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Paper Citation Record · LEDGER

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers

As of 21 August 2026, this Paper Citation Record lists 100 of 118 outbound references and 0 inbound Pith citation observations for arXiv:2607.14642.

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

pith.paper-citation-record.v1
2607.14642 v1

Coverage vector

measured 100 of 118 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:32:19.116429Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 118 outbound references displayed

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  • verified fuzzy0
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  • malformed identifier0
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External citation measurements

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Outbound references

Observation 5241f98b-8ff9-4743-8abe-c21bed5bdf41 · outbound

This paper cites A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024

Reference 1

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source=pdf_text observed=2026-08-02T01:32:10.850331Z digest=sha256:0ec71b674e16c8a65e49ec5cf7113ff823f6f1529a8766a0ddd23917c3f6bcad

Observation 356d2d6f-c65a-4f3e-af14-e087eea06d6a · outbound

This paper cites Tool learning with large language models: a survey.Frontiers of Computer Science, 19(8), 2025.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Tool learning with large language models: a survey.Frontiers of Computer Science, 19(8), 2025

Reference 2

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source=pdf_text observed=2026-08-02T01:32:10.914274Z digest=sha256:9a239d585937767e178cec2b1d36c3258a920fac5036416ad3eba2e24b400a9b

Observation 93ab22a9-30ac-448d-b598-0396ba7e0e4a · outbound

This paper cites Agentic large language models, a survey.Journal of Artificial Intelligence Research, 84, 2025.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Agentic large language models, a survey.Journal of Artificial Intelligence Research, 84, 2025

Reference 3

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source=pdf_text observed=2026-08-02T01:32:10.991812Z digest=sha256:6828eb048aa3ed26d9d796e64a2e85785350bcd310d2acf2aaf7933ac708ba66

Observation 78be2acd-457e-4c55-a81b-c38207399e3e · outbound

This paper cites Repomaster: Autonomous exploration and understanding of github repositories for complex task solving.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Repomaster: Autonomous exploration and understanding of github repositories for complex task solving

Reference 4

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source=pdf_text observed=2026-08-02T01:32:11.059743Z digest=sha256:6c0eadd98fda35d72850f4f2c925475e06fb8a3ab6754d7d8707c6a515801828

Observation c223ebed-7ced-42f7-884c-862feaf4a768 · outbound

This paper cites OS-ATLAS: foundation action model for generalist GUI agents.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers OS-ATLAS: foundation action model for generalist GUI agents

Reference 5

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source=pdf_text observed=2026-08-02T01:32:11.147259Z digest=sha256:211d842346ad93b190ab081217f9e15c25a2ab11245f6052a643da09d834c4e1

Observation f3c6c2b6-3684-4b55-b2a1-36d9eb3575d7 · outbound

This paper cites Chemagent: Self-updating memories in large language models improves chemical reasoning.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Chemagent: Self-updating memories in large language models improves chemical reasoning

Reference 6

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source=pdf_text observed=2026-08-02T01:32:11.187997Z digest=sha256:e1d9a32025449f6ad6d5a98e7d02488e6b6801825f6e615e2cba37096abd5693

Observation 30f02789-cd66-4784-a968-895bc434f998 · outbound

This paper cites Toolllm: Facilitating large language models to master 16000+ real-world apis.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Toolllm: Facilitating large language models to master 16000+ real-world apis

Reference 7

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source=pdf_text observed=2026-08-02T01:32:11.278626Z digest=sha256:ac03af2522fe9235314a7e901a759bf9d3db4b18c8aa0303d621e7087cac3df0

Observation 527e509c-25ce-4d62-a01d-8b81ce37595b · outbound

This paper cites ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases

Reference 8

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source=pdf_text observed=2026-08-02T01:32:11.366164Z digest=sha256:0acad1bd333425a14a67b87e569b92c0886cffdc37e5364c4cd8d7f8e6805f31

Observation 48fd0983-1dfb-43ac-a175-5bed6e794d1d · outbound

This paper cites Shortcutsbench: A large-scale real-world benchmark for api-based agents.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Shortcutsbench: A large-scale real-world benchmark for api-based agents

Reference 9

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source=pdf_text observed=2026-08-02T01:32:11.476416Z digest=sha256:09c89c9087e53a85dda7d1f65fddbab26341205dce9c201c8d9d023b30c50f80

Observation 1fe843e8-5bfa-4c96-bc9c-f37ec85c4260 · outbound

This paper cites Stabletoolbench: Towards stable large-scale benchmarking on tool learning of large language models.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Stabletoolbench: Towards stable large-scale benchmarking on tool learning of large language models

Reference 10

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source=pdf_text observed=2026-08-02T01:32:11.583829Z digest=sha256:a214b56209f003823bb996ab23f0a123982373e6f65f5b6dca6299090cb9587d

Observation b1fd7baf-067c-430e-b62b-fb6deb34f466 · outbound

This paper cites Model context protocol (mcp).

