Update branding to JEV InvariantOne-v1 and add agent routing & decision tags

194e27d verified 10 days ago

13.2 kB

---
language:
- en
license: apache-2.0
base_model:
- Qwen/Qwen3.5-4B-Base
pipeline_tag: text-classification
library_name: invariantone
tags:
- jev
- agent-routing
- tool-selection
- agentic-ai
- order-bias
- decision-making
- decision-model
- permutation-equivariance
- lora
- qwen
---
# Model Card: JEV InvariantOne-v1

**Release Version:** `InvariantOne-v1` (JEV InvariantOne)  
**Date:** September 24, 2026  
**License:** Apache-2.0  
**Base Backbone:** `Qwen/Qwen3.5-4B-Base` (Revision `1001bb4d826a52d1f399e183466143f4da7b741b`, Native BF16)  
**Primary Checkpoint:** D5-L4 (N_op=16, Seed 42 Primary; Replicated across Seeds 43, 44)  
---
## Try JEV InvariantOne
[Interactive Demo](https://huggingface.co/spaces/TanmaySah/InvariantOne-Demo) | [GitHub](https://github.com/tradertanmay/InvariantOne)
Try JEV InvariantOne-v1 directly in the browser on structured multi-candidate decision problems and AI agent routing.
---
## Quickstart
```bash
pip install invariantone==1.0.2
```
```python
from invariantone import InvariantOne
# Load packaged v1 weights (canonical package alias for TanmaySah/InvariantOne-v1)
model = InvariantOne.from_pretrained("invariantone-v1")

result = model.decide(
    state="Pressure is above the operating threshold.",
    question="What action should be taken?",
    options=[
        "Close the inlet valve",
        "Increase pressure",
        "Maintain current state",
        "Disable monitoring",
    ],
)
print(result.choice)
print(result.probabilities)
```
> **Note on Model Identifier:**  
> `"invariantone-v1"` is the official package alias that resolves the verified weights bundled directly inside `invariantone`. If you clone this repository locally, you can also load directly from the local directory via `InvariantOne.from_pretrained("./")`.
---
## 1. Model Overview: JEV InvariantOne
`JEV InvariantOne-v1` is an order-equivariant probabilistic decision model designed for multi-candidate decision-making, AI agent routing, and tool selection under complex operational constraints and relational rules. 
Standard autoregressive language models suffer from candidate-order and position sensitivity in joint multi-option evaluation. `JEV InvariantOne-v1` addresses this structural sensitivity through a decoupled comparative architecture:
1. **Candidate-Independent Scoring:** Each candidate action is scored independently conditioned on context, eliminating candidate order sensitivity and cross-option attention artifacts.
2. **Analytic Distributional Equivariance:** Option-span pooled representations pass through a lightweight direct comparative projection head, producing scalar scores normalized via calibrated softmax. The architecture is mathematically permutation-equivariant by construction in real arithmetic.
3. **Upper-Layer Relational Pre-Adaptation:** Parameter-efficient adaptation (LoRA on layers 28–31) trained across a diverse relational operator universe (N_op=16) instills strong, transferable relational reasoning capabilities into the comparative readout.
```
Context + Option_i ---> [Qwen3.5-4B (Frozen Layers 0-27 + LoRA Layers 28-31)]
                                     |
                          Option Span Mean Pooling
                                     |
       Direct Comparative Head: Projection (2560 -> 256) -> Scorer (256 -> 256 -> 1)
                                     |
                               Logit s_i
                                     |
                           Softmax([s_1, ..., s_K])
```
---

