Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always

Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/bayesian-elicitation-with-llms-model-size-helps-extra-reasoning-doesn-t-always

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID bayesian-elicitation-with-llms-model-size-helps-extra-reasoning-doesn-t-always | Route /signal-canvas/bayesian-elicitation-with-llms-model-size-helps-extra-reasoning-doesn-t-always

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/bayesian-elicitation-with-llms-model-size-helps-extra-reasoning-doesn-t-always

MCP example

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source_context

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  "query": "Bayesian Elicitation with LLMs: Model Size Helps, Extra \"Reasoning\" Doesn't Always",
  "normalized_query": "2604.01896",
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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always

PDF: https://arxiv.org/pdf/2604.01896v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.576Z

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Not build-ready: Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always

/buildability/bayesian-elicitation-with-llms-model-size-helps-extra-reasoning-doesn-t-always

Ignoreblocked

Subject: Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
First, larger, more capable models produce more accurate estimates
Implicationpartial
Explicitly stated in abstract as a key finding with clear comparative results
Verificationpartial
partial
Evidencepartial
increasing reasoning effort provides no consistent benefit
Implicationpartial
Directly stated in abstract as a key finding with experimental variation
Verificationpartial
partial
Evidencepartial
all models are severely overconfident: their 95% intervals contain the true value only 9–44% of the time, far below the expected 95%
Implicationpartial
Explicitly stated with clear numeric evidence in abstract
Verificationpartial
partial
Evidencepartial
a statistical recalibration technique called conformal prediction can correct this overconfidence, expanding the intervals to achieve the intended coverage
Implicationpartial
Directly stated in abstract as a key finding with specific technique mentioned
Verificationpartial
partial
Evidencepartial
giving models web search access degraded predictions for already-accurate models, while modestly improving predictions for weaker ones
Implicationpartial
Directly stated in abstract as preliminary experiment result
Verificationpartial
partial
Evidencepartial
Models performed well on commonly discussed topics but struggled with specialized health data
Implicationpartial
Directly stated in abstract with clear performance pattern
Verificationpartial
partial
Evidencepartial
These results indicate that LLM uncertainty estimates require statistical correction before they can be used in decision-making
Implicationpartial
Explicit conclusion stated in abstract based on experimental findings
Verificationpartial
partial
Evidencepartial
Large language models (LLMs) have been proposed as alternatives to human experts for estimating unknown quantities with associated uncertainty, a process known as Bayesian elicitation
Implicationpartial
Direct statement of research context and motivation in abstract
Verificationpartial
partial

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Bayesian Elicitation with LLMs: Model Size Helps, Extra "Reasoning" Doesn't Always

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