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arxiv: 2606.03110 · v2 · pith:67FNMIS7new · submitted 2026-06-02 · 💻 cs.CL

Coherence Maximization Improves Pluralistic Alignment

Taslim Mahbub , Yiding Pei , Shi Feng This is my paper

Pith reviewed 2026-06-28 10:19 UTC · model grok-4.3

classification 💻 cs.CL
keywords pluralistic alignmentinternal coherence maximizationin-context examplesvalue alignmentunsupervised methodspersona-specific examplesgeneralization
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The pith

Coherence maximization matches gold labels in pluralistic alignment

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that Internal Coherence Maximization can generate persona-specific examples for AI alignment by selecting labels that maximize mutual predictability among them, without needing human supervision. These examples perform as well as gold human labels on benchmarks for classification, preference, and open-ended generation. Coherence in the examples proves important beyond mere accuracy, leading to substantially better generalization when held constant. The method also demonstrates that for less represented personas, feedback on the model's least certain questions yields better results than arbitrary selections. This identifies coherence as a scalable way to specify values by leveraging encoded perspectives in pretrained models.

Core claim

Internal Coherence Maximization infers labels for in-context examples by maximizing their mutual predictability, producing persona-specific examples that steer a model toward a target group's values without human supervision. Across four benchmarks, these examples match the performance of gold labels. Coherence matters beyond individual label accuracy, as more coherent examples generalize substantially better when accuracy is held constant. For underrepresented personas, targeted human feedback on uncertain questions improves generalization over the same number of arbitrary labels.

What carries the argument

Internal Coherence Maximization (ICM), a method that infers labels by maximizing their mutual predictability to create coherent examples for persona alignment.

If this is right

  • ICM examples match gold label performance across classification, preference, and generation tasks.
  • More coherent examples generalize better than less coherent ones even at equal accuracy.
  • Targeted feedback on low-certainty questions benefits alignment for underrepresented personas.
  • Coherence is a key design principle for scalable value specification using pretrained models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Alignment pipelines could incorporate coherence checks to improve example quality from any source.
  • The findings suggest pretrained models hold extractable knowledge about a wide range of human values.
  • This method may apply to specifying values in other domains where multiple perspectives are needed.

Load-bearing premise

Pretrained language models already encode diverse human perspectives that can be extracted via coherence maximization without additional supervision.

What would settle it

An experiment that controls for label accuracy and finds no difference in generalization between high- and low-coherence example sets on the alignment benchmarks.

Figures

Figures reproduced from arXiv: 2606.03110 by Shi Feng, Taslim Mahbub, Yiding Pei.

Figure 1
Figure 1. Figure 1: Unsupervised coherence maximization matches gold-supervised performance; zero-shot prompting trails. Each bar is the mean across all (model, dataset) pairs of a condition’s score as a per￾centage of gold-supervised performance (y-axis starts at 70% for visual clarity), aggregated over 6 models and 4 datasets. Our method labels the in-context ex￾amples with no human supervision, selecting the labels that ar… view at source ↗
Figure 2
Figure 2. Figure 2: Test performance across four datasets and prompting conditions (Llama-3.1-70B). ICM-inferred labels [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Improvement over the zero-shot baseline, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Effect of collaborative elicitation on the three lowest-performing personas. Test accuracy (yellow) improves [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Test Accuracy Per Persona for GQA and OQA respectively for Llama-3.1-70B. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Coherent labels improve prediction stability. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Test accuracy as a function of the number of [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Absolute performance across six models and four datasets for all prompting conditions. GQA/OQA/PT [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
read the original abstract

Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensive human supervision remains an open challenge. We investigate what makes these examples effective, using Internal Coherence Maximization (ICM) -- which infers labels by maximizing their mutual predictability -- to generate persona-specific examples that steer a model toward a target group's values, without human supervision. Across four benchmarks spanning classification, preference, and open-ended generation, ICM-inferred in-context examples match the performance of gold labels. Crucially, coherence matters beyond individual label accuracy: with accuracy held constant, more coherent examples generalize substantially better than incoherent ones. For personas underrepresented in pretraining data, targeted human feedback on the questions where the model is least certain about a persona's values yields better generalization than the same number of labels on arbitrary questions. These results identify coherence as a key design principle for scalable value specification, leveraging the diverse human perspectives already encoded in pretrained language models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper introduces Internal Coherence Maximization (ICM), a method that infers persona-specific labels by maximizing mutual predictability among examples, and evaluates it on four benchmarks covering classification, preference, and open-ended generation tasks. It reports that ICM-generated in-context examples achieve performance matching gold labels, while demonstrating that coherence provides generalization benefits beyond label accuracy when accuracy is held constant via controlled matching. Additional results show that targeted human feedback on low-certainty questions improves outcomes for personas underrepresented in pretraining data.

