Paper Citation Record · LEDGER

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

As of 23 August 2026, this Paper Citation Record lists 87 of 87 outbound references and 5 inbound Pith citation observations for arXiv:2607.05155.

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

pith.paper-citation-record.v1
2607.05155 v1

Coverage vector

measured 87 of 87 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T07:57:43.000834Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:18:12.343176Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:09:10.342277Z

Reference resolution

87 of 87 outbound references displayed

verified exact0
verified fuzzy0
unresolved86
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4636831-ba28-49ec-9420-9d9004204895 · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 732f6555-3981-44dd-a3c0-9458b0301f07 · outbound

This paper cites The Claude Model Family.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments The Claude Model Family

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b3b172-b6e6-4bca-96a0-08bda14eb0cf · outbound

This paper cites Claude Opus 4.8 System Card.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Claude Opus 4.8 System Card

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d24d591d-37b8-4184-a985-90f903ff20e6 · outbound

This paper cites Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bbf5100-911c-4bee-9da9-096f27ec977c · outbound

This paper cites Self-organized criticality: An explanation of 1/f noise.Physical Review Letters, 59(4):381–384, 1987.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Self-organized criticality: An explanation of 1/f noise.Physical Review Letters, 59(4):381–384, 1987

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f01fb070-a75a-4bc0-bdcc-27117a7f6acd · outbound

This paper cites Application of the logistic function to bio-assay.Journal of the American Statistical Association, 39(227):357–365, 1944.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Application of the logistic function to bio-assay.Journal of the American Statistical Association, 39(227):357–365, 1944

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b178965b-c5bf-4417-a2ba-00ad557826a1 · outbound

This paper cites Establishing Task Scaling Laws via Compute-Efficient Model Ladders.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Establishing Task Scaling Laws via Compute-Efficient Model Ladders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 112643a7-dff6-405a-b733-501d50eedcaa · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b01ab9ac-10cd-4952-82a4-cc692c1c6142 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57fd2404-5238-4411-93f4-e2cb70846ef4 · outbound

This paper cites MLE-bench: Evaluating machine learning agents on machine learning engineering.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments MLE-bench: Evaluating machine learning agents on machine learning engineering

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6cfb9ad2-ae60-4a8b-9e9a-3d0287324c71 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Evaluating Large Language Models Trained on Code

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69d17f94-8c52-4124-84ea-a44724649fb1 · outbound

This paper cites ScienceAgentBench: Toward rigorous assessment of language agents for data-driven scientific discovery.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments ScienceAgentBench: Toward rigorous assessment of language agents for data-driven scientific discovery

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 865a234f-fb3e-4033-adbf-de6358e25840 · outbound

This paper cites LLF-Bench: Benchmark for Interactive Learning from Language Feedback.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments LLF-Bench: Benchmark for Interactive Learning from Language Feedback

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6881fdaa-50e7-4907-be81-cb9d72c33837 · outbound

This paper cites Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcda4993-f6c6-4743-80ac-dd446547cbe4 · outbound

This paper cites FrontierSWE: Benchmarking coding agents at the limits of human abilities.https://www.frontierswe.com/blog, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments FrontierSWE: Benchmarking coding agents at the limits of human abilities.https://www.frontierswe.com/blog, 2026

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3fa9e3c-b2da-46a4-b722-fe0430636231 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bcc2faf-d59f-4810-bd0d-a8079a128692 · outbound

This paper cites DeepSeek-V4: Towards highly efficient million-token context intelligence.https://huggingface.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments DeepSeek-V4: Towards highly efficient million-token context intelligence.https://huggingface

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a490dd8-b0bc-45f7-aef6-0a85af173efa · outbound

This paper cites NL2Repo-Bench: Towards long-horizon repository generation evaluation of coding agents.arXiv preprint arXiv:2512.12730, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments NL2Repo-Bench: Towards long-horizon repository generation evaluation of coding agents.arXiv preprint arXiv:2512.12730, 2025

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73000c7a-9880-4b6e-b1dc-6669b40dc8ab · outbound

This paper cites EvaLearn: Quantifying the learning capability and efficiency of LLMs via sequential problem solving.arXiv preprint arXiv:2506.02672, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments EvaLearn: Quantifying the learning capability and efficiency of LLMs via sequential problem solving.arXiv preprint arXiv:2506.02672, 2025

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3760e36-17c8-4e2f-a37e-5eabc56e6cb5 · outbound

This paper cites CL-bench Life: Can Language Models Learn from Real-Life Context?.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments CL-bench Life: Can Language Models Learn from Real-Life Context?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b43d2412-b158-4a29-998b-a5746a0433e2 · outbound

