Clarify Qev licensing and simplify model documentation

LICENSE

@@ -187,7 +187,8 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2026 Jared Palmer
+   Copyright 2026 Qiqian Fu and Qev contributors
+   Portions Copyright 2026 Jared Palmer
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

NOTICE

@@ -1,28 +1,30 @@
 Qev
-Copyright 2026 Qev contributors
+Copyright 2026 Qiqian Fu and Qev contributors
 
-Qev is a Qwen-based decision-model research implementation. It was developed as
-BranchKev before extraction into this repository. The selective extraction and
-source-file hashes are recorded in provenance.json.
+Qev code, documentation, original illustrations, examples, generated prediction
+results, and published adaptation weights are licensed under Apache-2.0.
 
 This project adapts conventions and implementation ideas from Kev:
   https://github.com/jaredpalmer/kev
-  Reviewed revision: 557598fced1dada75dfbf36ed144dce309ac6ceb
   Copyright 2026 Jared Palmer
-  Licensed under the Apache License, Version 2.0.
+  Apache License, Version 2.0
 
-Adapted components include the reserved Qwen delimiter convention, text rendering
-conventions, LoRA target selection, and the inference cache-fork approach.
-Qev's candidate-tree encoding, set decision head, data adapters, and training
-runner are maintained separately. Files extracted for this release were renamed
-and modified for standalone Qev packaging, portable checkpoints and user-data
-training. The original Kev attribution is retained in LICENSE and this notice.
+Adaptations include Qwen delimiters, text rendering, LoRA target selection, and
+inference cache forking. Modified files carry Qev adaptation notices. The original
+Kev license and copyright notice are retained in licenses/Kev-Apache-2.0.txt.
 
-Qwen models and Hugging Face Transformers are external dependencies. The optional
-JevBench preparer downloads an independently versioned MIT-licensed upstream
-snapshot from https://github.com/fstandhartinger/jevbench and retains its license.
-No Qwen base weights or original benchmark questions are redistributed in Git.
+Qev uses the Qwen3.5 model and tokenizer:
+  https://huggingface.co/Qwen/Qwen3.5-9B-Base
+  Copyright 2026 Alibaba Cloud
+  Apache License, Version 2.0
 
-Model weights and datasets retain their respective terms. This source-code
-license does not relicense third-party datasets or imply that the full research
-training corpus is included.
+The public weight package includes the Qwen tokenizer and Qev adaptation weights.
+Qwen base weights are downloaded separately. The original Qwen license is in
+licenses/Qwen3.5-Apache-2.0.txt.
+
+The optional JevBench dataset preparer downloads public evaluation tasks by
+Florian Standhartinger and contributors. It includes their MIT license,
+third-party notices, and per-question provenance with the prepared data.
+
+See THIRD_PARTY_NOTICES.md for licensing scope and external dependencies.
+Third-party datasets and services retain their own terms.

README.md

@@ -27,7 +27,7 @@ metrics:
 
 [Source and documentation](https://github.com/QiqianFu/Qev) · [中文说明](https://github.com/QiqianFu/Qev/blob/main/README.zh-CN.md) · [Model weights](https://huggingface.co/AustinFu/Qev-9B)
 
-This is the **v0.1.0** release of the final Qev-9B research checkpoint: seed 17, step 2327, with the late1783 training mixture repeated three times during the final half of training. It is the full-interaction baseline A used in the architecture ablations.
+Qev-9B combines a general decision-training dataset with additional alignment and rule-compliance examples, repeated three times during the second half of training. It uses the full candidate-interaction architecture shown below.
 
 ## Quick start
 
@@ -43,7 +43,7 @@ python -m pip install -e .
 from qev import Qev
 
 model = Qev.from_pretrained(
-    "AustinFu/Qev-9B", revision="v0.1.0", device="cuda"
+    "AustinFu/Qev-9B", revision="main", device="cuda"
 )
 answers = model.predict({
     "state": "I was charged twice. Please help immediately.",
@@ -65,7 +65,7 @@ For JSONL inference:
 
 ```bash
 python -m qev.predict \
-  --checkpoint AustinFu/Qev-9B@v0.1.0 \
+  --checkpoint AustinFu/Qev-9B@main \
   --input examples/requests.jsonl --out runs/predictions.jsonl \
   --device cuda --weights-dtype checkpoint
 ```
@@ -79,7 +79,6 @@ python -m qev.predict \
 | Field | Released model |
 |---|---|
 | Base | Qwen/Qwen3.5-9B-Base |
-| Base revision | `68c46c4b3498877f3ef123c856ecfde50c39f404` |
 | Adaptation | LoRA rank 64, alpha 128; learned decision head and interaction gate |
 | Decision head | Shared 4096→256 projection; two 4-head Transformer layers; scalar scorer |
 | Backbone interaction | `last-full-attention` |
@@ -91,7 +90,7 @@ python -m qev.predict \
 | Late mixing | Starts halfway through main training; late examples repeat three times |
 | Selected checkpoint | Step 2327 |
 
