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ARXIV:2605.13484 · LLM CALIBRATION · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13484LLM CALIBRATIONSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHKatarzyna Kobalczyk · Mihaela van der Schaar · arXiv
A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction.
Opportunity summary
Pain A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction. However, this view can hide substantial structure: models may be systematically overconfident on some kinds of inputs and underconfident…
Calibration is commonly evaluated by comparing model confidence with its empirical correctness, implicitly treating reliability as a function of the confidence score alone. However, this view can hide substantial structure: models may be systematically…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We further show that the discovered fields are actionable: they support local confidence correction and reduce calibration error in systematically miscalibrated regions where confidence-based…
LLM Calibration moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
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A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction.
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10.48550/arXiv.2605.13484A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction.
Abstract
Calibration is commonly evaluated by comparing model confidence with its empirical correctness, implicitly treating reliability as a function of the confidence score alone. However, this view can hide substantial structure: models may be systematically overconfident on some kinds of inputs and underconfident on others, causing global reliability diagnostics to obscure localised calibration failures. To address this, we formulate the problem of discovering hidden miscalibration regimes without assuming access to predefined data slices. We define the corresponding miscalibration field and propose a diagnostic framework for estimating it. Our approach learns a calibration-aware representation of the input space and estimates signed local miscalibration by kernel smoothing in the learned geometry. Across four real-world LLM benchmarks and twelve LLMs, we find that input-dependent calibration heterogeneity is prevalent. We further show that the discovered fields are actionable: they support local confidence correction and reduce calibration error in systematically miscalibrated regions where confidence-based methods such as isotonic regression and temperature scaling are less effective.
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PROBLEM
A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction. However, this view can hide substantial structure: models may be systematically overconfident on some kinds of inputs and underconfident on others, causi...
METHOD
Calibration is commonly evaluated by comparing model confidence with its empirical correctness, implicitly treating reliability as a function of the confidence score alone. However, this view can hide substantial structure: models may be systematically overconfident on some kind...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We further show that the discovered fields are actionable: they support local confidence correction and reduce calibration error in systematically miscalibrated regions where confidence-based methods such...
WHY NOW
LLM Calibration moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction. However, this view can hide substantial structure: models may be systematically overconfident on some kinds of inputs and underconfident on others, causing global reliability diagnostics to obscure localised calibration failures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Calibration is commonly evaluated by comparing model confidence with its empirical correctness, implicitly treating reliability as a function of the confidence score alone. However, this view can hide substantial structure: models may be systematically overconfident on some kinds of inputs and underconfident on others, causing global reliability diagnostics to obscure localised calibration failures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We further show that the discovered fields are actionable: they support local confidence correction and reduce calibration error in systematically miscalibrated regions where confidence-based methods such as isotonic regression and temperature scaling are less effective. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Calibration moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A diagnostic framework to discover and address input-dependent miscalibration in LLMs, improving local confidence correction.
Segment
LLM Calibration
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