This paper cites CL-bench: A benchmark for context learning.arXiv preprint arXiv:2602.03587, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments CL-bench: A benchmark for context learning.arXiv preprint arXiv:2602.03587, 2026

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c1af386-ae71-4832-b1c3-0ee67cf747db · outbound

This paper cites The Llama 3 Herd of Models.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments The Llama 3 Herd of Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40b8279d-1367-4817-a873-71b42c23df15 · outbound

This paper cites Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b5a05dc-39f8-4fce-a93c-387a838617d2 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca37178-d682-42ea-be6f-84203d0c6f06 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ff51093-4805-4598-a39d-68b265670f07 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GLM-5: from Vibe Coding to Agentic Engineering

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73ff0522-3efa-4149-9dc5-e4bee394a260 · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2523e60c-3c42-4315-a9af-b181b53a2d41 · outbound

This paper cites Measuring massive multitask language understanding.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Measuring massive multitask language understanding

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54d22059-df86-4576-8f40-02cd8a4c65ae · outbound

This paper cites Measuring mathematical problem solving with the MATH dataset.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Measuring mathematical problem solving with the MATH dataset

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e2a44c4-6fe0-4a55-b6d8-c27c0d7967b2 · outbound

This paper cites Scaling laws for single-agent reinforcement learning.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Scaling laws for single-agent reinforcement learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7a7b1a7-3149-4201-83cb-bdc89358b7b1 · outbound

This paper cites Training Compute-Optimal Large Language Models.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Training Compute-Optimal Large Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34df4243-24d7-4d25-a765-11b343cd2d55 · outbound

This paper cites Everything Is a Ralph Loop.https://ghuntley.com/loop/, January 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Everything Is a Ralph Loop.https://ghuntley.com/loop/, January 2026

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 328a289c-ed48-440e-89ba-c5b5d21cf6d2 · outbound

This paper cites ALE-bench: A benchmark for long-horizon objective-driven algorithm engineering.arXiv preprint arXiv:2506.09050, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments ALE-bench: A benchmark for long-horizon objective-driven algorithm engineering.arXiv preprint arXiv:2506.09050, 2025

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 210837f9-5455-4f29-96ae-c6a968a9bb08 · outbound

This paper cites Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik R

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7da9ee76-5a45-45bf-8fcf-2408bf9bc4d1 · outbound

This paper cites Scaling Laws for Neural Language Models.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Scaling Laws for Neural Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b886bcb1-417d-4c26-8913-2c0937be3a9f · outbound

This paper cites The Art of Scaling Reinforcement Learning Compute for LLMs.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments The Art of Scaling Reinforcement Learning Compute for LLMs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54bd7582-59b9-4158-b3e3-f583d7bbc9dc · outbound

This paper cites Measuring AI ability to complete long tasks.arXiv preprint arXiv:2503.14499, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Measuring AI ability to complete long tasks.arXiv preprint arXiv:2503.14499, 2025

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ecf02af-9595-477c-a464-f98d9e60071f · outbound

This paper cites Competition-Level Code Generation with AlphaCode.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Competition-Level Code Generation with AlphaCode

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cef4f759-52e3-404c-be62-fb76adab6c5a · outbound

This paper cites Introducing FrontierCode.https://cognition.ai/blog/frontier-code, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Introducing FrontierCode.https://cognition.ai/blog/frontier-code, 2026

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 026adc16-e3ac-4ca3-bc46-cd519e262cde · outbound

This paper cites MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI

Reference 40

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17e54dcf-b41c-41b5-b9fb-2af29b9d9467 · outbound

This paper cites FrontierCS: Evolving challenges for evolving intelligence.arXiv preprint arXiv:2512.15699, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments FrontierCS: Evolving challenges for evolving intelligence.arXiv preprint arXiv:2512.15699, 2025

Reference 41

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb355dfe-4034-43c7-b985-a751c46b1cb2 · outbound

This paper cites Humans still beat AI in the long horizon: Revisiting test-time scaling in the agent era.https://joyemang33.github.io/blog/ 2026/humans-dont-just-sample/, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Humans still beat AI in the long horizon: Revisiting test-time scaling in the agent era.https://joyemang33.github.io/blog/ 2026/humans-dont-just-sample/, 2026

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 881c1fe0-2cdf-48e5-b147-dacf0d347d52 · outbound

This paper cites MAA invitational competitions: American invitational mathematics examination (AIME).https://maa.org/maa-invitational-competitions/, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments MAA invitational competitions: American invitational mathematics examination (AIME).https://maa.org/maa-invitational-competitions/, 2026

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8bf8a6e-7811-4d3a-b6e1-6b1aef51ea03 · outbound

