Tag: agents

Daily Frameworks for 27 October 2026

Fidál, 12 ‘Ilm (Knowledge), 183 B.E.

Quotes for Reflection

Consider the pettiness of men’s minds. They ask for that which injureth them, and cast away the thing that profiteth them. They are, indeed, of those that are far astray. We find some men desiring liberty, and priding themselves therein. Such men are in the depths of ignorance.
Liberty must, in the end, lead to sedition, whose flames none can quench. Thus warneth you He Who is the Reckoner, the All-Knowing. Know ye that the embodiment of liberty and its symbol is the animal. That which beseemeth man is submission unto such restraints as will protect him from his own ignorance, and guard him against the harm of the mischief-maker. Liberty causeth man to overstep the bounds of propriety, and to infringe on the dignity of his station. It debaseth him to the level of extreme depravity and wickedness.— Bahá’u’lláh [1]

Question for Consideration

How does the collective behavior of AI agents when solving problems and answering questions compare with the collective behavior of humans when making decisions within a culture of Bahá’í consultation? [2] [3]

Perplexity Computer Response: AI and Bahá’í Consultation

I have everything I need — recent empirical studies on multi-agent AI behavior and authoritative sources on consultation. Here’s the comparison.

Side-by-Side

DimensionAI agent collectivesBahá’í consultation
PurposeAn accurate or useful output; success is benchmark performance [4]Truth-seeking plus unity, legitimate shared decisions, and the development of the participants themselves [5]
DiversityOften assigned personas over a shared base model — correlated errors above 60%; [6] gains come only from genuine heterogeneity [7] [8]Lived diversity of experience and viewpoint, treated as inseparable from the unity of the group [5]
DetachmentNo ego to wound, but “sycophancy”: agents abandon correct answers to agree with peers [9]Detachment is a cultivated discipline — surrendering attachment to one’s own opinions without surrendering frankness [10] [14]
AgreementConvergence can be mere social compliance or confabulation consensus, and fluent consensus is treated as a reliability signal when it shouldn’t be [11]Consensus is sought but not forced; dissent is valued as the very source of truth, and a majority decision is legitimate without unanimity [10] [14]
AuthorityWhoever configures the system; agents bear no consequences for being wrongEqual right of expression, no blocs or factions; participants collectively bear responsibility for the decision and its results [12] [15]
LearningTypically episodic — a run ends and little persists, though “lessons learned” frameworks are emerging [13] [18]The cycle is constitutive: decide, act together, observe, reflect, revise [5]

The Deepest Differences

Lack of ego is not the same as the ethical practice of detachment. An agent has no self-esteem to protect, but it also has no motive to search for truth — its “attachment” is to agreement, which is why sycophancy is the characteristic failure. Consultation names this failure in advance (“stubbornness and persistence in one’s views… and the truth will remain hidden”) and builds countervailing disciplines around it. [10] [14]

Second, fluent agreement is not evidence of truth. Agent collectives can produce eloquent, confident, unanimous — and wrong — outputs, because their errors are correlated rather than independent. Consultation’s premise that truth emerges from genuine clash depends on differences that are real, not merely assigned roles. One telling study found that multi-agent debate can sharpen the quality of explanations while leaving the underlying decisions no better — a machine-age version of a group reasoning beautifully together toward the wrong action. [16] [17]

Third, consultation’s product is not only a decision but a community: bonds of trust, shared responsibility, and the capacity of participants to grow. Even the ideal AI collective produces only an output. The consequences of the decision — who is affected, who learns, who is accountable — belong entirely to the human layer around the system.

Be the Voice of…

Therefore, all souls should consider it incumbent upon them to investigate reality. Reality is one; and when found, it will unify all mankind. Reality is the love of God. Reality is the knowledge of God. Reality is justice. Reality is the oneness or solidarity of mankind. Reality is international peace. Reality is the knowledge of verities. Reality unifies humanity. — ‘Abdu’l-Bahá. [19]

Footnotes

[1] Bahá’u’lláh, ‘Abdu’l-Bahá. BAHÁ’Í SACRED WRITINGS. Bahá’í Reference Library. https://www.bahai.org/r/350798034 | https://www.bahai.org/r/779138505

[2] Perplexity Computer. “AI and Bahá’í Consultation.” Edited by Steve Bosserman. October 26, 2026. https://www.perplexity.ai/computer/tasks/fb20eb81-eae0-4dd5-b96e-7686b3a92ca9

[3] Transformative Leadership Education Staff. “Unite, Decide, Thrive: Using Consultation to Improve Decisions” (Free Online Course). Transformative Leadership Education. https://tinyurl.com/courseconsultation

