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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 20 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 2026-08-20T06:33:59.587034+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=sha256:244b3dfe5cc47e4a423f120dce39c558b3cb8e981f8ab48ec7db37795cc7f294

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=sha256:1524caf489b74a48fa4fdce9c7aaa622f0bab723b3be4f1a4811e6bd978f42cc

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:01:22.201998Z digest=sha256:bad779c61f9ade71beecea271b00cfa687b9617317b43609d5955fb180f5a221

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=sha256:982a6a44f2ba75d135a105a63f7161d127e4048cb1270dc0182360ab517a8307

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:01:22.367415Z digest=sha256:76ed88f9e4a084d2707d0747179fb9aaba1dee5c300173f386ba9eef70e640cf

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=sha256:27cd16dd0eba3be72f26d940899dc14de21434ec0bb9468f26dab1984a4093ce

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=sha256:278a9a2a4326a7358df236adceb05c222fcd37f5e02b99dfb95b666b9a9d42d8

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:b6379f50886e97144a83b57f630db4929d288ad52af6e82f24eb0dfe2f734c8d

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:e03c90140e197c675c1f6eb59789a8ea321c2a6fe6861361060e8d639d101993

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:c96d7321fbd3f3f639f15f526cb15e76e85e8bee3c7a418924fb025ab993a0cc

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=sha256:100365f6c71dd66e96c7d1dbf1230fad130dd98a615c6d8eddd0c94987f49964

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:d18c6372e1483f95e70e492014447fba1d81615aa727e8e6db9e3dc9e76b98c2

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=sha256:40107bc1e7ebd216ee106da79c20b60ed3ba393852cf669a3bcca21777f411c8

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=sha256:70ca2e37b1368499a2fb0336fdaf93e5de6a0f8d88ebf7e13da817e5c433031e

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=sha256:9e26797962662695438e3babb846d005cf48c1ed7d40e8e14738ec15da76114c

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=sha256:02e1e1622d3f9853f0d82333d948cb97ecc31f44894d1a8a7e34bc1f35795409

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=sha256:821c62e929e94570b575592a6380e1c5bc21794ffaa64afafdbca5355135fbb0

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:bb643f01c889acd1b5f35a4790d06e7ac8d0f9a90eda357bfc4109108300bdd8

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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source=arxiv_source observed=2026-08-07T14:01:23.760219Z digest=sha256:cbb0818749e13b06970a12107212ea0e99b4d0a69f537fa3b1ae8efbe7158c42

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:f8a7446f865547398b8ab234c7f8c8510f9f4d881d0bdd5d7bdd49ebb4a03e33

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:f74e1e5df3131760902833075584a9986e593ec58396dca0364a364fac19f43f

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=sha256:7b40f769157b7884732827311144764877e9e97570c2bc9a9b95d1e022c8e61f

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=sha256:7f1df2890df7a77e65a784000eb7438e76d6fb0d06973893f6c4f6d8ac0bdef7

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:a9d671e1f5ed3a11e928845ec8fc4f5b76f5f1c238ff42e25b53d424e67f0484

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:de465c2f5cbe79208d8e6f19e34f5c77edf910704657be35f3abee107f7f10d5

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:bbd6fdf6ddebdf7fb28bbd328c33aeb399628a30f804e70865b7987921f220d6

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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source=arxiv_source observed=2026-08-07T14:01:24.515708Z digest=sha256:33cd04514f4f5a5065ac7be17256d033424fce894098cc01be744276be8e89e4

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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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:24.600283Z digest=sha256:ec9bdda9e8611dc337e62a5235c4d09de4fd4a760d34332fe5f25b6ff2b2b2f3

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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source=arxiv_source observed=2026-08-07T14:01:24.672118Z digest=sha256:3c70bc092d3661bea60da5e1dc41a28ddb888c5e0607e9807cca9a756d895598

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=sha256:2cfb934a6ee94e92db5f79309d8e5ec645b487a8c42a262794e91a16cab032e7

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=sha256:97a586c298960177019e14f6cda9313076b37fda394a4f54a7ac71e6d9f822a0

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:24.911865Z digest=sha256:b066deb297c2ad32c583c900acaef897ceb04318e9cfdf3e604b38d638f9a035

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:01:24.995849Z digest=sha256:939c562a65f6f9d5ed06d1baa2eac8e223a37edda6192f729dec70ac5fcf034f

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 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-07T14:01:25.047482Z digest=sha256:9ed273ba1ca93adcfcedc39ca4705133a550347e79086d7825ea9c313a811a58

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=sha256:7ce7c96b9f435070b23253c9434fe75c437e5281047ff835fdd8edbe1ee3d10c

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:dd2e2acadb47b02b8ef84d5aea0ada112c50db49cf8686b7c91318efc18b1572

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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unresolved
no resolver link, observed 2026-08-07T14:01:25.362381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:01:25.362381Z digest=sha256:b23e8ed8af2d2177eb8f069cde191d99b1ab93bca201c9258f1fb0929aef15e6

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=sha256:896326de2cd6146dc7ccb85e5db179277c54d576f3b4577e0a3fd34e7f5734f2

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=sha256:3fc5d99b0fd6adb4129a644b592331e2bb1c0b5c6db271310dd6b0ec4f939449

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=sha256:36864f76180b89a3fbfdba19b8923ad04ab04b32b9db18c50adcb637fd835f47

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=sha256:5b649affeb4792c60d9d7277cb85c0ed3782c9ae61d76ab703dd6a251af73574

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:fa2166068000ed6fdb6b7b56fc3543d7d1a0d251db429e1f4cd65fa0c5e9943c

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=sha256:7492aa4dd4a8b38779f99b42f2d8696edaf362eaa8344e5c1b46aabd2b5cea68

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:c91f1b9a37b0359782fdf9aed55de3ab4aa25626c649b05dc3290fb9c673db1a

Pith citing papers

No inbound Pith citation observations are available.