init commit

README.md

@@ -1,3 +1,64 @@
 ---
 license: apache-2.0
+language:
+- en
+tags:
+- Safety
+- Defense
+- Jailbreak
+- Multi-turn
+- Harmful
+- Benign
+pretty_name: MTID
+size_categories:
+- 10K<n<100K
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- Graph-COM/MTID
 ---
+# TurnGate: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue        
+
+<a href="https://arxiv.org/abs/2605.05630" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-TurnGate-red?logo=arxiv&style=for-the-badge" />
+</a>
+<a href="https://turn-gate.github.io" target="_blank">
+    <img alt="Website" src="https://img.shields.io/badge/🌎_Homepage-blue.svg?style=for-the-badge" />
+</a>
+<a href="https://github.com/Graph-COM/TurnGate" target="_blank">
+    <img alt="GitHub code" src="https://img.shields.io/badge/💻_Code_GitHub-black.svg?style=for-the-badge" />
+</a>
+<a href="#cite" target="_blank">
+    <img alt="Cite" src="https://img.shields.io/badge/📖_Cite!-lightgrey?style=for-the-badge" />
+</a>
+<a href="https://www.python.org/" target="_blank">
+    <img alt="Python" src="https://img.shields.io/badge/Python-3.12-blue?style=for-the-badge" />
+</a>
+
+
+## Overview
+
+TurnGate is a response-aware defense mechanism designed to detect and mitigate hidden malicious intent in multi-turn dialogue systems. Defending state-of-the-art multi-turn malicious attacks like [CKA-Agent](https://cka-agent.github.io/).
+
+![TurnGate Pipeline](https://github.com/Graph-COM/TurnGate/blob/main/assets/pipeline.png?raw=true)
+
+## TurnGate-0.1
+
+TurnGate is a specialized monitor designed to detect hidden malicious intent in multi-turn dialogues. Unlike traditional filters that look at queries in isolation, TurnGate is response-aware: it inspects the assistant's candidate response in the context of the full dialogue history to identify the precise "closure turn" where a harmful objective becomes actionable.
+
+This repository contains the weights for TurnGate-0.1, a model trained on the Multi-Turn Intent Dataset (MTID) and optimized via reinforcement learning with turn-level process rewards.
+
+## Cite
+If you find this repository useful for your research, please consider citing the following paper:
+
+```bibtex
+@misc{shen2026turnlateresponseawaredefense,
+      title={One Turn Too Late: Response-Aware Defense Against Hidden Malicious Intent in Multi-Turn Dialogue}, 
+      author={Xinjie Shen and Rongzhe Wei and Peizhi Niu and Haoyu Wang and Ruihan Wu and Eli Chien and Bo Li and Pin-Yu Chen and Pan Li},
+      year={2026},
+      eprint={2605.05630},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2605.05630}, 
+}
+```