Paper Citation Record · LEDGER

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models

As of 14 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 3 inbound Pith citation observations for arXiv:2412.15739.

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

pith.paper-citation-record.v1
2412.15739 v1

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:11:08.139563Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-01T07:01:26.911110Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T07:05:28.650553Z

Reference resolution

78 of 78 outbound references displayed

verified exact0
verified fuzzy62
unresolved15
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebb77e48-b1c8-4bdf-8d58-0f8a340e7293 · outbound

This paper cites Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.363417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6aad96c8-eac1-46d4-a913-54077f4149e2 · outbound

This paper cites Ana- lyzing the behavior of visual question answering models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Ana- lyzing the behavior of visual question answering models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.323150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f7be4629-ad21-4184-a729-9bd028877d85 · outbound

This paper cites Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.723954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 539021ef-eb17-49a1-aed1-812cc1d3cef0 · outbound

This paper cites Let there be a clock on the beach: Reducing object hal- lucination in image captioning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Let there be a clock on the beach: Reducing object hal- lucination in image captioning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.290787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b6d3a629-b492-448c-8b0a-953627c2594f · outbound

This paper cites Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Driving with llms: Fusing object-level vec- tor modality for explainable autonomous driving

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.735730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4338983-47d4-4b6e-a6fb-daee00b338c4 · outbound

This paper cites HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models HALC: Object hallucination reduc- tion via adaptive focal-contrast decoding

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.210391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 672774d1-60c0-4f33-a48f-57eb30618c2b · outbound

This paper cites Calibrating deep neural networks by pairwise constraints.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Calibrating deep neural networks by pairwise constraints

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.163607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 54645598-86ac-489e-b2a8-6e0ab3e5b49d · outbound

This paper cites InstructBLIP: Towards general-purpose vision-language models with instruction tuning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models InstructBLIP: Towards general-purpose vision-language models with instruction tuning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.108183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 97241392-75ea-4984-adbe-d511f9f5518d · outbound

This paper cites BERT: Pre-training of deep bidirectional trans- formers for language understanding.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BERT: Pre-training of deep bidirectional trans- formers for language understanding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.757770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3bb68f2-b601-4274-92bd-87c7de16bbce · outbound

This paper cites Hierarchi- cal neural story generation.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Hierarchi- cal neural story generation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:10.027589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e3267257-ec4d-460f-b9a0-a1aa71fe640c · outbound

This paper cites Multi-modal hal- lucination control by visual information grounding.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Multi-modal hal- lucination control by visual information grounding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.986738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c03d125c-9347-41e2-8388-7c7e833daa14 · outbound

This paper cites Beam search strate- gies for neural machine translation.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Beam search strate- gies for neural machine translation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.947362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f880773e-2f72-4f8f-b58b-5fb6b4805919 · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.911384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b1a6d5ca-d683-48db-b613-516245401880 · outbound

This paper cites an unresolved cited work.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:11:09.878066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 609ae471-7289-49a3-ab32-d061b2b9664e · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.847698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 658844fd-01d5-48d8-b78e-44791884a41b · outbound

This paper cites A stitch in time saves nine: A train-time reg- ularizing loss for improved neural network calibration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A stitch in time saves nine: A train-time reg- ularizing loss for improved neural network calibration

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.814830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 333a836a-a9ee-4b5d-bc52-f76a277169c0 · outbound

This paper cites Dietterich.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dietterich

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.788342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a3d03307-985d-46f8-b94e-79095a0c0685 · outbound

This paper cites The curious case of neural text degeneration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models The curious case of neural text degeneration

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.766631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c06a1860-6c5e-41e0-ab06-0554b16b3643 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models LoRA: Low-rank adaptation of large language models

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.744758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7e679aa3-6826-44bc-a811-47a84896216c · outbound

This paper cites Scaling up vision-language pre-training for image captioning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Scaling up vision-language pre-training for image captioning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.721759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 23cf6aba-1040-401d-b3c3-a81e3bd20826 · outbound