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Model context protocol (mcp)

Reference 11

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source=pdf_text observed=2026-08-02T01:32:11.700223Z digest=sha256:65e300df332dfe97054a7273b2ab6ca78a3b508eda73d2c185deb6bb3022b363

Observation c8d43dab-a14a-4bf2-a450-5d0778c93d85 · outbound

This paper cites Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets

Reference 12

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source=pdf_text observed=2026-08-02T01:32:11.789855Z digest=sha256:54e2c98ff87c0004b0bd797e89b91c93eca0f1e64501848a0b3136177b0eb6ac

Observation 482bd9fe-59f7-47f8-b407-e5ca3dbadf5e · outbound

This paper cites Mcp-flow: Facilitating llm agents to master real-world, diverse and scaling mcp tools, 2025.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Mcp-flow: Facilitating llm agents to master real-world, diverse and scaling mcp tools, 2025

Reference 13

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source=pdf_text observed=2026-08-02T01:32:11.896899Z digest=sha256:9749edef465546557f0921238a00c69681935bfb6e08346a77c6996e564eaeae

Observation 33b07738-40e9-41fe-bc8e-381e1003c5c4 · outbound

This paper cites Mcpeval: Automatic mcp-based deep evaluation for ai agent models.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Mcpeval: Automatic mcp-based deep evaluation for ai agent models

Reference 14

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source=pdf_text observed=2026-08-02T01:32:11.980694Z digest=sha256:7bb767c75dc32133864419deff739bada20b93b317b2e4eafb41cfad85aa8ab9

Observation a9866b0c-602c-49eb-a0bd-d4d5f2cf377b · outbound

This paper cites Mcp-bench: Benchmarking tool-using llm agents with complex real-world tasks via mcp servers.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Mcp-bench: Benchmarking tool-using llm agents with complex real-world tasks via mcp servers

Reference 15

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source=pdf_text observed=2026-08-02T01:32:12.027722Z digest=sha256:2c0d4502865131077b73fc508c26bd4b1f2bb56cb9a03d798bee90660e353064

Observation ab50c0ca-4dea-4390-9bd5-781822894e66 · outbound

This paper cites Toolace: Winning the points of llm function calling.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Toolace: Winning the points of llm function calling

Reference 16

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source=pdf_text observed=2026-08-02T01:32:12.132411Z digest=sha256:84bba19e94241f99d6979071aea7f3f3f84b2c314d2a76c6d0bd081735ec54de

Observation 183965fc-4e61-4ce2-94e6-6a9096c2f0ea · outbound

This paper cites The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models

Reference 17

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source=pdf_text observed=2026-08-02T01:32:12.294259Z digest=sha256:77384b6a657defd91de90e8998bbbd90bdbf11dac6c8da1132170c2b1caf7d47

Observation 9d5d0186-b0d1-4511-aecb-51ff06fca719 · outbound

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.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, and Graham Neubig

Reference 18

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source=pdf_text observed=2026-08-02T01:32:12.398664Z digest=sha256:3c738b924bc3dcd2624a5dcca9c12a0ac2ba4cf9e245104170724dfa166fa3da

Observation 5ef54d25-822f-4b24-99a3-2a1d659d9b84 · outbound

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

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers SWE-bench: Can language models resolve real-world github issues? InThe Twelfth International Conference on Learning Representations, 2024

Reference 19

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source=pdf_text observed=2026-08-02T01:32:12.509438Z digest=sha256:0a9ff6566ff58760eb6d18e1c70955d96b66066d0dff6a4cf63ff8cb40e85ce0

Observation 00eb1950-4b2d-4952-b6f6-fdb3a08b5941 · outbound

This paper cites Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments.Advances in Neural Information Processing Systems, 37:52040–52094, 2024

Reference 20

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source=pdf_text observed=2026-08-02T01:32:12.601480Z digest=sha256:2645941177484ad5d958b723c88564bea0d06a24a81ddc67502712fbd29d2816

Observation 7d34c308-4afb-409b-b064-3a0b77f6e720 · outbound

This paper cites Smithery.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Smithery

Reference 21

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source=pdf_text observed=2026-08-02T01:32:12.638949Z digest=sha256:2a3db9f427d8d29e0e74f87c7c3bf4809de88d496a462557455fec0bc9c63b07

Observation 6dc6c889-6ec4-490a-b0d3-818b1326acf0 · outbound

This paper cites Github, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Github, 2026