Significance. If the controlled comparisons hold, the work establishes coherence as a load-bearing design principle for scalable pluralistic alignment, showing that pretrained models can yield effective value specifications without extensive supervision. The accuracy-matched protocol, ablations, and cross-benchmark consistency are explicit strengths that support the central empirical claim without circularity.

minor comments (3)
  1. §3 (Benchmarks): the accuracy-matching protocol is described as explicit, but the exact procedure for constructing incoherent subsets while preserving per-example accuracy should be stated with pseudocode or a numbered step list to allow direct replication.
  2. Table 2 (or equivalent results table): report the number of runs, standard deviations, and any statistical tests for the coherence-vs-incoherence generalization gap to quantify the 'substantially better' claim.
  3. §4.3 (Targeted feedback): clarify how 'least certain' questions are selected (e.g., entropy threshold or ranking) and whether this selection is performed before or after ICM inference.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks and explicit controls

full rationale

The paper's central result—that ICM-generated examples match gold-label performance and that coherence improves generalization when accuracy is held constant—is established through controlled experiments on four external benchmarks (classification, preference, open-ended generation). ICM is defined as maximizing mutual predictability for label inference without reference to the target generalization metric, and the accuracy-matching protocol is described as an explicit post-hoc selection step rather than a definitional identity. No load-bearing self-citation, self-definitional loop, or fitted-input-renamed-as-prediction is present in the provided text; the derivation chain remains self-contained against the reported ablations and external data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that pretrained models already contain extractable diverse perspectives and that mutual predictability is a sufficient proxy for value alignment.

axioms (1)
  • domain assumption Pretrained language models encode diverse human perspectives that coherence maximization can surface without supervision.
    Explicitly invoked in the abstract's final sentence as the basis for the unsupervised approach.

pith-pipeline@v0.9.1-grok · 5691 in / 1144 out tokens · 29995 ms · 2026-06-28T10:19:35.756337+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

33 extracted references · 4 linked inside Pith

  1. [1]

    arXiv preprint arXiv:2212.08073 , year=

    Constitutional ai: Harmlessness from ai feedback , author=. arXiv preprint arXiv:2212.08073 , year=

    Pith/arXiv arXiv

  2. [2]

    Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=

    Collective constitutional ai: Aligning a language model with public input , author=. Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2024

  3. [3]

    Position: Towards Bidirectional Human-AI Alignment , author=

  4. [4]

    arXiv preprint arXiv:2408.10392 , year=

    Value alignment from unstructured text , author=. arXiv preprint arXiv:2408.10392 , year=

    arXiv

  5. [5]

    Methods in analytical political theory , pages=

    Reflective equilibrium , author=. Methods in analytical political theory , pages=. 2017 , publisher=

    2017

  6. [6]

    Truong and Andreas Haupt and Sanmi Koyejo , title =

    Sang T. Truong and Andreas Haupt and Sanmi Koyejo , title =. 2025 , publisher =

    2025

  7. [7]

    arXiv preprint arXiv:2404.10271 , year=

    Social choice should guide ai alignment in dealing with diverse human feedback , author=. arXiv preprint arXiv:2404.10271 , year=

    arXiv

  8. [8]

    arXiv preprint arXiv:2402.05070 , year=

    A roadmap to pluralistic alignment , author=. arXiv preprint arXiv:2402.05070 , year=

    Pith/arXiv arXiv

  9. [9]

    Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    Randomness, not representation: The unreliability of evaluating cultural alignment in llms , author=. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency , pages=

    2025

  10. [10]

    arXiv preprint arXiv:2510.26202 , year=

    What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data , author=. arXiv preprint arXiv:2510.26202 , year=

    Pith/arXiv arXiv

  11. [11]

    arXiv preprint arXiv:2410.14632 , year=

    Diverging Preferences: When do Annotators Disagree and do Models Know? , author=. arXiv preprint arXiv:2410.14632 , year=

    arXiv

  12. [12]

