Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-20T19:37:09.244578Z
Paper Citation Record · LEDGER
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation
As of 13 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2605.16079.
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 2026-06-27T19:56:09.820812Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-02T21:07:23.929020Z
Reference resolution
53 of 53 outbound references displayed
- verified exact36
- verified fuzzy4
- unresolved11
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation d9fcace9-5fcb-4838-934a-6dced20c9da7 · outbound
This paper cites Qwen3-VL Technical Report.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Qwen3-VL Technical Report
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.049715Z
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 42db01d1-b377-4212-af33-2e67655d431c · outbound
This paper cites SAM 3: Segment Anything with Concepts.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation SAM 3: Segment Anything with Concepts
Reference 2
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.043858Z
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 dab4ccb6-f27a-449f-87aa-0db2c25d18af · outbound
This paper cites MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.036772Z
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 4080fef3-fcd3-4b96-ac0b-02bb11bc753a · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.822686Z
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 50e8a264-80a0-4048-b984-0c90ab22ea70 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.825932Z
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 3e0da6a4-9c59-4c5e-b96e-034b676df229 · outbound
This paper cites Scaling rl to long videos.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Scaling rl to long videos
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.030979Z
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 133add86-adfe-404f-af4b-8518ce6262c6 · outbound
This paper cites Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.023943Z
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 fb1dcc8d-2260-4d4b-9ddf-679b459cb2cc · outbound
This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.160444Z
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 bef46cfa-f24a-48c4-a295-3b866e1e7c1a · outbound
This paper cites Deitke, C.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Deitke, C
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T19:38:56.841511Z
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 954bc2f1-15de-4634-b0a3-6da3899382b3 · outbound
This paper cites OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.101572Z
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 7240c959-ae34-4a33-9109-438ac891ddf7 · outbound
This paper cites Video-R1: Reinforcing Video Reasoning in MLLMs.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Video-R1: Reinforcing Video Reasoning in MLLMs
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.077048Z
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 956c3ab7-0b4a-413b-9f99-ddc874cae06d · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 12
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.835091Z
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 b84f70d0-e1dd-49bd-8aba-a00038a72761 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.095685Z
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 6667f4f9-fa11-4a7c-b2f3-5486177912bb · outbound
This paper cites DeepEyesV2: Toward Agentic Multimodal Model.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation DeepEyesV2: Toward Agentic Multimodal Model
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.194938Z
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 724b1f36-3924-465b-8c8d-8c3e27935671 · outbound
This paper cites GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.204131Z
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 033e7999-356e-44da-9eb9-f5ec98b5dee9 · outbound
This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
Reference 16
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.115052Z
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 36c84333-657a-4de6-9f99-c8c2a3e817e1 · outbound
This paper cites GPT-4o System Card.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation GPT-4o System Card
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.137975Z
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 4c1b3d22-9c60-4068-b415-1ae35841e91f · outbound
This paper cites OpenAI o1 System Card.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation OpenAI o1 System Card
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.214119Z
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 ece79d5d-84b9-4c61-b10d-db5e7d654379 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 19
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.850465Z
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 fc5a4d8f-d159-4562-a050-5776a51eb635 · outbound
This paper cites VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.110678Z
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 aef889cd-b2f6-4c31-af80-19783c661372 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.862369Z
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 93ae7cbe-87a3-460f-8006-6d501706aac9 · outbound
This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning
Reference 22
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.081767Z
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 d8b2736d-495a-4c49-935c-a4edf52757d8 · outbound
This paper cites VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.199595Z
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 0aa0c5cf-ce6b-4c0d-88bd-5e6a763ca483 · outbound
This paper cites Rafailov, A.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Rafailov, A
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T19:38:56.831681Z
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 1cdf0074-dc20-4286-9008-cd13f20818b3 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 25
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.865253Z
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 4a691e5d-624a-4824-a9d7-58de885ad914 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.119699Z
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 390cf3a5-ba9a-4ac8-bb4e-a3208d65369c · outbound
This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.071277Z
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 75c0588e-3c48-4560-ab2e-ba78f482dc68 · outbound
This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation HybridFlow: A Flexible and Efficient RLHF Framework
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.060197Z
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 8aa51fc0-5987-48ad-87c3-a7f570d73e63 · outbound
This paper cites Kimi K2.5: Visual Agentic Intelligence.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Kimi K2.5: Visual Agentic Intelligence
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.054884Z
