Paper Citation Record · LEDGER

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions

As of 24 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.10011.

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

pith.paper-citation-record.v1
2501.10011 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:28:29.845980Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5151a930-af36-4df4-8b7e-df234a20c345 · outbound

This paper cites Large language models are visual reasoning coordinators,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Large language models are visual reasoning coordinators,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.815312Z

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Observation 97733769-5075-41b0-9ffd-ae8eb34423ac · outbound

This paper cites LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions LLaVA-UHD: an LMM Perceiving Any Aspect Ratio and High-Resolution Images

Reference 2

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Observation 75d04fdb-6cb4-473d-b606-9ff0d3ec37ee · outbound

This paper cites Vision-language models for vision tasks: A survey,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Vision-language models for vision tasks: A survey,

Reference 3

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Observation 916d39be-f408-4891-8a77-665e334ef9e4 · outbound

This paper cites Vlp: A survey on vision-language pre-training,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Vlp: A survey on vision-language pre-training,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.779835Z

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Observation a68f78ec-5fda-431d-8509-1162291c382c · outbound

This paper cites Representing Online Handwriting for Recognition in Large Vision-Language Models.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Representing Online Handwriting for Recognition in Large Vision-Language Models

Reference 5

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Observation 246cb7ab-fa27-4b18-ba00-992c5ae3c952 · outbound

This paper cites Building a Modal-balanced BlockChain with Semantic Reconstruction.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Building a Modal-balanced BlockChain with Semantic Reconstruction

Reference 6

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Observation a6cf04f4-4195-4c16-bb39-2981b551f284 · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions A Survey on Hallucination in Large Vision-Language Models

Reference 7

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Observation 7e7e14d5-fe88-4b97-9713-cd882b7d69b1 · outbound

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

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Evaluating Object Hallucination in Large Vision-Language Models

Reference 8

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Observation b2afa959-0643-41ef-903a-0d5840addaf6 · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions A Survey of Hallucination in Large Foundation Models

Reference 9

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Observation 419a9c03-b1ab-4b3f-a7a5-e9d20dac948d · outbound

This paper cites Detecting and preventing hallucinations in large vision language models,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Detecting and preventing hallucinations in large vision language models,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.754365Z

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Observation 09a7831c-1b86-43b2-8e35-aafcffd6988b · outbound

This paper cites Aligning Large Multimodal Models with Factually Augmented RLHF.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 11

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Observation 196d5dea-7d58-463a-9d85-6a3148c17303 · outbound

This paper cites AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 12

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Observation 33fb0c23-d912-420e-870b-da0b2960742b · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Improved Baselines with Visual Instruction Tuning

Reference 13

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Observation 906c7a97-d2a6-4155-a90b-5ca1e415d781 · outbound

This paper cites Talk2face: A unified sequence-based framework for diverse face generation and anal- ysis tasks,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Talk2face: A unified sequence-based framework for diverse face generation and anal- ysis tasks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.735405Z

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Observation b728e16c-2255-4489-897b-2016d0f0b681 · outbound

This paper cites Anyface: Free- style text-to-face synthesis and manipulation,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Anyface: Free- style text-to-face synthesis and manipulation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.716229Z

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Observation 920dde6e-83d0-458a-8a58-0e95200970f4 · outbound

This paper cites Multi-caption text-to- face synthesis: Dataset and algorithm,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Multi-caption text-to- face synthesis: Dataset and algorithm,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:28:30.691733Z

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Observation 2c3bc237-ad1f-4d39-85ef-ed5332e619f0 · outbound

This paper cites CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning

Reference 17

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Source-reported events for the cited work

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Observation 5e59cfbd-714c-4805-8030-7e45f2682c53 · outbound

This paper cites Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models

Reference 18

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Observation ee4218e4-3f2c-403f-ba9c-ac3137568051 · outbound

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

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Mitigating hallucination in large multi-modal models via robust instruction tuning,

Reference 19

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Observation 15b3b455-0edb-4956-9222-bcae69388bb9 · outbound

This paper cites Ferret: Refer and Ground Anything Anywhere at Any Granularity.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Ferret: Refer and Ground Anything Anywhere at Any Granularity

Reference 20

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Observation be8a6406-8a4d-4354-8c6e-f3360fd0e08e · outbound

This paper cites SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusion

Reference 21

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Observation 9b5879e5-b7ed-4fac-9ba0-79354989deac · outbound

This paper cites One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization,

Reference 22

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Observation 1b5be744-efda-4d94-a754-10c839b04f4f · outbound

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Make-it-3d: High-fidelity 3d creation from a single image with diffu- sion prior,

Reference 23

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Observation 5cf9fd7a-f677-4439-8120-f6199eb45d98 · outbound

This paper cites Can SAM Boost Video Super-Resolution?.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Can SAM Boost Video Super-Resolution?

Reference 24

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Observation 93210828-6fb1-434a-aa66-67e49693db50 · outbound

This paper cites Hierarchical open-vocabulary universal image segmentation,.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Hierarchical open-vocabulary universal image segmentation,

Reference 25

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Observation 7e2d16d1-2dd2-4b4a-b3ce-e8040e7b5502 · outbound

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Segment anything,

Reference 26

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Observation 2b10b60e-591d-4483-8e9a-06fcde7b2013 · outbound

This paper cites Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models

Reference 27

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Llm-mhr: A llm-augmented multimodal hashtag recommendation algorithm,

Reference 28

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Observation 8b677bdb-7066-49b4-b906-94df89c128d5 · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 29

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Scaling instruction-finetuned language models,

Reference 30

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions High-fidelity 3d gan inversion by pseudo-multi-view optimization,

Reference 31

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions An image is worth 16x16 words: Transformers for image recognition at scale,

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions Yi: Open Foundation Models by 01.AI

Reference 33

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions xgen-mm (blip- 3): A family of open large multimodal models,

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models

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This paper cites OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation.

Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation

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Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions What matters when building vision-language models?

Reference 37

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Pith citing papers

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