ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models | Paper Citation Record

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This paper cites InstructBLIP: Towards general-purpose vision-language modelswithinstruction tuning.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models InstructBLIP: Towards general-purpose vision-language modelswithinstruction tuning

Reference 1

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Observation 65c84241-9047-41fd-8c0e-04c77d1e876e · outbound

This paper cites Visual instruction tun- ing.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Visual instruction tun- ing

Reference 2

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This paper cites Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

Reference 3

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This paper cites AudioChatLlama: To- wards general-purpose speech abilities for LLMs.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models AudioChatLlama: To- wards general-purpose speech abilities for LLMs

Reference 4

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This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 5

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This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 6

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Observation 2f833087-d913-489d-bcec-e5e876e61c6d · outbound

This paper cites MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning

Reference 7

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This paper cites Improved baselines with visual instructiontuning.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Improved baselines with visual instructiontuning

Reference 8

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Observation 5bf75526-4a76-49e6-bcca-674aa0ba026f · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 9

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Observation 244cdc3d-d993-49c9-a77c-8cd6fc3018f4 · outbound

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Multi-modal hallucination control by visual information grounding

Reference 10

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This paper cites Mitigatingobjecthallucinationsinlargevision-languagemodelsthroughvisualcontrastive decoding.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Mitigatingobjecthallucinationsinlargevision-languagemodelsthroughvisualcontrastive decoding

Reference 11

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Observation 9c1bc693-ceb7-450a-8f2a-3c261b0085e1 · outbound

This paper cites Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Don't Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models

Reference 12

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Direct preference optimization: Your language model is secretly a reward model

Reference 13

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Observation ce5991a8-9f4d-4c64-a64c-2ffda4e6b207 · outbound

This paper cites Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization, 2023.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization, 2023

Reference 14

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Hallucidoctor: Mitigating hallucinatory toxicity in visual in- struction data

Reference 15

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This paper cites Hallucination augmented contrastive learning for multimodal large language model.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Hallucination augmented contrastive learning for multimodal large language model

Reference 16

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation

Reference 17

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Observation 7df612aa-c1f6-4866-ad18-839abdd0b7c5 · outbound

This paper cites Reducing Hallucinations in Vision-Language Models via Latent Space Steering.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Reducing Hallucinations in Vision-Language Models via Latent Space Steering

Reference 18

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Observation f270d578-7df8-43ae-bb6f-8cfcc8ebd778 · outbound

This paper cites Attributes as operators: Factorizing unseen attribute- object compositions.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Attributes as operators: Factorizing unseen attribute- object compositions

Reference 19

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This paper cites Dávila, Alfredo Gómez-Rodríguez, and Marco Rodríguez-Andrade.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Dávila, Alfredo Gómez-Rodríguez, and Marco Rodríguez-Andrade

Reference 20

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This paper cites Geometric Algebra.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Geometric Algebra

Reference 21

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This paper cites Learning graph embeddings for composi- tionalzero-shotlearning.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Learning graph embeddings for composi- tionalzero-shotlearning

Reference 22

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Observation 96752a69-85a3-4335-87e3-407c0063d4df · outbound

This paper cites Open world compositional zero-shot learning.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Open world compositional zero-shot learning

Reference 23

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Shamma, Michael S

Reference 24

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This paper cites From red wine to red tomato: Composition with context.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models From red wine to red tomato: Composition with context

Reference 25

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This paper cites Neural-symbolic vqa: disentangling reasoning from vision and language understanding.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Neural-symbolic vqa: disentangling reasoning from vision and language understanding

Reference 26

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This paper cites Kim, and Vikas Singh.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Kim, and Vikas Singh

Reference 27

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Observation cf2bcca4-2a0b-4383-b87a-c42f6b165e13 · outbound

This paper cites Learningwithholographicreducedrepresentations.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Learningwithholographicreducedrepresentations

Reference 28

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Gunn and Steven De Keninck

Reference 29

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Geometric algebra trans- former

Reference 30

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This paper cites Gupta, Steven De Keninck, Max Welling, and Johannes Brandstetter.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Gupta, Steven De Keninck, Max Welling, and Johannes Brandstetter

Reference 31

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Evaluating object hallucination in large vision-language models

Reference 32

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Object hallucination in image captioning

