E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios | Paper Citation Record

Observation d23089d4-61f0-4a7b-bbdb-fe94e923f2a2 · outbound

This paper cites The claude 3 model family: A new standard for intelligence, 2024.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios The claude 3 model family: A new standard for intelligence, 2024

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Observation ea6b8ca4-b165-4f04-9d0d-af0f6d52fe4a · outbound

This paper cites MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers

Reference 2

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This paper cites VitaBench : Benchmarking LLM agents with versatile interactive tasks in real-world applications.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios VitaBench : Benchmarking LLM agents with versatile interactive tasks in real-world applications

Reference 3

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Measuring massive multitask language understanding

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This paper cites SWE -bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations, 2024.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios SWE -bench: Can language models resolve real-world github issues? In The Twelfth International Conference on Learning Representations, 2024

Reference 5

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Observation 436ef764-3d88-4ead-8003-b91e06f282c6 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Agentbench: Evaluating llms as agents

Reference 6

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Observation 9faf4ce9-fe33-44b6-8e92-0c9a08061033 · outbound

This paper cites MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers

Reference 9

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Observation b2da1654-1f38-42bc-b4af-0eceeb491e6d · outbound

This paper cites Gaia: a benchmark for general ai assistants.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Gaia: a benchmark for general ai assistants

Reference 10

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Observation 749906e3-22e1-4593-86d5-f25ef8f9a211 · outbound

This paper cites LiveMCPBench : Can agents navigate an ocean of MCP tools? arXiv preprint arXiv:2508.01780, 2025.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios LiveMCPBench : Can agents navigate an ocean of MCP tools? arXiv preprint arXiv:2508.01780, 2025

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Observation d9ecc0af-2ffc-4c5c-8908-5d515d2a7732 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Gorilla: Large language model connected with massive apis

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Observation bb7287b1-9d9f-4d33-85bf-9880568fbfa4 · outbound

This paper cites Gonzalez.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Gonzalez

Reference 13

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Observation 08aa23dd-41a1-44cc-990a-026ceebb4a5b · outbound

This paper cites Tool LLM : Facilitating large language models to master 16000+ real-world API s.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Tool LLM : Facilitating large language models to master 16000+ real-world API s

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Observation 83dddffe-a3f9-4319-a24b-1bd4c218947e · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Unresolved cited work

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Observation 8b4ae660-82e9-4357-833d-4fc9b015d405 · outbound

This paper cites Appworld: A controllable world of apps and people for benchmarking interactive coding agents.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Appworld: A controllable world of apps and people for benchmarking interactive coding agents

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Observation 9a51692f-8694-48a0-8fb4-0a9e6671e89a · outbound

This paper cites MCP -bench: Benchmarking tool-using LLM agents with complex real-world tasks via MCP servers.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MCP -bench: Benchmarking tool-using LLM agents with complex real-world tasks via MCP servers

Reference 18

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MCPMark : A benchmark for stress-testing realistic and comprehensive MCP use

Reference 19

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Observation 11c59c88-6112-4111-9690-e715c6280ce2 · outbound

This paper cites Hotpotqa: A dataset for diverse, explainable multi-hop question answering.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Hotpotqa: A dataset for diverse, explainable multi-hop question answering

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Observation 13800e47-32a7-4fbd-bf4d-4e79a626ef32 · outbound

This paper cites React: Synergizing reasoning and acting in language models.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios React: Synergizing reasoning and acting in language models

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This paper cites Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig

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This paper cites 2025 , month = dec, note =.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2025 , month = dec, note =

Reference 25

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Unresolved cited work

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This paper cites DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition

Reference 27

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Observation a604f60e-9eb3-4895-9865-55edc3c049b6 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Kimi K2: Open Agentic Intelligence

Reference 28

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Observation 663ee2bb-2bb6-4082-b032-e71ac3cff926 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios OpenAI GPT-5 System Card

Reference 29

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Observation c449061c-aa8b-4dcc-81fc-d62d550b3feb · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Gemini: A Family of Highly Capable Multimodal Models

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Qwen3 Technical Report

Reference 32

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Nature , volume=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International Conference on Learning Representations , year=

Reference 34

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2025 , eprint=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2024 , url=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International conference on data intelligence and cognitive informatics , pages=

Reference 38

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios ACM computing surveys , volume=

Reference 40

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 41

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Advances in Neural Information Processing Systems , volume=

Reference 42

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 2024 conference on empirical methods in natural language processing , pages=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2022 , journal=

Reference 44

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International Conference on Learning Representations , year=

Reference 45

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics , pages=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International Conference on Machine Learning , pages=

Reference 47

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Memory in the Age of AI Agents

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , pages=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios A Survey of Context Engineering for Large Language Models

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Advances in Neural Information Processing Systems , volume=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios MemGPT: Towards LLMs as Operating Systems

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2025 , eprint=

Reference 53

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2024 , eprint=

Reference 54

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2024 , url=

Reference 55

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios GPT-4 Technical Report

Reference 56

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

Reference 58

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios The Llama 3 Herd of Models

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Findings of EMNLP , year=

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 61

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=

Reference 62

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Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.740519Z

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Observation 406c0b95-5214-4750-984d-4ebdcdbd4249 · outbound

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Reference 64

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.746188Z

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Observation 77af0014-4020-42b7-8390-5fb5d7ca707a · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens

Reference 65

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.750693Z

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Observation eb91d6cb-16a4-428f-b9b2-63c5b50d4734 · outbound

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Reference 66

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.755689Z

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Observation 7ed6ac1d-fda7-4d4a-9110-b6dbfa8945d8 · outbound

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Reference 67

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.760396Z

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Observation 282e8ede-bdcc-413a-911b-d59fe69cb688 · outbound

