# Pingquanqi (Equalizer): A Cross-Domain Sociotechnical Framework for Human-Agent Interaction Governance

arXiv: https://arxiv.org/abs/2606.26573 · HTML: https://mlsanity.com/paper/2606.26573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Calibrated Language Models Must Hallucinate](https://mlsanity.com/paper/2311.14648/) (2023)
- [CiteAudit: You Cited It, But Did You Read It? A Benchmark for Verifying Scientific References in the LLM Era](https://mlsanity.com/paper/2602.23452/) (2026)
- [How Does Conversation Length Impact User's Satisfaction? A Case Study of Length-Controlled Conversations with LLM-Powered Chatbots](https://mlsanity.com/paper/2404.17025/) (2024)
- [Token Economics for LLM Agents: A Dual-View Study from Computing and Economics](https://mlsanity.com/paper/2605.09104/) (2026)
- [Do LLMs Benefit From Their Own Words?](https://mlsanity.com/paper/2602.24287/) (2026)
- [Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines](https://mlsanity.com/paper/2405.06478/) (2024)
- [The Siren Song of LLMs: How Users Perceive and Respond to Dark Patterns in Large Language Models](https://mlsanity.com/paper/2509.10830/) (2025)
- [ArXivBench: When You Should Avoid Using ChatGPT for Academic Writing](https://mlsanity.com/paper/2504.10496/) (2025)
- [Personality pairing improves human-AI collaboration](https://mlsanity.com/paper/2511.13979/) (2025)
- [Needs-aware Artificial Intelligence: AI that 'serves [human] needs'](https://mlsanity.com/paper/2202.04977/) (2022)
- [Designing AI Systems that Augment Human Performed vs. Demonstrated Critical Thinking](https://mlsanity.com/paper/2504.14689/) (2025)
- [Navigating the State of Cognitive Flow: Context-Aware AI Interventions for Effective Reasoning Support](https://mlsanity.com/paper/2504.16021/) (2025)
- [GazeMind: A Gaze-Guided LLM Agent for Personalized Cognitive Load Assessment](https://mlsanity.com/paper/2605.05790/) (2026)

## Abstract (arXiv)

LLM agents are transitioning from experimental tools to permanent infrastructure -- a computational layer as enduring as the electrical grid. Like any infrastructure, they carry a cost chain from physical capital through enterprise investment to user consumption, ending at the user's most irreplaceable resource: lifetime. When unoptimized, this chain leaks, consuming user lifetime without adequate compensation. This paper proposes Pingquanqi (Equalizer), a cross-domain sociotechnical framework for Human-Agent Interaction Governance (HAIGF). Its product form is an Agent framework-level embedded design specification, analogous to WCAG for web accessibility, whose goal is not to be purchased but adopted as a standard. Pingquanqi consists of four integrated components deployable as native middleware: (1) a user-state discrimination model enabling proactive knowledge leveling, (2) a Bayesian progressive stop-loss rule capping per-session interaction cost, (3) controlled friction mechanisms breaking self-reinforcing dependency loops, and (4) Lsteal, a transparency metric rendering token-to-lifetime cost conversion visible. A fifth mechanism, reflective summarization (F5), enables guided cognitive recollection. The framework is grounded in cross-cultural philosophy: Mao's epistemology of practice (On Practice, 1937) provides the basis for cross-session knowledge accumulation; Wang Yangming's unity of knowledge and action (zhi xing he yi, c. 1509) illuminates Lsteal's root -- knowing without acting is incomplete; and Hegel's unity of theory and practice demonstrates cross-traditional convergence. This paper argues Pingquanqi's primary economic beneficiary is the enterprise deploying Agent services -- through reduced wasted computation, improved user satisfaction, and sustained subscription revenue -- with individual user benefit as the natural downstream consequence.