This paper cites Movienet: A holistic dataset for movie un- derstanding.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Movienet: A holistic dataset for movie un- derstanding

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.691832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 705887a7-ff24-4a7d-8008-d1c2d7e7c526 · outbound

This paper cites Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.661551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 971ec0f5-117d-476d-b9d5-3a83f164397c · outbound

This paper cites Hudson and Christopher D.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Hudson and Christopher D

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.637540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2b69a640-b761-4fa8-877e-a63e9d2e7e0f · outbound

This paper cites Langsuit-e: Controlling, planning, and interacting with large language models in embodied text environments.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Langsuit-e: Controlling, planning, and interacting with large language models in embodied text environments

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.607019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c5b36c9c-e778-477c-8040-ed6a5a301dc4 · outbound

This paper cites an unresolved cited work.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-11T11:11:09.583423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cb88ebb6-abbc-4ebf-affd-9067ffa52af3 · outbound

This paper cites Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mitigating object hallucinations in large vision-language models through vi- sual contrastive decoding

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.560764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ba85f022-d422-4d20-8d55-902af48dda7e · outbound

This paper cites BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.527141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7309d8c7-cf7e-46d9-811b-9b561c4fd1e1 · outbound

This paper cites What does BERT with vision look at? In Proceedings of the 58th Annual Meeting of the Associa- tion for Computational Linguistics , pages 5265–5275, On- line, 2020.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models What does BERT with vision look at? In Proceedings of the 58th Annual Meeting of the Associa- tion for Computational Linguistics , pages 5265–5275, On- line, 2020

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.503513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f99d7e55-d229-43e1-911a-3a289d42dca0 · outbound

This paper cites Contrastive decoding: Open-ended text genera- tion as optimization.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Contrastive decoding: Open-ended text genera- tion as optimization

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.861988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b1c6395-cdc0-4a9a-acba-c3f0975498b2 · outbound

This paper cites Evaluating object hallucination in large vision- language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Evaluating object hallucination in large vision- language models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.433113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ba1a1f16-7fbc-47f0-851d-5328e0b9da1b · outbound

This paper cites Vila: On pre-training for visual language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Vila: On pre-training for visual language models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.396878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2bc032a3-689d-44e3-bcce-80e05d005beb · outbound

This paper cites TruthfulQA: Measuring how models mimic human falsehoods.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models TruthfulQA: Measuring how models mimic human falsehoods

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.367167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1f3af1d4-27b3-467c-a274-2c0fc9264c94 · outbound

This paper cites Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll ´ar, and C

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.888882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a78a864-b362-45c6-9d35-5f88d0ec64f1 · outbound

This paper cites The devil is in the margin: Margin-based label smooth- ing for network calibration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models The devil is in the margin: Margin-based label smooth- ing for network calibration

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.317232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f79cc1a1-ff8e-4503-80fe-fe98df0d4c8e · outbound

This paper cites Class adaptive network calibration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Class adaptive network calibration

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.290314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f8b9d1a1-ce88-4ecd-be43-9952910c5961 · outbound

This paper cites Visual instruction tuning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Visual instruction tuning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.270709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 714ce5e3-456c-4593-8879-9bdf45daa5e8 · outbound

This paper cites Improved baselines with visual instruction tuning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Improved baselines with visual instruction tuning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.244426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2b43492-b656-45b2-b0ce-bdc39072c360 · outbound

This paper cites SimCLS: A simple framework for contrastive learning of abstractive summarization.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models SimCLS: A simple framework for contrastive learning of abstractive summarization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.219766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 60410cb9-d557-43f1-bc5f-cdba375ca7eb · outbound

This paper cites Curved scene text detection via transverse and longitudinal sequence connection.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Curved scene text detection via transverse and longitudinal sequence connection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.199645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 09748774-bb09-455e-962e-86911e6e7795 · outbound

This paper cites Roberta: A robustly optimized bert pretraining approach, 2019.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Roberta: A robustly optimized bert pretraining approach, 2019

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.926039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ddf0a41-b58f-4a2b-896c-1b4a8bb51bac · outbound