## 2. Model Architecture & Specifications

| Parameter | Specification |
| :--- | :--- |
| **Base Model** | `Qwen/Qwen3.5-4B-Base` (32 transformer layers, d_model = 2560) |
| **Backbone Status** | Layers 0–27 strictly frozen (zero parameter updates) |
| **Adaptation** | Low-Rank Adaptation (LoRA) on upper layers 28–31: r = 8, α = 16 (layers 28–30: `in_proj_qkv`, `out_proj`; layer 31: `q_proj`, `v_proj`, `o_proj`) |
| **Active LoRA Parameters** | 585,728 parameters |
| **Readout Head Architecture** | `DirectComparativeHead`: Projection (`Linear(2560 → 256, bias=True)` → `LayerNorm(256)`) + Absolute Scorer (`Linear(256 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`) |
| **Projection Block Parameters** | 656,128 parameters |
| **Absolute Scoring Network Parameters** | 66,049 parameters |
| **Active Head Parameters** | 722,177 parameters (frozen `absolute_only` inference mode) |
| **Auxiliary Stored Pairwise Comparator** | 262,657 parameters (`pair_net`, stored in checkpoint but bypassed during frozen `absolute_only` inference) |
| **Total Stored Head Parameters** | 984,834 parameters (in checkpoint file `head.pt`) |
| **Total Active Adapted Parameters** | 1,307,905 parameters (585,728 LoRA + 722,177 Head; <0.05% of base model) |
| **Pooling Method** | Option-span mean pooling over candidate option token span |
| **Scoring Scheme** | Independent candidate scoring with calibrated comparative softmax readout (see formulation below) |
| **Calibration Temperature** | τ* = 1.0091 (pre-calibrated on validation split) |

### Candidate Scoring Formulation

Candidate choice probabilities are computed by independent forward passes through the adapted backbone and comparative head, followed by temperature-calibrated softmax normalization:

```text
p_i = exp(s_i / tau*) / sum_j exp(s_j / tau*)
```

