pith:UN2O3Z3Z
LLMs are not (consistently) Bayesian: Quantifying internal (in)consistencies of LLMs' probabilistic beliefs
Large language models do not consistently update probabilistic beliefs according to Bayesian rules.
arxiv:2605.06915 v2 · 2026-05-07 · cs.LG
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Claims
Some of these approaches produce (nearly) Bayesian updates; others seem to use a learned heuristic. Surprisingly, the non-Bayesian heuristic updates often outperform exact Bayesian computation in terms of downstream task performance -- indicating the LLMs' probabilistic models of the world are misspecified.
That LLMs possess stable internal probabilistic beliefs that can be reliably elicited and compared to Bayesian standards via prompting or other interfaces, and that the introduced information processing gap accurately quantifies internal inconsistencies rather than surface-level response artifacts.
LLMs do not consistently perform Bayesian updates on probabilistic beliefs; heuristic approaches often outperform exact Bayesian computation on downstream tasks, indicating misspecified internal models of the world.
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Receipt and verification
| First computed | 2026-05-28T01:04:41.768137Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
a374ede779682235b648a8ed700c76f246378e96401b2db890275350035f5bfe
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/UN2O3Z3ZNARDLNSIVDWXADDW6J \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a374ede779682235b648a8ed700c76f246378e96401b2db890275350035f5bfe
Canonical record JSON
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