Paper Citation Record · LEDGER

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 12 inbound Pith citation observations for arXiv:2505.13426.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.13426 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:21:40.641794Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:21:12.064728Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

48 of 48 outbound references displayed

verified exact0
verified fuzzy13
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 632309ab-ceac-4221-83a5-87b08d498007 · outbound

This paper cites an unresolved cited work.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:21:41.340425Z

Source-reported events for the cited work

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Observation 24134682-60cd-461a-b811-80fa3ec1cec0 · outbound

This paper cites Qwen2.5-VL Technical Report.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Qwen2.5-VL Technical Report

Reference 2

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Observation 279d5243-cd23-4ba9-affe-6dd70ebc6c66 · outbound

This paper cites Deep blue.Artificial intelligence, 134(1-2):57–83, 2002.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Deep blue.Artificial intelligence, 134(1-2):57–83, 2002

Reference 3

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Observation 3fec197c-b599-4d52-8449-4d313ca7646f · outbound

This paper cites R1-v: Reinforcing super generalization ability in vision-language models with less than $3.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning R1-v: Reinforcing super generalization ability in vision-language models with less than $3

Reference 4

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Observation f83a8618-2e4b-4f9d-80ce-38bf088742d0 · outbound

This paper cites Next token prediction towards multimodal intelligence: A comprehensive survey, 2024.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Next token prediction towards multimodal intelligence: A comprehensive survey, 2024

Reference 5

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Observation 369135b3-c9ed-42dd-8a5e-39620b1cf007 · outbound

This paper cites Pca-bench: Evaluating multimodal large language models in perception-cognition-action chain, 2024.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Pca-bench: Evaluating multimodal large language models in perception-cognition-action chain, 2024

Reference 6

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Observation 1a6d9cb4-f391-46e4-aaeb-b0cca2fb1a16 · outbound

This paper cites Can VLMs Play Action Role-Playing Games? Take Black Myth Wukong as a Study Case.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Can VLMs Play Action Role-Playing Games? Take Black Myth Wukong as a Study Case

Reference 7

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Unresolved cited work

Reference 8

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Observation 67ab1055-ff75-4e04-977b-dcc0f3ffde41 · outbound

This paper cites GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

Reference 9

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Observation 1c4d1528-a51b-4f91-89a1-44542b429875 · outbound

This paper cites FlowReasoner: Reinforcing Query-Level Meta-Agents.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning FlowReasoner: Reinforcing Query-Level Meta-Agents

Reference 10

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Observation c351b643-633e-4b3b-8341-6134f6b7f1a8 · outbound

This paper cites VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making

Reference 11

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This paper cites Mmevalpro: Calibrating multimodal benchmarks towards trustworthy and efficient evaluation, 2025.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Mmevalpro: Calibrating multimodal benchmarks towards trustworthy and efficient evaluation, 2025

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:41.249782Z

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Observation 8f7321ce-28c0-413c-8135-2ff7b7a81489 · outbound

This paper cites Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

Reference 13

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This paper cites Learning multiple layers of features from tiny images.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Learning multiple layers of features from tiny images

Reference 14

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Observation 5854a8e8-56d2-44d4-8836-6ee06d98abf4 · outbound

This paper cites JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning JARVIS-VLA: Post-Training Large-Scale Vision Language Models to Play Visual Games with Keyboards and Mouse

Reference 15

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This paper cites MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning

Reference 16

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This paper cites Playing atari with deep reinforcement learning, 2013.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Playing atari with deep reinforcement learning, 2013

Reference 17

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Unresolved cited work

Reference 18

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Gpt-4v(ision) system card

Reference 19

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning hello-gpt-4o, 2024

Reference 20

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This paper cites BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Reference 21

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This paper cites Generative agents: Interactive simulacra of human behavior.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Generative agents: Interactive simulacra of human behavior

Reference 22

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Llms are greedy agents: Effects of rl fine-tuning on decision-making abilities, 2025

Reference 23

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 25

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning HybridFlow: A Flexible and Efficient RLHF Framework

Reference 26

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Maddison, Arthur Guez, L

Reference 27

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Mastering the game of go with deep neural networks and tree search.nature, 529(7587):484–489, 2016

Reference 28

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Cradle: Empowering Foundation Agents Towards General Computer Control

Reference 29

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Reference 30

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 31

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This paper cites Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 34

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Are large vision language models good game players?, 2025

Reference 35

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Are Large Vision Language Models Good Game Players?

Reference 36

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

Reference 37

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Waytowich, Devin White, MD Sunbeam, and Vinicius G

Reference 38

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning SmartPlay: A Benchmark for LLMs as Intelligent Agents

Reference 39

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 40

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Fine-tuning large vision-language models as decision-making agents via reinforcement learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:41.059318Z

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Easyr1: An efficient, scalable, multi-modality rl training framework

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:41.044872Z

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Llamafactory: Unified efficient fine-tuning of 100+ language models

Reference 43

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning aha moment

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:41.019518Z

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This paper cites Output Format Description First describe the board in <perception></perception>.

G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Output Format Description First describe the board in <perception></perception>

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:40.986156Z

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning (row1,col1) (row2,col2)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:40.970513Z

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Reference 49

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G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning Output Format Description First describe the board in <perception></perception>

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:21:40.955184Z

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Pith citing papers

Observation b6a2d442-edd3-43fe-b415-f99bd643a4e8 · inbound

Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models cites this paper.

Perception, Reason, Think, and Plan: A Survey on Large Multimodal Reasoning Models G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 49

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UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning cites this paper.

UAV-VL-R1: Generalizing Vision-Language Models via Supervised Fine-Tuning and Multi-Stage GRPO for UAV Visual Reasoning G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 36

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Observation be41000b-fa61-4774-89c7-5dbac2f99424 · inbound

Explain Before You Answer: A Survey on Compositional Visual Reasoning cites this paper.

Explain Before You Answer: A Survey on Compositional Visual Reasoning G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 159

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Observation 3c920247-9f73-4a74-b31d-de700b27b369 · inbound

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey cites this paper.

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 239

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:21:48.400095Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bd874107-7871-46f7-a6e0-00b32ff204d2 · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:02:25.071498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 956c631a-0ef5-4bf5-8f4f-2c7720d85ba0 · inbound

Gym-V: A Unified Vision Environment System for Agentic Vision Research cites this paper.

Gym-V: A Unified Vision Environment System for Agentic Vision Research G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T10:05:26.028670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 58c72bb5-6fd8-4fc1-87b9-46224ea591f5 · inbound

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning cites this paper.

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 74

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:16:08.788471Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c91ea36f-f7fd-4ea2-bbdc-c0320bde6f33 · inbound

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:26:19.110488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9da3c042-ac19-4f9d-b2fa-fe0fe1f26042 · inbound

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.994016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 69838789-0ba6-4fc1-aaae-0b6f5097178e · inbound

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning cites this paper.

RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T04:13:05.251364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3f6d3dd1-ceba-448d-8587-55ad11df59ff · inbound

Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning cites this paper.

Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:34:41.170593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e6a6b4c-2bcb-419c-8434-262108d00612 · inbound

CAST: Game Solvers as Turn-Level Teachers for LLM Agents cites this paper.

CAST: Game Solvers as Turn-Level Teachers for LLM Agents G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T02:58:29.293555Z

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Unavailable: canonical work link unavailable.