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Paper Citation Record · LEDGER

SPyCE: Skill-Policy Co-evolution for Multimodal Agents

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.13854.

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

pith.paper-citation-record.v1
2607.13854 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:36:03.356888Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • unresolved36
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Outbound references

Observation 4b2925a7-5b1a-4941-9625-6dbccfd61d42 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022

Reference 1

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source=pdf_text observed=2026-08-02T03:36:00.008227Z digest=sha256:9f6b20df5be7bbd1525cb7098f5875e2fc3b6016ab8491311cc52cc82d6779f0

Observation 522348c1-bff6-443f-a74c-7ce4bd3cb50c · outbound

This paper cites Multimodal Chain-of-Thought Reasoning in Language Models.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Multimodal Chain-of-Thought Reasoning in Language Models

Reference 2

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source=pdf_text observed=2026-08-02T03:36:00.051835Z digest=sha256:328e6e421dccd482cdf6587d71c36ab37e2dd263313d13a4da964a529c40ffbd

Observation 14182f4d-e2a4-4255-8f9d-f482b6f5c3c0 · outbound

This paper cites Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models.Advances in Neural Information Processing Systems, 36: 5168–5191, 2023.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Ddcot: Duty-distinct chain-of-thought prompting for multimodal reasoning in language models.Advances in Neural Information Processing Systems, 36: 5168–5191, 2023

Reference 3

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source=pdf_text observed=2026-08-02T03:36:00.110003Z digest=sha256:e55dcdd56c5eec8d923c16540438b4e4b05cb9c43107bdcc65a02fdb08d4a60a

Observation 5f5e0752-8b3e-46f7-aa8c-2404351e2a89 · outbound

This paper cites an unresolved cited work.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-02T03:36:00.217669Z digest=sha256:532a6ad8e143d070a54e0c053aa4c1f79d80c781744674feb27e20d0edadbd82

Observation 0881aa9a-4249-46de-aed8-3c1b32aec5eb · outbound

This paper cites Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models

Reference 5

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source=pdf_text observed=2026-08-02T03:36:00.317683Z digest=sha256:fb51e4abc70f68cc59a11e4f21d7bd8c77133171aff6766e147c9bf9a529c15b

Observation fcd914d0-14ab-4d8c-b153-a3e5c238b593 · outbound

This paper cites MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action

Reference 6

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source=pdf_text observed=2026-08-02T03:36:00.424537Z digest=sha256:2986431f904d5305c2b7db7efff814bd4bff7a73ee6c4897f3d2740ca36e6711

Observation 1e9b79e7-9ccc-40df-9b4b-7dfe59155a83 · outbound

This paper cites ViperGPT: Visual Inference via Python Execution for Reasoning.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents ViperGPT: Visual Inference via Python Execution for Reasoning

Reference 7

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source=pdf_text observed=2026-08-02T03:36:00.547732Z digest=sha256:c892f644ca87a629bc4eecb1d95d14bf52255d21224be836f2b75f5c2198b542

Observation 507e273e-646d-4f4a-a988-cc322dcb61b8 · outbound

This paper cites Llava-plus: Learning to use tools for creating multimodal agents.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Llava-plus: Learning to use tools for creating multimodal agents

Reference 8

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source=pdf_text observed=2026-08-02T03:36:00.643911Z digest=sha256:7f316fb0adb3d7ea5577f20e3d9917fd92cd4c827bba303ddc048993e662fa06

Observation 47489981-3aee-4372-8f0e-7407fe3379c4 · outbound

This paper cites OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning

Reference 9

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source=pdf_text observed=2026-08-02T03:36:00.764120Z digest=sha256:4f73f297c68c1c191bdc0ce0f6735f8b8dbb4512164c08d6e5e27fd171c088cd

Observation 5c8f4c3e-7337-458a-94ad-2eec51b9f79f · outbound

This paper cites DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning

Reference 10

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source=pdf_text observed=2026-08-02T03:36:00.882675Z digest=sha256:e3742f100546c6e50e6a868592e90a9b2fe32b665983cfe08327d4ab595f7c05

Observation e715fc43-b212-409b-b912-8cff4e0a98a8 · outbound

This paper cites VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use.CoRR, abs/2505.19255, 2025.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents VTool-R1: VLMs Learn to Think with Images via Reinforcement Learning on Multimodal Tool Use.CoRR, abs/2505.19255, 2025

Reference 11

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source=pdf_text observed=2026-08-02T03:36:01.003869Z digest=sha256:1e47b81be2e4d252495227d15883d5840f7d591fd219fc59998a2fad2fa47eea

