Distribird arXiv:2608.11210 matches LLM baseline on 24-parameter prior quality while enforcing local execution and source tracing
Distribird automates literature-derived priors for Bayesian model calibration using a local multi-agent pipeline that matches baseline quality while adding source tracing and scope validation. The approach prioritizes auditability and data locality over marginal gains in distribution fit. It targets domains where model parameters carry physical meaning supported by published measurements.
The system deploys literature search, relevance-weighted extraction, and AIC-based distribution fitting for physically interpretable parameters. When literature is absent it substitutes documented uninformative alternatives. All LLM calls execute locally; only search terms reach public databases. Evaluation compared Qwen3 27B, Gemma 4 31B, and Mistral Small 119B against a single-prompt baseline, confirming equivalent point-estimate quality under controlled conditions.
Local execution and explicit confidence reporting address two documented failure modes in prior Bayesian workflows: leakage of unpublished model details and overconfident priors lacking provenance. The validity layer's rejection rate on out-of-scope requests demonstrates a measurable safeguard absent from direct LLM prompting. These properties align with reproducibility requirements in process-based modeling rather than raw distributional accuracy.
Operational deployment now depends on integration into existing calibration pipelines such as those using Stan or PyMC. Traceability logs enable post-hoc sensitivity checks against the cited source values. Future releases can benchmark against expanded parameter sets drawn from hydrology, systems biology, and materials science repositories to test generalization beyond the initial 10 domains.
Distribird maintainers: at least 8 peer-reviewed papers will report using its generated priors in calibration sections by December 2027
Sources (3)
- [1]Primary Source(https://arxiv.org/abs/2608.11210)
- [2]Supporting Source(https://arxiv.org/abs/2305.14303)
- [3]Supporting Source(https://joss.theoj.org/papers/10.21105/joss.04567)