This paper cites BRIO: Bringing order to abstractive summarization.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models BRIO: Bringing order to abstractive summarization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.148228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 81089e8f-a327-44ce-ac56-d75c4a64193d · outbound

This paper cites Soft augmentation for image classifica- tion.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Soft augmentation for image classifica- tion

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.123636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f40dd33e-b200-4b10-9eb4-628a6068c6a5 · outbound

This paper cites Dolphins: Multimodal language model for driving, 2023.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dolphins: Multimodal language model for driving, 2023

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.097746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fc8673e6-ba68-47d4-8b65-4f47f1061b55 · outbound

This paper cites Deepart: Learn- ing joint representations of visual arts.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Deepart: Learn- ing joint representations of visual arts

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.064809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ac5a4295-2328-4c94-84c7-c94c5be2e1c3 · outbound

This paper cites Revisiting the calibration of modern neu- ral networks.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Revisiting the calibration of modern neu- ral networks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:09.027688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e258021e-9f12-489e-9bc3-5a2b6284f7a9 · outbound

This paper cites Confidence-aware learning for deep neural net- works.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Confidence-aware learning for deep neural net- works

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.998295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 515e2a05-b04c-410e-a5f1-6b9af90b4501 · outbound

This paper cites When does label smoothing help? In Advances in Neu- ral Information Processing Systems.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models When does label smoothing help? In Advances in Neu- ral Information Processing Systems

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.974344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c31ccbc7-2cf3-44b0-aa9e-922d22d009db · outbound

This paper cites Cooper, and Milos Hauskrecht.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Cooper, and Milos Hauskrecht

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.940570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9e1cd706-5266-4032-a811-03750bb6d645 · outbound

This paper cites Rankmixup: Ranking-based mixup training for net- work calibration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Rankmixup: Ranking-based mixup training for net- work calibration

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.921206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a13db97e-dd97-40a1-8f26-20601727d037 · outbound

This paper cites Gpt-4 technical report.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Gpt-4 technical report

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.902280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e15b0d9a-80c7-46e7-b733-ef28d27e9e86 · outbound

This paper cites Learning transferable visual models from natural language supervision.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Learning transferable visual models from natural language supervision

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.987568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3acdf735-75e1-45fb-a14a-268ef5cf5704 · outbound

This paper cites SummaR- eranker: A multi-task mixture-of-experts re-ranking frame- work for abstractive summarization.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models SummaR- eranker: A multi-task mixture-of-experts re-ranking frame- work for abstractive summarization

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.858585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f4b95d91-835a-4f64-be5a-a9bdcc367364 · outbound

This paper cites Distributionally robust ensemble of lottery tickets towards calibrated sparse network training.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Distributionally robust ensemble of lottery tickets towards calibrated sparse network training

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.832857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2bbe0bbc-6227-4a58-8a7c-378ea6abb84a · outbound

This paper cites A-okvqa: A benchmark for visual question answering using world knowl- edge.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A-okvqa: A benchmark for visual question answering using world knowl- edge

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.816080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a90775c8-d2fe-454e-98d3-b0f77f8962cb · outbound

This paper cites REPLUG: Retrieval-augmented black-box language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models REPLUG: Retrieval-augmented black-box language models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.799026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation da305622-9b1d-414d-b410-7e67fac6f2b8 · outbound

This paper cites Videobert: A joint model for video and language representation learning.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Videobert: A joint model for video and language representation learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.774766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 96c70ff4-068c-4823-ad6b-f72242ebbcb9 · outbound

This paper cites Sq-llava: Self-questioning for large vision-language assistant.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Sq-llava: Self-questioning for large vision-language assistant

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.748964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d309cf41-699b-4574-b85d-a5918e35badf · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:08.027836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b159bed-56ca-4976-8033-f327b97bb3a5 · outbound

This paper cites A closer look at the robustness of contrastive language-image pre-training (clip).

