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Paper Citation Record · LEDGER

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models

As of 7 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2505.20236.

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

pith.paper-citation-record.v1
2505.20236 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:01:25.978139Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 577c4f42-b60b-44d0-bc42-e6663dffb170 · outbound

This paper cites Pixtral 12B.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Pixtral 12B

Reference 1

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source=arxiv_source observed=2026-08-07T14:01:22.013740Z digest=sha256:381dd00271a4ee364ed31e74e44b0fee41fea76778c08402f2746282b67e61a3

Observation d8bd690b-7249-45e7-9db2-74af12102b38 · outbound

This paper cites Qwen2.5-VL Technical Report.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Qwen2.5-VL Technical Report

Reference 2

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source=arxiv_source observed=2026-08-07T14:01:22.100408Z digest=sha256:766d228db4f2a55873369c9bd60138f7bba691196af682443a78b1baed8c303a

Observation 368f3ad0-0e61-4f1a-be60-79e97a696b23 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:01:22.201998Z digest=sha256:5c7aaa72d1873eb179b54a1d430af4c144a0eaaf9b1bc233f8a61630a5d15994

Observation 46b8f820-b59d-47cc-9e92-8bb0df96f279 · outbound

This paper cites VoiceBench: Benchmarking LLM-Based Voice Assistants.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models VoiceBench: Benchmarking LLM-Based Voice Assistants

Reference 4

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source=arxiv_source observed=2026-08-07T14:01:22.298371Z digest=sha256:8b6107d622dc5fa6e806de5a90efcd226b5a86ee67eec04f603e8c1790a553f6

Observation 0a8a4407-06e2-42a4-a39d-1c84d66a1c4a · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:01:22.367415Z digest=sha256:cdb5aba75ec73b209a9d4ad634c48357f210278b23bb9b2f2e16f644ea92a2d5

Observation c52f5b08-33f5-4921-a7eb-fdd7fb77eefd · outbound

This paper cites Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning

Reference 6

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source=arxiv_source observed=2026-08-07T14:01:22.483974Z digest=sha256:1871087a640b5172862be172213e7b56208d854878b634d0ed27b14f7602421a

Observation 0584dd42-db82-4da3-bdb9-921479950611 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 7

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source=arxiv_source observed=2026-08-07T14:01:22.586032Z digest=sha256:92b34764abff313da1b833317f8bf237a52cab7765f017c6d1b7525e04cee658

Observation 520fad86-b4f8-4cbc-82cd-e83f17e10919 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-07T14:01:22.692185Z digest=sha256:5f373be92d287a4da2ab796e24052bc95adb924134b241195244d5c70ff59a84

Observation 839f82ac-2372-4ace-ae8b-7ca6f029ae5a · outbound

This paper cites IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations

Reference 9

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source=arxiv_source observed=2026-08-07T14:01:22.788224Z digest=sha256:fb0046560cd3cb94fc2b5295b1430830770e410fa65734de5d5eec7bd5ad9ea9

Observation a0186ef2-f6bd-45a8-b24c-b4e3f6cb4faa · outbound

This paper cites The Llama 3 Herd of Models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models The Llama 3 Herd of Models

Reference 10

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source=arxiv_source observed=2026-08-07T14:01:22.912852Z digest=sha256:4ce109edcef318622990ea0597b92bed3ae28fcda880b9d0200e8a5cd1e0f557

Observation fbe1f2cb-9467-4b61-be0e-41a1325d5fe6 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 11

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source=arxiv_source observed=2026-08-07T14:01:23.025879Z digest=sha256:9c67623e1bc84230d204e28151ad5d54e60d3832a872f0eebae3b5a66aac28ee

Observation 783aeaef-3f43-4248-90a0-ca1006418410 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 12

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source=arxiv_source observed=2026-08-07T14:01:23.143535Z digest=sha256:4cb8d67eb343ca3874a0506f3276e5335c9f88b6e0cbae3a270e8f5d6de26f75

Observation 2bf9ee16-4a31-4cbb-a89b-bbbe64aa3707 · outbound

This paper cites Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos

Reference 13

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source=arxiv_source observed=2026-08-07T14:01:23.214406Z digest=sha256:27889fa9d5cab2cc28ed00f6ea31311372b814f764c1a9b715acef667e824203

Observation f7a87000-bebd-4d58-9554-fd88f6ea092e · outbound

This paper cites Kimi-VL Technical Report.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Kimi-VL Technical Report

Reference 14

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source=arxiv_source observed=2026-08-07T14:01:23.280081Z digest=sha256:e537a09e75ce0600bbbff10dd6fd8aa9d04def8800cf5e4c86a2f298a360b242

Observation 660bbce5-be51-4d44-8c54-537bd684d169 · outbound

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

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Reference 15

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source=arxiv_source observed=2026-08-07T14:01:23.379055Z digest=sha256:3f4902610a959c188fc318ec5d36052ede331bdd39e34da29e5809dac182bd44

