Paper Citation Record · LEDGER

MLGym: A New Framework and Benchmark for Advancing AI Research Agents

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2502.14499.

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

pith.paper-citation-record.v1
2502.14499 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:43.090307Z

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

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 24af59e5-79f5-48b6-a9b5-fdcb8693094c · inbound

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review cites this paper.

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 122

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:57:38.473587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f76081b5-bdb8-4bf5-9fc4-b728fddc0f48 · inbound

MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem cites this paper.

MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:43.090307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52d62860-0f53-413c-8dc2-a3af42c12106 · inbound

Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks cites this paper.

Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T12:18:53.333828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13645ed6-a5b8-4d94-942a-6771b241bbe6 · inbound

TextAtari: 100K Frames Game Playing with Language Agents cites this paper.

TextAtari: 100K Frames Game Playing with Language Agents MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:57.997567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 463cd5b2-c0d1-4b07-9eca-843fb111b21c · inbound

LLM-First Search: Self-Guided Exploration of the Solution Space cites this paper.

LLM-First Search: Self-Guided Exploration of the Solution Space MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T10:28:47.225949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79bccf71-c36d-4f0c-ba6c-fac4d942ced4 · inbound

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey cites this paper.

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T06:00:19.472486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a9e3bef-3d00-48e7-bb34-5fb578784dcc · inbound

Kaleidoscopic Teaming in Multi Agent Simulations cites this paper.

Kaleidoscopic Teaming in Multi Agent Simulations MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:35:49.121636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0860c8ab-af57-4a9f-bc7e-58eb1e415f44 · inbound

Deep Research Agents: A Systematic Examination And Roadmap cites this paper.

Deep Research Agents: A Systematic Examination And Roadmap MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T23:26:56.783174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9824b926-79df-498e-ab20-0cee30bbb560 · inbound

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems cites this paper.

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-15T23:21:42.565484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9593ae03-a769-400c-bd17-8ebad3f56a97 · inbound

DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence cites this paper.

DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T12:11:34.231652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9e7f922-74b4-4365-a969-2385d37e313f · inbound

AgentCE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments cites this paper.

AgentCE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:30:49.503880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f16f896e-a368-43f4-8557-60b61edd8e3f · inbound

AI-Driven Research for Databases cites this paper.

AI-Driven Research for Databases MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:55:59.963254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1c404655-6b82-45f4-bfab-9a9a3c85b18f · inbound

TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration cites this paper.

TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:46:35.120334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e22e6dff-ce38-43e4-bfff-2fbdc3bd2b30 · inbound

EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents cites this paper.

EO-Gym: A Multimodal, Interactive Environment for Earth Observation Agents MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:13:58.361015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74cfeb6c-6026-46a0-ad7b-a6d6a568ebf1 · inbound

Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight cites this paper.

Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:00:56.553489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 16597c5b-5b3f-4237-8ce9-65cc80a6bd3c · inbound

Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight cites this paper.

Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:29:52.723765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3343f866-4dd0-4b75-b324-dea8922abc84 · inbound

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

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:21:25.265962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e6a903ea-d8a9-4c5f-a5a4-2ad2b5968289 · inbound

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

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 69

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T13:25:46.030035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6ef9c2b0-10b2-4462-942c-e288a2fa4801 · inbound

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

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-12T17:14:49.310598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb6bf2b1-c166-4a23-b57c-4b04936e025b · inbound

MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility cites this paper.

MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T20:03:44.085167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7f48d165-c0ec-4440-8ddd-e272d23f420d · inbound

AI for Auto-Research: Roadmap & User Guide cites this paper.

AI for Auto-Research: Roadmap & User Guide MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-20T10:33:12.639602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6dc2c65c-838e-40c2-8d4f-b500548d840c · inbound

AI for Auto-Research: Roadmap & User Guide cites this paper.

AI for Auto-Research: Roadmap & User Guide MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 135

Resolution: unresolved; no resolver link, observed 2026-08-02T13:43:40.325453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e24dbd26-fbef-49fe-82f7-367b0c4149cb · inbound

SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems cites this paper.

SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T03:26:29.555018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9cf0516c-52f2-4d55-afa1-a277f036a1c3 · inbound

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research cites this paper.

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T08:33:15.813204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ddd90b3-696c-4d5e-941f-f6a3714ce425 · inbound

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research cites this paper.

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T00:39:16.507175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation be25b1b2-71ba-4e48-bac1-dffb1720bbf0 · inbound

InquiTree: Evaluating AI Agents in the Scientific Inquiry Loop with Paper-Derived Research Trees cites this paper.

InquiTree: Evaluating AI Agents in the Scientific Inquiry Loop with Paper-Derived Research Trees MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T04:07:37.266203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9362f5de-c93a-4a7d-ae64-b221944a01d1 · inbound

Benchmarking AI Agents for Addressing Scientific Challenges Across Scales cites this paper.

Benchmarking AI Agents for Addressing Scientific Challenges Across Scales MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:28:04.333266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe5d0459-1a22-417e-a3b0-1d2c71d1afa2 · inbound

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data? cites this paper.

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data? MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:22.222379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 21be2fa4-d534-4af3-9fd3-422ffd310c30 · inbound

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data? cites this paper.

TerraBench: Can Agents Reason Over Heterogeneous Earth-System Data? MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:37:25.449237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cd66105-af08-4e2f-87c2-a77ade7a068b · inbound

Environment-Grounded Automated Prompt Optimization for LLM Game Agents cites this paper.

Environment-Grounded Automated Prompt Optimization for LLM Game Agents MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-06-27T00:40:18.662768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 954b2b2f-e649-4138-a210-40e2995b1809 · inbound

Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist cites this paper.

Discovering Crystal Structure Prediction Algorithms with an AI Co-Scientist MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:19:46.994246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 82ea1e40-c1c7-43a5-aaf0-47e24585bd20 · inbound

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? cites this paper.

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-07-04T16:49:57.820516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6fab8d45-91dc-469c-8eeb-8e5e0e8c7039 · inbound

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? cites this paper.

NatureBench: Can Coding Agents Match the Published SOTA of Nature-Family Papers? MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-12T12:32:39.706055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db995256-b2bf-4c09-bff4-0a297d14f7e0 · inbound

Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data cites this paper.

Autoresearch with Coding Agents: Generalizers and Metric-Maximizers on Quran Recitation Data MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T16:16:31.350666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90d01cd4-8504-4389-8829-49cd3e2e4d33 · inbound

One Run Is Not an Idea: The Implementation Lottery in Automated Research cites this paper.

One Run Is Not an Idea: The Implementation Lottery in Automated Research MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T12:50:04.327582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b22e7f-9d05-42f0-907f-9ffcb04fa167 · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details MLGym: A New Framework and Benchmark for Advancing AI Research Agents

Reference 169

Resolution: unresolved; no resolver link, observed 2026-08-05T15:25:40.067169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.