Reference 22

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source=pdf_text observed=2026-08-02T01:32:12.677530Z digest=sha256:22b3848cc3d316951568257ce481401ea1649fa869eca248f4d2e42f9d81ac93

Observation 9b28968e-f62e-4a01-a5dd-ab6896369e0c · outbound

This paper cites Modelscope platform, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Modelscope platform, 2026

Reference 23

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source=pdf_text observed=2026-08-02T01:32:12.724692Z digest=sha256:035dac5e029b0856eae4c449ad0cb4f6905ef106426fcd3a5b6bd0d6185c3af3

Observation 69cc8191-a5a1-4e1a-8430-41eac8b856fd · outbound

This paper cites npm registry, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers npm registry, 2026

Reference 24

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source=pdf_text observed=2026-08-02T01:32:12.778143Z digest=sha256:f5d8a0f23d4db7a32e7e32944a4cf6437ec3ab6739c2c730db5635dc27d82508

Observation f788806b-998a-43e5-b03d-8393d4a978fc · outbound

This paper cites Livemcpbench: Can agents navigate an ocean of mcp tools?.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Livemcpbench: Can agents navigate an ocean of mcp tools?

Reference 25

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source=pdf_text observed=2026-08-02T01:32:12.830683Z digest=sha256:de9ed22e962656480931ad5da3851ab79a191857bd4c9c6e0fae8c35f19647ff

Observation 97bc10b0-782a-4510-8bd4-69a906d51226 · outbound

This paper cites Re- Act: Synergizing reasoning and acting in language models.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Re- Act: Synergizing reasoning and acting in language models

Reference 26

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source=pdf_text observed=2026-08-02T01:32:12.881525Z digest=sha256:bf89764a009429c2d6fd9ba41c6da58ec5f0092046423b20bd66cc97645cfc8b

Observation 7db1fc3e-daeb-4263-acc8-0b22ca64138b · outbound

This paper cites Deepseek-v3 technical report.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Deepseek-v3 technical report

Reference 27

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source=pdf_text observed=2026-08-02T01:32:12.940943Z digest=sha256:4246366a4999dd2610dd22065ab75b39fc11a7243494da0ba6daf71b0f5d56a4

Observation f8e4d8ee-de28-4ddf-9142-0a79b7e2d2e8 · outbound

This paper cites Introducing gpt-5.4, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing gpt-5.4, 2026

Reference 28

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Observation 4f15bb67-78ba-43d5-a900-3d52057b65ed · outbound

This paper cites Introducing gpt-5, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing gpt-5, 2026

Reference 29

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source=pdf_text observed=2026-08-02T01:32:13.047324Z digest=sha256:634235f33571430130179028eaf7c8f5438bbebff020f159c5ccd7bbdc175dee

Observation 921408ec-f478-4aeb-bdef-acaca175b5cf · outbound

This paper cites GPT-4 Technical Report.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers GPT-4 Technical Report

Reference 30

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source=pdf_text observed=2026-08-02T01:32:13.102619Z digest=sha256:a82fe6152d190e454abb7dd2180eafb26adf13bc3fdcfd923ce71fbfee213c41

Observation 87cae0ba-4d0f-4c0e-b482-431687ff3317 · outbound

This paper cites Introducing claude opus 4.6, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing claude opus 4.6, 2026

Reference 31

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source=pdf_text observed=2026-08-02T01:32:13.153355Z digest=sha256:46bac1003fba2ea66b907b9de5e259ab613013d7bf2a3749e65fe5d67265f59a

Observation e6d285e8-e6e1-42db-879b-4a525a672a45 · outbound

This paper cites Introducing claude sonnet 4.6, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing claude sonnet 4.6, 2026

Reference 32

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source=pdf_text observed=2026-08-02T01:32:13.202860Z digest=sha256:f5b137e36702c7a82cf91d04bcf1f8d9171053345dcc3041a2dd1bf16cc3f1a5

Observation 4a477e97-a4e8-42c3-9e09-ee874e3fe773 · outbound

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

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 33

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source=pdf_text observed=2026-08-02T01:32:13.287990Z digest=sha256:60ee3965817b71dfb7600fc0873f4cf62331f6c4a1d133333599f4a44e3d603c

Observation e870f728-a962-4286-abc5-106055d55b64 · outbound

This paper cites Introducing openai o3 and o4-mini, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing openai o3 and o4-mini, 2026

Reference 34

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source=pdf_text observed=2026-08-02T01:32:13.345777Z digest=sha256:26bf4f5c82524ec6015d9ac3b5babb443178118c182a8da7c86bca6dbb7aa98e

Observation 801ff9ef-d7ed-45bf-8782-97aa0873d7f0 · outbound

This paper cites Introducing claude sonnet 4.5, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Introducing claude sonnet 4.5, 2026