Observation d4e6d02b-185a-47c8-b668-a2626a337952 · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models LLaVA-OneVision: Easy Visual Task Transfer

Reference 16

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source=arxiv_source observed=2026-08-07T14:01:23.443069Z digest=sha256:7ef0227c477c6c8d55bb1b3d62c14e4cd03248c344fe82dcf592d50f74f06453

Observation 8db16a5d-58ee-48fd-b298-057a8c3876c4 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 17

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:23.511582Z digest=sha256:cabbc6337fde7a156dc2f165dfae92d21ec174881062d32ec2a695b71a401e72

Observation 12de5b99-01a3-4278-83ce-e0e4dd4be166 · outbound

This paper cites Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?

Reference 18

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source=arxiv_source observed=2026-08-07T14:01:23.643676Z digest=sha256:84694bcc1f769a1e8143b97f5954fe3e59ae5cd4a81a9e36e78fa4e5e6a097ff

Observation 5fa8cfb7-796a-42b6-932c-c9a508807abb · outbound

This paper cites Teaching Models to Express Their Uncertainty in Words.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Teaching Models to Express Their Uncertainty in Words

Reference 19

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no resolver link, observed 2026-08-07T14:01:23.760219Z

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source=arxiv_source observed=2026-08-07T14:01:23.760219Z digest=sha256:be8609e2a7b365bcae04d97cd8a8f5ba91f9da388e4b0cb64c5a5f6e98cdcdbf

Observation 31ac3cbe-04e4-4174-ae7b-6b35555b57ab · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 20

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source=arxiv_source observed=2026-08-07T14:01:23.883414Z digest=sha256:7b14527803f3cd2f48715b7d8e727b07e653265612343ef1e807d9a0a7c0ec86

Observation 9b1faf96-b556-46f0-a79c-9c9a84189980 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 21

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source=arxiv_source observed=2026-08-07T14:01:23.976050Z digest=sha256:964ecb92de651926b4f655628868428512f3b584eb313318d523f06d6fa0b152

Observation 421bfe05-7e31-41b5-ad92-4b487724c3f8 · outbound

This paper cites Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Reference 22

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no resolver link, observed 2026-08-07T14:01:24.087885Z

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source=arxiv_source observed=2026-08-07T14:01:24.087885Z digest=sha256:b3a2babbc9b388f047b000b838cdfd10eb4be3e550168fb5a9b7cddc793b2c7a

Observation bda0b752-27ba-482c-a26b-fef51d8d9cea · outbound

This paper cites Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities

Reference 23

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source=arxiv_source observed=2026-08-07T14:01:24.159098Z digest=sha256:5acabf3a06b46c38795eb7f32dc63b43a0186e40b025244f0657613bb47772bd

Observation 784c145e-d28b-486f-8ede-99c514aad554 · outbound

This paper cites Competitive Programming with Large Reasoning Models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Competitive Programming with Large Reasoning Models

Reference 24

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source=arxiv_source observed=2026-08-07T14:01:24.248102Z digest=sha256:0dd217878cf1edec22a499c3f5297175fbeba32c218a24b76303e5b581500d99

Observation 1369b012-4f31-40f6-beb0-002c58439b0d · outbound

This paper cites GPT-4o System Card.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models GPT-4o System Card

Reference 25

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source=arxiv_source observed=2026-08-07T14:01:24.321343Z digest=sha256:64a0ad1471a2609968852e2687dee607b92290a01ba4d29a204345ca77470f3b

Observation 299fdc9e-7526-4e0d-aac4-2a71948a3229 · outbound

This paper cites OpenAI o1 System Card.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models OpenAI o1 System Card

Reference 26

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no resolver link, observed 2026-08-07T14:01:24.419906Z

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source=arxiv_source observed=2026-08-07T14:01:24.419906Z digest=sha256:96af2ce8aea9669503673cc1d26e59a9a654877e94e3adace74d42017cb62d4d

Observation 73f42e94-ba71-46a6-9b1d-6bc050c3a39c · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-07T14:01:24.515708Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:24.515708Z digest=sha256:ec8122884b4438ba4590ec9dc297b02635449b19ff50ebc0229c49e620064c67

Observation f0e75ab4-d008-43ec-b0e7-367f2f3232d2 · outbound

This paper cites Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models

Reference 28

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no resolver link, observed 2026-08-07T14:01:24.600283Z

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source=arxiv_source observed=2026-08-07T14:01:24.600283Z digest=sha256:086bee1e73bbc0d8597c75b8f93382966e76e22634fb22fe7c32f3c627fdfc81

Observation 29f1d055-0c40-4292-bd67-dbf5c2773828 · outbound

This paper cites Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought

Reference 29

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no resolver link, observed 2026-08-07T14:01:24.672118Z