    International Conference on Machine Learning , pages=

    Whose opinions do language models reflect? , author=. International Conference on Machine Learning , pages=. 2023 , organization=

    2023

  13. [13]

    arXiv preprint arXiv:2506.10139 , year=

    Unsupervised Elicitation of Language Models , author=. arXiv preprint arXiv:2506.10139 , year=

    arXiv

  14. [14]

    Nature machine intelligence , volume=

    Principles alone cannot guarantee ethical AI , author=. Nature machine intelligence , volume=. 2019 , publisher=

    2019

  15. [15]

    Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    The role and limits of principles in AI ethics: Towards a focus on tensions , author=. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society , pages=

    2019

  16. [16]

    Minds and machines , volume=

    Artificial intelligence, values, and alignment , author=. Minds and machines , volume=. 2020 , publisher=

    2020

  17. [17]

    Advances in neural information processing systems , volume=

    Training language models to follow instructions with human feedback , author=. Advances in neural information processing systems , volume=

  18. [18]

    arXiv preprint arXiv:2503.18991 , year=

    Inverse reinforcement learning with dynamic reward scaling for llm alignment , author=. arXiv preprint arXiv:2503.18991 , year=

    arXiv

  19. [19]

    arXiv preprint arXiv:2311.10934 , year=

    Case repositories: Towards case-based reasoning for ai alignment , author=. arXiv preprint arXiv:2311.10934 , year=

    arXiv

  20. [20]

    arXiv preprint arXiv:2501.16448 , year=

    What is Harm? Baby Don't Hurt Me! On the Impossibility of Complete Harm Specification in AI Alignment , author=. arXiv preprint arXiv:2501.16448 , year=

    arXiv

  21. [21]

    Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

    Steerable pluralism: Pluralistic alignment via few-shot comparative regression , author=. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society , volume=

  22. [22]

    arXiv preprint arXiv:2507.21509 , year=

    Persona vectors: Monitoring and controlling character traits in language models , author=. arXiv preprint arXiv:2507.21509 , year=

    Pith/arXiv arXiv

  23. [23]

    arXiv preprint arXiv:2511.01689 , year=

    Open character training: Shaping the persona of AI assistants through constitutional AI , author=. arXiv preprint arXiv:2511.01689 , year=

    arXiv

  24. [24]

    arXiv preprint arXiv:2511.02966 , year=

    Inference-Time Personalized Alignment with a Few User Preference Queries , author=. arXiv preprint arXiv:2511.02966 , year=

    arXiv

  25. [25]

    arXiv preprint arXiv:2408.11779 , year=

    Personality alignment of large language models , author=. arXiv preprint arXiv:2408.11779 , year=

    arXiv

  26. [26]

    Advances in Neural Information Processing Systems , volume=

    Self-supervised alignment with mutual information: Learning to follow principles without preference labels , author=. Advances in Neural Information Processing Systems , volume=

  27. [27]

    arXiv preprint arXiv:2509.25369 , year=

    Generative value conflicts reveal LLM priorities , author=. arXiv preprint arXiv:2509.25369 , year=

    arXiv

  28. [28]

    arXiv preprint arXiv:2509.01418 , year=

    On the Alignment of Large Language Models with Global Human Opinion , author=. arXiv preprint arXiv:2509.01418 , year=

    arXiv

  29. [29]

    Statutory Construction and Interpretation for Artificial Intelligence , journal =

    He, Luxi and Nadeem, Nimra and Liao, Michel and Chen, Howard and Chen, Danqi and Cuéllar, Mariano-Florentino and Henderson, Peter , year =. Statutory Construction and Interpretation for Artificial Intelligence , journal =

  30. [30]

    arXiv preprint arXiv:2403.19154 , year=

    Star-gate: Teaching language models to ask clarifying questions , author=. arXiv preprint arXiv:2403.19154 , year=

    arXiv

  31. [31]

    arXiv preprint arXiv:2506.10949 , year=

    Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors , author=. arXiv preprint arXiv:2506.10949 , year=

    arXiv

  32. [32]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Whose boat does it float? improving personalization in preference tuning via inferred user personas , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  33. [33]

    arXiv preprint arXiv:2512.01351 , year=

    Benchmarking Overton Pluralism in LLMs , author=. arXiv preprint arXiv:2512.01351 , year=

    arXiv