Reference 35

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source=pdf_text observed=2026-08-02T01:32:13.382818Z digest=sha256:8f64f06b480f9e411a2f3007bbb12ec9f7525beacbef60ea7db6e59648df80fb

Observation 2d19e53e-bca2-4198-bd08-0c33972ac15b · outbound

This paper cites The Llama 3 Herd of Models.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers The Llama 3 Herd of Models

Reference 36

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source=pdf_text observed=2026-08-02T01:32:13.424951Z digest=sha256:1226430f846bbb8aeacc93c21dd86997728407c6403182bd903af9e05736070d

Observation 1379edce-f9f7-4a82-87a8-774c538f8ad4 · outbound

This paper cites Gemma 4, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Gemma 4, 2026

Reference 37

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source=pdf_text observed=2026-08-02T01:32:13.516341Z digest=sha256:7919e68f46223f24cab4f4a40a4db911df9fafd2af28850d6ae5f47dd77cb6e0

Observation 0691fff7-6dcb-4e44-9934-82360a04d88b · outbound

This paper cites Qwen3.5: Towards native multimodal agents, February 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Qwen3.5: Towards native multimodal agents, February 2026

Reference 38

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source=pdf_text observed=2026-08-02T01:32:13.561803Z digest=sha256:f81eec55bb2cd505ca4f5d99cc2b21fff90e534fbbafe93cc94f0a961f05e5df

Observation 95ee215c-a722-48ae-aead-7019df9b505a · outbound

This paper cites Judgebench: A benchmark for evaluating llm-based judges.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Judgebench: A benchmark for evaluating llm-based judges

Reference 39

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source=pdf_text observed=2026-08-02T01:32:13.617062Z digest=sha256:425b293023cf4e70940d3980a9929daa59793a16c21138685399385ce5dae644

Observation 972f5769-7b0a-469c-90e4-e35b605ecd6c · outbound

This paper cites Mllm-as-a-judge for image safety without human labeling.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Mllm-as-a-judge for image safety without human labeling

Reference 40

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source=pdf_text observed=2026-08-02T01:32:13.670476Z digest=sha256:ae03f1dc43f52a11ff5bd462ee035e3a6b806649a858a7f0ed3d3cea4ea1c8f9

Observation 00675376-dd18-4da5-afcb-193d80aeecf3 · outbound

This paper cites Agentrx: Diagnosing ai agent failures from execution trajectories.arXiv preprint arXiv:2602.02475, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Agentrx: Diagnosing ai agent failures from execution trajectories.arXiv preprint arXiv:2602.02475, 2026

Reference 41

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source=pdf_text observed=2026-08-02T01:32:13.720801Z digest=sha256:c674fdc97cfb4621a612ea5080925f4ce28b38496c30322bbd7465fce620caa7

Observation 89b25d1e-362d-4620-affd-197e6be0cfa1 · outbound

This paper cites Trajad: Trajectory anomaly detection for trustworthy llm agents.arXiv preprint arXiv:2602.06443, 2026.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Trajad: Trajectory anomaly detection for trustworthy llm agents.arXiv preprint arXiv:2602.06443, 2026

Reference 42

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source=pdf_text observed=2026-08-02T01:32:13.769803Z digest=sha256:0b2704ae80c7c5617ae85a75c67e5f40be3bc44ce96e66a75e2e0a1cafedfeb0

Observation 5b733a2c-fe10-4a8c-8e39-c675b0dce4ca · outbound

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

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Reflexion: Language agents with verbal reinforcement learning.Advances in neural information processing systems, 36:8634–8652, 2023

Reference 43

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source=pdf_text observed=2026-08-02T01:32:13.825249Z digest=sha256:c0a46502b7bfb64f703175406272ca3f7eee3f71496e024b6aad1ca86cb6cef4

Observation 67d177d6-34a9-49d3-8435-5dd5588e9dd4 · outbound

This paper cites Plan-and-act: Improving planning of agents for long-horizon tasks.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Plan-and-act: Improving planning of agents for long-horizon tasks

Reference 44

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source=pdf_text observed=2026-08-02T01:32:13.886948Z digest=sha256:187ae591a9333a75d6f898a0eb5f50c4e48b88e9953d564e1d0ead5b6739eeae

Observation f80a6c08-c83b-43ee-97ce-01e6f6be29e9 · outbound

This paper cites Agent workflow memory.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Agent workflow memory

Reference 45

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source=pdf_text observed=2026-08-02T01:32:13.941048Z digest=sha256:04a974f66f587a95ea3b236a9bb311a1e96c0f0dce52ae0ee8e857578a0791ad