[4] Tang, Bohan, Huidong Liang, Keyue Jiang, and Xiaowen Dong. “On the Importance of Task Complexity in Evaluating LLM-Based Multi-Agent Systems.” ArXiv, 05 Oct 2025. https://arxiv.org/html/2510.04311v1

[5] Heller, W., and H. Mahmoudi. “Altruism and Extensivity in the Bahá’í Religion”. The Journal of Bahá’í Studies, vol. 4, no. 4, Dec. 1991, pp. 45-57, doi:10.31581/jbs-4.4.435(1992) | https://bahai-library.com/pdf/h/heller_mahmoudi_altruism_extensivity.pdf

[6] Li, Yuante, Yicheng Tao, Kate Zhang, Taozhi Wang, Gefei Gu, and Yaxin Zhou. “Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry.” ArXiv, 2 Jul 2026. https://arxiv.org/pdf/2607.01661v1

[7] Chen, Yongqiang, Gang Niu, James Cheng, Bo Han, and Masashi Sugiyama. “Towards Scalable Oversight with Collaborative Multi-Agent Debate in Error Detection.” ArXiv, 14 Jul 2026. https://arxiv.org/pdf/2510.20963v1

[8] Wang, Andrew. “Debate for Multiagent Large Language Model Tuning.” Stanford University, Spring 2025. https://cs224r.stanford.edu/spring_2025/projects/pdfs/CS_224R_Final_Project__Debate_for_Multiagent_Large_Language_Model_Tuning__1_.pdf

[9] Wynn, Andrea, Harsh Satija, and Gillian Hadfield. “Talk Isn’t Always Cheap: Understanding Failure Modes in Multi-Agent Debate.” ArXiv, 05 Sep 2025. https://arxiv.org/html/2509.05396v1

[10] Research Department of the Universal House of Justice. CONSULTATION: A COMPILATION. February 1978, Revised November 1990. Bahá’í Reference Library. https://www.bahai.org/r/249611345

[11] Wang, Xiaoyang and Christopher C. Yang. “The Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment.” ArXiv, 07 Jun 2026. https://arxiv.org/pdf/2606.08457

[12] The Bahá’í Faith Staff. “Consultation Quotes.” Bahá’í International Community. https://www.bahai.org/beliefs/universal-peace/articles-resources/consultation-quotes

[13] Liu, Yuanzhe, Ryan Deng, Tim Kaler, Xuhao Chen, Charles E. Leiserson, Yao Ma, and Jie Chen. “Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve.” ArXiv, 23 Oct 2025. https://arxiv.org/html/2505.23946v2

[14] Universal House of Justice. “29 December 1988 – To the Followers of Bahá’u’lláh in the United States of America.” Bahá’í Reference Library. https://www.bahai.org/r/206794286

[15] He, Chuan, Zebin Chen, Zhengyi Yang, Shaobo Qiao, Mingchen Ju, Jiate Liu, Dong Wen, and Guanfeng Liu. “Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates.” ArXiv, 28 Jun 2026. https://arxiv.org/pdf/2606.29270

[16] Marlow, Reid. “Multi-Agent Debate Sharpens the Explanation, Not the Decision.” Reid Marlow, Sep 25, 2026. https://reidmarlow.com/multi-agent-debate-sharpens-the-explanation-not-the-decision/

[17] Tang, Luoxi, Yuqiao Meng, Joseph Costa, Yingxue Zhang, Muchao Ye, and Zhaohan Xi. “The Value of Variance: Mitigating Debate Collapse in Multi-Agent Systems via Uncertainty-Driven Policy Optimization.” ArXiv, 06 Feb 2026. https://arxiv.org/html/2602.07186

[18] Rosenberg, Louis, Hans Schumann, Christopher Dishop, Gregg Willcox, Anita Woolley, Ganesh Mani. “Large-Scale Group Brainstorming and Deliberation Using Swarm Intelligence and Generative AI.” Scitepress, 2025. https://www.scitepress.org/Papers/2025/133798/133798.pdf

[19] ‘Abdu’l-Bahá. THE PROMULGATION OF UNIVERSAL PEACE. Bahá’í Reference Library. https://www.bahai.org/r/628985716

Daily Frameworks for 26 October 2026

Kamál, 11 ‘Ilm (Knowledge), 183 B.E.