where $s_i$ is the unnormalized scalar score for candidate option $i$, and τ* = 1.0091 is the frozen calibration temperature.
### DirectComparativeHead Architectural Details
The frozen `DirectComparativeHead` uses a modular architecture for candidate evaluation:
1. **Shared Option Projection (`self.proj`):**
   - `Linear(2560 → 256, bias=True)` → `LayerNorm(256)`
   - Parameters: 656,128
2. **Absolute Scoring Network (`self.abs_net`):**
   - `Linear(256 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`
   - Parameters: 66,049
3. **Active Head Parameters in Frozen `absolute_only` Mode:** 722,177 parameters (656,128 + 66,049).
4. **Auxiliary Stored Pairwise Comparator (`self.pair_net`):**
   - `Linear(1024 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`
   - Parameters: 262,657
   - *Note on Inactive Weights:* The auxiliary `pair_net` remains present in the checkpoint (`head.pt`), but is completely bypassed during frozen InvariantOne-v1 `absolute_only` inference.
5. **Total Stored Head Parameters:** 984,834 parameters (722,177 active + 262,657 stored auxiliary).
### Cryptographic Checkpoint Signatures
* **Primary Checkpoint (`Seed 42`, N_op=16)**:
  - LoRA Weights: `adapter_model.pt` (Frozen Phase 7R research checkpoint)
    - SHA-256: `a0000d3ec984d155fbad5abc854556904ecffe78b997900b677a8d8d5811e4d2`
  - Head Weights: `head.pt` (Frozen Phase 7R research checkpoint)
    - SHA-256: `190d867dc302a89d420d19a8374a5db252ea2e71e4f40c68064ded23fc321384`
* **Replication Checkpoint (`Seed 43`, N_op=16)**:
  - LoRA: `adapter_model_seed43.pt` (Frozen Phase 7R research checkpoint)
  - Head: `head_seed43.pt` (Frozen Phase 7R research checkpoint)
* **Replication Checkpoint (`Seed 44`, N_op=16)**:
  - LoRA: `adapter_model_seed44.pt` (Frozen Phase 7R research checkpoint)
  - Head: `head_seed44.pt` (Frozen Phase 7R research checkpoint)
---
## 3. Verified Performance & Benchmark Evaluation
`InvariantOne-v1` was evaluated on `FINAL-HOLDOUT-V2` (1,536 four-choice items) under strict preregistered one-shot governance. Prior to inference, SHA-256 checksums of all strata were verified against the locked manifest (`final_holdout_v2_manifest.json`).
> **Scope Note on Candidate Options Count (K):**  
> The runtime supports arbitrary K ≥ 2; the reported scientific evaluation results are for K=4 unless otherwise stated. Experimental generalization to arbitrary K has not been independently established in the scientific benchmarks.
### Definitive Benchmark Summary
| Stratum | Evaluation Focus | D0 (Baseline) | B1-Raw | B1-Sym (24-Perm) | `InvariantOne-v1` (Seed 42) | `InvariantOne-v1` (Mean ± SD) |
| :--- | :--- | :---: | :---: | :---: | :---: | :---: |
| **`FINAL-REAL`** | Real operational control tasks (N=256) | 89.45% | 91.41% | 92.58% | **94.14%** | **93.36% ± 1.35%** |
| **`FINAL-L1`** | Familiar-Operator Compositional Recombination (N=256) | 32.81% | 38.28% | 43.75% | **89.84%** | **89.32% ± 0.45%** |
| **`FINAL-L2a`** | Semantic Extrapolation (N=256) | 28.12% | 45.31% | 50.39% | **86.33%** | **87.76% ± 2.48%** |
| **`FINAL-L2b`** | Relational Extrapolation (N=256) | 22.27% | 62.50% | **65.23%** | 55.08% | 53.91% ± 2.03% |
| **`FINAL-L3`** | Double Extrapolation (N=512) | 25.20% | 60.94% | **65.43%** | 56.84% | 54.10% ± 4.91% |
| **`Aggregate`** | Total locked holdout pool (N=1,536) | 37.17% | 59.90% | 63.80% | **73.18%** (1,124/1,536) | **71.74% ± 1.97%** |
### Counterfactual Sensitivity & Minimal-Pair Tracking
| Stratum | Micro Accuracy | Minimal-Pair Accuracy | Correct Override Switch Rate |
| :--- | :---: | :---: | :---: |
| **`FINAL-L1`** | 89.84% | **87.50%** | **99.12%** |
| **`FINAL-L2a`** | 86.33% | **85.16%** | **97.32%** |
| **`FINAL-L2b`** | 55.08% | 31.25% | 39.60% |
| **`FINAL-L3`** | 56.84% | 39.45% | 53.16% |
### Reproducibility & Public Artifacts
The v1 public release includes the runtime, tests, checkpoint hashes, model card, and release manifest. The full FINAL-HOLDOUT-V2 evaluation corpus and generation pipeline are not included in this model repository.
- **Release Manifest:** [`INVARIANTONE_V1_RELEASE_MANIFEST.json`](https://github.com/tradertanmay/InvariantOne/blob/main/INVARIANTONE_V1_RELEASE_MANIFEST.json)
- **Reproduction Test:** [`tests/test_research_reproduction.py`](https://github.com/tradertanmay/InvariantOne/blob/main/tests/test_research_reproduction.py)
---
## 4. Invariance Guarantees & Systems Efficiency
### Structural Invariance Audit
* **Distributional Equivariance**: The candidate-independent scoring architecture is exactly permutation-equivariant by construction in real arithmetic. In the finite-precision implementation audit, the measured distributional TVD was 0.000000 to six decimal places across all tested permutations on 128 samples (3,072 evaluations; Mean TVD = 0.000000, Max TVD = 0.000000).
* **Top-1 Tie-Breaking Instability**: Discrete argmax selection showed a 1.56% top-choice flip rate in tied/near-tied cases due to tie-breaking behavior under finite floating-point precision. The underlying continuous probabilistic output distribution is strictly permutation-equivariant in real arithmetic.
### Computational Efficiency (Measured on NVIDIA A100-SXM4-40GB)
* **Benchmark Provenance Note:** A100 performance figures are from the frozen FINAL-HOLDOUT-V2 benchmarking run; release-wheel functionality was independently smoke-tested in a fresh Python 3.12 environment.
* **Forward Passes**: 4 candidate forward passes per sample vs 24 full-context forward passes for B1-Sym (6.0× reduction).
* **Wall-Clock Latency**: **83.71 ms** vs **425.31 ms** (B1-Sym) ⟹ **5.08× speedup**.
* **Throughput**: **11.95 samples/sec** vs **2.35 samples/sec** (B1-Sym).
* **Peak VRAM**: **8,941.2 MB** vs **14,614.2 MB** (B1-Sym) ⟹ **38.8% memory footprint reduction**.
---
## 5. Capabilities vs Frontier Boundaries
### Confirmed Capabilities
1. **Real-Task Decision Accuracy**: Outperforms unadapted baselines and 24-permutation causal scoring on real operational decision tasks (94.14%).
2. **Compositional Recombination**: Excels at counterfactual state tracking and rule execution for learned operator families (89.84% accuracy, 99.12% override switch rate).
3. **Semantic Domain Generalization**: Robustly transfers learned relational operators to completely novel technical and engineering domains (86.33%, +35.94 points over B1-Sym).
4. **Order-Equivariant Efficiency**: Eliminates position bias with zero prompt permutation overhead, achieving 5.08× lower latency than permutation averaging.
### Localized Frontier Limitations
1. **Zero-Shot Novel Operator Grammars**: Performance drops to 55.08% on L_2b and 56.84% on L_3. While D5 rescues independent candidate comparative scoring from near-chance baseline failure (D0 = 25.20%, Δ = +31.64 points), exhaustive permutation-averaged full-context causal inference (B1-Sym = 65.43%) retains an 8.59-point advantage on completely novel relational operators.
2. **Mechanistic Hypothesis**: The observed advantage is consistent with a benefit from joint full-context token processing, but the evaluation does not establish this as the unique causal mechanism.
3. **Capacity Ceiling (N_op=16 vs N_op=32)**: Training with 32 operator families saturates held-out transfer (+1.37% on L_3, p=0.2075) while causing substantial in-distribution catastrophic degradation on L_1 (75.52% vs 89.32%). Nop=16 was selected as the frozen operating point under the fixed D5-L4 adaptation budget.
---
## Release Status
InvariantOne-v1 is a frozen model release. The published model weights, architecture configuration, calibration temperature, and reported FINAL-HOLDOUT-V2 results correspond to the released v1 checkpoint and will not be changed in place.
Future model improvements will be released under a new version and evaluated separately.

	---
	language:
	- en
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-4B-Base
	pipeline_tag: text-classification
	library_name: invariantone
	tags:
	- jev
	- agent-routing
	- tool-selection
	- agentic-ai
	- order-bias
	- decision-making
	- decision-model
	- permutation-equivariance
	- lora
	- qwen
	---

	# Model Card: JEV InvariantOne-v1

	Release Version: `InvariantOne-v1` (JEV InvariantOne)
	Date: September 24, 2026
	License: Apache-2.0
	Base Backbone: `Qwen/Qwen3.5-4B-Base` (Revision `1001bb4d826a52d1f399e183466143f4da7b741b`, Native BF16)
	Primary Checkpoint: D5-L4 (N_op=16, Seed 42 Primary; Replicated across Seeds 43, 44)

	---

	## Try JEV InvariantOne

	[Interactive Demo](https://huggingface.co/spaces/TanmaySah/InvariantOne-Demo) \| [GitHub](https://github.com/tradertanmay/InvariantOne)

	Try JEV InvariantOne-v1 directly in the browser on structured multi-candidate decision problems and AI agent routing.

	---

	## Quickstart

	```bash
	pip install invariantone==1.0.2
	```

	```python
	from invariantone import InvariantOne

	# Load packaged v1 weights (canonical package alias for TanmaySah/InvariantOne-v1)
	model = InvariantOne.from_pretrained("invariantone-v1")

	result = model.decide(
	state="Pressure is above the operating threshold.",
	question="What action should be taken?",
	options=[
	"Close the inlet valve",
	"Increase pressure",
	"Maintain current state",
	"Disable monitoring",
	],
	)

	print(result.choice)
	print(result.probabilities)
	```

	> Note on Model Identifier:
	> `"invariantone-v1"` is the official package alias that resolves the verified weights bundled directly inside `invariantone`. If you clone this repository locally, you can also load directly from the local directory via `InvariantOne.from_pretrained("./")`.

	---

	## 1. Model Overview: JEV InvariantOne

	`JEV InvariantOne-v1` is an order-equivariant probabilistic decision model designed for multi-candidate decision-making, AI agent routing, and tool selection under complex operational constraints and relational rules.