Observation b38dd7e2-2843-4b1c-a295-3ad9ec74f383 · outbound

This paper cites Thinking with programming vision: Towards a unified view for thinking with images.arXiv preprint arXiv:2512.03746, 2025.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Thinking with programming vision: Towards a unified view for thinking with images.arXiv preprint arXiv:2512.03746, 2025

Reference 12

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source=pdf_text observed=2026-08-02T03:36:01.088885Z digest=sha256:e2e42fa9326bb567c90a81cf16860c1260c1499d61084b7b912fc99b41517b84

Observation 7d640adc-2bd2-4322-835c-1f8505c65335 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 13

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source=pdf_text observed=2026-08-02T03:36:01.200616Z digest=sha256:c31e4d5150eaf822161ad60224474f828efefdedb39489f5ce06c7492013e738

Observation 49615960-ddee-45c5-8a08-5fe4f8df94e8 · outbound

This paper cites Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

Reference 14

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source=pdf_text observed=2026-08-02T03:36:01.314015Z digest=sha256:8fd6c9ac6202f2633125e60a59cafa4060015bd3f70a52d2f439f9e266d4cfb8

Observation 48dd30da-0baa-4045-a145-50a1e88729e8 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 15

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source=pdf_text observed=2026-08-02T03:36:01.426563Z digest=sha256:03eda5a566143a3bd81ce8dd3182815b47341286b04c60ac3fed705303bba1a5

Observation b63697d7-6d9a-450a-b44f-e81f3ccfe7e9 · outbound

This paper cites Expel: Llm agents are experiential learners.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Expel: Llm agents are experiential learners

Reference 16

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source=pdf_text observed=2026-08-02T03:36:01.549991Z digest=sha256:0f1239f5e6b078e8da25cbb39306355083ed4aef0f119d7cc354455fc92c9269

Observation 4165808b-2843-445b-91b9-d009252c6e25 · outbound

This paper cites Dynamic cheatsheet: Test-time learning with adaptive memory.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Dynamic cheatsheet: Test-time learning with adaptive memory

Reference 17

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Observation 6ee7bba5-b674-45b5-9482-41c296ee34c2 · outbound

This paper cites Memp: Exploring Agent Procedural Memory.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Memp: Exploring Agent Procedural Memory

Reference 18

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source=pdf_text observed=2026-08-02T03:36:01.717725Z digest=sha256:dd87ebeba7fae022d6dd3068c67a38b8710b88eb369089c4ac2e9c4e38e08b69

Observation 7608c57b-5ffa-4056-b88a-6bcfa38f8ee3 · outbound

This paper cites Agent kb: Leveraging cross-domain experience for agentic problem solving.arXiv preprint arXiv:2507.06229, 2025.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Agent kb: Leveraging cross-domain experience for agentic problem solving.arXiv preprint arXiv:2507.06229, 2025

Reference 19

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source=pdf_text observed=2026-08-02T03:36:01.728489Z digest=sha256:349b24b3a2313329d457a85d49c67f39aed8b90f896bbdbaf5461ae276625846

Observation 2387f6d3-f8be-4343-b8a1-a6495ac4d498 · outbound

This paper cites Instructblip: Towards general-purpose vision-language models with instruction tuning.Advances in neural information processing systems, 36:49250–49267, 2023.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Instructblip: Towards general-purpose vision-language models with instruction tuning.Advances in neural information processing systems, 36:49250–49267, 2023

Reference 20

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source=pdf_text observed=2026-08-02T03:36:01.762282Z digest=sha256:ed03fb909c169eba58b3a15adbf0be4a03f78323e40f9ebef06d1de0489b04fd

Observation 2382d4da-00da-4dd9-b126-2e049a9f1c01 · outbound

This paper cites Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Visual instruction tuning.Advances in neural information processing systems, 36:34892–34916, 2023

Reference 21

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source=pdf_text observed=2026-08-02T03:36:01.840280Z digest=sha256:8606210f3d16d1345a54e1325440cdd604259ffdd0f55e73abc69f16726f13ed

Observation 706aecf5-74b5-4870-b270-574ef2eb54d7 · outbound

This paper cites Qwen2.5-VL Technical Report.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Qwen2.5-VL Technical Report

Reference 22

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source=pdf_text observed=2026-08-02T03:36:01.957078Z digest=sha256:1fb19e43f88c8b50a8640349e369fd183d3bf9b3cced99bd361f0e77e6489d7b

Observation 859c1e85-c79d-4530-841e-dc1c95e126b5 · outbound

This paper cites GPT-4V(ision) System Card.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents GPT-4V(ision) System Card

Reference 23

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Observation c44c27a9-2900-4134-9a9d-0017be668d03 · outbound

This paper cites Introducing OpenAI o3 and o4-mini, 2025.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Introducing OpenAI o3 and o4-mini, 2025