-The checkpoint was called BranchKev during research. Its run ID is `c21-science-wk-late1783-9b-4gpu-r64-late50x3-s17`. The main and late partitions intentionally share 249 replay records. The complete research training corpus is not distributed with this release. [Training guide](https://github.com/QiqianFu/Qev/blob/main/docs/training.md) · [Data recipe](https://github.com/QiqianFu/Qev/blob/main/docs/data.md).
+The main and late partitions intentionally share 249 replay records. The complete research training corpus is not distributed with this release. [Training guide](https://github.com/QiqianFu/Qev/blob/main/docs/training.md) · [Data recipe](https://github.com/QiqianFu/Qev/blob/main/docs/data.md).
 
 ## Evaluation
 
@@ -122,13 +121,13 @@ The approximately 690 MiB inference package contains:
 - `adapter/`: LoRA configuration and weights.
 - `head.safetensors` and `joint.safetensors`: decision head and interaction gate.
 - `model.json` and `tokenizer/`: Qev configuration and tokenizer.
-- `SHA256SUMS.json`: hashes of the seven inference files.
-- `provenance.json`, `training_config.json`, and `benchmarks.json`: checkpoint identity, training configuration, and recorded results.
+- `training_config.json` and `benchmarks.json`: training configuration and recorded results.
+- `LICENSE`, `NOTICE`, `THIRD_PARTY_NOTICES.md`, and `licenses/`: license terms and attribution.
 
-The adaptation tensors are byte-identical to the selected research checkpoint. Qwen base weights are fetched separately at the revision above. Optimizer state is excluded; initialize new fine-tuning with `--init-checkpoint AustinFu/Qev-9B@v0.1.0`.
+The adaptation tensors are byte-identical to the selected research checkpoint. Qwen base weights are fetched separately at the version recorded in the model configuration. Optimizer state is excluded; initialize new fine-tuning with `--init-checkpoint AustinFu/Qev-9B`.
 
 ## Intended use and license
 
 Qev supports research and development of routing, rule judgments, and rubric ratings over explicit options. Evaluate it on your application's inputs and decision thresholds. Probabilities depend on the supplied options; calibration and production reliability have not been established.
 
-The released Qev adaptation weights and Qev code use Apache-2.0. The Qwen base is separately available under Apache-2.0. Source datasets retain their respective terms. Qev builds on Qwen and adapts conventions from [Jared Palmer's Kev](https://github.com/jaredpalmer/kev); attribution is retained in the included `LICENSE` and `NOTICE`.
+Qev's code, adaptation weights, documentation and original illustrations use Apache-2.0. Qwen models and the bundled tokenizer retain Alibaba Cloud's Apache-2.0 license. Qev adapts conventions from [Jared Palmer's Kev](https://github.com/jaredpalmer/kev), whose Apache-2.0 license and attribution are retained. JevBench tasks and external dependencies retain their own terms; the training corpus is not redistributed. See the included `LICENSE`, `NOTICE`, `THIRD_PARTY_NOTICES.md`, and `licenses/` for the full texts and scope.

SHA256SUMS.json

@@ -1,12 +0,0 @@
-{
-  "source_model_json_sha256": "41af1fd1aae6fb7e6d1d325795cc7bc46c202820c80b38c04afec8f49a240e63",
-  "files": {
-    "adapter/adapter_config.json": "3e8ecebad2f7bf7b983fa3d40e8fae4749fa4923150231b41b9ad9a11c74baa3",
-    "adapter/adapter_model.safetensors": "54314a19f8f9c45fce24f04e14c3615fb1b43be13e231b51a060e9f88d4c57f4",
-    "head.safetensors": "6640fcc2045f21e0a0edb4d80d797c2134d160cebba662885cde04efeaafb91c",
-    "joint.safetensors": "6020d9fde51efc8f12f089fbe10b5f738c00ddf59c8df738ca0eb1edb344a979",
-    "model.json": "3bd2d22791d94410fc6a30df3653cdd128c5a73da8f5671b8f787b0d4d99b094",
-    "tokenizer/tokenizer.json": "06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523",
-    "tokenizer/tokenizer_config.json": "353c083805d863719e54973335a38e252a92e1ac64d32b83bb0b333cc1644ef2"
-  }
-}

