Paper Citation Record · LEDGER

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception

As of 19 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2504.20468.

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

pith.paper-citation-record.v1
2504.20468 v2

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:33:05.345013Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T06:41:59.641410Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:46:37.512830Z

Reference resolution

51 of 51 outbound references displayed

verified exact0
verified fuzzy12
unresolved39
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f74fe1c5-afc0-44df-b04b-2d83b2218500 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:04.973486Z

Source-reported events for the cited work

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Observation 83d5ab26-c756-45fe-95e8-f5543279ecf2 · outbound

This paper cites GPT-4 Technical Report.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception GPT-4 Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:04.979571Z

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Observation 7401bee4-6b9c-42e4-ad70-22646d7e5df7 · outbound

This paper cites Claude 3.5 sonnet model card adden- dum.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Claude 3.5 sonnet model card adden- dum

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.258593Z

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Observation 86f4b135-64ef-41b1-8111-aeec0f920f0c · outbound

This paper cites M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 4

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Observation 6756bd0e-254c-4793-bdc6-c8c6c3e9c854 · outbound

This paper cites Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.237791Z

Source-reported events for the cited work

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

Observation fedec3f3-83b3-4eb0-bf96-66e21d772863 · outbound

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

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.000352Z

Source-reported events for the cited work

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Observation ef61f654-560b-49c6-bac9-15da76807eec · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.004793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10323c3d-609f-4353-aeaf-3d3083b8c1c9 · outbound

This paper cites ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.009426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe1be5be-9b7b-434b-b338-37be4edffc2a · outbound

This paper cites CogVLM2: Visual Language Models for Image and Video Understanding.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception CogVLM2: Visual Language Models for Image and Video Understanding

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.014955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d1571af-5f14-4e77-a9bf-00de90935b21 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception LoRA: Low-Rank Adaptation of Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.135084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eae33eae-ce0c-40eb-836e-d94817d73068 · outbound

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

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Opera: Alleviating hallucination in multi- modal large language models via over-trust penalty and retrospection-allocation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.178855Z

Source-reported events for the cited work

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Observation 94a0be69-dbbb-4c94-b16d-a67eba80bd3d · outbound

This paper cites Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.145229Z

Source-reported events for the cited work

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Observation 030ac135-0bf5-4959-bbaa-739b2429b5a9 · outbound

This paper cites A Survey on Locality Sensitive Hashing Algorithms and their Applications.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception A Survey on Locality Sensitive Hashing Algorithms and their Applications

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.150783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e8f358a-ab6f-4c27-aa3b-f1d9defae5c3 · outbound

This paper cites Towards mitigating llm hallucination via self reflection.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Towards mitigating llm hallucination via self reflection

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.159719Z

Source-reported events for the cited work

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Observation 8ec3ddae-d807-488d-85bb-b90caf06ea65 · outbound

This paper cites Hallucination augmented contrastive learn- ing for multimodal large language model.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Hallucination augmented contrastive learn- ing for multimodal large language model

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.130995Z

Source-reported events for the cited work

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Observation 771c014e-6e3a-4f89-95c7-a39d1ade5a01 · outbound

This paper cites Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision

Reference 16

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Source-reported events for the cited work

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Observation 4d5eba18-17db-4d5a-bd67-ab6efa75a11c · outbound

This paper cites Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.114482Z

Source-reported events for the cited work

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Observation d05943c0-d16a-4748-a43b-b4073ced610c · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Evaluating Object Hallucination in Large Vision-Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.174886Z

Source-reported events for the cited work

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Observation e712f285-e383-4609-88ba-d00334775e5d · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.179414Z

Source-reported events for the cited work

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Observation 690f3d1d-cc6b-4b8c-b6fb-fb641d95ea2e · outbound

This paper cites Mitigating hallucination in large multi-modal models via robust instruction tuning.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.184293Z

Source-reported events for the cited work

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Observation 41cbf034-0290-4eaf-8db4-8234d5825153 · outbound

This paper cites Improved baselines with visual instruction tuning.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Improved baselines with visual instruction tuning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.189681Z

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Observation 88ce785f-54b8-440d-b85b-afaab5bc5635 · outbound

This paper cites Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Llava-next: Im- proved reasoning, ocr, and world knowledge, 2024

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.194559Z

Source-reported events for the cited work

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Observation 4737e319-5e48-4a9a-8c48-c35ebcf25a93 · outbound

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Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Visual instruction tuning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.056392Z

Source-reported events for the cited work

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

Observation 475c9ee5-a16e-4dfa-9331-4fd0802519ce · outbound

This paper cites Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.204107Z

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Observation 2d3d16c1-4a0b-43cf-b146-11772b6a4143 · outbound

This paper cites MMBench: Is Your Multi-modal Model an All-around Player?.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception MMBench: Is Your Multi-modal Model an All-around Player?