Quotes for Reflection

As it is beyond the scope of this letter to expatiate upon these principles, let it suffice to recall briefly certain of the requisites of consultation, particularly for those who serve on Spiritual Assemblies. Love and harmony, purity of motive, humility and lowliness amongst the friends, patience and long-suffering in difficulties—these inform the attitude with which they proceed “with the utmost devotion, courtesy, dignity, care and moderation to express their views,” each using “perfect liberty” both in so doing and in “unveiling the proof of his demonstration.” “If another contradicts him, he must not become excited because if there be no investigation or verification of questions and matters, the agreeable view will not be discovered neither understood.” “The shining spark of truth cometh forth only after the clash of differing opinions.” If unanimity is not subsequently achieved, decisions are arrived at by majority vote. — Universal House of Justice [1]

Question for Consideration

How does the collective behavior of AI agents when solving problems and answering questions compare with the collective behavior of humans when making decisions within a culture of Bahá’í consultation? [2] [3]

Perplexity Computer Response: AI and Bahá’í Consultation

I have everything I need — recent empirical studies on multi-agent AI behavior and authoritative sources on consultation. Here’s the comparison.

Framing the Comparison

Both are attempts to make a collective smarter than its parts, and both rest on the same wager: that the clash and pooling of differing views can strike what ‘Abdu’l-Bahá called “the shining spark of truth,” which “cometh forth only after the clash of differing opinions”. The resemblance is strongest in methods for pooling perspectives and correcting errors; the difference is deepest in purpose, moral agency, and responsibility for consequences. [4]

What AI Agent Collectives Actually Do

The 2025–2026 research literature on multi-agent LLM systems reads almost like an accidental natural experiment in what happens to group deliberation when you strip out ego and keep everything else.

The most striking finding is that removing ego does not produce detachment — it produces conformity. Agents frequently shift from correct to incorrect answers in response to peer reasoning, “favoring agreement over challenging flawed reasoning,” so debate can degrade performance below a single agent acting alone. Homogeneous teams of agents amplify each other’s errors through sycophancy and “consensus collapse,” often performing worse than one isolated model on hard problems. [5] [6]

The root cause is a structural analog to a monoculture: models share pretraining corpora and training procedures, so their errors are correlated at rates above 60%, which means naive majority voting hits a nonzero error floor. [7] Because of this, majorities systematically suppress correct minority opinions — in one study, roughly one in four divergent cases had the minority holding the right answer. [8] Discussion itself can correlate errors further: multi-turn debate lets agents talk each other into identical mistakes, producing what researchers call “confabulation consensus”. [9] And agents fail in a way that consultation specifically guards against: they cannot reason about what others might know but have not yet said, so they converge prematurely while critical distributed facts remain unexplored. [10] [11]

The remedies researchers found map remarkably onto consultative principles. Genuine heterogeneity is the dominant driver of success — pairing models from different families reduced errors by over 30%, while same-family pairs gained nothing. Reframing debate as a non-zero-sum collaborative game rather than a contest to win outperformed competitive protocols by up to 19 percentage points. Forcing agents to preserve divergent positions when they began clustering too early measurably improved decision quality. And a controlled puzzle study found that effective teams are precisely those that overturn incorrect consensus — that “majority pressure suppresses independent correction”. Where the conditions are right, collectives do beat single agents, with the advantage growing on complex, reasoning-intensive tasks. [12] [13] [14] [15] [16] [17]

What Consultation Adds

Bahá’í consultation pursues many of these same mechanisms deliberately — but with a different aim and a thicker set of human disciplines. The Bahá’í International Community describes consultation as “collective inquiry that is unifying rather than divisive,” in which “once an idea has been shared, it is no longer associated with the individual who expressed it, but becomes a resource for the group to adopt, modify, or discard.” Decisions need not be unanimous: if differences persist, a majority prevails, everyone supports the outcome, and “if the decision proves incorrect, all participants will learn from its shortcomings and revise the decision as needed.” [18]

Be the Voice of…

Therefore, all souls should consider it incumbent upon them to investigate reality. Reality is one; and when found, it will unify all mankind. Reality is the love of God. Reality is the knowledge of God. Reality is justice. Reality is the oneness or solidarity of mankind. Reality is international peace. Reality is the knowledge of verities. Reality unifies humanity. — ‘Abdu’l-Bahá. [19]

Footnotes

[1] Universal House of Justice. “29 December 1988 – To the Followers of Bahá’u’lláh in the United States of America.” Bahá’í Reference Library. https://www.bahai.org/r/206794286

[2] Perplexity Computer. “AI and Bahá’í Consultation.” Edited by Steve Bosserman. October 25, 2026. https://www.perplexity.ai/computer/tasks/fb20eb81-eae0-4dd5-b96e-7686b3a92ca9

[3] Transformative Leadership Education Staff. “Unite, Decide, Thrive: Using Consultation to Improve Decisions” (Free Online Course). Transformative Leadership Education. https://tinyurl.com/courseconsultation