	Standard autoregressive language models suffer from candidate-order and position sensitivity in joint multi-option evaluation. `JEV InvariantOne-v1` addresses this structural sensitivity through a decoupled comparative architecture:
	1. Candidate-Independent Scoring: Each candidate action is scored independently conditioned on context, eliminating candidate order sensitivity and cross-option attention artifacts.
	2. Analytic Distributional Equivariance: Option-span pooled representations pass through a lightweight direct comparative projection head, producing scalar scores normalized via calibrated softmax. The architecture is mathematically permutation-equivariant by construction in real arithmetic.
	3. Upper-Layer Relational Pre-Adaptation: Parameter-efficient adaptation (LoRA on layers 28–31) trained across a diverse relational operator universe (N_op=16) instills strong, transferable relational reasoning capabilities into the comparative readout.

	```
	Context + Option_i ---> [Qwen3.5-4B (Frozen Layers 0-27 + LoRA Layers 28-31)]
	\|
	Option Span Mean Pooling
	\|
	Direct Comparative Head: Projection (2560 -> 256) -> Scorer (256 -> 256 -> 1)
	\|
	Logit s_i
	\|
	Softmax([s_1, ..., s_K])
	```

	---

	## 2. Model Architecture & Specifications

	\| Parameter \| Specification \|
	\| :--- \| :--- \|
	\| Base Model \| `Qwen/Qwen3.5-4B-Base` (32 transformer layers, d_model = 2560) \|
	\| Backbone Status \| Layers 0–27 strictly frozen (zero parameter updates) \|
	\| Adaptation \| Low-Rank Adaptation (LoRA) on upper layers 28–31: r = 8, α = 16 (layers 28–30: `in_proj_qkv`, `out_proj`; layer 31: `q_proj`, `v_proj`, `o_proj`) \|
	\| Active LoRA Parameters \| 585,728 parameters \|
	\| Readout Head Architecture \| `DirectComparativeHead`: Projection (`Linear(2560 → 256, bias=True)` → `LayerNorm(256)`) + Absolute Scorer (`Linear(256 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`) \|
	\| Projection Block Parameters \| 656,128 parameters \|
	\| Absolute Scoring Network Parameters \| 66,049 parameters \|
	\| Active Head Parameters \| 722,177 parameters (frozen `absolute_only` inference mode) \|
	\| Auxiliary Stored Pairwise Comparator \| 262,657 parameters (`pair_net`, stored in checkpoint but bypassed during frozen `absolute_only` inference) \|
	\| Total Stored Head Parameters \| 984,834 parameters (in checkpoint file `head.pt`) \|
	\| Total Active Adapted Parameters \| 1,307,905 parameters (585,728 LoRA + 722,177 Head; <0.05% of base model) \|
	\| Pooling Method \| Option-span mean pooling over candidate option token span \|
	\| Scoring Scheme \| Independent candidate scoring with calibrated comparative softmax readout (see formulation below) \|
	\| Calibration Temperature \| τ* = 1.0091 (pre-calibrated on validation split) \|

	### Candidate Scoring Formulation

	Candidate choice probabilities are computed by independent forward passes through the adapted backbone and comparative head, followed by temperature-calibrated softmax normalization:

	```text
	p_i = exp(s_i / tau) / sum_j exp(s_j / tau)
	```

	where $s_i$ is the unnormalized scalar score for candidate option $i$, and τ* = 1.0091 is the frozen calibration temperature.

	### DirectComparativeHead Architectural Details

	The frozen `DirectComparativeHead` uses a modular architecture for candidate evaluation:
	1. Shared Option Projection (`self.proj`):
	- `Linear(2560 → 256, bias=True)` → `LayerNorm(256)`
	- Parameters: 656,128
	2. Absolute Scoring Network (`self.abs_net`):
	- `Linear(256 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`
	- Parameters: 66,049
	3. Active Head Parameters in Frozen `absolute_only` Mode: 722,177 parameters (656,128 + 66,049).
	4. Auxiliary Stored Pairwise Comparator (`self.pair_net`):
	- `Linear(1024 → 256, bias=True)` → `SiLU` → `Linear(256 → 1, bias=True)`
	- Parameters: 262,657
	- Note on Inactive Weights: The auxiliary `pair_net` remains present in the checkpoint (`head.pt`), but is completely bypassed during frozen InvariantOne-v1 `absolute_only` inference.
	5. Total Stored Head Parameters: 984,834 parameters (722,177 active + 262,657 stored auxiliary).