Reference 25

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Observation 7e7edded-d986-44fb-8aa0-8c2b50a4c02f · outbound

This paper cites Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Mmbench: Is your multi-modal model an all-around player? In European Conference on Computer Vision, pages 216–233

Reference 26

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Observation 1126ba43-9af7-49e6-bc11-6aa9acb846c8 · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.219974Z

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Observation 526101fd-7373-4a8f-a926-cdd667139a14 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 28

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Observation 64187fd5-d148-4def-8eed-a8451c78ea94 · outbound

This paper cites Hello gpt-4o.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Hello gpt-4o

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:06.013685Z

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Observation 6abdc6f4-e6c5-4a08-b184-17bc51be04c7 · outbound

This paper cites Scalable diffusion models with transformers.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Scalable diffusion models with transformers

Reference 30

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Observation dae203b3-f86a-4dcc-a999-8d05a0b9c5f3 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Direct preference optimization: Your language model is secretly a reward model

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:05.978938Z

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Observation d3d9adeb-91de-48c3-a655-683391fd31ec · outbound

This paper cites Object Hallucination in Image Captioning.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Object Hallucination in Image Captioning

Reference 32

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Observation b5f53078-1a11-413d-975e-763cbc8b696f · outbound

This paper cites Scienceqa: A novel resource for question answering on scholarly articles.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Scienceqa: A novel resource for question answering on scholarly articles

Reference 33

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Observation c352b03b-526e-4333-9a1f-e73af44dff72 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Proximal Policy Optimization Algorithms

Reference 34

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Observation 3de345a3-61b9-4658-b921-f970338e013e · outbound

This paper cites Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Conceptual captions: A cleaned, hypernymed, im- age alt-text dataset for automatic image captioning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:05.946670Z

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Observation 2d6785af-aa05-4322-bf4f-79939be67186 · outbound

This paper cites Intervening anchor token: Decod- ing strategy in alleviating hallucinations for mllms.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Intervening anchor token: Decod- ing strategy in alleviating hallucinations for mllms

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:05.925034Z

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Observation e588c4fa-40ad-45cf-8594-b5e998efc6b5 · outbound

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

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception LLaMA: Open and Efficient Foundation Language Models

Reference 37

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Observation b21539f5-cfc7-4f20-a131-23be82d88eeb · outbound

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

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 38

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This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

Reference 39

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Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Reinforcement Learning for LLM Post-Training: A Survey

Reference 40

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Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback

Reference 41

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Observation 1c919ffd-fe1c-45f5-8134-9be42f27bbcf · outbound

This paper cites SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.298081Z

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This paper cites MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.303738Z

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Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Qwen2 Technical Report

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.308800Z

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Observation 567af210-f6e2-4d43-a72b-7afeaaa97596 · outbound

This paper cites MiniCPM-V: A GPT-4V Level MLLM on Your Phone.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception MiniCPM-V: A GPT-4V Level MLLM on Your Phone

Reference 45

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Observation 342c07b7-357b-4e85-b4c6-c93df8277f38 · outbound

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Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.318848Z

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This paper cites Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:33:05.908738Z

Source-reported events for the cited work

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Observation 014d6c58-fba8-4a97-bee2-342641acc0c1 · outbound

This paper cites MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities

Reference 48

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This paper cites Debiasing Multimodal Large Language Models via Penalization of Language Priors.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Debiasing Multimodal Large Language Models via Penalization of Language Priors

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.333246Z

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Unavailable: canonical work link unavailable.

Observation d81f5793-b83a-4b86-858e-c5c55205bff8 · outbound

This paper cites Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.339602Z

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Observation 5782e471-7e5e-412d-8b52-4fefb243f4a1 · outbound

This paper cites Self-Supervised Visual Preference Alignment.

Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception Self-Supervised Visual Preference Alignment

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T05:33:05.345013Z

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Pith citing papers

Observation 29629cb6-bc1b-4997-b951-261d3053ae57 · inbound

When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models cites this paper.

When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models Antidote: A Unified Framework for Mitigating LVLM Hallucinations in Counterfactual Presupposition and Object Perception

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:46:37.514043Z

Source-reported events for the cited work

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