Reference 24

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source=pdf_text observed=2026-08-02T03:36:02.187738Z digest=sha256:740427cdb2d152a8c8e01debed2bdc7afd697a41b84b8934fa80dc2176854252

Observation 8a07dd2c-5e46-4e3c-a6a4-0da3de1841a4 · outbound

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

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Generative agents: Interactive simulacra of human behavior

Reference 25

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source=pdf_text observed=2026-08-02T03:36:02.319782Z digest=sha256:1f4efa0755aa83e28239b865881bd462a623160655563974f404eb4e3fdd6dc2

Observation 23dc7971-2694-4e15-8ff0-2807d609843c · outbound

This paper cites Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134, 1998.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Planning and acting in partially observable stochastic domains.Artificial intelligence, 101(1-2):99–134, 1998

Reference 26

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Observation de01759e-1aec-4ab5-a021-a49a7cc38d66 · outbound

This paper cites Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Back to basics: Revisiting reinforce-style optimization for learning from human feedback in llms

Reference 27

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source=pdf_text observed=2026-08-02T03:36:02.549781Z digest=sha256:1229aa882986a40e1c658b19fe957609e693fde999930844ae0e14a3754b651d

Observation a6102b35-0eea-42e5-9a20-4fa10884e65a · outbound

This paper cites Tir-bench: A comprehensive benchmark for agentic thinking-with-images reasoning.arXiv preprint arXiv:2511.01833, 2025.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Tir-bench: A comprehensive benchmark for agentic thinking-with-images reasoning.arXiv preprint arXiv:2511.01833, 2025

Reference 28

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source=pdf_text observed=2026-08-02T03:36:02.688456Z digest=sha256:3c5e4925330330b6ffe94a4eadc5f64aa00009a0f3aec4dd865b35269cfec6b5

Observation a473a47c-c172-4216-a09c-d50b94c12305 · outbound

This paper cites Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? InEuropean Conference on Computer Vision, pages 169–186.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? InEuropean Conference on Computer Vision, pages 169–186

Reference 29

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source=pdf_text observed=2026-08-02T03:36:02.806915Z digest=sha256:252cfad79d5bee397ebb6c83f6a0bf7b36b1c6e0472b22957198c7857e7d22aa

Observation 70cb2750-42be-4619-b04d-79f8f17167e9 · outbound

This paper cites Measuring multimodal mathematical reasoning with math-vision dataset.Advances in Neural Information Processing Systems, 37:95095–95169, 2024.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Measuring multimodal mathematical reasoning with math-vision dataset.Advances in Neural Information Processing Systems, 37:95095–95169, 2024

Reference 30

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Observation e264f7eb-b7b9-4a6c-9cad-5f04b507f36a · outbound

This paper cites an unresolved cited work.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Unresolved cited work

Reference 31

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source=pdf_text observed=2026-08-02T03:36:02.993693Z digest=sha256:575be05166be2b658efe6d3432891c6778f7d24c5e75331174dcb1beb726aafc

Observation f8c0f3ce-0d6c-4147-8bee-f227489b5d49 · outbound

This paper cites Chartqapro: A more diverse and challenging benchmark for chart question answering.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Chartqapro: A more diverse and challenging benchmark for chart question answering

Reference 32

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Observation 30517f5f-991c-43fa-85a7-ad9d5c2e2fcd · outbound

This paper cites V*: Guided visual search as a core mechanism in multimodal llms.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents V*: Guided visual search as a core mechanism in multimodal llms

Reference 33

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Observation 352a93be-d382-479f-9330-7063b731b13e · outbound

This paper cites Divide, conquer and combine: A training-free framework for high-resolution image perception in multimodal large language models.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Divide, conquer and combine: A training-free framework for high-resolution image perception in multimodal large language models

Reference 34

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Observation e045e36c-641e-479c-8108-2a08a3f9a12e · outbound

This paper cites Qwen3-VL Technical Report.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents Qwen3-VL Technical Report

Reference 35

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source=pdf_text observed=2026-08-02T03:36:03.352494Z digest=sha256:0c06e07415d3033f074bc97a4ea57dac337cee9597168a1c3c9df2b771e42679

Observation 11289315-ae72-4ef3-a911-08befa22b31e · outbound

This paper cites New Embedding Models and API Updates, 2024.

SPyCE: Skill-Policy Co-evolution for Multimodal Agents New Embedding Models and API Updates, 2024

Reference 36

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unresolved
no resolver link, observed 2026-08-02T03:36:03.356888Z

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source=pdf_text observed=2026-08-02T03:36:03.356888Z digest=sha256:dbd90d162516c4187d8f256152f490ace8b7478df6284e29eb0a832ca642ed1c

Pith citing papers

No inbound Pith citation observations are available.