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Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses — arXiv:2608.19206

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.19206
License (as archived): CC0-1.0
Archived: 2026-10-18T11:36:38.741Z
SHA-256 of the archived text: 5c0adc748da8e8f90b382ed076ca1f921f64cd5bd8a7a5074f49a5eab81f91e0
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How to cite

Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses — arXiv:2608.19206 — arXiv (en), https://arxiv.org/abs/2608.19206 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_41fb07caed04 (Metraipolis archive dc_41fb07caed04, sha256 5c0adc748da8e8f90b382ed076ca1f921f64cd5bd8a7a5074f49a5eab81f91e0)

BibTeX

@misc{dc41fb07caed04,
  title = {Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses — arXiv:2608.19206},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.19206},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_41fb07caed04; sha256 5c0adc748da8e8f90b382ed076ca1f921f64cd5bd8a7a5074f49a5eab81f91e0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.19206. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creativity. While crucial for mitigating misinformation, this alignment may also restrict speculative Research and Development (R&D) by encouraging what this work operationally treats as semantic overfitting and diversity collapse. In this paper, we propose a Rust-based multi-agent orchestration that uses the contrast between narrative daydreaming and executive control as a functional analogy, not as a neurocognitive claim. The system instigates an Epistemological Friction loop between a high-entropy generating agent and a web-grounded evaluating agent, mediated by a low-entropy semantic bottleneck intended to reduce noise and repetition. Initial experiments generated diverse, viability-rated hypotheses across physical and social-science domains. We additionally report an exploratory paired baseline and ablation study comparing the full system against direct prompting, self-reflection, removal of the semantic filter, removal of search grounding, and removal of lateral lenses. The results place direct prompting among the weakest conditions across most observed metrics, but they do not show a general superiority of the full system over simple self-reflection. Instead, they suggest that each architecture shifts the balance between originality, feasibility, diversity, and empirical grounding in different ways, and that the full system provides its main advantages when hypotheses must survive strong physical, empirical, or institutional constraints. These findings do not show that hallucination is useful in isolation; they suggest that speculative generation gains value only when constrained by architecture, empirical grounding, and explicit evaluation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-21 in cs.MA).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2608.19206 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T11:30:10.500Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-21?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).