Controlling Multimodal LLMs via Reward-guided Decoding | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Controlling Multimodal LLMs via Reward-guided Decoding GPT-4 Technical Report

Reference 1

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This paper cites Understanding Alignment in Multimodal LLMs: A Comprehensive Study.

Controlling Multimodal LLMs via Reward-guided Decoding Understanding Alignment in Multimodal LLMs: A Comprehensive Study

Reference 2

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This paper cites Hallucination of Multimodal Large Language Models: A Survey.

Controlling Multimodal LLMs via Reward-guided Decoding Hallucination of Multimodal Large Language Models: A Survey

Reference 3

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This paper cites PaliGemma: A versatile 3B VLM for transfer.

Controlling Multimodal LLMs via Reward-guided Decoding PaliGemma: A versatile 3B VLM for transfer

Reference 4

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This paper cites An Introduction to Vision-Language Modeling.

Controlling Multimodal LLMs via Reward-guided Decoding An Introduction to Vision-Language Modeling

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This paper cites Rank analysis of incomplete block designs: I.

Controlling Multimodal LLMs via Reward-guided Decoding Rank analysis of incomplete block designs: I

Reference 6

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This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Controlling Multimodal LLMs via Reward-guided Decoding Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 7

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This paper cites Language Models are Few-Shot Learners.

Controlling Multimodal LLMs via Reward-guided Decoding Language Models are Few-Shot Learners

Reference 8

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This paper cites The (r) evolution of multi- modal large language models: A survey.

Controlling Multimodal LLMs via Reward-guided Decoding The (r) evolution of multi- modal large language models: A survey

Reference 9

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This paper cites End-to- end object detection with transformers.

Controlling Multimodal LLMs via Reward-guided Decoding End-to- end object detection with transformers

Reference 10

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Observation 0d560a61-1267-4238-8b4c-a8e4fc62b192 · outbound

This paper cites Plug and play language models: A simple approach to controlled text generation.

Controlling Multimodal LLMs via Reward-guided Decoding Plug and play language models: A simple approach to controlled text generation

Reference 11

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Observation d48f62ed-f031-4991-b33f-ff70f1455e3b · outbound

This paper cites Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding.

Controlling Multimodal LLMs via Reward-guided Decoding Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding

Reference 12

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This paper cites Reward-augmented decod- ing: Efficient controlled text generation with a unidirectional reward model.

Controlling Multimodal LLMs via Reward-guided Decoding Reward-augmented decod- ing: Efficient controlled text generation with a unidirectional reward model

Reference 13

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Observation d9812ea6-4b95-47ed-be5f-1e9a817c7c31 · outbound

This paper cites The Llama 3 Herd of Models.

Controlling Multimodal LLMs via Reward-guided Decoding The Llama 3 Herd of Models

Reference 14

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

Controlling Multimodal LLMs via Reward-guided Decoding Multi-modal hal- lucination control by visual information grounding

Reference 15

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This paper cites Value Augmented Sampling for Language Model Alignment and Personalization.

Controlling Multimodal LLMs via Reward-guided Decoding Value Augmented Sampling for Language Model Alignment and Personalization

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This paper cites Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality.

Controlling Multimodal LLMs via Reward-guided Decoding Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality

Reference 17

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

Controlling Multimodal LLMs via Reward-guided Decoding Lora: Low- rank adaptation of large language models

Reference 18

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Observation 826fd636-5466-42e6-addf-efbb1a1c091e · outbound

This paper cites Args: Alignment as reward-guided search.

Controlling Multimodal LLMs via Reward-guided Decoding Args: Alignment as reward-guided search

Reference 19

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Observation b3ec0fdf-f59d-440d-9450-2477156f23d6 · outbound

This paper cites RewardBench: Evaluating Reward Models for Language Modeling.

Controlling Multimodal LLMs via Reward-guided Decoding RewardBench: Evaluating Reward Models for Language Modeling

Reference 20

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

Controlling Multimodal LLMs via Reward-guided Decoding Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 21

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This paper cites Sequential monte carlo steering of large lan- guage models using probabilistic programs.

