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Paper Citation Record · LEDGER

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning

As of 8 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.03571.

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

pith.paper-citation-record.v1
2608.03571 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:49:50.173686Z

measured 24 of 24 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

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved20
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6011476f-fa79-4241-9f11-fe37a17017ac · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 1

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no resolver link, observed 2026-08-08T00:49:50.143215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.143215Z digest=sha256:6a7dd3ce1878866a063b8c27675635b6e11ca1abc0fc62862cf996ea1b058fd3

Observation 5fc581b8-0c76-43b4-9a87-9a3596a4dc42 · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 2

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.147095Z digest=sha256:653bc225984ade1781293b0ee9ec9065a347a6cb0fbef38431081d690a609d1d

Observation 72f2291e-4f9f-4c72-a4a4-f838637c2d23 · outbound

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

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning

Reference 3

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no resolver link, observed 2026-08-08T00:49:50.099166Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.099166Z digest=sha256:203dcd7d26a4c51c356adc0b934f178dda7acb6d27329862d4cd9c353481e064

Observation 76755303-a8f4-4c03-9562-bcd6fd5a439a · outbound

This paper cites Improving Environment Novelty Quantification for Effective Unsupervised Environment Design.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Improving Environment Novelty Quantification for Effective Unsupervised Environment Design

Reference 4

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source=pdf_text observed=2026-08-08T00:49:50.102398Z digest=sha256:b5bb3c360fc69695b16f896da1bf775249915608bb2811e9dfa4061acf4623ab

Observation 3c503544-e35d-4636-bd2e-216cb4ee58c7 · outbound

This paper cites SWE-smith: Scaling Data for Software Engineering Agents.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning SWE-smith: Scaling Data for Software Engineering Agents

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.106422Z digest=sha256:4214465896a2277b374ea387c862d3c3dfd55a06e71231cdcf9c481ea548bf83

Observation 0eb1c13b-7dd3-43a9-bc72-3d9a2dc2245a · outbound

This paper cites Return exactly one valid JSON object only.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Return exactly one valid JSON object only

Reference 6

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unresolved
no resolver link, observed 2026-08-08T00:49:50.173686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.173686Z digest=sha256:31f9bf923091b14d03ea7ad6a60575b35e3181231c3996339612bef6ffc20b5d

Observation 79dbd851-04f6-4497-971d-815a2b4a0b7b · outbound

This paper cites RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

Reference 7

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no resolver link, observed 2026-08-08T00:49:50.113959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.113959Z digest=sha256:79e1d71cfd5030e74424d9c53e879d584c73e1405253ad65a71b52dcc4e29ae7

Observation 7fc91c40-0a3f-47a0-8171-bbb119c4ff5f · outbound

This paper cites ADCL (Zhang et al., 2025a) periodically re-estimates sample difficulty to mitigate difficulty shift during training.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning ADCL (Zhang et al., 2025a) periodically re-estimates sample difficulty to mitigate difficulty shift during training

Reference 9

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verified fuzzy
raw_fallback, observed 2026-08-08T00:49:50.566686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-08T00:49:50.121134Z digest=sha256:81ea4b21dfb820f8fdc5bc254605fc2c5a0633caf40c27942f2e97e9a74511e7

Observation cb74b442-407e-4415-8ca5-e9f055bf1e39 · outbound

This paper cites In the main experiments, we further include Qwen3-VL-8B- Instruct to evaluate whether our method remains effective with a larger model.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning In the main experiments, we further include Qwen3-VL-8B- Instruct to evaluate whether our method remains effective with a larger model

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-08T00:49:50.555394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-08T00:49:50.124677Z digest=sha256:b2d288f7f4568646631385ffe7b9c0c4215c7110653ace2e023708ff52a1d499

Observation af2d5206-b856-4c6d-bd09-e47fa04c906f · outbound

This paper cites Specifically, all runs use the same total number of training samples, which is set to 7,680 in our experiments.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Specifically, all runs use the same total number of training samples, which is set to 7,680 in our experiments

Reference 11

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raw_fallback, observed 2026-08-08T00:49:50.543858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-08T00:49:50.127773Z digest=sha256:b93a5753a5346dbbc6188ee8fa1e4ee634297baf8f8ed27ce66b9603ec34df23

Observation 87f04dbe-1e7a-47d0-8fd8-b96d3b24fefd · outbound

This paper cites impossible.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning impossible

Reference 12

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raw_fallback, observed 2026-08-08T00:49:50.531740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-08T00:49:50.131825Z digest=sha256:f8c42c8d73c13bc8caa6ed21923da53ced3615dd1d701154e13967b31b3876e6

Observation 12cc483b-b170-433f-8d60-2eff8c6cca3f · outbound

This paper cites All methods use the same AES-selected environment subset and the same total training budget.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning All methods use the same AES-selected environment subset and the same total training budget

