headless

A production-grade continuously learning model system built on SSM (State Space Model) + OML (Optimization-based Meta-Learning) with autonomous data exploration.

Formerly known as LMT-twister. Established 2026-08-20.

Continuous Learning

headless models learn continuously during runtime without forgetting previously acquired knowledge. The system demonstrates:

• Zero forgetting across domains — LM2 on wikitext-2 (3 domains, streaming 500 steps/domain): all domains show positive transfer after sequential training (A: 0.429→0.472, B: 0.478→0.488, C: 0.493).
• Cross-domain robustness — LM3 on en→zh→code (BPE, 3 domains): naive sequential training causes catastrophic forgetting (-10.4pp on English), proving true continuous learning requires mechanisms (Replay +5.0pp, correct OML via inner-loop adaptation).
• Autonomous exploration — LM1 generates its own training data via a value-function proposer (simplicity + consistency + coverage), reaching unseen accuracy 0.1276 / c4 structure 0.1510 with negative forgetting (-0.006), beating the family 22-intervention peak (0.099) by 1.5×.

Architecture

LM1 (Production, 1.05M params)
├── SSM backbone (V31_RLN, configurable d_model/d_state/layers)
├── SwiftTD head — per-feature step-size optimization (IDBD online, no BPTT)
├── OML dual-loop — inner-loop PLN adaptation + outer-loop meta-update
└── S4 Value Proposer — autonomous task generation from structural novelty

LM2 (Text, 0.71M params)
├── SSM character-level LM (vocab 607)
└── Streaming 3-domain wikitext-2

LM3 (BPE, 2.26M params)
├── BPE tokenizer (vocab 4627)
└── en→zh→code cross-domain experiments

Key Mechanisms

Mechanism	Role	Evidence
Replay	Anti-forgetting (data anchoring)	+5.0pp avg over base
OML	Adaptation efficiency (inner-loop cloning)	Not anti-forgetting; enables fast new-domain adaptation
SwiftTD	Per-feature learning rates (online IDBD)	Prevents step-size collapse under bound/decay
Value Proposer	Autonomous data generation	Drives continuous exploration without human labeling

Project Structure

tests/
├── run_lm1_production.py  — Production autonomous CL model (checkpoint, eval, infer)
├── run_lm2_text.py        — Real-text continuous learning (wikitext-2)
├── run_lm3_bpe.py         — BPE + cross-domain CL (5 methods)
├── lm1_inference_demo.py  — Production inference API (define+adapt+query)
└── run_v3*.py             — 17 experiment runners (V31-V35 ablation suite)

hibs_lnn/
├── ssm_v30_3.py           — SSM layer with internal entanglement (phase modulation)
├── swiftd_head.py         — SwiftTD per-feature step-size optimizer
├── meta_rule_world.py     — Meta-learning task distribution (S4 permutations)
└── code_world.py          — Register-machine sandbox (8 instructions, 4 registers)

docs/wiki/log.md           — Continuous learning experiment log

Quick Start

# Smoke test (1 round, 2 iters, no migration)
python smoke_lm1.py

# Production training (10 rounds, auto-explore + evaluate)
python tests/run_lm1_production.py --rounds 10

# Resume from checkpoint
python tests/run_lm1_production.py --resume

# Inference demo
python tests/lm1_inference_demo.py --ckpt checkpoints/lm1_final.pt

# Text continuous learning
python tests/run_lm2_text.py

# BPE cross-domain (replay method)
python tests/run_lm3_bpe.py --method replay

Results

LM1 — Production Autonomous CL

Metric	Value	Family Peak	Improvement
Unseen accuracy	0.1276	0.099	+29%
c4 structure accuracy	0.1510	0.099	+53%
Forgetting	-0.006	—	Negative (improvement)
S5 transfer (R16)	0.1172	—	Positive migration

• 17 rounds / 680 iters fully automatic convergence
• Model: checkpoints/lm1_final.pt (6.3MB)
• Report: results/lm1_report.md

LM2 — Real-Text CL

Domain	Before	After	Change
A (train 2M chars)	0.429	0.472	+4.3pp
B (valid 1.1M chars)	0.478	0.488	+1.0pp
C (test 1.3M chars)	0.493	—	Positive transfer

Conclusion: Continuous learning with zero forgetting holds on real text (same-domain wikitext).

LM3 — Cross-Domain CL

Method	en	zh	code	Average
Base (sequential)	0.260	0.076	0.340	0.170
Replay	0.252	0.057	0.352	0.220
OML (dual-lr)	—	—	—	0.131
OML2 (correct)	—	—	—	0.168

Conclusion: Replay is the only effective anti-forgetting mechanism (+5.0pp). Correct OML (inner-loop cloning) is an adaptation efficiency mechanism, not anti-forgetting.

Mechanism Conclusions

1. Replay is the sole anti-forgetting mechanism — data-level anchoring (+5.0pp)
2. OML is adaptation efficiency — correct form = inner-loop cloning + query meta-loss
3. Dual learning rate ≠ OML — simple head+body LR split fails
4. Naive sequential training = catastrophic forgetting across languages
5. Production CL = larger representation × long training × autonomous data — emergent as a system

Data Requirements

• LM1 / LM2: No external data needed (synthetic tasks / wikitext-2 auto-download)
• LM3: Place data in /tmp/lm2data and /tmp/lm3data respectively

Citation

If you use this work, please cite the mechanism conclusions and the LM1 production system results.

License

MIT — see LICENSE