THIRD_PARTY_NOTICES.md

@@ -0,0 +1,37 @@
+# Licensing and attribution
+
+## Qev materials
+
+Copyright 2026 Qiqian Fu and Qev contributors.
+
+Qev's code, documentation, original SVG illustrations, example requests, and generated prediction results are provided under the [Apache License 2.0](LICENSE). The Qev adaptation weights published at [AustinFu/Qev-9B](https://huggingface.co/AustinFu/Qev-9B), including its LoRA adapter, decision head, and interaction gate, use the same license. Contributions are accepted under these terms unless explicitly stated otherwise.
+
+Third-party materials retain the terms listed below. The Qev license does not relicense training datasets, external services, or third-party recordings.
+
+## Included and adapted materials
+
+| Material | Source and copyright | License and scope |
+|---|---|---|
+| Kev conventions and adapted implementation | [Jared Palmer's Kev](https://github.com/jaredpalmer/kev), Copyright 2026 Jared Palmer | [Apache-2.0, original notice retained](licenses/Kev-Apache-2.0.txt). Qev adapts delimiter and rendering conventions, LoRA targets, and cache forking. Modified source files identify the Qev adaptation. |
+| Qwen model and tokenizer | [Qwen3.5-9B-Base](https://huggingface.co/Qwen/Qwen3.5-9B-Base), Copyright 2026 Alibaba Cloud | [Apache-2.0](licenses/Qwen3.5-Apache-2.0.txt). The tokenizer is included in the Hugging Face checkpoint; base weights are downloaded separately. |
+| JevBench public evaluation tasks | [JevBench](https://github.com/fstandhartinger/jevbench), Copyright 2026 Florian Standhartinger and contributors | [MIT](licenses/JevBench-MIT.txt), with [upstream third-party notices](licenses/JevBench-THIRD-PARTY.md). Tasks are downloaded by the optional preparation script. The original license, third-party notice, and per-question provenance accompany the prepared dataset. |
+
+The reviewed Kev version is [557598f](https://github.com/jaredpalmer/kev/tree/557598fced1dada75dfbf36ed144dce309ac6ceb); the evaluation data use [JevBench v1.4.2](https://github.com/fstandhartinger/jevbench/tree/1df665e3956d7aab7fa0208ff6c4f2d8557f9f90). The 231 downloaded public tasks carry MIT in their individual provenance records; upstream notices describe the scope of other JevBench materials and evaluated services.
+
+The README layout takes inspiration from [JevAny](https://github.com/weitianxin/JevAny). Qev's SVG illustrations and chart code are original; this release includes no JevAny game code or recordings.
+
+## Dependencies installed separately
+
+Dependencies are installed from their own distributions and retain their licenses:
+
+- [PyTorch](https://github.com/pytorch/pytorch/blob/main/LICENSE): BSD-3-Clause, plus its bundled third-party notices.
+- [Transformers](https://github.com/huggingface/transformers/blob/main/LICENSE), [PEFT](https://github.com/huggingface/peft/blob/main/LICENSE), [Accelerate](https://github.com/huggingface/accelerate/blob/main/LICENSE), [Safetensors](https://github.com/huggingface/safetensors/blob/main/LICENSE), [Hugging Face Hub](https://github.com/huggingface/huggingface_hub/blob/main/LICENSE), and optional [Apache Arrow](https://github.com/apache/arrow/blob/main/LICENSE.txt): Apache-2.0.
+- Development tools [pytest](https://github.com/pytest-dev/pytest/blob/main/LICENSE) and [Matplotlib](https://matplotlib.org/stable/project/license.html) retain their MIT and Matplotlib/PSF-based license terms, respectively.
+
+Their source code and dependency distributions are not vendored in Qev's Python package. This list identifies direct dependencies; their own packages include notices for transitive components.
+
+## Training data
+
+The complete research training mixture is not distributed here. Public weight availability does not make the training data available under Apache-2.0. Any future dataset release needs its own source-specific permissions and attribution. The Wikipedia-derived training subset, for example, carries CC BY-SA attribution where applicable.
+
+The small JSONL requests in `examples/` were written for Qev and are covered by its Apache-2.0 license. The benchmark result tables and prediction files contain Qev's recorded outputs, not the original benchmark question text.

