Add HOTE-8B model card

README.md

@@ -7,4 +7,94 @@ base_model:
 datasets:
 - rl-research/dr-tulu-sft-data
 - rl-research/dr-tulu-rl-data
----
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- deep-research
+- agent
+- reinforcement-learning
+- tool-use
+- open-ended-evolution
+- qwen3
+model-index:
+- name: HOTE-8B
+  results:
+  - task:
+      type: text-generation
+      name: Long-form deep research
+    dataset:
+      name: HealthBench
+      type: HealthBench
+    metrics:
+    - type: score
+      value: 54.4
+      name: HealthBench score
+  - task:
+      type: text-generation
+      name: Long-form deep research
+    dataset:
+      name: ResearchQA
+      type: ResearchQA
+    metrics:
+    - type: score
+      value: 76.9
+      name: ResearchQA score
+  - task:
+      type: text-generation
+      name: Long-form deep research
+    dataset:
+      name: DeepResearchBench
+      type: DeepResearchBench
+    metrics:
+    - type: score
+      value: 45.9
+      name: DeepResearchBench score
+---
+
+# HOTE-8B
+
+HOTE-8B is an 8B-parameter deep research model trained with **Hybrid Open-Ended Tri-Evolution (HOTE)**, a reinforcement-learning framework for open-ended research agents. The model is introduced in [Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher](https://arxiv.org/abs/2606.13710) (arXiv:2606.13710v2, 2026-06-15).
+
+HOTE trains a deep research system through the co-evolution of three roles:
+
+- **Solver**: plans, searches, integrates retrieved evidence, and writes long-form research reports with citations.
+- **Judge**: generates and updates rubrics, evaluates multiple solver responses, and provides rewards beyond deterministic-answer tasks.
+- **Proposer**: searches for weaknesses identified by the judge and proposes challenging but learnable research tasks.
+
+The framework uses a dual-mode strategy with both tool-use and no-tool training. According to the paper, this improves training efficiency while allowing the tool-use and no-tool modes to benefit each other.
+
+## Repository Contents
+
+This repository contains the following checkpoint folders:
+
+- `step_700/`: HOTE-8B deep research model checkpoint.
+- `step_700_query/`: proposer checkpoint used in the HOTE framework.
+
+## Intended Use
+
+HOTE-8B is intended for research on long-form deep research agents, search-augmented report generation, open-ended agent evolution, and reinforcement learning for non-verifiable tasks.
+
+The model is most useful when integrated with a search-enabled agent runtime. In the paper, the solver operates with ReAct-style actions including thinking, tool calls, final answers, and citations. The model weights alone do not provide web search, browsing, paper search, citation validation, or tool execution.
+
+
+## Limitations
+
+- The model is designed for deep research workflows and should be paired with robust tool execution, citation validation, and source-quality checks.
+- The model may generate inaccurate, incomplete, outdated, or unsupported claims, especially without retrieval tools.
+- The paper notes that evolution slows as training progresses and that the upper bound may still be constrained by model scale.
+- The HOTE method still relies on initial training data; fully data-free open-ended deep research evolution is left for future work.
+- Research outputs in sensitive domains such as healthcare, law, finance, or public policy should be reviewed by qualified experts.
+
+## Citation
+
+```bibtex
+@misc{piao2026hybridopenendedtrievolutionmakes,
+  title = {Hybrid Open-Ended Tri-Evolution Makes Better Deep Researcher},
+  author = {Hongming Piao and Chi Liu and Mengzhuo Chen and Yan Shu and Xidong Wang and Derek Li and Ying Wei and Bryan Dai},
+  year = {2026},
+  eprint = {2606.13710},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.AI},
+  url = {https://arxiv.org/abs/2606.13710}
+}
+```