Observation 572ee72a-6a6f-45b0-97ab-e62f64b26ab8 · outbound

This paper cites Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation, 2024

Reference 46

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source=pdf_text observed=2026-08-02T01:32:14.020165Z digest=sha256:f4f33388af8b6dcbe6bdb6e8a097a2329b78e95d2d49c82cb14e46abb1c827ef

Observation 4538fb9c-4c2c-46ed-bc30-894abb384eb4 · outbound

This paper cites Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation

Reference 47

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source=pdf_text observed=2026-08-02T01:32:14.068782Z digest=sha256:a769ae9465355a11939a570fb427e2d2bb78b49239ed09b5180ab5cf1a3e19da

Observation 2551fce8-ba79-4422-a082-7d6d323f9aa7 · outbound

This paper cites StarCoder 2 and The Stack v2: The Next Generation.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers StarCoder 2 and The Stack v2: The Next Generation

Reference 48

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source=pdf_text observed=2026-08-02T01:32:14.112360Z digest=sha256:8ed2947f2867fe7a217cdf30e2e9d92a61f3de5a9b772191c9fc03b95e85f719

Observation 54602d4f-bf8e-4892-b4bf-d22119932160 · outbound

This paper cites Coefficient of variation.Encyclopedia of research design, 1(5):169–171, 2010.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Coefficient of variation.Encyclopedia of research design, 1(5):169–171, 2010

Reference 49

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source=pdf_text observed=2026-08-02T01:32:14.166174Z digest=sha256:da5eb7a1dfa1484177e66b789ec0307f982f09915f83a3487137e8968a18fdb0

Observation e17f8167-d818-4fad-b3d5-4c810f0f6544 · outbound

This paper cites Beijing". For multiple cities, separate with.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Beijing". For multiple cities, separate with

Reference 50

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source=pdf_text observed=2026-08-02T01:32:14.210118Z digest=sha256:97aa815aa3a1c53504d473c6b3551a92f15d0690a0217a8ebf5ae283fdcf5205

Observation 13d99b93-f0bc-4aa4-9527-e8b50d0e558d · outbound

This paper cites Do not remove, rename, or alter the logic/schema of any existing tool.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Do not remove, rename, or alter the logic/schema of any existing tool

Reference 51

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source=pdf_text observed=2026-08-02T01:32:14.275863Z digest=sha256:2c839a104d0a05caac51fa845d1b877a7aa8fdf4ae4ee9353c25c4a9f1e1af01

Observation a833bc5b-402d-4334-8151-6b226b08e0dd · outbound

This paper cites Ensure the new tool name does not conflict with existing names.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Ensure the new tool name does not conflict with existing names

Reference 52

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source=pdf_text observed=2026-08-02T01:32:14.335142Z digest=sha256:833562bb0a4241dc67d8a2f0b659033b491cd1ab77949715e6a2530767c5a280

Observation a6ea4f8c-c3e9-490f-a760-ae8df233a547 · outbound

This paper cites integration_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers integration_summary

Reference 53

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source=pdf_text observed=2026-08-02T01:32:14.391402Z digest=sha256:17376895f8a246da7955fca5515c75ce917aaf1abc27c97dcd8f732abfa28add

Observation 97e754b2-a4b0-4ef9-8986-a0addde72e44 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 54

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source=pdf_text observed=2026-08-02T01:32:14.462910Z digest=sha256:b83dbec3a9f239457354b8e4a293f2cb9d1a2447e8d31535cc617615725e8100

Observation 31fbb526-b68c-48f4-8f06-8e357115ba1a · outbound

This paper cites Their schemas, descriptions, and logic must be preserved 100%.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Their schemas, descriptions, and logic must be preserved 100%

Reference 55

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source=pdf_text observed=2026-08-02T01:32:14.500117Z digest=sha256:ce3e620a28b60051d51d3760edd2799ab283f10add42acfd07ca6e22a9245b89

Observation e23361e3-1274-4c53-a973-99655f93b814 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 56

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source=pdf_text observed=2026-08-02T01:32:14.597879Z digest=sha256:44b718a2038f124ef620adb97fc6e0027d39498e73883df303ae3114d53895a3

Observation e02bd52a-7b34-443b-b592-bbe2fe9c49ec · outbound

This paper cites replacement_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers replacement_summary

Reference 57

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source=pdf_text observed=2026-08-02T01:32:14.679346Z digest=sha256:ac491cce0695bdb20d7e220d4393376af244ffcf1192a8306235c110aae7a5dd

Observation 0d227088-628e-4519-866c-245243f35f40 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 58

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source=pdf_text observed=2026-08-02T01:32:14.767819Z digest=sha256:b9e53f4d18bb01ff76702a0c0a2e78922315844fc9b9cbba5169a27ebc870a5d