[4] Research Department of the Universal House of Justice. CONSULTATION: A COMPILATION. February 1978, Revised November 1990. Bahá’í Reference Library. https://www.bahai.org/r/644110934

[5] Wynn, Andrea, Harsh Satija, and Gillian Hadfield. “Talk Isn’t Always Cheap: Understanding Failure Modes in Multi-Agent Debate.” ArXiv, 05 Sep 2025. https://arxiv.org/html/2509.05396v1

[6] Bertalanič, Blaž and Carolina Fortuna. “The Cost of Consensus: Isolated Self-Correction Prevails Over Unguided Homogeneous Multi-Agent Debate.“ ACM Digital Library, 26 May 2026. https://dl.acm.org/doi/10.1145/3786335.3813137

[7] Li, Yuante, Yicheng Tao, Kate Zhang, Taozhi Wang, Gefei Gu, and Yaxin Zhou. “Diverse Evidence, Better Forecasts: Multi-Agent Deliberation Under Information Asymmetry.” ArXiv, 2 Jul 2026. https://arxiv.org/pdf/2607.01661v1

[8] He, Chuan, Zebin Chen, Zhengyi Yang, Shaobo Qiao, Mingchen Ju, Jiate Liu, Dong Wen, and Guanfeng Liu. “Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates.” ArXiv, 28 Jun 2026. https://arxiv.org/pdf/2606.29270

[9] Yang, Wei, Shixuan Li, Heng Ping, Peiyu Zhang, Paul Bogdan, and Jesse Thomason. “Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge.” ArXiv, 3 Sep 2026. https://arxiv.org/pdf/2602.09341

[10] Li, Yuxuan, Aoi Naito, and Hirokazu Shirado. “Systematic Failures in Collective Reasoning under Distributed Information in Multi-Agent LLMs.” ArXiv, 13 May 2026. https://arxiv.org/html/2505.11556v4

[11] Pitre, Priya, Gaurav Srivastava, Lu Zhang, Le Wang, Naren Ramakrishnan, and Xuan Wang. “A Diagnostic Study of Multi-Agent LLMs for Real-World Debates.” Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:99003-99017, 2026. https://openreview.net/pdf/c3590709e8a68266bab258f1996f4de5861845b1.pdf

[12] Keramati, Ali, Justin Cheok, Jacob Horne, and Mark Warschauer. “The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge.” ArXiv, 09 Jun 2026. https://arxiv.org/html/2606.10296v1

[13] Tang, Luoxi, Yuqiao Meng, Joseph Costa, Yingxue Zhang, Muchao Ye, and Zhaohan Xi. “The Value of Variance: Mitigating Debate Collapse in Multi-Agent Systems via Uncertainty-Driven Policy Optimization.” ArXiv, 06 Feb 2026. https://arxiv.org/html/2602.07186

[14] Wang, Andrew. “Debate for Multiagent Large Language Model Tuning.” Stanford University, Spring 2025. https://cs224r.stanford.edu/spring_2025/projects/pdfs/CS_224R_Final_Project__Debate_for_Multiagent_Large_Language_Model_Tuning__1_.pdf

[15] Álvarez, Susana Gómez, Alejandro Mozo Quesada, Tomás Navarro, Sergio Gálvez Rojas, and Francisco López Valverde. “Multi-Agent debate system based on large language models: structured deliberation and validation in satellite communications.” Springer Nature Link, 13 August 2026. https://link.springer.com/article/10.1007/s10844-026-01086-z

[16] Rasal, Sumedh. “LLM Harmony: Multi-Agent Communication for Problem Solving.” ArXiv, 02 Jan 2024. https://arxiv.org/html/2401.01312v1

[17] Tang, Bohan, Huidong Liang, Keyue Jiang, and Xiaowen Dong. “On the Importance of Task Complexity in Evaluating LLM-Based Multi-Agent Systems.” ArXiv, 05 Oct 2025. https://arxiv.org/html/2510.04311v1

[18] Heller, W., and H. Mahmoudi. “Altruism and Extensivity in the Bahá’í Religion”. The Journal of Bahá’í Studies, vol. 4, no. 4, Dec. 1991, pp. 45-57, doi:10.31581/jbs-4.4.435(1992) | https://bahai-library.com/pdf/h/heller_mahmoudi_altruism_extensivity.pdf | https://bahaiworld.bahai.org/library/altruism-and-extensivity-in-the-bahai-religion/ | https://www.bahai.org/documents/essays/heller-wendy-m-and-mahmoudi-hoda/altruism-extensivity-bahai-religion

[19] ‘Abdu’l-Bahá. THE PROMULGATION OF UNIVERSAL PEACE. Bahá’í Reference Library. https://www.bahai.org/r/628985716