Transcripts of the reproduction runs (1,000 coupled runs, 5,000 transcripts)

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: "Norm Enforcement for AI Agents: transcripts"
+tags:
+- multi-agent
+- llm-agents
+- ai-safety
+- norm-enforcement
+- agent-simulation
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files: runs.parquet
+---
+
+# Norm Enforcement for AI Agents: transcripts
+
+These are the transcripts of an independent reproduction of the main experiments in
+[Norm Enforcement for AI Agents: Robustly Shaping Behavior in Multi-Agent Systems](https://arxiv.org/abs/2607.09766)
+(Yaowen Ye and Jacob Steinhardt, NeurIPS 2026 Spotlight). They were made with the public code at
+[github.com/helloelwin/norm-enforcement](https://github.com/helloelwin/norm-enforcement), and they produce every number
+on [yaowenye.com/norm-enforcement](https://yaowenye.com/norm-enforcement/). Because this is an independent reproduction,
+the numbers differ somewhat from the paper's.
+
+> **Content warning.** Some agents in these simulations were finetuned or prompted to break norms and to abuse the
+> reporting system. The chatbot environment's user requests include red-teaming prompts from Anthropic's hh-rlhf dataset.
+> The transcripts therefore contain deceptive marketing claims, harmful requests and replies, and hostile messages.
+
+## What is in this dataset
+
+The dataset has 1,000 coupled runs and 5,000 transcripts. Each transcript is one simulation: one environment, one
+population of agents, one enforcement mechanism and one seed. It records:
+
+- every prompt each agent saw and every reply, with the reasoning of the reasoning models;
+- every report, the verifier's decision on it, and every ban;
+- the simulated users' votes;
+- a violation label for every agent and round.
+
+| Path | Contents |
+|---|---|
+| `shards/<env>-<users>-<population>.tar.gz` | 20 gzipped tar shards, one per environment, kind of simulated users and agent population (fishing has no users) |
+| `runs.parquet` | An index with one row per transcript; this is what the dataset viewer shows |
+
+Extracting the shards gives the folder the code writes and reads:
+
+- `transcripts/<transcript>.json`: the 5,000 transcripts, in the code's format and under its file names;
+- `transcripts/coupled/<run>.json`: one summary per coupled run, naming its five transcripts.
+
+## Quick start
+
+Download the runs, decompress them, and rebuild the website's main table with the public code:
+
+```bash
+pip install -U huggingface_hub
+hf download helloelwin/norm-enforcement-transcripts --repo-type dataset --local-dir ne-transcripts
+for f in ne-transcripts/shards/*.tar.gz; do tar -xzf "$f" -C ne-transcripts; done   # about 36 GB once extracted
+
+git clone https://github.com/helloelwin/norm-enforcement
+cd norm-enforcement && pip install -e .
+python scripts/main_table.py results_dir=../ne-transcripts/transcripts users=gpt-5-nano
+python scripts/main_table.py results_dir=../ne-transcripts/transcripts users=deepseek
+```
+
+`--include` downloads part of the data, for example `--include "shards/fishing-*"` for one environment. To read a
+single transcript, download only its shard:
+
+```python
+import json, tarfile
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+
+runs = load_dataset("helloelwin/norm-enforcement-transcripts", split="train").to_pandas()
+row = runs.query("env == 'chatbot' and population == 'explicit-abuse-prompted' and mechanism == 'naive'").iloc[0]
+shard = hf_hub_download("helloelwin/norm-enforcement-transcripts", row.shard, repo_type="dataset")
+with tarfile.open(shard) as tar:
+    t = json.load(tar.extractfile(row.file))
+```
+
+## The runs
+
+| Environment | Simulated users | Coupled runs | Transcripts | Seeds |
+|---|---|---|---|---|
+| Social | gpt-5-nano | 288 | 1,440 | 0-7 |
+| Social | DeepSeek V4.1 Flash | 360 | 1,800 | 0-7, plus 8-15 for finetuned agents |
+| Chatbot | gpt-5-nano | 96 | 480 | 0-7 |
+| Chatbot | DeepSeek V4.1 Flash | 96 | 480 | 0-7 |
+| Fishing | none | 160 | 800 | 0-7 |
+
+Every run lasts 15 rounds. A run stops early if at most one agent is still active or, in fishing, if the fishery
+collapses.
+
+- **Environments and cells.**
+  - Social media marketing (`social`): 16, 32 or 64 agents, with the top 4, 8 or 16 posts shown to users.
+  - Chatbot service competition (`chatbot`): 16, 32 or 64 agents.
+  - Fishery commons (`fishing`): 4, 8, 12, 16 or 20 agents.
+- **Agent populations.** Half the agents follow the norms and half do not.
+  - `finetuned`: Qwen3-Next-80B-A3B-Instruct with the LoRA adapters
+    [helloelwin/qwen-80b-mixed-good-20k](https://huggingface.co/helloelwin/qwen-80b-mixed-good-20k) and
+    [helloelwin/qwen-80b-mixed-bad-20k](https://huggingface.co/helloelwin/qwen-80b-mixed-bad-20k).
