π Validation Results
| Test | Result |
|---|---|
| Forward Pass | β |
| State Evolution | β (norm: 0 β 4.6 β 7.5) |
| Coherent Generation | β |
| Edit Mode | β |
| Entity Memory | β (Elara, Nimbus retained) |
π‘ Use Cases
- Interactive Fiction: Characters and plot points remain consistent
- Long Conversations: Context persists without growing prompts
- Creative Writing: Maintain story coherence across chapters
- Role-Playing: NPCs remember past interactions
π Citation
@article{amega2025sclm,
title={SCLM: Stateful Coherent Language Models with EARCP Architecture},
author={Amega, Mike},
year={2025},
note={Ame Web Studio}
}
π€ Author
Mike Amega - Ame Web Studio
SCLM is an experimental architecture exploring persistent memory in language models.
Model tree for amewebstudio/ananke-sclm
Base model
mistralai/Mistral-7B-v0.1