ARXIV:2602.24195 · AI RELIABILITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

arXiv

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger…

METHOD

Full abstract

Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance. However, existing uncertainty metrics have practical constraints, such as being designed only for specific modalities, reliant on external tools, or computationally expensive. We introduce UMPIRE, a training-free uncertainty quantification framework for MLLMs that works efficiently across various input and output modalities without external tools, relying only on the models' own internal modality features. UMPIRE computes the incoherence-adjusted semantic volume of sampled MLLM responses for a given task instance, effectively capturing both the global semantic diversity of samples and the local incoherence of responses based on internal model confidence. We propose uncertainty desiderata for MLLMs and provide theoretical analysis motivating UMPIRE's design. Extensive experiments show that UMPIRE consistently outperforms baseline metrics in error detection and uncertainty calibration across image, audio, and video-text benchmarks, including adversarial and out-of-distribution settings. We also demonstrate UMPIRE's generalization to non-text output tasks, including image and audio generation.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance.

WHY NOW

AI Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainUMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.24195 · AI RELIABILITY · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

arXiv

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

AI Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainUMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.24195

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback55 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

AI Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Despite their capabilities, Multimodal Large Language Models (MLLMs) may produce plausible but erroneous outputs, hindering reliable deployment. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Accurate uncertainty metrics could enable escalation of unreliable queries to human experts or larger models for improved performance.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
AI Reliability moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

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Methods

Materials

PDF linked

Markets

AI Reliability

Competitors

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Competitive landscape

UMPIRE offers a training-free framework for uncertainty quantification in MLLMs using incoherence-adjusted semantic volume for error detection and uncertainty calibration.

Segment

AI Reliability

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

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Substitute

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Unknown

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Buzz

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PDF

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References(55)

SMELLNET: A Large-scale Dataset for Real-world Smell Recognition

2025Dewei Feng, Carol Li et al.

Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

2025Abdelrahman Abouelenin, Atabak Ashfaq et al.

Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models

2024Zijun Chen, Wenbo Hu et al.

Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks

2024Gregory Kang Ruey Lau, Wenyang Hu et al.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation

2024Gianni Franchi, Dat Nguyen Trong et al.

VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation

2024Ruiyang Zhang, Hu Zhang et al.

GPT-4o System Card

2024OpenAI Aaron Hurst, Adam Lerer et al.

Reference-free Hallucination Detection for Large Vision-Language Models

2024Qing Li, Chenyang Lyu et al.

Detecting hallucinations in large language models using semantic entropy

2024Sebastian Farquhar, Jannik Kossen et al.

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

2024Chaoyou Fu, Yuhan Dai et al.

Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities

2024Alexander Nikitin, Jannik Kossen et al.

Hallucination of Multimodal Large Language Models: A Survey

2024Zechen Bai, Pichao Wang et al.

Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering

2024Zaid Khan, Yun Fu

Vision-language models for medical report generation and visual question answering: a review

2024Iryna Hartsock, Ghulam Rasool

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

2024Jun Zhan, Junqi Dai et al.

INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

2024Chao Chen, Kai Liu et al.

Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs

2024Shengbang Tong, Zhuang Liu et al.

Hallucination Augmented Contrastive Learning for Multimodal Large Language Model

2023Chaoya Jiang, Haiyang Xu et al.

Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites

2023Lei Wang, Jiabang He et al.

A medical multimodal large language model for future pandemics

2023Fenglin Liu, Tingting Zhu et al.

Showing 20 of 55 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkFine-Grained Uncertainty Quantification for Long-Form Language Model Outputs: A Comparative Study

5.0

Extension

Builds On ThisThe Anatomy of Uncertainty in LLMs

4.0

Builds On ThisFrom Entropy to Calibrated Uncertainty: Training Language Models to Reason About Uncertainty

3.0

Builds On ThisEvaluating the Relevance of Uncertainty Estimators for LLM Hallucination

4.0

Builds On ThisVerbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities

4.0

Commercially relevant

Higher ViabilityLLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy

7.0

Higher ViabilityWalking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models

7.0

Higher ViabilityClustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models

7.0

Higher ViabilityLinking Perception, Confidence and Accuracy in MLLMs

7.0

Higher ViabilityTowards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models

8.0

Conflicting

none indexed

Related Resources

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.24195
Route: /paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume
Active tab: read
Artifact: uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

Available agents

Read extractor
Build planner
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Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume/paper-pack
REST build passport API/api/v1/paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

Canonical ID uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume | Route /paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

/buildability/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

Watchwatch

Subject: Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

Paper ref

uncertainty-quantification-for-multimodal-large-language-models-with-incoherence-adjusted-semantic-volume

arXiv id

2602.24195

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

5f013ba7209b1e3c5bcc32b5714d4c1d6bf30d34f21c95d2d1308f967141e993

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume

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