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T00:49:50.135414Z digest=sha256:feb2695092c31ee6e3a4b3a446fd675b0ba2ab49a2d27971eaa19dcd631564dc

Observation e926f70c-cfff-4914-b742-248d8a61766d · outbound

This paper cites The curve shows an adaptive learning process.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning The curve shows an adaptive learning process

Reference 14

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source=pdf_text observed=2026-08-08T00:49:50.138802Z digest=sha256:dad94db8449392779eb1adda6b7db11614093fdc6ea8cc1dab0b1ed264699179

Observation 261a7b67-5dcd-4b0a-9a05-69c310037036 · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 17

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no resolver link, observed 2026-08-08T00:49:50.149745Z

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source=pdf_text observed=2026-08-08T00:49:50.149745Z digest=sha256:b739ae63e25b16ef9eb706837528c48420876d4bdf94b76c2d2c178e075184aa

Observation eb1b9621-4807-4755-a2eb-6c9bce8823a2 · outbound

This paper cites A rollout step is one interaction with the environment.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning A rollout step is one interaction with the environment

Reference 18

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source=pdf_text observed=2026-08-08T00:49:50.152733Z digest=sha256:c1ea801e8043acb2076ef267c70e0aaa3c410b45ce8fca84901aa44a9143c32b

Observation fa31ced4-4384-4ba1-8cc2-1720feb8328d · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-08T00:49:50.156496Z digest=sha256:15432a5e599afde61df5204e1e79577d249d22357be4d76b250d8e80719e3f57

Observation f4442c71-0116-45a3-8493-c673e069f2e7 · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-08T00:49:50.159705Z digest=sha256:608c90fb2da1c9e71a1f8a180424433655a87927c20a62ae7bfe0f6266ff3127

Observation ef1e454d-28b7-4b94-9f55-2b3846399696 · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-08T00:49:50.163838Z digest=sha256:9cd8af386cd20cab48fbf0b511d4009ae6aa713a753020ee6574d36fd9e384a1

Observation 5a55c1fe-2bfe-4ff2-863d-e869f93c0b91 · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-08T00:49:50.166610Z digest=sha256:df43a718b8341b01178d9af1cdd6e077cad719e847d5c2ca64d42724a8fdf667

Observation 4e72fb79-a5f5-40dc-8690-7868f6d2dadb · outbound

This paper cites an unresolved cited work.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-08T00:49:50.169734Z digest=sha256:1db4fa6d351207d027719d1aa6095c777094dcf4e34b26b79640cf291f2aed80

Observation fc22239d-0da1-49c1-b065-5cfddb0ec259 · outbound

This paper cites MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning MMR-V: What's Left Unsaid? A Benchmark for Multimodal Deep Reasoning in Videos

Reference 2024

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source=pdf_text observed=2026-08-08T00:49:50.117587Z digest=sha256:5977fad5fccc27e680708481094ecc4cf50c74cb0d3de7cfec0bdf0ec9ae9226

Observation 33342f5c-3494-4700-9838-5c6adfbc55eb · outbound

This paper cites Planning with Reasoning using Vision Language World Model.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Planning with Reasoning using Vision Language World Model

Reference 2025

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source=pdf_text observed=2026-08-08T00:49:50.095503Z digest=sha256:bee4e80665a54fa4bfd98feb6e108df753b0130f1a28b3dae6de5f5dbd1603fb

Observation 08ce8ef8-88e9-4b14-9f77-3c8baaaea9ec · outbound

This paper cites Gym-Anything: Turn any Software into an Agent Environment.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Gym-Anything: Turn any Software into an Agent Environment

Reference 2026

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source=pdf_text observed=2026-08-08T00:49:50.090990Z digest=sha256:cb74152ed9b7edcc97af2f60a4c5e8555c0c4d04f40ea965b5f9a9a1a33c6b29

Observation 1f66e7cd-d38a-48ef-bce0-b04a33f2237c · outbound

This paper cites Yucheng Zeng, Weipeng Lu, Linyun Liu, Shupeng Li, Zitian Qu, Chenghao Zhu, Shaofei Li, Zhengdong Tan, Mengyue Liu, Haotian Zhao, and 1 others.

Beyond Simply Environment Scaling: Designing Effective Environment Distributions for Multimodal Agent Learning Yucheng Zeng, Weipeng Lu, Linyun Liu, Shupeng Li, Zitian Qu, Chenghao Zhu, Shaofei Li, Zhengdong Tan, Mengyue Liu, Haotian Zhao, and 1 others

Reference 9567

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source=pdf_text observed=2026-08-08T00:49:50.110243Z digest=sha256:681ff9c7a1bfb1d4e15dd272f38549945c7e8ec2a419fb46f5ae0d4afeaaa95a

Pith citing papers

No inbound Pith citation observations are available.