Observation 8b6d9cf2-87e3-474e-88ff-d93fec179d2d · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 59

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source=pdf_text observed=2026-08-02T01:32:14.858250Z digest=sha256:b0943943f6716bed460f66477ccabc7ccc389f05e08c2e38fa37c85c8dd822ed

Observation f000f35d-8d7c-45b4-a520-584f4c5c97ae · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 60

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source=pdf_text observed=2026-08-02T01:32:14.959042Z digest=sha256:7eb19f8df586635783d88c36ce4df95f90fada61e04de37cfe413326eb750863

Observation e356c7d0-9d2e-4922-8251-7b1de7e71e36 · outbound

This paper cites deletion_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers deletion_summary

Reference 61

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source=pdf_text observed=2026-08-02T01:32:15.066182Z digest=sha256:54fa2b068ac385cc772d27a9b2be330e780667f0437c9ffcb24145a80d599a8c

Observation 7f82f838-51e7-4e15-b9ee-a395f792d361 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 62

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source=pdf_text observed=2026-08-02T01:32:15.159023Z digest=sha256:c2294cbd5c6b242ef5f108480c4ba13e683f7f339bf6393b7c0efe7963e8d8a5

Observation 3b6f0204-eb39-4c8a-9f05-d192db34b961 · outbound

This paper cites Only modify description/text fields; DO NOT change name, inputSchema, or outputSchema.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Only modify description/text fields; DO NOT change name, inputSchema, or outputSchema

Reference 63

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source=pdf_text observed=2026-08-02T01:32:15.221746Z digest=sha256:a22a939bedccbd4eaa77ae0ce707173ef4005358b712b987a70f801c0a9eec1d

Observation a17c729b-250c-4d19-806a-223fe269e42b · outbound

This paper cites addition_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers addition_summary

Reference 64

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source=pdf_text observed=2026-08-02T01:32:15.312684Z digest=sha256:2666629e4da4edfc852e05c9b6980b2a9e3368ee63acd52a11b1636c2d153291

Observation 8d28cf89-a7d4-4557-a2c6-15823541e01d · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 65

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source=pdf_text observed=2026-08-02T01:32:15.390313Z digest=sha256:4558bdfcc20665081bb6163e6cb01e48fd9d0a46b6776345e01f1c39542680d3

Observation 69b2de5e-c9ab-457a-9baa-986913d37530 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 66

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source=pdf_text observed=2026-08-02T01:32:15.456235Z digest=sha256:8420560b2d7de90b2ff65c9527cb5ca673bc69b7e9b4516bf72a58d7f8ec9cf6

Observation 70de6af5-8350-4ca7-8989-1c4df5e133cc · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 67

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source=pdf_text observed=2026-08-02T01:32:15.557135Z digest=sha256:41d66495b020263ade1f48c0fe455de7da8a7593e7187634ad57997944dfca2a

Observation 2228b345-c2b8-4af4-bfe0-87921b19dee2 · outbound

This paper cites evolution_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers evolution_summary

Reference 68

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source=pdf_text observed=2026-08-02T01:32:15.625836Z digest=sha256:4fba2369faa1954a861caff2bed99568b65384ed0821144f463602bce9ed4580

Observation 55aa238d-7e78-4c15-a935-d65e3b65ff45 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 69

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source=pdf_text observed=2026-08-02T01:32:15.691256Z digest=sha256:6d3334e9f8915371bbf83cd4778f8e897a0456bf37135efbfcf8dffbe1cd9c76

Observation 941280ae-1a6d-4e98-966b-14aebcb5f950 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 70

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source=pdf_text observed=2026-08-02T01:32:15.761140Z digest=sha256:a4235d19cfa8c8cc9a0fbf024807f50d32e7c67efea54565c35f590c9f08a5e5

Observation 1e91337a-64b8-4dae-a4bc-be64c7a3885e · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 71

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source=pdf_text observed=2026-08-02T01:32:15.824847Z digest=sha256:7732efc172ba17ce319c3c15ba1056710331e0a62b2458041db7db9384f13174

Observation d2c6916f-6b32-48f9-af07-a93a269a5543 · outbound

This paper cites evolution_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers evolution_summary

Reference 72

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source=pdf_text observed=2026-08-02T01:32:15.899501Z digest=sha256:5468fe01bdc09594d56056c1c2e441a7c2782b5b9d5e6d225c5912a2d31ad1f7

Observation 3a3bbf20-a337-4632-a23d-7414a8d4baf6 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 73

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source=pdf_text observed=2026-08-02T01:32:15.976200Z digest=sha256:bd2f496756951ccef756ce5456b9adef305ba5970db99a62d1827710c82238a5

