Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T13:37:00.301396Z
Paper Citation Record · LEDGER
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding
As of 14 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2509.00484.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T09:12:18.190583Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T03:45:58.423192Z
Reference resolution
57 of 57 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c4f6cb42-b89a-43b4-b278-7082640825dd · outbound
This paper cites Phi-3 technical report: A highly capable lan- guage model locally on your phone, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Phi-3 technical report: A highly capable lan- guage model locally on your phone, 2024
Reference 1
- Resolution
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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 e94cf7cb-74d9-45c1-87d5-acfb09d28294 · outbound
This paper cites Claude-3.7-sonnet.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Claude-3.7-sonnet
Reference 2
- Resolution
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Source-reported events for the cited work
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Observation c7ca1495-48b3-4dfe-b77c-81d475bd9ab4 · outbound
This paper cites Qwen2.5-vl technical report, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Qwen2.5-vl technical report, 2025
Reference 3
- Resolution
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Source-reported events for the cited work
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Observation 5915fc07-4818-4255-af09-a49ab2e26d7f · outbound
This paper cites Mllm-as-a-judge: Assessing multimodal llm-as-a-judge with vision-language benchmark.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mllm-as-a-judge: Assessing multimodal llm-as-a-judge with vision-language benchmark
Reference 4
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Source-reported events for the cited work
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Observation ff2e0499-c770-4847-af77-bff4b4272419 · outbound
This paper cites From captions to rewards (carevl): Leveraging large language model experts for en- hanced reward modeling in large vision-language models,.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding From captions to rewards (carevl): Leveraging large language model experts for en- hanced reward modeling in large vision-language models,
Reference 5
- Resolution
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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 458599d7-441e-49c8-9027-5eaadcfd82f2 · outbound
This paper cites Mmbench-video: A long-form multi-shot benchmark for holistic video under- standing.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mmbench-video: A long-form multi-shot benchmark for holistic video under- standing
Reference 6
- Resolution
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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 35785f6a-bbaa-4cc6-a1bf-9492b07eec96 · outbound
This paper cites Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in 9 video analysis.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in 9 video analysis
Reference 7
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Source-reported events for the cited work
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Observation 815a0060-ae0c-4654-93e8-1f18529321d6 · outbound
This paper cites Gemini 2.5 flash, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Gemini 2.5 flash, 2025
Reference 8
- Resolution
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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 9a0723b1-0a7a-4fcf-90d8-62cd592b9499 · outbound
This paper cites Gemini 2.5 pro, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Gemini 2.5 pro, 2025
Reference 9
- Resolution
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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 f147d787-2081-4768-bfc0-2a5f8439d302 · outbound
This paper cites The llama 3 herd of models, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding The llama 3 herd of models, 2024
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 46713bf0-5af4-40a9-a194-5bd26af5e271 · outbound
This paper cites Mmworld: Towards multi- discipline multi-faceted world model evaluation in videos,.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mmworld: Towards multi- discipline multi-faceted world model evaluation in videos,
Reference 11
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Source-reported events for the cited work
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Observation 6b80a8a4-4a30-46fc-b486-6b10d4d7e303 · outbound
This paper cites Video-mmmu: Evaluating knowledge acquisition from multi-discipline pro- fessional videos, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Video-mmmu: Evaluating knowledge acquisition from multi-discipline pro- fessional videos, 2025
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 0a1592f8-5a7b-4c9e-9f7f-3d6f256a387f · outbound
This paper cites Flex-judge: Think once, judge anywhere, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Flex-judge: Think once, judge anywhere, 2025
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 464475ca-6ce4-4df4-ba44-0c3398e8f56f · outbound
This paper cites Smith, and Hannaneh Hajishirzi.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Smith, and Hannaneh Hajishirzi
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation 7c133eb4-541b-4797-93b6-8be641df03cd · outbound
This paper cites Vhelm: A holistic evaluation of vision language models.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Vhelm: A holistic evaluation of vision language models
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 98ca7ad7-047c-4738-b074-a748151bdfd1 · outbound
This paper cites Llava-onevision: Easy visual task transfer, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Llava-onevision: Easy visual task transfer, 2024
Reference 16
- Resolution
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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 5e0806f5-8935-4239-b0a0-74bb98530c8e · outbound
This paper cites Aria: An open multimodal native mixture-of-experts model, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Aria: An open multimodal native mixture-of-experts model, 2025
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 27ba31a1-08a6-4816-86bb-1ece49a1a1ed · outbound
This paper cites Mvbench: A comprehensive multi-modal video understand- ing benchmark.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mvbench: A comprehensive multi-modal video understand- ing benchmark
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation e2e7aee3-320c-4b6e-acb2-dca4c9ec5f35 · outbound
This paper cites Vl-rewardbench: A challenging benchmark for vision-language generative reward models.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Vl-rewardbench: A challenging benchmark for vision-language generative reward models
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation 0f641bec-ef5d-4e35-b963-eb12b8510092 · outbound
This paper cites Holistic evaluation of language models, 2023.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Holistic evaluation of language models, 2023
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation b3d25fb7-0624-470d-aa62-27254b37cfd4 · outbound