	### Cryptographic Checkpoint Signatures

	* Primary Checkpoint (`Seed 42`, N_op=16):
	- LoRA Weights: `adapter_model.pt` (Frozen Phase 7R research checkpoint)
	- SHA-256: `a0000d3ec984d155fbad5abc854556904ecffe78b997900b677a8d8d5811e4d2`
	- Head Weights: `head.pt` (Frozen Phase 7R research checkpoint)
	- SHA-256: `190d867dc302a89d420d19a8374a5db252ea2e71e4f40c68064ded23fc321384`
	* Replication Checkpoint (`Seed 43`, N_op=16):
	- LoRA: `adapter_model_seed43.pt` (Frozen Phase 7R research checkpoint)
	- Head: `head_seed43.pt` (Frozen Phase 7R research checkpoint)
	* Replication Checkpoint (`Seed 44`, N_op=16):
	- LoRA: `adapter_model_seed44.pt` (Frozen Phase 7R research checkpoint)
	- Head: `head_seed44.pt` (Frozen Phase 7R research checkpoint)

	---

	## 3. Verified Performance & Benchmark Evaluation

	`InvariantOne-v1` was evaluated on `FINAL-HOLDOUT-V2` (1,536 four-choice items) under strict preregistered one-shot governance. Prior to inference, SHA-256 checksums of all strata were verified against the locked manifest (`final_holdout_v2_manifest.json`).

	> Scope Note on Candidate Options Count (K):
	> The runtime supports arbitrary K ≥ 2; the reported scientific evaluation results are for K=4 unless otherwise stated. Experimental generalization to arbitrary K has not been independently established in the scientific benchmarks.

	### Definitive Benchmark Summary

	\| Stratum \| Evaluation Focus \| D0 (Baseline) \| B1-Raw \| B1-Sym (24-Perm) \| `InvariantOne-v1` (Seed 42) \| `InvariantOne-v1` (Mean ± SD) \|
	\| :--- \| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| `FINAL-REAL` \| Real operational control tasks (N=256) \| 89.45% \| 91.41% \| 92.58% \| 94.14% \| 93.36% ± 1.35% \|
	\| `FINAL-L1` \| Familiar-Operator Compositional Recombination (N=256) \| 32.81% \| 38.28% \| 43.75% \| 89.84% \| 89.32% ± 0.45% \|
	\| `FINAL-L2a` \| Semantic Extrapolation (N=256) \| 28.12% \| 45.31% \| 50.39% \| 86.33% \| 87.76% ± 2.48% \|
	\| `FINAL-L2b` \| Relational Extrapolation (N=256) \| 22.27% \| 62.50% \| 65.23% \| 55.08% \| 53.91% ± 2.03% \|
	\| `FINAL-L3` \| Double Extrapolation (N=512) \| 25.20% \| 60.94% \| 65.43% \| 56.84% \| 54.10% ± 4.91% \|
	\| `Aggregate` \| Total locked holdout pool (N=1,536) \| 37.17% \| 59.90% \| 63.80% \| 73.18% (1,124/1,536) \| 71.74% ± 1.97% \|

	### Counterfactual Sensitivity & Minimal-Pair Tracking

	\| Stratum \| Micro Accuracy \| Minimal-Pair Accuracy \| Correct Override Switch Rate \|
	\| :--- \| :---: \| :---: \| :---: \|
	\| `FINAL-L1` \| 89.84% \| 87.50% \| 99.12% \|
	\| `FINAL-L2a` \| 86.33% \| 85.16% \| 97.32% \|
	\| `FINAL-L2b` \| 55.08% \| 31.25% \| 39.60% \|
	\| `FINAL-L3` \| 56.84% \| 39.45% \| 53.16% \|

	### Reproducibility & Public Artifacts

	The v1 public release includes the runtime, tests, checkpoint hashes, model card, and release manifest. The full FINAL-HOLDOUT-V2 evaluation corpus and generation pipeline are not included in this model repository.

