🌀 Negentropy-claude-opus-4.7-9B: A Reasoning Experimental Model Based on Trace Inversion
💡 Abstract
Based on current public information, commercial models like OpenAI's GPT series and Anthropic's Claude series have clearly hidden their true internal reasoning chains. What we ultimately see through APIs or frontend interfaces are often just "Reasoning Bubbles"—highly compressed and summarized versions of the original massive reasoning content. For models aiming to improve capabilities through data distillation, these overly compressed reasoning chains fail to provide sufficient step-level learning signals. On the contrary, because the logical leaps are too large and intermediate derivations are missing, forcing smaller models to learn these summaries directly often leaves them confused and unable to master true reasoning abilities.
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🚀 Negentropy-claude-opus-4.7-9B is a 9B-level reasoning-enhanced model. Its name is derived from Negentropy in information theory, symbolizing the reconstruction of highly ordered, logically rigorous reasoning chains from compressed and fragmented information. It also draws inspiration from the movie Tenet.
✨ This model builds upon the success of our 4B version, employing the same "Trace Inversion" technique: using the Trace-Inverter-4B model to restore the compressed "Reasoning Bubbles" leaked by Claude-Opus-4.7 in public interfaces into full Chains-of-Thought (CoT). These reconstructed traces serve as high-value supervisory signals for fine-tuning the 9B parameter base.
[!IMPORTANT]
Core: By scaling up to 9B parameters, this model further validates the "Negentropy" hypothesis. The increased capacity allows for more nuanced logical reconstruction and a deeper inheritance of the "Opus style" reasoning depth.
📖 1. Negentropy Theory: Scaling the Inversion (Introduction)
In the current model distillation paradigm, the "Information Entropy" trap is particularly damaging for mid-sized models:
Commercial Black Boxes: Models like Claude-Opus or GPT-5.5 provide only "Reasoning Bubbles" via API.
Information Loss: This compression increases "entropy," forcing models to mimic surface-level conclusions rather than underlying logic.
Negentropy-4.7-9B addresses this by using "negative entropy" to restore order. We believe that reasoning capability scales not just with parameters, but with the quality of the process. By providing the 9B base with full, inverted thinking chains, we provide a "logical gradient" that is far more effective than simple outcome-based distillation.
🚀 2. Model Positioning
This model is designed as the "Goldilocks" solution for high-fidelity reasoning: it offers significantly deeper logical capacity than the 4B version while remaining efficient enough for deployment on mid-range hardware.
It is best suited for:
🧠 Advanced Logical Deduction: Solving complex problems that require a larger internal state and more sophisticated planning.
🏗 Reasoning Teacher Model: Generating high-quality CoT data for even smaller models (e.g., 0.8B - 4B).
🖥 Workstation Inference: Delivering "Pro-level" thinking capabilities on hardware like RTX 3090/4090/5090.
🎓 Academic Research: Investigating the scaling laws of reasoning recovery via trace inversion.
🧪 3. The Negentropy Pipeline: Data Construction and Training
❤️ This project was completed alongside Kyle Hessling. Special thanks to him for providing the compute cluster support that made the training of this larger 9B variant possible. You can find him on X here: 🔗 @KyleHessling1
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The training pipeline remains consistent with our established methodology:
🛠 Stage 1: Trace Capture
To establish a robust foundation for trace capture, we sourced our initial data from two high-fidelity synthetic datasets. These corpora provide the critical "Reasoning Bubbles" required for our subsequent inversion pipeline:
We employed the Jackrong/Trace-Inverter-4B as the universal "decoder" to bridge the logical gaps in the Claude-Opus summaries, generating the Negentropy Trace.
🛠 Stage 3: SFT
The Unsloth framework was used to fine-tune the Qwen3.5-9B base. The larger parameter count allows the model to better internalize the complex dependencies within the reconstructed reasoning chains.
[!TIP]
Why 9B?
While the 4B model is a "speed demon," the 9B model is a "logic specialist." It provides the extra "brainpower" needed to handle multi-step problems where the 4B version might occasionally lose the thread. It represents the sweet spot for users who need GPT-4 level logic in a local, private environment.
✨ 4. Key Capabilities
🧬 Superior Logical Fidelity: Higher capacity leads to more stable and detailed thinking chains, especially in math and coding tasks.
🧩 Complex Instruction Following: Better at maintaining multi-faceted constraints throughout a long reasoning process.
⚡ Optimized Performance: Despite its size, when used with Unsloth and 4-bit quantization, it remains incredibly fast and memory-efficient.
🏗 Native CoT Integration: Seamlessly uses <think>...</think> tags for transparent and auditable reasoning.
📝 5. Recommended Usage
Example (Complex Logic):
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1User: If all A are B, and some B are C, does it necessarily follow that some A are C? Explain your reasoning.
2Assistant:
3<think>
41. Identify the premises:
5 - P1: All A are B (A ⊆ B).
6 - P2: Some B are C (B ∩ C ≠ ∅).
72. Goal: Determine if "Some A are C" (A ∩ C ≠ ∅) is a necessary conclusion.
83. Logical Analysis:
9 - "All A are B" means the set A is entirely contained within set B.
10 - "Some B are C" means there is an overlap between set B and set C.
114. Testing for counterexamples:
12 - Let B be the set of "Mammals," A be the set of "Dogs," and C be the set of "Cats."
13 - All Dogs (A) are Mammals (B). [P1 holds]
14 - Some Mammals (B) are Cats (C). [P2 holds]
15 - Does it follow that some Dogs (A) are Cats (C)? No.
165. Visualization (Venn Diagram):
17 - Circle A is inside Circle B.
18 - Circle C overlaps with Circle B.
19 - However, Circle C does not necessarily touch or overlap with Circle A.
20</think>
21Conclusion: No, it does not necessarily follow. While all A are B and some B are C, the part of B that overlaps with C could be entirely separate from the part of B that contains A.
⚠️ 6. Limitations
[!CAUTION]
Inference Latency: Compared to the 4B version, the 9B model will have a lower tokens-per-second rate, though still much faster than commercial APIs.
Hardware Requirements: Requires at least 8GB-12GB of VRAM (depending on quantization) for comfortable inference.
Hallucination: Even at 9B, "logic-style hallucinations" can occur where the format is perfect but the facts are incorrect.
🔭 7. Future Vision
The success of the 9B model completes the second phase of the "Negentropy Project."
MoE Explorations: Our next goal is to apply Trace Inversion to 27B and 35B Mixture-of-Experts (MoE) architectures.
Infinite Reasoning: Experimenting with longer context windows to allow for multi-thousand step reasoning traces.
🙏 8. Acknowledgements
Thanks to Claude-Opus-4.7 for the logical inspiration.
Thanks to the Unsloth team for making large-model fine-tuning accessible.
Theoretical basis: How to Steal Reasoning Without Reasoning Traces (arXiv:2603.07267).
📖 9. Citation
bibtex
1@misc{jackrong_negentropy_47_9b,
2 title = {Negentropy-claude-opus-4.7-9B: Advanced Reasoning via Trace Inversion},
3 author = {Jackrong},
4 year = {2026},
5 publisher = {Hugging Face}
6}
