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ouroboros-v24
MMJBDS/ouroboros-v24
ouroboros-v24 is a text generation model from MMJBDS. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
Ouroboros V24 is the latest iteration of a cognitive architecture designed for autonomous financial decision-making. Built on a 35B-parameter Mixture-of-Experts (MoE) base model with ~3B active parameters, trained thr…
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Updated Apr 22, 2026
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- other
- Access
- Public
- Created
- Apr 22, 2026
- Updated
- Apr 22, 2026
- SHA
- 9f117a12
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Model card
From the Hugging Face model README
Ouroboros V24: Cognitive Architecture for Reflexive Financial Reasoning
Ouroboros V24 is the latest iteration of a cognitive architecture designed for autonomous financial decision-making. Built on a 35B-parameter Mixture-of-Experts (MoE) base model with ~3B active parameters, trained through 24 iterative rounds of multi-reward GRPO with a 54-dimensional cognitive reward topology.
⚠️ Weights are not publicly released. This model card documents the architecture and training methodology. For research collaboration inquiries, contact the author.
Architecture
Base Model
- Type: Mixture-of-Experts (MoE)
- Total Parameters: ~35B
- Active Parameters: ~3B per token
- Context Window: 32K tokens
Training Methodology
- Algorithm: R-GRPO (Reflexive Group Relative Policy Optimization)
- Training Rounds: 24 iterative cycles (V1 → V24)
- Adapter Strategy: 20-layer sequential LoRA merge chain
- Reward Architecture: SCRGNDWMT (9-tier, 54 sub-dimensions)
9-Tier Reward Topology (SCRGNDWMT)
| Tier | Name | Sub-dimensions | Description |
|---|---|---|---|
| S | Structure | 6 | XML formatting, JSON decision blocks |
| C | Content | 7 | Domain expertise, data fidelity, causal depth |
| R | Reasoning | 5 | Temporal-causal chains, counterfactual depth |
| G | Game Theory | 5 | K-level thinking, deception detection, coalition |
| N | Narrative | 4 | Scenario construction, debate, arc coherence |
| D | Data Fidelity | 3 | Numerical accuracy, source attribution |
| W | World Model | 6 | Regime detection, cross-market transmission, macro |
| M | Metacognition | 7 | Self-awareness, Bayesian confidence, falsification |
| T | Temporal-Causal | 5 | Causal chains, temporal depth, granularity |
V24 Upgrades (from V22)
- C7 (CausalChainDepthV2): Multi-step causal chains with time-lag annotations
- M7 (BayesianConfidence): Calibrated confidence field in JSON decisions
- W3 (CrossMarketPath): Structural contagion paths (Market A → Mechanism → Market B)
- M5 (FalsificationV2): Quantitative, price-based invalidation conditions
Key Training Parameters
| Parameter | Value |
|---|---|
| Learning rate | 5 × 10⁻⁷ |
| Group size | 12 |
| Max completion tokens | 1000 |
| Temperature | 1.15 |
| β-annealing | Stable (β=0.05) ↔ Break-up (β=0.03) |
| LoRA rank | ≥ 10 |
Key Results
Reflexive Intelligence Emergence
During V17 training, reflexive reasoning emerged through a discontinuous phase transition at Step 153 — after 150+ steps of zero reflexivity scores, the capability appeared spontaneously and sustained. This is documented in Papers 1-3 of the research program.
V24 Training (ongoing)
- 54-dimensional reward actively guiding cognitive development
- Bayesian confidence calibration observed from Step 18
- Cross-market causal reasoning emerging by Step 25
- Zero gradient failures through 55+ steps
Research Program
This model is part of a six-paper research program:
| Paper | Title | DOI |
|---|---|---|
| P1 | Reflexive Intelligence in LLMs | 10.5281/zenodo.19557261 |
| P2 | Observer Depth (ReflexBench) | 10.5281/zenodo.19627242 |
| P3 | When Rewards Collide (Multi-Reward GRPO) | 10.5281/zenodo.19665969 |
| P4 | Ouroboros V22 Architecture | 10.5281/zenodo.19666786 |
| P5 | The Cognitive Lifecycle | 10.5281/zenodo.19666806 |
| P6 | Cognitive Reward Topology | 10.5281/zenodo.19666829 |
Related Resources
| Resource | Link |
|---|---|
| ReflexBench Dataset | MMJBDS/reflexbench |
| ReflexBench Eval Results | MMJBDS/reflexbench-eval |
| Papers Repository | github.com/mmjbds/ouroboros-papers |
| Evaluation Code | github.com/mmjbds/reflexbench |
Citation
@article{zhang2026ouroborosv22,
title={Ouroboros V22: Bayesian Scenario Simulation and Recurrent Depth Cognition},
author={Zhang, Mian},
year={2026},
doi={10.5281/zenodo.19666786}
}
@article{zhang2026topology,
title={Cognitive Reward Topology: A Nine-Tier Architecture for Multi-Reward GRPO},
author={Zhang, Mian},
year={2026},
doi={10.5281/zenodo.19666829}
}
Author
- Mian Zhang — Independent AI Researcher
- ORCID: 0009-0001-9556-3839
- Email: [email protected]
- GitHub: @mmjbds
- Twitter/X: @Henry_Avery666
- LinkedIn: henryavery-mianzhang
License
This model card is released under CC BY 4.0. Model weights are not publicly available.
- Task
- Text Generation
- Library
- transformers
- License
- other
- Languages
- en, zh
- Created
- Apr 22, 2026
- Updated
- Apr 22, 2026