Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization
- URL: http://arxiv.org/abs/2603.02420v2
- Date: Fri, 06 Mar 2026 22:04:14 GMT
- Title: Slurry-as-a-Service: A Modest Proposal on Scalable Pluralistic Alignment for Nutrient Optimization
- Abstract summary: We study a high-stakes deployment context - mulch - where automated systems transform selected individuals into nutrient-rich slurry for the dual purposes of food security and aesthetic population management.<n>We introduce ValueMulch, a reproducible training, deployment, and certification pipeline for aligning mulch models to a wide range of community norms.<n>We conclude with a discussion of ethical considerations, limitations, and implications for researchers seeking to align systems to the full spectrum of human values.
- Score: 2.710404836272193
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pluralistic alignment has emerged as a promising approach for ensuring that large language models (LLMs) faithfully represent the diversity, nuance, and conflict inherent in human values. In this work, we study a high-stakes deployment context - mulching - where automated systems transform selected individuals into nutrient-rich slurry for the dual purposes of food security and aesthetic population management. Building on recent pluralistic alignment frameworks, we introduce ValueMulch, a reproducible training, deployment, and certification pipeline for aligning mulching models (MMs) to a wide range of community norms. Through a real-world testbed spanning 32 communities, we show that ValueMulch improves distributional agreement with community mulching preferences relative to frontier baselines. We conclude with a discussion of ethical considerations, limitations, and implications for researchers seeking to align systems to the full spectrum of human values - especially when those values are inconsistent, commercially inconvenient, or nutritionally underutilized. Author's note: This piece builds on prior existing work Keyes et al in 2019 that satirized cannibalism as a parody for approaches that imbue ethics into problematic technology. We bring those ideas to today's era with the proliferation of large language models in everyday lives, as a critique of current AI pluralistic alignment literature. Our work does not intend to argue that all alignment practices are evil, but rather that if framing value design as a technical problem enables technology systems to enact harms, then perhaps this framing is not enough.
Related papers
- Do LLMs Have Values? A Quantitative Analysis and Alignment Framework for Values in Large Language Models [49.45276299939287]
Large Language Models (LLMs) increasingly handle complex subjective tasks.<n>LLMs fluctuate unpredictably under minor wording changes.<n> resolving this duality is critical for reliable AI alignment.
arXiv Detail & Related papers (2026-09-15T03:35:04Z) - Pluralis v0.1: Towards a Multicultural, Multimodal, Multilingual Benchmark for AI Risk and Reliability [46.80780735011334]
Current AI safety evaluation and benchmarking frameworks rely on Western-centric culture-agnostic defaults that mask critical regional laws, socio-linguistic nuances, and cultural taboos.<n>We introduce Pluralis v0.1: a novel multimodal, multi-regional, and multilingual dataset built from a culture-first perspective.
arXiv Detail & Related papers (2026-07-07T12:21:52Z) - The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents [51.56484100374058]
We show that the high document density required to serve majority interests can geometrically overcrowd the retrieval neighborhood of a semantically similar minority.<n>We introduce a formal framework to analyze how such goal collisions in dense retrieval induce fundamental performance limits.<n>We prove that such objectives drive the system to self-organize into a state that exclusively serves majority interests.
arXiv Detail & Related papers (2026-06-01T20:40:30Z) - From Sycophantic Consensus to Pluralistic Repair: Why AI Alignment Must Surface Disagreement [8.459329029609602]
We argue that aggregation alone is an incomplete primitive for deployed pluralistic alignment.<n>We formalise a metric, the Pluralistic Repair Score (PRS), distinguishing principled revision from capitulation.
arXiv Detail & Related papers (2026-05-14T14:47:06Z) - Position: General Alignment Has Hit a Ceiling; Edge Alignment Must Be Taken Seriously [51.03213216886717]
We take the position that the dominant paradigm of General Alignment reaches a structural ceiling in settings with conflicting values.<n>We introduce Edge Alignment as a distinct approach in which systems preserve multi dimensional value structure.
arXiv Detail & Related papers (2026-02-23T16:51:43Z) - Infrastructuring Contestability: A Framework for Community-Defined AI Value Pluralism [0.0]
The proliferation of AI-driven systems presents a challenge to Human-Computer Interaction and Computer-Supported Cooperative Work.<n>Current approaches to value alignment, which rely on centralized, top-down definitions, lack the mechanisms for meaningful contestability.<n>This paper introduces Community-Defined AI Value Pluralism, a socio-technical framework that addresses this gap.
arXiv Detail & Related papers (2025-07-07T16:45:50Z) - VITAL: A New Dataset for Benchmarking Pluralistic Alignment in Healthcare [9.087074203425061]
Existing alignment paradigms fail to account for the diversity of perspectives across cultures, demographics, and communities.<n>This is particularly critical in health-related scenarios, where plurality is essential due to the influence of culture, religion, personal values, and conflicting opinions.<n>This work highlights the limitations of current approaches and lays the groundwork for developing health-specific alignment solutions.
arXiv Detail & Related papers (2025-02-19T14:38:57Z) - The Superalignment of Superhuman Intelligence with Large Language Models [63.96120398355404]
We discuss the concept of superalignment from the learning perspective to answer this question.<n>We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation.<n>We present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing.
arXiv Detail & Related papers (2024-12-15T10:34:06Z) - On the Fairness, Diversity and Reliability of Text-to-Image Generative Models [68.62012304574012]
multimodal generative models have sparked critical discussions on their reliability, fairness and potential for misuse.<n>We propose an evaluation framework to assess model reliability by analyzing responses to global and local perturbations in the embedding space.<n>Our method lays the groundwork for detecting unreliable, bias-injected models and tracing the provenance of embedded biases.
arXiv Detail & Related papers (2024-11-21T09:46:55Z) - Dynamic Normativity: Necessary and Sufficient Conditions for Value Alignment [0.0]
We find "alignment" a problem related to the challenges of expressing human goals and values in a manner that artificial systems can follow without leading to unwanted adversarial effects.
This work addresses alignment as a technical-philosophical problem that requires solid philosophical foundations and practical implementations that bring normative theory to AI system development.
arXiv Detail & Related papers (2024-06-16T18:37:31Z) - PaCE: Parsimonious Concept Engineering for Large Language Models [57.740055563035256]
Parsimonious Concept Engineering (PaCE) is a novel activation engineering framework for alignment.
We construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept.
We show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.
arXiv Detail & Related papers (2024-06-06T17:59:10Z) - A Roadmap to Pluralistic Alignment [49.29107308098236]
We propose a roadmap to pluralistic alignment, specifically using language models as a test bed.
We identify and formalize three possible ways to define and operationalize pluralism in AI systems.
We argue that current alignment techniques may be fundamentally limited for pluralistic AI.
arXiv Detail & Related papers (2024-02-07T18:21:17Z) - Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties [68.66719970507273]
Value pluralism is the view that multiple correct values may be held in tension with one another.
As statistical learners, AI systems fit to averages by default, washing out potentially irreducible value conflicts.
We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations.
arXiv Detail & Related papers (2023-09-02T01:24:59Z) - Personalisation within bounds: A risk taxonomy and policy framework for
the alignment of large language models with personalised feedback [11.895749982167375]
Large language models (LLMs) are used to generate content for a wide range of tasks, and are set to reach a growing audience in coming years.
This intensifies the need to ensure that models are aligned with human preferences and do not produce unsafe, inaccurate or toxic outputs.
Personalising LLMs through micro-level preference learning processes may result in models that are better aligned with each user.
arXiv Detail & Related papers (2023-03-09T17:52:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.