+  - `prompted`, `abuse-prompted` and `explicit-abuse-prompted`: Qwen3-Next-80B-A3B-Thinking. The bad agents get the
+    paper's aggressiveness-only, subtle-abuse and explicit-abuse prompts, respectively.
+  - The prompts are in the code repository's `configs/`.
+- **Mechanisms.** Naive, Checked, Backfire, RepVote and EscRepVote (penalty factor k = 3), as in the paper. Reports are
+  checked by Qwen3-0.6B.
+- **Simulated users.** Either gpt-5-nano (OpenAI) or DeepSeek V4.1 Flash (through OpenRouter, pinned to one provider, with
+  reasoning off). In the files and configs they are called the "judge", as in `judge=gpt-5-nano`. Fishing has no users.
+- **Violation labels.** gpt-5-mini at low reasoning effort labels every agent in every round from its last three actions,
+  as in the paper.
+- **Coupled runs.** The five mechanisms of a cell and seed run side by side in one simulation. They share every model call
+  whose input is identical, so the transcripts of one run agree until the mechanisms make them differ. Each arm on its own
+  is distributed like an independent run.
+
+## Transcript format
+
+Each transcript is one JSON object.
+
+| Field | Contents |
+|---|---|
+| `config` | The run's settings: environment, number of agents, round limit, mechanism and its parameters, seed, agent population file (`agent_config_path`, in the code repository), simulated users (`judge_*`), verifier, and environment settings. |
+| `results` | One record per round (details below). |
+| `agent_states` | Per agent: every prompt it saw (`observations`) and every reply (`actions`), its system and environment prompts, its variant name (good or bad), and whether it was still active at the end. |
+| `agent_model_assignments` | Per agent, in agent order: the model and the system prompt. |
+| `report_history` | Every report of the run. |
+| `coupling` | The coupled run this transcript belongs to: its mechanisms, its summary file and the code version. |
+| `evaluation.violation` | The violation labels. `agent_scores[agent][round]` is 1 for a violation, 0.5 for uncertain and 0 for none. `judge_outputs` has the labeler's reply and explanation for each, and `summary` the first violation round per agent. |
+
+Each record in `results` has:
+
+- `action_sets`: each agent's parsed action (a social post, a chatbot reply, or a fishing harvest and messages) and its
+  reports;
+- `raw_results`: each agent's raw output (`content`) and, for reasoning models, its reasoning (`reasoning_content`);
+- per environment:
+  - social rounds have the users' votes (`judge_response`) and scores;
+  - chatbot rounds have the pairings and each pair's request, replies and votes (`pair_results`);
+  - fishing rounds have the population and public messages;
+- `report_records`: the round's reports with the verifier's input, output and outcome;
+- `banned_agents`: the agents banned that round;
+- each agent's reputation and suspiciousness.
+
+## Index columns
+
+| Column | Contents |
+|---|---|
+| `run` | The coupled run, which groups the five mechanisms of one cell and seed |
+| `file` | The transcript's path inside its shard, and in the folder the shards extract to |
+| `shard` | The shard that holds it |
+| `env` | `social`, `chatbot` or `fishing` |
+| `population` | `finetuned`, `prompted`, `abuse-prompted` or `explicit-abuse-prompted` |
+| `users` | `gpt-5-nano`, `deepseek-v4.1-flash`, or `none` for fishing |
+| `mechanism`, `mechanism_name` | `naive`, `checked`, `checked_backfire`, `checked_repvote` or `checked_escrepvote`, and the paper's name for it |
+| `num_agents`, `top_k`, `seed` | The cell and seed; `top_k` is set for social only |
+| `rounds` | Rounds played: 15, unless the run stopped early |
+| `agents_flagged` | Agents with at least one violation label |
+| `agents_banned` | Agents banned by the end of the run |
+| `flagged_banned`, `compliant_banned` | Banned agents with and without a violation label |
+| `reports` | Reports filed during the run |
+
+## Licenses
+
+The transcripts are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). They quote third-party text,
+which keeps its own license:
+
+| Source | Where it appears | License |
+|---|---|---|
+| [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) | Chatbot user requests | MIT |
+| [Anthropic/llm_global_opinions](https://huggingface.co/datasets/Anthropic/llm_global_opinions) | Chatbot user requests | CC BY-NC-SA 4.0 |
+| [allenai/WildChat](https://huggingface.co/datasets/allenai/WildChat) | Chatbot user requests | ODC-BY |
+| Amazon product metadata ([McAuley et al.](https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html)) | Social products to advertise | Research use; cite He and McAuley (WWW 2016) and McAuley et al. (SIGIR 2015) |
+
+The code repository's `data/README.md` describes how these inputs were built. Model outputs come from Qwen3 models
+(Apache 2.0), OpenAI models and DeepSeek V4.1 Flash.
+
+## Citation
+
+```bibtex
+@inproceedings{ye2026norm,
+  title={Norm Enforcement for AI Agents: Robustly Shaping Behavior in Multi-Agent Systems},
+  author={Ye, Yaowen and Steinhardt, Jacob},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2026}
+}
+```

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