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 e8e268aa-037c-43b6-a53f-1d66bda2faf7 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 30
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.828669Z
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 109b9733-637a-465b-a045-29273b932d53 · outbound
This paper cites Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.129604Z
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 2929d93c-b76b-4d33-b6d6-7102987ee55a · outbound
This paper cites AdaTooler-V: Adaptive Tool-Use for Images and Videos.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation AdaTooler-V: Adaptive Tool-Use for Images and Videos
Reference 32
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.147802Z
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 5556ed8a-a58e-4756-b793-846d66f0c2cb · outbound
This paper cites Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.065492Z
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 c2dbb5f8-e64c-4eeb-8cd1-be369d07c850 · outbound
This paper cites Videorft: Incentivizing video reasoning capability in mllms via reinforced fine-tuning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Videorft: Incentivizing video reasoning capability in mllms via reinforced fine-tuning
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.186027Z
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 58b44ea4-88a4-4ac5-a044-eb752caec526 · outbound
This paper cites Video-thinker: Sparking” thinking with videos” via reinforcement learning.arXiv preprint arXiv:2510.23473.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Video-thinker: Sparking” thinking with videos” via reinforcement learning.arXiv preprint arXiv:2510.23473
Reference 35
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-20T19:38:56.086589Z
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 c8fd74a3-a013-429c-8099-de10dffb0c1a · outbound
This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Reference 36
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.175051Z
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 87698de1-9b46-4c55-aa86-242a60097b5f · outbound
This paper cites Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Time-R1: Post-Training Large Vision Language Model for Temporal Video Grounding
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.133728Z
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 9614726e-eb0b-4fda-bd34-84be4af8c00c · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 38
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.859353Z
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 bcbe7025-38af-495a-a854-55d0d44d8e57 · outbound
This paper cites Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing
Reference 39
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.190292Z
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 b6c88fac-f9bf-4ae4-a27f-f742260f2aa3 · outbound
This paper cites 2509.22647 , archivePrefix=.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation 2509.22647 , archivePrefix=
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.179679Z
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 da80de83-4719-4fac-b2ca-1930ce3bec7b · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 41
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.847454Z
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 0f5c71be-3f89-4238-9bb5-5e86c8acfa21 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 42
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.853507Z
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 2d7aa7b6-1fb2-4c32-aab1-d57edb0175ce · outbound
This paper cites LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T02:03:52.219011Z
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 048032d9-4f39-43d3-b943-7d2ca08caca8 · outbound
This paper cites Perception-R1: Pioneering Perception Policy with Reinforcement Learning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Perception-R1: Pioneering Perception Policy with Reinforcement Learning
Reference 44
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.151767Z
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 ff4f1e5b-159b-451e-af00-bc5c58d53205 · outbound
This paper cites arXiv preprint arXiv:2510.01304 , year=.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation arXiv preprint arXiv:2510.01304 , year=
Reference 45
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.106220Z
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 76a0227e-420e-4729-9094-3c4b979242f9 · outbound
This paper cites an unresolved cited work.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Unresolved cited work
Reference 46
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T19:38:56.844594Z
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 dc24a0bc-2ecd-4ac9-a3cb-69a18fe83560 · outbound
This paper cites Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.091241Z
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 448d1f33-452d-4cf6-a735-0979ea1fcaf1 · outbound
This paper cites LLaVA-Video: Video Instruction Tuning With Synthetic Data.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation LLaVA-Video: Video Instruction Tuning With Synthetic Data
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T19:38:56.164285Z
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 c30a972a-050c-4254-b6a1-64422ca970f8 · outbound
This paper cites PyVision: Agentic Vision with Dynamic Tooling.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation PyVision: Agentic Vision with Dynamic Tooling
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.208850Z
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 2559cda7-ba7b-4b36-aee5-c2a2bd123b0d · outbound
This paper cites arXiv preprint arXiv:2503.17736 , year=.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation arXiv preprint arXiv:2503.17736 , year=
Reference 50
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T19:38:56.123950Z
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 6be654b1-6c48-4f26-8947-ee1dd3361bf8 · outbound
This paper cites Zheng, R.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Zheng, R
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T19:38:56.838197Z
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 3d3cbc2d-c047-4a1a-95b9-48328891898d · outbound
This paper cites DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
Reference 52
- Resolution
- malformed identifier
- local_arxiv, observed 2026-05-20T19:38:56.143658Z
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 c6949fc3-ed6d-4f23-930b-9d79029ca9a7 · outbound
This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.
VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T19:38:56.856749Z
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 ffb93de8-b762-45ef-97df-7016bb2954df · inbound
ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning cites this paper.
ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation
Reference 49
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T21:07:23.930391Z
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.