Reference 33

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 34

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This paper cites Hallusion- bench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Hallusion- bench: An advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 35

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This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 36

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Relational composition in neural networks: A survey and call to action

Reference 37

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This paper cites Attention Is All You Need.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Attention Is All You Need

Reference 38

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Pooling Methods in Deep Neural Networks, a Review

Reference 39

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This paper cites Look, ma, no matrices! InACM SIGGRAPH 2024 Talks, SIGGRAPH ’24, New York, NY, USA, 2024.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Look, ma, no matrices! InACM SIGGRAPH 2024 Talks, SIGGRAPH ’24, New York, NY, USA, 2024

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Unresolved cited work

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This paper cites Vector-derivedtransformationbinding: Animprovedbinding operation for deep symbol-like processing in neural networks.Neural Comput., 31(5), 2019.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Vector-derivedtransformationbinding: Animprovedbinding operation for deep symbol-like processing in neural networks.Neural Comput., 31(5), 2019

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Thomason

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This paper cites Fodor and Zenon W.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Fodor and Zenon W

Reference 44

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This paper cites Tensor product variable binding and the representation of symbolic structures in connectionist systems.Artificial Intelligence, 46(1), 1990.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Tensor product variable binding and the representation of symbolic structures in connectionist systems.Artificial Intelligence, 46(1), 1990

Reference 45

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Touretzky and Geoffrey E

Reference 46

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Acomparisonofvectorsymbolicarchitectures

Reference 47

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models URLhttps://api.semanticscholar.org/ CorpusID:52182870

Reference 48

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Lawrence Zitnick

Reference 49

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models A-okvqa: A benchmark for visual question answering using world knowledge

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:03.179524Z

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Gqa: Anewdatasetforreal-worldvisualreasoning andcompositionalquestionanswering

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

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models A Survey on Hallucination in Large Vision-Language Models

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ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Recasting self-attention with holographic reduced representations

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e1c7a04f-d5cb-499b-915e-dbbf12055c8d · outbound

This paper cites C Hyperparameters We combine ReCo with two widely used VLMs: InstructBLIP [1] and MiniGPT4 [6] and we follow the same training procedure for both.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models C Hyperparameters We combine ReCo with two widely used VLMs: InstructBLIP [1] and MiniGPT4 [6] and we follow the same training procedure for both

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:02.582920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fca8ebd2-4660-4810-b6eb-bddde40aa2d1 · outbound

This paper cites Istherea ⟨object⟩intheimage?.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Istherea ⟨object⟩intheimage?

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:02.370209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fb697e78-856d-47b7-b27d-45d477237cb9 · outbound

This paper cites Describe the image.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Describe the image

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:02.213267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 467719fa-6326-4ade-828e-4fda07a0371d · outbound

This paper cites Is there a direct contect between⟨object1⟩and⟨object2⟩.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Is there a direct contect between⟨object1⟩and⟨object2⟩

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:02.061761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9172db05-304c-4447-a5dc-58d5c44a03b7 · outbound

This paper cites Similarly to AMBER, MME also consists of multiple types of discriminative questions such asartworkrelated, coderelated,and numericalrelatedquestions.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Similarly to AMBER, MME also consists of multiple types of discriminative questions such asartworkrelated, coderelated,and numericalrelatedquestions

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:07:01.907463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 52ea0ef1-694d-4ef7-8632-d014c358a236 · outbound

This paper cites According to the image, in 2017, was Tencent the company with the highest revenue from video games, with Sony as the second-highest earner?.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models According to the image, in 2017, was Tencent the company with the highest revenue from video games, with Sony as the second-highest earner?

Reference 61

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T22:07:01.737365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 19dedbb1-325a-4384-81a1-87f078f54257 · outbound

This paper cites an unresolved cited work.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Unresolved cited work

Reference 1995

Resolution: unresolved; no resolver link, observed 2026-08-06T22:06:57.173208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb719b69-1ad7-47a5-bf46-527f460fdfe2 · outbound

This paper cites an unresolved cited work.

ReCo: Reminder Composition Mitigates Hallucinations in Vision-Language Models Unresolved cited work

Reference 2017

Resolution: verified exact; doi, observed 2026-08-06T22:07:00.139850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers