Paper Citation Record · LEDGER

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.03788.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.03788 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:54:19.613972Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact2
verified fuzzy5
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f106cfc3-b819-4af6-af64-c11baf4ed319 · outbound

This paper cites VQD: Visual Query Detection in Natural Scenes.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding VQD: Visual Query Detection in Natural Scenes

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:54:19.995191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 53d7e6cc-7bdc-4f73-9b7d-05b76929ab4f · outbound

This paper cites A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.503477Z

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Observation 528858d6-e646-40af-aa2d-851d24f2d46f · outbound

This paper cites Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.511692Z

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Observation 7d986630-f109-4611-ab01-d4dad73997ea · outbound

This paper cites DeBERTa: Decoding-enhanced BERT with Disentangled Attention.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding DeBERTa: Decoding-enhanced BERT with Disentangled Attention

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.515277Z

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Observation a7369a8e-c3f8-462f-9775-2e96ae9140ba · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Distilling the Knowledge in a Neural Network

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.519231Z

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Observation 7791a210-2eb7-4b48-9bc5-d29c874b9e13 · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.531484Z

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Observation 1a2ed9a4-8718-42b1-8f1c-92b4be223a2b · outbound

This paper cites Language Models (Mostly) Know What They Know.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Language Models (Mostly) Know What They Know

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.535260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a47baca-48bd-4ff6-b2c7-bced60e43cbc · outbound

This paper cites Uncertainty-Aware Evaluation for Vision-Language Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Uncertainty-Aware Evaluation for Vision-Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.542896Z

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Unavailable: canonical work link unavailable.

Observation 29909f85-0646-46b3-92da-cef124afc3d8 · outbound

This paper cites Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.546731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bd506f9-05b4-4273-b90b-7d7cbb7fc6a2 · outbound

This paper cites LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.550795Z

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Observation 71cb0814-b590-4b44-a6c2-ec3922f90705 · outbound

This paper cites Slake: A semantically- labeled knowledge-enhanced dataset for medical visual question answering.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Slake: A semantically- labeled knowledge-enhanced dataset for medical visual question answering

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:20.032176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3f3a41b2-4a8a-4cf1-a2d4-157447258a67 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.566442Z

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Observation 638672cf-8d23-4acb-8333-8d2a41af96bf · outbound

This paper cites Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.570695Z

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Observation 51c01721-af72-4fab-947e-c88201bc642a · outbound

This paper cites A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.575021Z

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Unavailable: canonical work link unavailable.

Observation cac051d6-62b7-483d-b6cb-f32efad5cfd1 · outbound

This paper cites Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:20.020196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7b9fc1e-b139-4d0d-b2d9-c9771d613f93 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.583631Z

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Observation f82b17f6-5445-4319-8d61-3e5b7b7ee5c3 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.588230Z

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Observation 3b18bc8a-730c-4733-be3f-46de5b383a73 · outbound

This paper cites COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding COPU: Conformal Prediction for Uncertainty Quantification in Natural Language Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.593315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17d5fe97-a88f-4836-a1f3-21295eb71081 · outbound

This paper cites Benchmarking LLMs via Uncertainty Quantification.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Benchmarking LLMs via Uncertainty Quantification

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.601232Z

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Observation c0cde35f-4ca5-41ba-a5a8-911a5e0c6d3c · outbound

This paper cites BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.609432Z

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Unavailable: canonical work link unavailable.

Observation b358de03-1109-43a6-8241-d12fe5e8937a · outbound

This paper cites Distribution Statement “A” (Approved for Public Release, Distribution Unlimited).

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:20.008092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32cc1297-6605-44c2-848d-53dda7029126 · outbound

This paper cites Mistral 7B.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Mistral 7B

Reference 1957

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.523675Z

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Unavailable: canonical work link unavailable.

Observation 692e433c-3f85-4f85-aae9-54704f0f2517 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 1983

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.494769Z

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Observation df4fe64c-34cf-4d44-bc53-2f24566d212f · outbound

This paper cites Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.554640Z

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Observation cafcb103-ce07-40fa-a1de-bc41e1b5a059 · outbound

This paper cites On Domain-Adaptive Post-Training for Multimodal Large Language Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding On Domain-Adaptive Post-Training for Multimodal Large Language Models

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.486637Z

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Unavailable: canonical work link unavailable.

Observation d07cf3de-7c63-405a-aa0a-66cfdf7a456d · outbound

This paper cites Conformal Language Modeling.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Conformal Language Modeling

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.562429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 594b804e-8065-447c-bece-24fc9fe9d047 · outbound

This paper cites The Llama 3 Herd of Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding The Llama 3 Herd of Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.507486Z

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Observation e93b0ff0-392b-4232-a1bb-64d78e2c2c84 · outbound

This paper cites VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.605281Z

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Observation 4abc8c8f-6f97-486d-ac07-1cb83fbc2002 · outbound

This paper cites GPT-4 Technical Report.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding GPT-4 Technical Report

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.481765Z

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Observation 98f50b02-9378-4573-9b5b-31142428528c · outbound

This paper cites Explaining Multi-modal Large Language Models by Analyzing their Vision Perception.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Explaining Multi-modal Large Language Models by Analyzing their Vision Perception

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.499010Z

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Unavailable: canonical work link unavailable.

Observation 32ec6484-02e9-4d69-8de1-1209c021b0fc · outbound

This paper cites Ad- dressing uncertainty in llms to enhance reliability in generative ai.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Ad- dressing uncertainty in llms to enhance reliability in generative ai

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:20.044723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b910364-32d7-4efa-8944-546d2bfa3e16 · outbound

This paper cites CursorCore: Assist Programming through Aligning Anything.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding CursorCore: Assist Programming through Aligning Anything

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:54:19.864090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f93fe253-2eaf-422f-b3b9-3b892a1b1d04 · outbound

This paper cites An empirical study of in-context learning in llms for machine translation.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding An empirical study of in-context learning in llms for machine translation

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:54:20.058355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 08139543-4f96-4fb7-97f1-2b8cc04c518d · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Calibrating Uncertainty Quantification of Multi-Modal LLMs using Grounding Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-16T04:54:19.597151Z

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Pith citing papers

No inbound Pith citation observations are available.