This paper cites Merrill, Alexander G.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Merrill, Alexander G

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91e9f2a4-293f-49ca-adc2-bfec779246a4 · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6b770bb-1edc-4a58-97ca-832b32585cf4 · outbound

This paper cites GPT-4 Technical Report.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GPT-4 Technical Report

Reference 46

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a851a6a5-d2c6-477f-bf96-244b56372cab · outbound

This paper cites Learning to reason with LLMs.https://openai.com/index/learning-to-reason-with-llms/, 2024.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Learning to reason with LLMs.https://openai.com/index/learning-to-reason-with-llms/, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d27af57-53f9-429e-a886-97f8b5f06485 · outbound

This paper cites Introducing SWE-bench verified.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Introducing SWE-bench verified

Reference 48

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be1cf6c6-bf4b-44a7-a72f-bd0d64a9af93 · outbound

This paper cites Computer-using agent.https://openai.com/index/computer-using-agent/, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Computer-using agent.https://openai.com/index/computer-using-agent/, 2025

Reference 49

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a1aaa90-bc35-47e1-b1a8-2381540d2446 · outbound

This paper cites GPT-4.5 System Card.https://cdn.openai.com/gpt-4-5-system-card-2272025.pdf, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GPT-4.5 System Card.https://cdn.openai.com/gpt-4-5-system-card-2272025.pdf, 2025

Reference 50

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78be564a-c477-4f0e-8910-22ba64918f9a · outbound

This paper cites Update to GPT-5 System Card: GPT-5.2.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Update to GPT-5 System Card: GPT-5.2

Reference 51

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e47e857d-0523-466f-9746-0b9b56a260c6 · outbound

This paper cites OpenAI GPT-5 System Card.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments OpenAI GPT-5 System Card

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70905830-041f-486b-b4cc-9343160de6a8 · outbound

This paper cites Follow a Goal.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Follow a Goal

Reference 53

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e16acc0c-60f2-4a96-8241-aebfbf01853c · outbound

This paper cites GPT-5.4 Thinking System Card.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GPT-5.4 Thinking System Card

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation fc34b859-fde2-4635-981c-05305836231f · outbound

This paper cites GPT-5.5 System Card.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GPT-5.5 System Card

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation 55cb6e29-b47e-46a7-911f-8a59565b5b1f · outbound

This paper cites How predictable is language model benchmark performance?.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments How predictable is language model benchmark performance?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation 8afc3c7d-9e70-430e-bce6-d4eb4626ee1c · outbound

This paper cites GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation f91086f6-b85b-41a4-bb86-9a70c300f0b3 · outbound

This paper cites PRBench: End-to-end paper reproduction in physics research.arXiv preprint arXiv:2603.27646, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments PRBench: End-to-end paper reproduction in physics research.arXiv preprint arXiv:2603.27646, 2026

Reference 58

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation f1e1595b-1e44-4dd1-b005-eb1d9a436803 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 59

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation ee29afa1-7422-4610-8ab1-71a255dac09e · outbound

This paper cites HCAST: Human-Calibrated Autonomy Software Tasks.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments HCAST: Human-Calibrated Autonomy Software Tasks

Reference 60

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation c7c6e6ae-4d1e-41ba-9124-1a301b67e354 · outbound

This paper cites Observational Scaling Laws and the Predictability of Language Model Performance.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Observational Scaling Laws and the Predictability of Language Model Performance

Reference 61

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation 29cf167a-bdf0-43fa-ae28-3af81c202ecb · outbound

This paper cites Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction

Reference 62

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 0c2d66e0-eecb-4956-b3d5-71cb9d17f48c · outbound

This paper cites CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 82694a33-b01e-4646-8691-20ad6d64bcdc · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Observation bfdd95cd-b5f7-4f98-b4d2-9fbad01feefb · outbound

This paper cites PaperBench: Evaluating AI’s ability to replicate AI research.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments PaperBench: Evaluating AI’s ability to replicate AI research

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 712d4198-2fad-4628-9742-ca7899573f11 · outbound

This paper cites Agents’ last exam.arXiv preprint arXiv:2606.05405, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Agents’ last exam.arXiv preprint arXiv:2606.05405, 2026

Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 1defa02b-5608-43f3-a65d-70e52db69747 · outbound

This paper cites SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios

Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation d112d6e9-5606-40dc-a387-121b2e1da382 · outbound

This paper cites BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 252f20b3-6da8-46dd-b279-166d986c5e88 · outbound

This paper cites Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory

Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 22315c62-4bfa-47c5-a42d-840f1a112ec2 · outbound

