Sentience.Cascade.II

Recursive Language Model (RLM) · Hybrid Mind Frame 1.147B Parameters · 64K Context Window · Dual T4 Trained

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Overview

Sentience.Cascade.II is not a Large Language Model (LLM).
It is a Recursive Language Model (RLM) — a novel architecture where every forward pass includes multiple self-recursive refinement steps, episodic short and long-term memory, and a fully wired Hybrid Mind module that runs as one integrated frame, not as sequential pipeline stages.

All cognitive subsystems operate inside a single unified forward pass.

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Architecture

Component	Detail
Architecture type	Recursive Language Model (RLM)
Parameters	~1.147B
Context window	64,000 tokens
Attention	Grouped Query Attention (16 heads / 4 KV heads)
Positional encoding	RoPE (θ=500,000)
FFN	SwiGLU
Normalisation	RMSNorm
Weight format	safetensors (float32 on disk, bfloat16 for training)
Vocabulary	65,536 (BPE ByteLevel)

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Hybrid Mind Frame — Self-Automated (S.A.) Modules

All modules are active simultaneously inside each transformer layer. None are optional pipeline steps — they are weights baked into the model.

Module	Role
S.A. Meta Learning Gate	Scales activation magnitude as a proxy learning signal
S.A. Reinforcement Learning Head	Scalar reward prediction per forward pass
S.A. Continual Learning Gate	Soft forgetting-protection via decay gates
S.A. Adaptive Learning Scale	Per-token hidden-state scaling
S.A. Rewrite Gate	Token-level hidden-state rewriting delta
S.A. NLP Head	Span boundary logits for structured extraction
S.A. Problem Solving Head	8-class step-type classification
S.A. Innovation Noise	Trainable exploration noise (active during training only)
S.A. Debug Probe	4-class anomalous activation detector
S.A. Advanced Short-Term Memory	512-slot episodic rolling buffer
S.A. Advanced Long-Term Memory	1024-slot consolidated episodic store
S.A. Recursive Seed Learning	Multi-step (×4) recursive refinement loop
S.A. Self-Evaluation & Reward	Scalar self-score head
S.A. Goal & Constraint Engine	Residual goal-projection delta
S.A. Memory Consolidation	Automatic STM→LTM every 8 layers
S.A. Introspection Interface	64-dim interpretable summary of hidden state
S.A. Recursive Outer Loop Gate	Final gate before residual output
Conversational Intelligence	32-class dialog-act classification head
MultiModal (Text/Image/Audio/Video)	Linear projection from ViT-L / mel-spec / video dims

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Recursive Language Model Core

Unlike a standard transformer that processes tokens once per layer, Sentience.Cascade.II applies a RecursiveSeedLayer after all transformer blocks. This layer runs num_recursive_steps=4 passes of attention + FFN with a shared-weight inner loop, allowing the model to internally "think again" before producing logits.

This is the defining feature of the RLM architecture:

Output is not produced after one pass — it is refined recursively.

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Memory System

• Short-Term Memory (512 slots): Updated every forward pass via a write gate.
  Cross-attended by every layer, giving the model persistent intra-context state.
• Long-Term Memory (1024 slots): Consolidated from short-term every 8 layers via a separate consolidation gate with 0.99/0.01 EMA blend.
  Persists across training steps when fine-tuning.

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Multimodal Support

Three input projection heads accept external embeddings:

Modality	Input dim	Projection
Image	1024 (ViT-L patch)	Linear → 2048
Audio	128 (mel-spectrogram)	Linear → 2048
Video	1024 (frame embedding)	Linear → 2048

These are additive prefix embeddings — concatenate modality tokens before input_ids.

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Chat Template

<|system|>You are Sentience.Cascade.II, a recursive reasoning model.
<|user|>What is consciousness?
<|assistant|>

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Fine-Tuning

This is the base pretrained initialisation — weights are randomly initialised and the tokenizer is bootstrapped. Fine-tune on your domain corpus using standard causal-LM training.

Recommended fine-tune config:

from transformers import TrainingArguments

args = TrainingArguments(
    output_dir           = "./sc2-finetuned",
    per_device_train_batch_size = 1,
    gradient_accumulation_steps = 16,
    num_train_epochs     = 3,
    learning_rate        = 2e-4,
    lr_scheduler_type    = "cosine",
    warmup_ratio         = 0.03,
    bf16                 = True,
    gradient_checkpointing = True,
    save_strategy        = "steps",
    save_steps           = 500,
    logging_steps        = 10,
    report_to            = "none",
)

Note: Because SentienceCascadeModel is a custom architecture, you will need to register it with the HuggingFace AutoModel registry or load it with trust_remote_code=True after placing the model code in the repo.

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Citation

@misc{sentiencecascade2,
  author       = {GODsStrongestSoldier},
  title        = {Sentience.Cascade.II: A Recursive Language Model with Hybrid Mind Frame},
  year         = {2025},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/GODsStrongestSoldier/Sentience.Cascade.II}},
}

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License

Apache 2.0