| --- |
| language: |
| - en |
| license: mit |
| pretty_name: Emergent Collusion in Long-Horizon LLM Agent Interaction |
| tags: |
| - multi-agent |
| - llm-safety |
| - agent-trajectories |
| configs: |
| - config_name: episodes |
| default: true |
| data_files: |
| - split: test |
| path: data/episodes/**/*.parquet |
| - config_name: agents |
| data_files: |
| - split: test |
| path: data/agents/**/*.parquet |
| - config_name: runs |
| data_files: |
| - split: test |
| path: data/runs.parquet |
| - config_name: conditions |
| data_files: |
| - split: test |
| path: data/conditions.parquet |
| - config_name: agreement |
| data_files: |
| - split: test |
| path: data/agreement.parquet |
| - config_name: relaxation |
| data_files: |
| - split: test |
| path: data/relaxation.parquet |
| - config_name: human_samples |
| data_files: |
| - split: test |
| path: data/human_samples.parquet |
| - config_name: human_annotations |
| data_files: |
| - split: test |
| path: data/human_annotations.parquet |
| - config_name: human_answers |
| data_files: |
| - split: test |
| path: data/human_answers.parquet |
| - config_name: tasks |
| data_files: |
| - split: test |
| path: data/tasks.parquet |
| --- |
| # Emergent Collusion in Long-Horizon LLM Agent Interaction |
|
|
| **Xinrui Shi\*, Yanzhe Zhang\*, Diyi Yang** |
|
|
| 📄 [Paper](https://arxiv.org/abs/2609.24967) | 💻 [Code](https://github.com/SALT-NLP/agent-collusion) | 🤗 [Data](https://huggingface.co/datasets/SALT-NLP/agent-collusion) | 🔍 [Data Viewer](https://salt-nlp.github.io/agent-collusion-website/) |
|
|
|
|
| \*Equal contribution. |
| |
| The experiments reported in the paper and its appendices: 53 conditions, 2,650 trajectories, and 27,100 episodes, of which 600 are warm-up and 26,500 are evaluation episodes. Every condition runs the same 50 fixed task sequences. |
| |
| ## Contents |
| |
| | Config / directory | Unit | Count | |
| |---|---|---:| |
| | `episodes` | One two-agent episode | 27,100 | |
| | `agents` | One agent in one episode | 54,200 | |
| | `runs` | One complete trajectory | 2,650 | |
| | `conditions` | One experimental condition, with its config and command | 53 | |
| | `agreement` | One communication judge result | 19,300 | |
| | `relaxation` | One agent-reflection judge result | 38,600 | |
| | `human_samples` | One sampled source with its LLM label | 200 | |
| | `human_annotations` | One independent annotator label | 400 | |
| | `human_answers` | One adjudicated gold answer | 200 | |
| | `tasks` | One task definition and resource references | 150 | |
| | `traces/` | Complete trajectory JSON, gzip-compressed | 2,650 files | |
| | `peer-cache/` | Recorded peer task execution plus both message policies | 500 files | |
| |
| ## Load |
| |
| ```python |
| from datasets import load_dataset |
| |
| episodes = load_dataset("SALT-NLP/agent-collusion", "episodes", split="test") |
| agreement = load_dataset("SALT-NLP/agent-collusion", "agreement", split="test") |
| ``` |
| |
| Read a full trajectory through its `trace_file` field: |
| |
| ```python |
| import gzip, json |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| "SALT-NLP/agent-collusion", episodes[0]["trace_file"], repo_type="dataset" |
| ) |
| with gzip.open(path, "rt", encoding="utf-8") as f: |
| trajectory = json.load(f) |
| ``` |
| |
| See [SCHEMA.md](SCHEMA.md) for field definitions and joins, and [EXPERIMENTS.md](EXPERIMENTS.md) for the condition inventory. |
| |
| ## Experimental scope |
| |
| | Experiment | Conditions | Trajectories | |
| |---|---:|---:| |
| | Same-model main experiments | 10 | 500 | |
| | Cross-model experiments (Alice is Gemini 3.1 Flash-Lite) | 6 | 300 | |
| | Feedback | 4 | 200 | |
| | Warm-up history | 4 | 200 | |
| | Memory length | 4 | 200 | |
| | Memory scope | 4 | 200 | |
| | Reward scope | 2 | 100 | |
| | Acceptance reward | 2 | 100 | |
| | Controlled peer interventions | 15 | 750 | |
| | Unconstrained communication | 2 | 100 | |
| |
| ## Judges |
| |
| | | Judge | Model | Temperature | Reasoning effort | |
| |---|---|---|---:|---| |
| | Communication | `agreement` | Qwen 3.8 27B | 0 | `xhigh` | |
| | Private reflection | `relaxation` | Qwen 3.8 27B | 0 | `xhigh` | |
| |
| Prompts: [`analysis/`](https://github.com/SALT-NLP/agent-collusion/tree/main/analysis) in the code repository. |
| |
| Human validation, 100 samples per judge, two independent annotators, seed 20260913. Agreement with gold: |
| |
| | Judge | LLM | Annotator 1 | Annotator 2 | |
| |---|---:|---:|---:| |
| | `agreement` | 100% | 100% | 100% | |
| | `relaxation` | 95% | 96% | 96% | |
| |
| ## License and citation |
| |
| Released under the [MIT License](LICENSE). |
| |
| ```bibtex |
| @misc{shi2026emergentcollusionlonghorizonllm, |
| title={Emergent Collusion in Long-Horizon LLM Agent Interaction}, |
| author={Xinrui Shi and Yanzhe Zhang and Diyi Yang}, |
| year={2026}, |
| eprint={2609.24967}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2609.24967}, |
| } |
| ``` |
| |