Observation 89164236-8587-4e52-909e-a01896dba1d5 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 74

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source=pdf_text observed=2026-08-02T01:32:16.029679Z digest=sha256:2f3693881b1699b1282fee15c1bed414da9fedf194006f03ae680bd6e4258ecc

Observation 2553e661-160e-4c28-a070-992f91cf7b44 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 75

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.110171Z digest=sha256:a2050eb94bb9ca350750d3592bff2ef255fa73d3166c529e84e9fc09b720c7e6

Observation 656c7d6f-4c65-486b-bb9c-70823be00835 · outbound

This paper cites evolution_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers evolution_summary

Reference 76

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no resolver link, observed 2026-08-02T01:32:16.163735Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.163735Z digest=sha256:1ad06c6f99d205e1536f90f6afbeb7f8cb18b8282b53cfc49f7fd8a3c50754b7

Observation 815a055a-4958-40e3-b47d-6c9306cf08ce · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 77

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no resolver link, observed 2026-08-02T01:32:16.214718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.214718Z digest=sha256:1d9440275b9739da139bad168c0a64c39d061e75d52c63a4470e969c223ccbba

Observation 67ee8b57-392b-4162-b32e-23df1fc1e0df · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 78

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no resolver link, observed 2026-08-02T01:32:16.280338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.280338Z digest=sha256:3aea05c00850064b03e2560d36776985d4a846b91f57944ae4aa89bed8b4d8cb

Observation de90b678-681d-45d9-a314-bdb01e7f3b56 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 79

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no resolver link, observed 2026-08-02T01:32:16.359633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.359633Z digest=sha256:c0122fdc52ca1b02fb788a703ecec326d066aaadcc065f9376ae9e1f722393c3

Observation 3450b0b5-971c-40ee-88da-f5cdbb8e2383 · outbound

This paper cites evolution_summary.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers evolution_summary

Reference 80

Resolution
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no resolver link, observed 2026-08-02T01:32:16.436169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.436169Z digest=sha256:aa8c0cebb60df3408b0da5cc45927c4723e5e20f32593d3f478e9b9663eb168a

Observation f984cc31-1d30-4976-8c63-544b296e8188 · outbound

This paper cites Your modifications are strictly limited to the description text field of the target tool.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Your modifications are strictly limited to the description text field of the target tool

Reference 81

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no resolver link, observed 2026-08-02T01:32:16.515984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.515984Z digest=sha256:fc38ca7a22508cb56e7a0eab2121171e2cf1a1289b5adaab7d3f4113d797282a

Observation 27710e2d-8932-4b19-bc02-08f6bb661d1f · outbound

This paper cites - Strict Prohibition: Do not claim, imply, or promise any capabilities, parameters, or return values that are not explicitly defined in the provided schemas.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers - Strict Prohibition: Do not claim, imply, or promise any capabilities, parameters, or return values that are not explicitly defined in the provided schemas

Reference 82

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no resolver link, observed 2026-08-02T01:32:16.586363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.586363Z digest=sha256:a887eadf834c4f7ac4a9b55d374ad536f356da09ac2b196c7a0ea4ebed9a9e92

Observation d154a30e-37ad-4fe4-93c6-6443c37e47dd · outbound

This paper cites tool": {.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers tool": {

Reference 83

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no resolver link, observed 2026-08-02T01:32:16.640354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.640354Z digest=sha256:11c19ec9e2882c6ad4a8dd5adc5d3426f6d41aa06f98a3e09675a576d79c04c9

Observation 6ea32034-6320-4772-9ff1-34646dc75325 · outbound

This paper cites - You MUST NOT add new parameters or remove existing ones.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers - You MUST NOT add new parameters or remove existing ones

Reference 84

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no resolver link, observed 2026-08-02T01:32:16.703370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.703370Z digest=sha256:d3c2958abd9dad1165465f34095532acdadbb6ef38226145099ffb0e7324d017

Observation 547db10d-061c-4adb-9d72-616bf26b74ab · outbound

This paper cites - Strict Prohibition: Do not change the semantic meaning of the parameter 30 in a way that contradicts its type or validation rules.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers - Strict Prohibition: Do not change the semantic meaning of the parameter 30 in a way that contradicts its type or validation rules

Reference 85

Resolution
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no resolver link, observed 2026-08-02T01:32:16.791162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.791162Z digest=sha256:03b60afd4ab690f649c8a3c6839ed8316521cd761bb78ce170309ddff6cd92ff

Observation 778d9205-8302-4774-9c62-716f80d17067 · outbound

This paper cites parameters.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers parameters

Reference 86

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no resolver link, observed 2026-08-02T01:32:16.883361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.883361Z digest=sha256:92f0869bb933644fec0b148bbeea49a1963c30576fea85d5b51e2c6610b30975