Controlling Multimodal LLMs via Reward-guided Decoding Sequential monte carlo steering of large lan- guage models using probabilistic programs

Reference 22

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This paper cites Cascade reward sampling for efficient decoding-time align- ment.

Controlling Multimodal LLMs via Reward-guided Decoding Cascade reward sampling for efficient decoding-time align- ment

Reference 23

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Observation 58053729-0009-4ac4-bf4f-2ff1c2de74c5 · outbound

This paper cites Vlfeedback: A large-scale ai feedback dataset for large vision-language models alignment.

Controlling Multimodal LLMs via Reward-guided Decoding Vlfeedback: A large-scale ai feedback dataset for large vision-language models alignment

Reference 24

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Observation 67a17eee-0953-42c8-9389-3abfde6e5385 · outbound

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

Controlling Multimodal LLMs via Reward-guided Decoding Evaluating object hallucination in large vision-language models

Reference 25

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

Controlling Multimodal LLMs via Reward-guided Decoding Mitigating hallucination in large multi-modal models via robust instruction tuning

Reference 26

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This paper cites Improved baselines with visual instruction tuning.

Controlling Multimodal LLMs via Reward-guided Decoding Improved baselines with visual instruction tuning

Reference 27

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This paper cites Don’t throw away your value model! generating more preferable text with value-guided monte-carlo tree search decoding.

Controlling Multimodal LLMs via Reward-guided Decoding Don’t throw away your value model! generating more preferable text with value-guided monte-carlo tree search decoding

Reference 28

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This paper cites Investigating and mitigating object hallucinations in pretrained vision-language (clip) models.

Controlling Multimodal LLMs via Reward-guided Decoding Investigating and mitigating object hallucinations in pretrained vision-language (clip) models

Reference 29

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This paper cites SmolVLM: Redefining small and efficient multimodal models.

Controlling Multimodal LLMs via Reward-guided Decoding SmolVLM: Redefining small and efficient multimodal models

Reference 30

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This paper cites Scaling open-vocabulary object detection.

Controlling Multimodal LLMs via Reward-guided Decoding Scaling open-vocabulary object detection

Reference 31

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This paper cites Controlled decod- ing from language models.

Controlling Multimodal LLMs via Reward-guided Decoding Controlled decod- ing from language models

Reference 32

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This paper cites Training language models to follow instructions with human feedback.

Controlling Multimodal LLMs via Reward-guided Decoding Training language models to follow instructions with human feedback

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This paper cites Learning transferable visual models from natural language supervi- sion.

Controlling Multimodal LLMs via Reward-guided Decoding Learning transferable visual models from natural language supervi- sion

Reference 34

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

Controlling Multimodal LLMs via Reward-guided Decoding Direct preference optimization: Your language model is secretly a reward model

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This paper cites A critical look at to- kenwise reward-guided text generation.

Controlling Multimodal LLMs via Reward-guided Decoding A critical look at to- kenwise reward-guided text generation

Reference 36

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

Controlling Multimodal LLMs via Reward-guided Decoding Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 37

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This paper cites Object hallucination in image cap- tioning.

Controlling Multimodal LLMs via Reward-guided Decoding Object hallucination in image cap- tioning

Reference 38

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Observation 38a8d27a-7220-42b2-932b-d2eb8fd01b5d · outbound

This paper cites Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment.

Controlling Multimodal LLMs via Reward-guided Decoding Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

Reference 39

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This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Controlling Multimodal LLMs via Reward-guided Decoding Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 40

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This paper cites PaliGemma 2: A Family of Versatile VLMs for Transfer.