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Reference 68

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.764950Z

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Observation 675b1cd0-4f2c-4c40-8ab1-15a7a90acc31 · outbound

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Reference 69

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.769104Z

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Observation 5b474b89-7286-437b-b831-70e4656ac215 · outbound

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Reference 70

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.773339Z

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Observation c1621d39-f659-46ca-bc26-366eb2c5dd1d · outbound

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Reference 71

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.777967Z

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Observation 7528bc7f-a0f9-4df1-849e-436e67b079dc · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics , pages=

Reference 72

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.782018Z

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Observation 99ab2634-dbab-408f-aacc-94ed92c26b89 · outbound

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Reference 73

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.786270Z

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Observation cb0d518f-abed-445a-92b8-ad7e3f72a157 · outbound

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Reference 74

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.790666Z

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Observation f4e0c5d8-a6ea-4edc-823a-ea00f21c9de1 · outbound

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Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.795216Z

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Observation 8afbb7c1-3b38-46ca-ba0d-27d0bc9faff1 · outbound

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Reference 76

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.799581Z

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Observation 8859dca0-bb2a-4149-a0d5-6097e69d4df5 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 31st International Conference on Computational Linguistics , pages=

Reference 77

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.803559Z

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Observation e156d0fb-af87-4346-8ef1-6d2152d6efbb · outbound

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Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.807788Z

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Observation 44f87baf-dc03-4aed-ad53-d6bed3fdc143 · outbound

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Reference 79

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.812271Z

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Observation 381ba74f-8ae1-4531-a890-2d81d47b7e2e · outbound

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Reference 80

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.816811Z

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Observation 490982e3-58d5-4116-9428-249c276dca2e · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 12th International Conference on Learning Representations, ICLR 2024 , year=

Reference 81

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.821686Z

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Observation 66aabc91-d026-49a9-9114-13af427fd794 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Advances in Neural Information Processing Systems , volume=

Reference 82

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.825968Z

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Observation c62427a9-6ed0-4651-98ae-77923081505d · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Findings of the Association for Computational Linguistics: ACL 2024 , year=

Reference 83

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.829949Z

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Observation 782e5bcf-6efd-4c10-af8f-53dfd08f4ac6 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 84

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.834143Z

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Observation 72afd11d-82b9-420c-8bb8-3589a8d170bb · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 12th International Conference on Learning Representations, ICLR 2024 , year=

Reference 85

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.839059Z

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Observation 0f36f000-37f3-4b01-93b6-e936a14d57d9 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 86

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.843281Z

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Observation 543eb8a8-7eae-49d6-88b4-e0cbab2b4dfc · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios AGENTIF: Benchmarking Instruction Following of Large Language Models in Agentic Scenarios

Reference 87

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.848292Z

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Observation 7aa57ca6-37ac-430d-84a2-c058854ff408 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios arXiv preprint arXiv:2511.10507 , year=

Reference 88

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.853058Z

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Observation d4b68599-789b-435c-8a86-cbb9ed050131 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics , pages=

Reference 89

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.858208Z

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Observation 49a59eb3-7bd5-4e50-998f-ff7c12f7cd3d · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) , year=

Reference 90

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.863309Z

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Observation 70374c0c-db4f-431b-b5c2-f166eb60edff · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems

Reference 91

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.867599Z

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Observation 2b78b768-697d-45d1-b113-e0137f379788 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 92

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.872098Z

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Observation ca890e42-18c7-42ef-88d0-6cd076503697 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Transactions of the Association for Computational Linguistics , volume=

Reference 93

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.876103Z

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Observation 175eca42-47d8-4753-877c-3e004cb7779d · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Proceedings of the 28th International Conference on Computational Linguistics , pages=

Reference 94

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.880269Z

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Observation a1396ab5-b73d-4700-8790-27b4dddf5f64 · outbound

This paper cites an unresolved cited work.

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Reference 95

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.884644Z

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Observation b75ab52b-5a9d-46b5-ab88-2bbc3d0e8fe1 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International Conference on Learning Representations , year=

Reference 96

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.889357Z

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Observation 61a4762a-5f90-4194-b209-5115cc89f4c6 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

Reference 97

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.893475Z

Source-reported events for the cited work

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Observation 33ebb7a5-735a-4953-9847-79c801f70974 · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Advances in Neural Information Processing Systems , volume=

Reference 98

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.897596Z

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Observation e17a205b-ca14-43f9-99cf-8f4373ec772b · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios International Conference on Machine Learning , pages=

Reference 99

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.901925Z

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Observation d84e1a8e-12d0-4a91-aacf-80ad70baba5c · outbound

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Reference 100

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.906261Z

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Unavailable: canonical work link unavailable.

Observation fe91f08b-68b0-443e-9035-f008b32777f9 · outbound

This paper cites The eleventh international conference on learning representations , year=.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios The eleventh international conference on learning representations , year=

Reference 101

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.910350Z

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Observation 861899d9-528d-4ee6-846c-ef0544b13a53 · outbound

This paper cites The eleventh international conference on learning representations , year=.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios The eleventh international conference on learning representations , year=

Reference 102

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.914493Z

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Observation 4bc5a407-1aa8-4c21-8077-3807b207ef9e · outbound

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Reference 103

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.918914Z

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Observation fe172b63-e2f3-44e8-a740-76bd65b24e7e · outbound

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E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios 2021 , eprint=

Reference 104

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.924418Z

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Observation f4865f98-4fd9-45cb-b06f-b5a092efe053 · outbound

This paper cites Program Synthesis with Large Language Models.

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios Program Synthesis with Large Language Models

Reference 105

Resolution: unresolved; no resolver link, observed 2026-07-30T15:06:50.929024Z

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