This paper cites Video- safetybench: A benchmark for safety evaluation of video lvlms, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Video- safetybench: A benchmark for safety evaluation of video lvlms, 2025
Reference 21
- Resolution
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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 98d78dd1-154c-4bec-a7e5-98f16ac30b4b · outbound
This paper cites Rm-bench: Benchmarking reward models of lan- guage models with subtlety and style, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Rm-bench: Benchmarking reward models of lan- guage models with subtlety and style, 2024
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation 84c079c1-9c8e-45f8-88ab-dc640f1875a1 · outbound
This paper cites Videogpt+: Integrating image and video encoders for enhanced video understanding, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Videogpt+: Integrating image and video encoders for enhanced video understanding, 2024
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation 0e34abe0-122c-4574-b29c-40ec34abdf74 · outbound
This paper cites Smith, Hannaneh Hajishirzi, and Nathan Lambert.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Smith, Hannaneh Hajishirzi, and Nathan Lambert
Reference 24
- Resolution
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Observation c6042620-dd95-4a61-8c82-05f9793f1ca9 · outbound
This paper cites Hello gpt-4o.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Hello gpt-4o
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation 6bf86790-0c14-48f3-b2db-25ecedcd3c29 · outbound
This paper cites Gpt-4o mini: advancing cost-efficient intel- ligence.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Gpt-4o mini: advancing cost-efficient intel- ligence
Reference 26
- Resolution
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Observation 6f9cb145-28d3-4e6d-85a5-1bcc0d88753b · outbound
This paper cites Training language models to follow instructions with human feedback.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Training language models to follow instructions with human feedback
Reference 27
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Source-reported events for the cited work
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This paper cites Vibe-eval: A hard eval- uation suite for measuring progress of multimodal language models, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Vibe-eval: A hard eval- uation suite for measuring progress of multimodal language models, 2024
Reference 28
- Resolution
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Observation e5b36f29-b815-469a-890e-d4050996fa38 · outbound
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VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Unresolved cited work
Reference 29
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Observation d3f52317-6b7d-44eb-b8b5-3ed8ba05952a · outbound
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VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Unresolved cited work
Reference 30
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Observation 83617b98-f60b-49d7-b30a-d613979447b1 · outbound
This paper cites Direct preference optimization: Your language model is secretly a reward model.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Direct preference optimization: Your language model is secretly a reward model
Reference 31
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Observation 36f21a8a-9fe1-45cb-9dec-68ae68bd6cbc · outbound
This paper cites Scaling llm test-time compute optimally can be more effec- tive than scaling model parameters, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Scaling llm test-time compute optimally can be more effec- tive than scaling model parameters, 2024
Reference 32
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Observation 7126e7c9-d619-4a1a-9a8f-c9fb22f570b3 · outbound
This paper cites Aligning large mul- timodal models with factually augmented rlhf.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Aligning large mul- timodal models with factually augmented rlhf
Reference 33
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Observation 67911071-2d2e-487b-9976-26b7bc9df062 · outbound
This paper cites Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution, 2024
Reference 34
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Observation 8977e9be-1c7b-417f-9469-318b75a1d9ea · outbound
This paper cites Visualprm: An effective process reward model for multimodal reasoning, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Visualprm: An effective process reward model for multimodal reasoning, 2025
Reference 35
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Observation d15c199f-cb9d-4b60-b885-5527adb7502c · outbound
This paper cites Skywork-vl re- ward: An effective reward model for multimodal understand- ing and reasoning, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Skywork-vl re- ward: An effective reward model for multimodal understand- ing and reasoning, 2025
Reference 36
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Source-reported events for the cited work
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Observation fadbf3fa-85e4-4c10-8c66-5827de2b86d9 · outbound
This paper cites Videohallucer: Evaluating intrinsic and extrinsic hallucinations in large video-language models,.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Videohallucer: Evaluating intrinsic and extrinsic hallucinations in large video-language models,
Reference 37
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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 764544fd-5763-42c9-9c73-f8c0d29e2473 · outbound
This paper cites Internvideo2.5: Empowering video mllms with long and rich context modeling, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Internvideo2.5: Empowering video mllms with long and rich context modeling, 2025
Reference 38
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Observation 261d4f87-715b-453d-ba4e-37e0ce250b29 · outbound
This paper cites Unified multimodal chain-of-thought reward model through reinforcement fine- tuning, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Unified multimodal chain-of-thought reward model through reinforcement fine- tuning, 2025
Reference 39
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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 be6f6f8b-4e21-4054-91a3-b5eb23fa6389 · outbound
This paper cites Unified reward model for multimodal understanding and generation, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Unified reward model for multimodal understanding and generation, 2025
Reference 40
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Source-reported events for the cited work
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Observation b1340748-d163-4569-8cf2-7f7e62c61eba · outbound
This paper cites reword- bench: Benchmarking and improving the robustness of re- ward models with transformed inputs, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding reword- bench: Benchmarking and improving the robustness of re- ward models with transformed inputs, 2025
Reference 41
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Source-reported events for the cited work