Controlling Multimodal LLMs via Reward-guided Decoding PaliGemma 2: A Family of Versatile VLMs for Transfer

Reference 41

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

Controlling Multimodal LLMs via Reward-guided Decoding Aligning Large Multimodal Models with Factually Augmented RLHF

Reference 42

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Observation e80dd9b1-5646-4091-99f2-c0e139688bce · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Controlling Multimodal LLMs via Reward-guided Decoding Gemini: A Family of Highly Capable Multimodal Models

Reference 43

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This paper cites Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training.

Controlling Multimodal LLMs via Reward-guided Decoding Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training

Reference 44

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Observation a82b1c07-6d07-4831-ad0d-e46326757cc6 · outbound

This paper cites mDPO: Conditional Preference Optimization for Multimodal Large Language Models.

Controlling Multimodal LLMs via Reward-guided Decoding mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Reference 45

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Observation f5bee5c2-4487-4ccc-a420-ecd5e506f822 · outbound

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

Controlling Multimodal LLMs via Reward-guided Decoding AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation

Reference 46

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This paper cites Fudge: Controlled text genera- tion with future discriminators.

Controlling Multimodal LLMs via Reward-guided Decoding Fudge: Controlled text genera- tion with future discriminators

Reference 47

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Observation 1dadae72-8f9d-4a43-98a3-363fc9f4b752 · outbound

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

Controlling Multimodal LLMs via Reward-guided Decoding Woodpecker: Hallucination Correction for Multimodal Large Language Models

Reference 48

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Observation 84cbdba1-2f16-4ba6-b72f-98472b1082cb · outbound

This paper cites Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional hu- man feedback.

Controlling Multimodal LLMs via Reward-guided Decoding Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional hu- man feedback

Reference 49

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Observation ae856375-f6d8-44ce-8fe2-1619c0597096 · outbound

This paper cites Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness.

Controlling Multimodal LLMs via Reward-guided Decoding Rlaif-v: Aligning mllms through open-source ai feedback for super gpt-4v trustworthiness

Reference 50

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Observation 2b6617e6-dee4-468c-9fce-02b3789331cf · outbound

This paper cites Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective.

Controlling Multimodal LLMs via Reward-guided Decoding Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective

Reference 51

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This paper cites Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models.

Controlling Multimodal LLMs via Reward-guided Decoding Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models

Reference 52

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Observation 2fbbeb2a-fc82-43fe-b537-59b5b329929d · outbound

This paper cites Multimodal chain-of-thought reasoning in language models.

Controlling Multimodal LLMs via Reward-guided Decoding Multimodal chain-of-thought reasoning in language models

Reference 53

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Observation 9a6e7ddc-b304-4ad9-ba1e-0ff10c7e9035 · outbound

This paper cites Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance.

Controlling Multimodal LLMs via Reward-guided Decoding Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance

Reference 54

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Observation 641aad0b-c7e6-4738-bd4b-3560e69da43c · outbound

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

Controlling Multimodal LLMs via Reward-guided Decoding Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization

Reference 55

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Observation fa86e7c7-b053-4781-b60f-33adc81f7be3 · outbound

This paper cites Aligning modalities in vision large lan- guage models via preference fine-tuning.

Controlling Multimodal LLMs via Reward-guided Decoding Aligning modalities in vision large lan- guage models via preference fine-tuning

Reference 56

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Observation 5369d77b-72fb-4a3e-b16e-483daf65cdb6 · outbound

This paper cites Analyzing and mitigating object hallucination in large vision-language models.

Controlling Multimodal LLMs via Reward-guided Decoding Analyzing and mitigating object hallucination in large vision-language models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T19:52:50.961526Z

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Observation d23d70d0-a0c0-4148-8f1a-b3ad230b8e11 · outbound

This paper cites Calibrated Self-Rewarding Vision Language Models.

Controlling Multimodal LLMs via Reward-guided Decoding Calibrated Self-Rewarding Vision Language Models

Reference 58

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Observation 9c058615-4b94-457e-9c0b-7ae63f453588 · outbound

This paper cites Describe this image in detail.

Controlling Multimodal LLMs via Reward-guided Decoding Describe this image in detail

Reference 59

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T19:52:50.950962Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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