# Toward Agentic Governance: What Shapes LLM-Agent Intervention in Public Forums?

arXiv: https://arxiv.org/abs/2606.00603 · HTML: https://mlsanity.com/paper/2606.00603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 30 May 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 26 of 26)

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- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
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- [Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training](https://mlsanity.com/paper/2401.05566/) (2024)
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- [A Holistic Approach to Undesired Content Detection in the Real World](https://mlsanity.com/paper/2208.03274/) (2022)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Mind the Gap: Assessing Temporal Generalization in Neural Language Models](https://mlsanity.com/paper/2102.01951/) (2021)
- [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://mlsanity.com/paper/2605.29358/) (2026)
- [The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail](https://mlsanity.com/paper/2110.08300/) (2021)

## Abstract (arXiv)

LLM agents are increasingly used in moderation-relevant public forum workflows, where their choices to answer, acknowledge, repair, or decline are routinely challenged by users, platforms, and regulators. The same agent often returns different responses on identical content, so any defense based on the agent's behavior cannot be reliably reproduced. The variation is structural. Four deployment choices typically invisible to the operator each shift the agent's response rate, and their combinations can produce substantially different interventions on the same forum posts. The four choices are (1) which model version is currently served, which can change between calls without notice; (2) the model's weight-release status (open-weight, with weights publicly downloadable, vs. closed-weight, with weights held by the provider); (3) which provider serves the request; and (4) which system-prompt policy is in force. Across LLMs spanning both open-weight and closed-weight families, we find that the previously reported tendency to decline more on visible than hidden challenges aligns with the open/closed weight boundary in our panel more than with access surface. Every closed-weight cell declines more on visible challenges; every open-weight cell reverses this or shows no gap. Auditable forum-agent governance requires awareness of all four choices, not just the model name, since each independently shifts behavior.