	- Release Manifest: [`INVARIANTONE_V1_RELEASE_MANIFEST.json`](https://github.com/tradertanmay/InvariantOne/blob/main/INVARIANTONE_V1_RELEASE_MANIFEST.json)
	- Reproduction Test: [`tests/test_research_reproduction.py`](https://github.com/tradertanmay/InvariantOne/blob/main/tests/test_research_reproduction.py)

	---

	## 4. Invariance Guarantees & Systems Efficiency

	### Structural Invariance Audit
	* Distributional Equivariance: The candidate-independent scoring architecture is exactly permutation-equivariant by construction in real arithmetic. In the finite-precision implementation audit, the measured distributional TVD was 0.000000 to six decimal places across all tested permutations on 128 samples (3,072 evaluations; Mean TVD = 0.000000, Max TVD = 0.000000).
	* Top-1 Tie-Breaking Instability: Discrete argmax selection showed a 1.56% top-choice flip rate in tied/near-tied cases due to tie-breaking behavior under finite floating-point precision. The underlying continuous probabilistic output distribution is strictly permutation-equivariant in real arithmetic.

	### Computational Efficiency (Measured on NVIDIA A100-SXM4-40GB)
	* Benchmark Provenance Note: A100 performance figures are from the frozen FINAL-HOLDOUT-V2 benchmarking run; release-wheel functionality was independently smoke-tested in a fresh Python 3.12 environment.
	* Forward Passes: 4 candidate forward passes per sample vs 24 full-context forward passes for B1-Sym (6.0× reduction).
	* Wall-Clock Latency: 83.71 ms vs 425.31 ms (B1-Sym) ⟹ 5.08× speedup.
	* Throughput: 11.95 samples/sec vs 2.35 samples/sec (B1-Sym).
	* Peak VRAM: 8,941.2 MB vs 14,614.2 MB (B1-Sym) ⟹ 38.8% memory footprint reduction.

	---

	## 5. Capabilities vs Frontier Boundaries

	### Confirmed Capabilities
	1. Real-Task Decision Accuracy: Outperforms unadapted baselines and 24-permutation causal scoring on real operational decision tasks (94.14%).
	2. Compositional Recombination: Excels at counterfactual state tracking and rule execution for learned operator families (89.84% accuracy, 99.12% override switch rate).
	3. Semantic Domain Generalization: Robustly transfers learned relational operators to completely novel technical and engineering domains (86.33%, +35.94 points over B1-Sym).
	4. Order-Equivariant Efficiency: Eliminates position bias with zero prompt permutation overhead, achieving 5.08× lower latency than permutation averaging.

	### Localized Frontier Limitations
	1. Zero-Shot Novel Operator Grammars: Performance drops to 55.08% on L_2b and 56.84% on L_3. While D5 rescues independent candidate comparative scoring from near-chance baseline failure (D0 = 25.20%, Δ = +31.64 points), exhaustive permutation-averaged full-context causal inference (B1-Sym = 65.43%) retains an 8.59-point advantage on completely novel relational operators.
	2. Mechanistic Hypothesis: The observed advantage is consistent with a benefit from joint full-context token processing, but the evaluation does not establish this as the unique causal mechanism.
	3. Capacity Ceiling (N_op=16 vs N_op=32): Training with 32 operator families saturates held-out transfer (+1.37% on L_3, p=0.2075) while causing substantial in-distribution catastrophic degradation on L_1 (75.52% vs 89.32%). Nop=16 was selected as the frozen operating point under the fixed D5-L4 adaptation budget.

	---

	## Release Status

	InvariantOne-v1 is a frozen model release. The published model weights, architecture configuration, calibration temperature, and reported FINAL-HOLDOUT-V2 results correspond to the released v1 checkpoint and will not be changed in place.

	Future model improvements will be released under a new version and evaluated separately.