Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 44 inbound Pith citation observations for arXiv:2410.10818.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T19:06:46.147800Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T23:39:03.234328Z
Reference resolution
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External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation d5b6241f-c506-4dde-888a-5ebddaa3d455 · inbound
LLaVA-Video: Video Instruction Tuning With Synthetic Data cites this paper.
LLaVA-Video: Video Instruction Tuning With Synthetic Data TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 216
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Source-reported events for the cited work
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Observation 43e812f6-e863-40dd-8404-0d52591f11a0 · inbound
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No! cites this paper.
Can Multimodal LLMs do Visual Temporal Understanding and Reasoning? The answer is No! TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 4
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 12dd80fa-586b-45d7-9b83-9d90bb319641 · inbound
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding cites this paper.
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2019
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8728683d-c1dd-4cae-8913-cb685f76a829 · inbound
Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos cites this paper.
Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 3
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Source-reported events for the cited work
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Observation 333f9254-8eb1-45d4-bb86-ce42e95dab06 · inbound
HD-EPIC: A Highly-Detailed Egocentric Video Dataset cites this paper.
HD-EPIC: A Highly-Detailed Egocentric Video Dataset TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 8
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Source-reported events for the cited work
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Observation 4d7bc3d6-cca5-45e2-9860-084fb8cfc5d9 · inbound
Seed1.5-VL Technical Report cites this paper.
Seed1.5-VL Technical Report TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 12
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Source-reported events for the cited work
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Observation a6bc6d7e-b309-427f-aa28-fe817f779cc3 · inbound
RTime-QA: A Benchmark for Atomic Temporal Event Understanding in Large Multi-modal Models cites this paper.
RTime-QA: A Benchmark for Atomic Temporal Event Understanding in Large Multi-modal Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 8
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0068b704-aecd-46cc-a8c5-175b3944aa05 · inbound
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos cites this paper.
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 6
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 358b7850-828e-4e44-905f-3a296da34640 · inbound
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding cites this paper.
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 25
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Source-reported events for the cited work
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Observation 8c97c53f-09c4-49cc-a2be-eebe6f273efc · inbound
Fostering Video Reasoning via Next-Event Prediction cites this paper.
Fostering Video Reasoning via Next-Event Prediction TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 5
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 305f6973-6c73-49c7-a805-d19b14918540 · inbound
Deep Temporal Reasoning in Video Language Models: A Cross-Linguistic Evaluation of Action Duration and Completion through Perfect Times cites this paper.
Deep Temporal Reasoning in Video Language Models: A Cross-Linguistic Evaluation of Action Duration and Completion through Perfect Times TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 7
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a22fff6-cf20-4c15-89bc-b46ffd20a18e · inbound
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs cites this paper.
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 26
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Source-reported events for the cited work
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Observation b3e7157c-7b90-4817-989b-f5200612b389 · inbound
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning cites this paper.
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation e849daa4-bf69-475c-8e19-353675a6c28c · inbound
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models cites this paper.
ProactiveVideoQA: A Comprehensive Benchmark Evaluating Proactive Interactions in Video Large Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bbe643d1-35b2-4d2e-9e1a-e0b64b67fc02 · inbound
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? cites this paper.
GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2024
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f91d00e8-c6f6-491f-b1a5-7bdc970597a8 · inbound
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding cites this paper.
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ad492195-bd08-400d-a93c-a65b26054333 · inbound
CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos cites this paper.
CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2024
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15f9e0ce-387e-4e56-a998-6d9afa19ef88 · inbound
AdsQA: Towards Advertisement Video Understanding cites this paper.
AdsQA: Towards Advertisement Video Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 5
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation af06448f-fba1-4f4c-bcbd-94a2990c6610 · inbound
NeMo: Needle in a Montage for Video-Language Understanding cites this paper.
NeMo: Needle in a Montage for Video-Language Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 12
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6a8aaab0-b1f6-4fde-bec6-dbbb05df58b9 · inbound
LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling cites this paper.
LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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Source-reported events for the cited work
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Observation d12b6c69-c134-451c-910b-20cabb957091 · inbound
See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models cites this paper.
See, Hear, and Understand: Benchmarking Audiovisual Human Speech Understanding in Multimodal Large Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 3
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Source-reported events for the cited work
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Observation c84d5d53-66bb-46aa-8f21-429c8c812f36 · inbound
From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models cites this paper.
From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 6
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Unavailable: canonical work link unavailable.
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GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models cites this paper.
GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 13
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Source-reported events for the cited work
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Observation 6968e55c-af11-43d4-910d-b458eb5ba282 · inbound
POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs cites this paper.
POINTS-Long: Adaptive Dual-Mode Visual Reasoning in MLLMs TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 6
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Source-reported events for the cited work
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Observation 4d934730-f911-4785-8ed2-1e50700138de · 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 TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 6
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Source-reported events for the cited work
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Observation ebfb5115-25aa-42a9-8026-15d9f12fafa5 · inbound
VideoNet: A Large-Scale Dataset for Domain-Specific Action Recognition cites this paper.
VideoNet: A Large-Scale Dataset for Domain-Specific Action Recognition TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 21
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Source-reported events for the cited work
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Observation 83c7d2dd-2323-4155-b984-2468ff757f85 · inbound
TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models cites this paper.
TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 4
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Source-reported events for the cited work
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TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models cites this paper.
TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 4
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EvoGround: Self-Evolving Video Agents for Video Temporal Grounding cites this paper.
EvoGround: Self-Evolving Video Agents for Video Temporal Grounding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 17
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Source-reported events for the cited work
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Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding cites this paper.
Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 9
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Source-reported events for the cited work
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FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding cites this paper.
FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding cites this paper.
FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding cites this paper.
FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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Source-reported events for the cited work
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Observation e6080247-df3f-4d8b-944b-8736e0d26d6a · inbound
Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning cites this paper.
Learning Spatiotemporal Sensitivity in Video LLMs via Counterfactual Reinforcement Learning TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9aca9f84-e2ee-4eb9-a074-cb6fe0b21f5e · inbound
The TIME Machine: On The Power of Motion for Efficient Perception cites this paper.
The TIME Machine: On The Power of Motion for Efficient Perception TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
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The TIME Machine: On The Power of Motion for Efficient Perception cites this paper.
The TIME Machine: On The Power of Motion for Efficient Perception TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
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The TIME Machine: On The Power of Motion for Efficient Perception cites this paper.
The TIME Machine: On The Power of Motion for Efficient Perception TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 7
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IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streams cites this paper.
IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streams TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 5
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 2a848339-a283-4131-80eb-97e8b045757f · inbound
YoCausal: How Far is Video Generation from World Model? A Causality Perspective cites this paper.
YoCausal: How Far is Video Generation from World Model? A Causality Perspective TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 18
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f8049609-c217-4dd0-9b2a-88c25912653e · inbound
TeachObs: A Human-Validated Benchmark for Multimodal Teaching Observation and Model Evaluation cites this paper.
TeachObs: A Human-Validated Benchmark for Multimodal Teaching Observation and Model Evaluation TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 2
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 8ba65bc2-aaee-4236-9f7a-44708d0b76f4 · inbound
Benchmarking Visual State Tracking in Multimodal Video Understanding cites this paper.
Benchmarking Visual State Tracking in Multimodal Video Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 12
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation a21a8d1f-e3e0-4d18-957e-68a4b6df0fbb · inbound
MAOAM: Unified Object and Material Selection with Vision-Language Models cites this paper.
MAOAM: Unified Object and Material Selection with Vision-Language Models TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 142
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 4544884c-2f76-421b-ae9b-7d6aabf2f597 · inbound
APT: Atomic Physical Transitions for Causal Video-Language Understanding cites this paper.
APT: Atomic Physical Transitions for Causal Video-Language Understanding TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 5
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation fe62423f-d61e-4782-aac6-fa2f3577de9c · inbound
Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation cites this paper.
Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Reference 6
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.