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source=arxiv_source observed=2026-08-07T14:01:24.672118Z digest=sha256:0d18debc4aab11463a7f56f24efbf279a0f68f823900e3dcbf62323667007a53

Observation 4c7c4b9e-7866-42ff-b8c3-bd4ba4b8fde3 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-07T14:01:24.745046Z digest=sha256:b53ed103170bde10146a435bd0dd6e8f04cbb48c44493d939481c91f9f53a109

Observation 96363283-fa5d-4d18-81af-988b2ada11e7 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 31

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source=arxiv_source observed=2026-08-07T14:01:24.847837Z digest=sha256:36a3ad1b7069778d3098f47720bdd0de15e5024165c7922509f6b90426339ed4

Observation f9d64c17-4280-444f-b441-c7b5b070541e · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 32

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no resolver link, observed 2026-08-07T14:01:24.911865Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:24.911865Z digest=sha256:90c1bd9c337fafde23f0f1c9e88d629c6d859fd9520b5fdd7eff57eb61aa3d8e

Observation a72a6b96-0e25-4210-bb07-e86db1b9b0f8 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 33

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raw_fallback, observed 2026-08-07T14:01:27.346906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:01:24.995849Z digest=sha256:0a4fe481747c30bb7f6d0c64befcb3edcaf5120585f73cfd98893054c444b564

Observation 65e97d08-87bc-493e-a73f-9e4b9d3f9984 · outbound

This paper cites VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering

Reference 34

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verified exact
local_arxiv, observed 2026-08-07T14:01:26.473041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T14:01:25.047482Z digest=sha256:aa72c430bd256f743dd3f827b1973d5b7dce14daa7411f0434ab77e66a4a5d9e

Observation 49fca5c3-60ad-4a8b-8911-1f4f555b816d · outbound

This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 35

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.160503Z digest=sha256:edbf420a2d412d48bfca278744f3c02d3b1697777b190cad4eaaff0b25bcd203

Observation 0023d82c-4754-4593-bf77-e98fe5ffe04f · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 36

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source=arxiv_source observed=2026-08-07T14:01:25.253135Z digest=sha256:ac266b8b860cf759d83220ec8438f2dfa25fc66b43710a9271aa71f189eadf4e

Observation 32e8d7ba-05ad-43b1-b1ed-a0a1ee967a4e · outbound

This paper cites On Verbalized Confidence Scores for LLMs.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models On Verbalized Confidence Scores for LLMs

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.362381Z digest=sha256:8e4f090138d85ae8c51de239faca0d20b7e5941126d6137f35ba030c4fe8f131

Observation e2430b32-9e4a-421e-8186-4e9ec2ca701e · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.453877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.453877Z digest=sha256:79e94b5f4004881a1775fe0f4bf94b07024acd65265e9809725fbe4c29124106

Observation e87c8039-d94f-47b3-bd9c-671458260439 · outbound

This paper cites an unresolved cited work.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Unresolved cited work

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.547974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.547974Z digest=sha256:a1e056eca444f1a3975db4a282424ea9e2e44eec5551ed7f8d9f4ba8f81fbf53

Observation c62b61a3-1ded-4373-b33f-4ac3ef07d44f · outbound

This paper cites Object-Level Verbalized Confidence Calibration in Vision-Language Models via Semantic Perturbation.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Object-Level Verbalized Confidence Calibration in Vision-Language Models via Semantic Perturbation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.677292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.677292Z digest=sha256:cae4bea6511636f17ac4b30d6e58047fd8ee746afc7b49ccc8ef5f46fcf6293e

Observation 38df3b57-ede7-4009-858b-2ca0244844da · outbound

This paper cites Instruction-Following Evaluation for Large Language Models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models Instruction-Following Evaluation for Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.765160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.765160Z digest=sha256:ee126837ceeddc060a171e68a29e159a1ce0e31dcf08279a0cbb5a0802c1e06f

Observation bbe0ee83-2b7a-4ec4-b01a-5d9eb00677f7 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.854052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.854052Z digest=sha256:458bc5a031ddd6e0dad1eb0afc109303e5042fea821c82291ba11ab26dfc333b

Observation 634c2005-6315-4346-a502-bac238a50906 · outbound

This paper cites online" 'onlinestring :=.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models online" 'onlinestring :=

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.922314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.922314Z digest=sha256:7904677af7cc4536f69292d1bbc278cc9fd2403d485028ebfcf3f7baa46d6aa8

Observation deb1b7ee-7405-4a9c-bcc0-52673db637c7 · outbound

This paper cites write newline.

Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models write newline

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T14:01:25.978139Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.978139Z digest=sha256:27714d96bafbd02eb2ddfac2d8998463d386035dd40b0fd0032db9f761ac1aea

Pith citing papers

No inbound Pith citation observations are available.