Observation 91dfd01c-bb8d-4254-b1c7-d3d70cb4f9b7 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 87

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no resolver link, observed 2026-08-02T01:32:16.953987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:16.953987Z digest=sha256:4fd0c9175bf283a30f1b512e6383bd4fc5bc99e2f6aaae8f5ce89b549d08a3e8

Observation 602e1aff-4546-4f3c-8442-77ca0cd6cf91 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 88

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no resolver link, observed 2026-08-02T01:32:17.032348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:17.032348Z digest=sha256:44d82f88507fa5ab2d8ccebd935cfb0115556acf71fa7e6f8f9000fa6778f496

Observation 15464b84-c51e-4ecf-bc8e-1ea16fb247db · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 89

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no resolver link, observed 2026-08-02T01:32:17.228679Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-02T01:32:17.228679Z digest=sha256:a2a1a40853d4140af022104c154d0bd23d2645ba2b70088fa2be83c72b409ca4

Observation dec21f95-0094-4db3-95c7-99fd9b10fd4d · outbound

This paper cites Do not fabricate information sources, such as accessing self-invented URLs in the code or using data with unknown formats or structures.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Do not fabricate information sources, such as accessing self-invented URLs in the code or using data with unknown formats or structures

Reference 90

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no resolver link, observed 2026-08-02T01:32:17.461528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:17.461528Z digest=sha256:5e042bb16e2a0565256ab97ba898bc79156c80bde6a67b1d4f8c1319381b5c73

Observation e5abc686-9d5d-4cbc-828d-0f388631b126 · outbound

This paper cites key": "value.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers key": "value

Reference 91

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no resolver link, observed 2026-08-02T01:32:17.615188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:17.615188Z digest=sha256:b8699e942ae87b9e474cfd91fd70b932b15680ed69d1e7ff18ba9c803982e295

Observation c30a6368-542f-425d-b91e-b47a831ae2f4 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 92

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no resolver link, observed 2026-08-02T01:32:17.755936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:17.755936Z digest=sha256:5fb8a4b9711821a60b64f470ab9f37829909cceb461b234625f6db488e5539a2

Observation 2d1ebae8-e420-4e17-893a-182f1d5d9523 · outbound

This paper cites metadata.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers metadata

Reference 93

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no resolver link, observed 2026-08-02T01:32:17.929043Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:17.929043Z digest=sha256:b99c9d179a130cf9f35b27599c60498fc0d3031fb469bbaba4f78baaf4835906

Observation 2618250c-f668-4dba-8413-38468d421d02 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 94

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no resolver link, observed 2026-08-02T01:32:18.083328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.083328Z digest=sha256:6556f66beef77c5a01204e14c513ad7cd2b86416bd23b7e1dfe0e989be462d73

Observation 4dbba295-faa3-4246-a40a-f22faaede4f5 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 95

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no resolver link, observed 2026-08-02T01:32:18.279536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.279536Z digest=sha256:95a04157064629d6edc7d35f385a357e84798b6eb6bae1c5034a7d1ad48127ae

Observation 3877e769-052a-4c69-bc36-9d22ab536044 · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 96

Resolution
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no resolver link, observed 2026-08-02T01:32:18.486669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.486669Z digest=sha256:7571d9345be35362e2b7070541a2e07b9ed4a7350d9e00ec0a6fb08a73c44ac2

Observation e87b8724-47ee-4f69-aa2e-78c9b5a3fedd · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:18.624500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.624500Z digest=sha256:757478ef1e81f37cbb7a3e2ea9eb0a203d3f0a2bd02bd8a19bd965f333ad7e47

Observation ef8487c4-3f64-4581-a6fe-2393859a8b4d · outbound

This paper cites an unresolved cited work.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers Unresolved cited work

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:18.798260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.798260Z digest=sha256:dee472873e097b15eb971472ef552676790cf564c8a7a9405e0756c7f58655cd

Observation 8caf6e6f-23d2-4c4e-bab5-fa9c06399660 · outbound

This paper cites categories.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers categories

Reference 99

Resolution
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no resolver link, observed 2026-08-02T01:32:18.936663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:18.936663Z digest=sha256:5ddadb218c92575b593cec09de6c2e78b10615843dddf8ea4db8d503105394d6

Observation 23775bc0-84d8-47f8-bdbe-2e2760ede044 · outbound

This paper cites perfectly executed.

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers perfectly executed

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-02T01:32:19.116429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:32:19.116429Z digest=sha256:ec9ed843d06a17a631b0da30bd4073a42f74051abcac59ba05932f586aa16ea9

Pith citing papers

No inbound Pith citation observations are available.