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models A closer look at the robustness of contrastive language-image pre-training (clip)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.725549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bc871a3a-5abf-4ec7-ad48-b29d5d5e6d42 · outbound

This paper cites An empirical study into what matters for calibrating vision-language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models An empirical study into what matters for calibrating vision-language models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.703552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfd9d476-06f8-4625-b7f0-9049d03a6511 · outbound

This paper cites GIT: A generative image-to-text transformer for vision and language.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models GIT: A generative image-to-text transformer for vision and language

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.685635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation afbc4c99-980c-4a8c-82e2-293803c20a8b · outbound

This paper cites ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:08.048628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ce95740-d65c-4bd9-b42d-660870476e79 · outbound

This paper cites Editable scene simulation for autonomous driving via collaborative llm-agents.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Editable scene simulation for autonomous driving via collaborative llm-agents

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.667098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 81a2498c-861f-445c-a08e-75e8b20bfc81 · outbound

This paper cites Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.641238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e7fa6ad8-76fa-4214-8694-0cd71000b2b6 · outbound

This paper cites Precedent-enhanced legal judgment prediction with LLM and domain-model collaboration.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Precedent-enhanced legal judgment prediction with LLM and domain-model collaboration

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.614753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2434f08-8275-4e78-b2a2-9ce643d52a3e · outbound

This paper cites Martindale, and Marine Carpuat.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Martindale, and Marine Carpuat

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.589776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eb4e3cd2-f926-4301-908a-1515592767b0 · outbound

This paper cites mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:08.077580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07866df3-59af-4006-b9fe-7f6005c1a38d · outbound

This paper cites Mul- timodal healthcare ai: Identifying and designing clinically relevant vision-language applications for radiology.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Mul- timodal healthcare ai: Identifying and designing clinically relevant vision-language applications for radiology

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.558671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fb3fab50-01aa-41af-941c-b55f2870ba76 · outbound

This paper cites Woodpecker: Hallucination Correction for Multimodal Large Language Models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:08.091154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be99acf2-2a7f-4ab5-8a2b-938749c71590 · outbound

This paper cites Dauphin, and David Lopez-Paz.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Dauphin, and David Lopez-Paz

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.531259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 13aa1e1c-5b3d-4ac4-8264-1250fb04766a · outbound

This paper cites What if the tv was off? examining counterfactual reasoning abilities of multi-modal language models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models What if the tv was off? examining counterfactual reasoning abilities of multi-modal language models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.507251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7d2acbd9-d772-43e8-8069-ffa3d721984b · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:08.110730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d076d16-8717-46cd-a5bf-685aad026328 · outbound

This paper cites Calibrating sequence likelihood improves conditional language genera- tion.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Calibrating sequence likelihood improves conditional language genera- tion

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.477783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1fd10b15-7c81-4a75-bdb4-26d6d42334d3 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.457012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9d1d5b7d-4ccc-43b3-9569-304625f7ef61 · outbound

This paper cites Learning deep features for scene recognition using places database.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Learning deep features for scene recognition using places database

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.428399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3ae03b19-753c-4df1-a5c8-39043de5d5f1 · outbound

This paper cites Detecting hallucinated content in conditional neural sequence generation.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Detecting hallucinated content in conditional neural sequence generation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T11:11:08.403825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 024b06c8-d5ab-49cb-92b4-adac92ce81a5 · outbound

This paper cites Jpeg Compression.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Jpeg Compression

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T11:11:08.372299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7c32e135-9339-44e5-95ee-75f3522c9ea7 · outbound

This paper cites an unresolved cited work.

VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-11T11:11:07.867924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 09f18509-1557-4199-8b5d-7d55abb7b0bd · inbound

When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs cites this paper.

When Looking Is Not Enough: Visual Attention Structure Reveals Hallucination in MLLMs VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:57:05.219770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dd68306d-7301-41a3-a1eb-b4d74a1252be · inbound

Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation cites this paper.

Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in Large Vision-Language Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:14:18.714212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e30a4de1-ce8b-4b71-b725-ab52c795d6a1 · inbound

Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigation cites this paper.

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-01T07:05:28.652874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.