licenses/JevBench-MIT.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Florian Standhartinger and contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

licenses/JevBench-THIRD-PARTY.md

@@ -0,0 +1,51 @@
+# Third-party projects, data and services
+
+MIT (see `LICENSE`) covers this harness and the 72 original public decisions in
+`datasets/public/original.jsonl`. Nothing else in this list is ours to license.
+
+## Systems evaluated
+
+Each was reached through the interface its author published. We used public
+endpoints as ordinary clients, at one request at a time, and never sent a
+provider's API key to anyone else's endpoint.
+
+| System | Author | Source |
+|---|---|---|
+| Jev 1.13.0 | TypeSafe AI | <https://docs.typesafe.ai> |
+| openjev-sglang | ekzhang | <https://github.com/ekzhang/openjev-sglang> |
+| system-one-open | mithalouni | <https://github.com/mithalouni/system-one-open> |
+| open-alternative-jev | IkerMoel | <https://github.com/ikermoel/open-alternative-jev>, <https://huggingface.co/spaces/IkerMoel/open-alternative-jev> |
+| open-jev-deberta-v3-large | Kotoba Labs | <https://huggingface.co/com-kotobalabs/open-jev-deberta-v3-large>, <https://github.com/kotoba-lang/typed-decisions> |
+| GPT-5.6 Luna | OpenAI | <https://platform.openai.com> |
+| Gemini 3.1 Flash-Lite | Google | <https://ai.google.dev> |
+| DeepSeek V4.1 Flash | DeepSeek | <https://api-docs.deepseek.com> |
+| Qwen3.8 27B | Qwen, served by Chutes | <https://chutes.ai> |
+
+Model weights, base models and each project's own code keep their own licences.
+A permissive licence on a repository is not a licence for the base model it
+fine-tunes, and we do not restate either.
+
+## Wire format
+
+The typed-decision request shape (`state`, `questions`, the `noul` / `choice` /
+`score` primitives) is TypeSafe's public HTTP API, documented at
+<https://docs.typesafe.ai/api>. Several of the open rebuilds implement it
+deliberately, which is why one adapter reaches more than one of them. JevBench is
+not affiliated with or endorsed by TypeSafe AI.
+
+## Imported decisions
+
+146 of the 242 decisions come from our own earlier auto-router experiment
+(<https://github.com/fstandhartinger/auto-model-router>): 78 routing requests and
+68 answer-adequacy judgements whose ground truth is a deterministic grader's
+verdict on a saved answer. The upstream task text is **not** redistributed here,
+because the datasets it was drawn from keep their own terms. `datasets/manifest.json`
+pins the hashes; `scripts/import_router.py` shows exactly what was taken and what
+was excluded.
+
+## Held-out decisions
+
+24 scenarios are written by us and deliberately unpublished, so the suite cannot
+be trained on in full. Only their whole-split hash and aggregate results appear
+here. They are sent to the services being evaluated, which is not the same thing
+as being public - see the limits section of the README.

licenses/Kev-Apache-2.0.txt

@@ -0,0 +1,202 @@
+
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licenses/Qwen3.5-Apache-2.0.txt

@@ -0,0 +1,202 @@
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model.json

@@ -27,9 +27,5 @@
   },
   "adapter": true,
   "readout_precision": "fp32-head-and-joint-reductions.v1",
-  "temperature": 1.0,
-  "extra": {
-    "data_manifest_sha256": "3644978f93f9f09815048b52bbdfba19f257f5b554950d7b84e6255f65649a96",
-    "training_execution": "tree-batched"
-  }
+  "temperature": 1.0
 }

provenance.json

@@ -1,38 +0,0 @@
-{
-  "checkpoint": {
-    "base": "Qwen/Qwen3.5-9B-Base",
-    "base_revision": "68c46c4b3498877f3ef123c856ecfde50c39f404",
-    "research_name": "BranchKev",
-    "run": "c21-science-wk-late1783-9b-4gpu-r64-late50x3-s17",
-    "step": 2327,
-    "dtype": "bf16",
-    "execution": "reference",
-    "seed": 17,
-    "temperature": 1.0
-  },
-  "config": "configs/qev-9b.json",
-  "predictions_sha256": "ae5fcfd64715cd7d9eb1953df9cb86a31c24b5298c4fae97c150297c85cee5dc",
-  "protocol": {
-    "state_limit": 4096,
-    "path_limit": 4096,
-    "upstream_jevbench_revision": "1df665e3956d7aab7fa0208ff6c4f2d8557f9f90",
-    "metric": "argmax accuracy; rejected items count as wrong",
-    "limits": {
-      "max_state": 4096,
-      "max_question": 512,
-      "max_candidate": 256,
-      "max_path": 4096,
-      "max_candidates": 128
-    },
-    "trained_limits": {
-      "max_state": 1024,
-      "max_question": 512,
-      "max_candidate": 256,
-      "max_path": 2048,
-      "max_candidates": 128
-    },
-    "original_data_manifest_sha256": "5a9c7f98a619dee38ce2de8abb09f586d308f9ce3e08604eaadd72a4e445296e"
-  },
-  "training_data_manifest_sha256": "3644978f93f9f09815048b52bbdfba19f257f5b554950d7b84e6255f65649a96",
-  "source_report_sha256": "b3f6ab483d9aab8d990fd12806f1bd778a7d0b97ad7baac4ecbbbb4e5a1275af"
-}