This paper cites A statistical distribution function of wide applicability.Journal of Applied Mechanics, 18(3): 293–297, 1951.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments A statistical distribution function of wide applicability.Journal of Applied Mechanics, 18(3): 293–297, 1951

Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation f8cf512a-ebdb-4e42-8857-b0716d1e392e · outbound

This paper cites RE-bench: Evaluating frontier AI R&D capabilities of language model agents against human experts.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments RE-bench: Evaluating frontier AI R&D capabilities of language model agents against human experts

Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 7c0d626a-d78c-4aaa-b494-6941e0d25c4c · outbound

This paper cites Sigmoid function.https://en.wikipedia.org/wiki/Sigmoid_function, 2025.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Sigmoid function.https://en.wikipedia.org/wiki/Sigmoid_function, 2025

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d6660aa-82ab-4b57-903c-a210987b8135 · outbound

This paper cites StreamBench: Towards benchmarking continuous improvement of language agents.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments StreamBench: Towards benchmarking continuous improvement of language agents

Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47937986-7f5f-4b95-b26e-823417d4f43e · outbound

This paper cites Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 82f25be7-e4ef-4f8b-a507-683c691cfcf5 · outbound

This paper cites RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments RoadmapBench: Evaluating Long-Horizon Agentic Software Development Across Version Upgrades

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 7b4ae5b9-58ca-4e14-bf39-7f8ab6ce8281 · outbound

This paper cites AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?

Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 83dd5da8-a8d8-400f-84f0-19ca19df90eb · outbound

This paper cites ProgramBench: Can Language Models Rebuild Programs From Scratch?.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments ProgramBench: Can Language Models Rebuild Programs From Scratch?

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3419edba-ac58-47af-9f84-e4b284203e24 · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d2e2253-0e65-405a-9e00-2bec3401796b · outbound

This paper cites GLM-5.1: Towards long-horizon tasks.https://z.ai/blog/glm-5.1, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments GLM-5.1: Towards long-horizon tasks.https://z.ai/blog/glm-5.1, 2026

Reference 79

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ffc4346-e530-4b02-b3e9-dc7f9b48d6ed · outbound

This paper cites Prescriptive Scaling Reveals the Evolution of Language Model Capabilities.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Reference 80

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00de3fbb-5693-4642-97aa-b58b811c76c6 · outbound

This paper cites SWE-AGI: Benchmarking specification-driven software construction with MoonBit in the era of autonomous agents.arXiv preprint arXiv:2602.09447, 2026.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments SWE-AGI: Benchmarking specification-driven software construction with MoonBit in the era of autonomous agents.arXiv preprint arXiv:2602.09447, 2026

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a011244a-6d00-4119-a029-e0ee87c7f1a5 · outbound

This paper cites LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners

Reference 82

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f56a403-bb20-49d5-9cb9-a7ee627a9e52 · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 83

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e6ca54-87a1-4472-b024-0179f90f2e44 · outbound

This paper cites Within one task, the conditional expected score-growth rate is a weighted cut from unlocked score nodes to locked score nodes of the task graph.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Within one task, the conditional expected score-growth rate is a weighted cut from unlocked score nodes to locked score nodes of the task graph

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b30b25d8-f8f9-442b-9c10-ab5aff42b72b · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d9bd9f2-87a3-4a43-863b-97f3b7a59aea · outbound

This paper cites an unresolved cited work.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Unresolved cited work

Reference 86

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

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Observation 17b0d53d-3e14-4212-9ece-320167a3b5c4 · outbound

This paper cites Throughout the section, we denote raw interaction time byt > 0, and the raw task/benchmark score byS(t).

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Throughout the section, we denote raw interaction time byt > 0, and the raw task/benchmark score byS(t)

Reference 87

Resolution: malformed identifier; no resolver link, observed 2026-07-11T07:57:43.000834Z

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Pith citing papers

Observation c7ecb65b-43ab-4cc8-9de2-de63cc58e571 · inbound

Sample-Efficient Learning from Agent Experience cites this paper.

Sample-Efficient Learning from Agent Experience EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T08:43:22.628158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bb0945f-10e2-4d11-910e-6d1883fab4cb · inbound

Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI cites this paper.

Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-01T05:03:45.411378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e34d1c5-958c-46d1-ae55-e7b29e03fa3f · inbound

The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation cites this paper.

The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T06:59:15.205579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e58e4473-b390-4895-91ea-d33b71e320b1 · inbound

GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks cites this paper.

GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:09:10.439914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ce60c5f6-a37b-472c-b4e8-d654b87278e2 · inbound

VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World? cites this paper.

VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World? EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T15:18:12.343176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.