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Observation a3b188c3-f4bb-49fc-8167-943cb1724c22 · outbound
This paper cites Llava- critic: Learning to evaluate multimodal models.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Llava- critic: Learning to evaluate multimodal models
Reference 42
- Resolution
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- raw_fallback, observed 2026-08-05T13:37:03.469920Z
Source-reported events for the cited work
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Observation 4d72ee90-a197-4dfe-9da4-becf901f93c4 · outbound
This paper cites Thinking in space: How mul- timodal large language models see, remember, and recall spaces.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Thinking in space: How mul- timodal large language models see, remember, and recall spaces
Reference 43
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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 5475c51c-cf2d-499c-9648-ceeaf6c12bd3 · outbound
This paper cites Minicpm-v: A gpt-4v level mllm on your phone, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Minicpm-v: A gpt-4v level mllm on your phone, 2024
Reference 44
- Resolution
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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 0acb75a3-7223-4a1d-bdf4-067cc6752f96 · outbound
This paper cites Multimodal rewardbench: Holistic evalua- tion of reward models for vision language models, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Multimodal rewardbench: Holistic evalua- tion of reward models for vision language models, 2025
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:03.057857Z
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 601e0b30-c8e5-4ebb-bdbf-6d04c41722a4 · outbound
This paper cites mplug- owl3: Towards long image-sequence understanding in multi- modal large language models, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding mplug- owl3: Towards long image-sequence understanding in multi- modal large language models, 2024
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:03.000377Z
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 fff5b201-f05a-416a-a0cd-f7f81c4c8b98 · outbound
This paper cites Internlm-xcomposer2.5-reward: A simple yet effec- tive multi-modal reward model, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Internlm-xcomposer2.5-reward: A simple yet effec- tive multi-modal reward model, 2025
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.910320Z
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 6e28fccb-51f0-498a-b7ec-7ccd3f04d21f · outbound
This paper cites Video instruction tuning with synthetic data, 2024.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Video instruction tuning with synthetic data, 2024
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.800246Z
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 d100f179-064a-4808-bcab-4546d9d6392c · outbound
This paper cites R1-reward: Train- ing multimodal reward model through stable reinforcement learning, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding R1-reward: Train- ing multimodal reward model through stable reinforcement learning, 2025
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.665649Z
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 4d94a7f3-44f2-43b8-ab82-8e8bb70059ee · outbound
This paper cites Mm-rlhf: The next step forward in mul- timodal llm alignment, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mm-rlhf: The next step forward in mul- timodal llm alignment, 2025
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.524756Z
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 de729c4a-47e1-433b-b0ad-a1ff6cee003c · outbound
This paper cites Mmvu: Measuring expert-level multi- discipline video understanding.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Mmvu: Measuring expert-level multi- discipline video understanding
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.304836Z
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 9d797eb8-6c3d-49e7-9467-33eb1fe8a745 · outbound
This paper cites Generative rlhf-v: Learning principles from multi- modal human preference, 2025.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Generative rlhf-v: Learning principles from multi- modal human preference, 2025
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:02.086954Z
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 d19521cb-91bd-47fc-89d7-b44065f3fdc1 · outbound
This paper cites Input Frames.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Input Frames
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:01.739502Z
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 e337c64c-0f18-4685-95e0-eaae06bf12b8 · outbound
This paper cites When placed in water, there is a violent reaction.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding When placed in water, there is a violent reaction
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:01.387620Z
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 d92b6859-9d44-4444-94ba-8e7f4ae697f7 · outbound
This paper cites - Silver (\\(Ag\\)):\n - Silver is a very unreactive metal and does not react with water under normal conditions.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding - Silver (\\(Ag\\)):\n - Silver is a very unreactive metal and does not react with water under normal conditions
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:00.984353Z
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 d9ae3455-a6ed-4695-9bc7-0d6a12cc0ed0 · outbound
This paper cites - Iron (Fe) reacts with steam (not cold water easily in a simple setup like this video) and silver (Ag) is a noble - metal that does not react with water under normal conditions.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding - Iron (Fe) reacts with steam (not cold water easily in a simple setup like this video) and silver (Ag) is a noble - metal that does not react with water under normal conditions
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:00.820297Z
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 ec53e020-5ad6-4f07-a864-0be5f63ac85e · outbound
This paper cites Also, when phenolphthalein is added (the pink - colour change indicates a basic solution), which is consistent with the reaction of alkali metals with water.
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding Also, when phenolphthalein is added (the pink - colour change indicates a basic solution), which is consistent with the reaction of alkali metals with water
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T13:37:00.514904Z
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.
Pith citing papers
Observation b67f1f99-ad3a-43e0-975b-10d945926009 · inbound
Social Caption: Evaluating Social Understanding in Multimodal Models cites this paper.
Social Caption: Evaluating Social Understanding in Multimodal Models VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T09:12:18.190583Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85a1b947-7ccc-4cf7-9702-476b1f28f9cd · inbound
Video Understanding Reward Modeling: A Robust Benchmark and Performant Reward Models cites this paper.
Video Understanding Reward Modeling: A Robust Benchmark and Performant Reward Models VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T03:45:58.426040Z
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.