AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition | ScienceToStartup | ScienceToStartup

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References (62)

[1]

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

2025DeepSeek-AI, A. Liu et al.

[2]

The inadequacy of offline large language model evaluations: A need to account for personalization in model behavior

2025Angelina Wang, Daniel E. Ho et al.

[3]

Where LLM Agents Fail and How They can Learn From Failures

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"AgentNoiseBench provides a framework to evaluate the robustness of language model agents in noisy environments, optimizing their real-world performance."

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AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition

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References (62)

Founder's Pitch

"AgentNoiseBench provides a framework to evaluate the robustness of language model agents in noisy environments, optimizing their real-world performance."

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AgentNoiseBench: Benchmarking Robustness of Tool-Using LLM Agents Under Noisy Condition

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References (62)

Founder's Pitch

"AgentNoiseBench provides a framework to evaluate the robustness of language model agents in noisy environments, optimizing their real-world performance."

Commercial Viability Breakdown

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