Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents
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Abstract (arXiv)
An email assistant should not complete less work simply because a user phrases the same request differently. Yet most benchmarks test each task with only one canonical request, leaving this form of robustness largely unmeasured. We test whether email assistants remain reliable when the requested information, available evidence, and expected outcome stay fixed, but the communication style or English variety changes. We construct validated variants along five communication-style axes and four rule-based dialect conditions, and evaluate them on three benchmarks: a retrieval-augmented generation (RAG) pipeline and two tool-using agents. Indirect requests reduce performance on all three benchmarks, while formal requests reduce performance on both agentic benchmarks. Examining the systems more closely shows that these failures have different causes. Verbose requests mainly hurt a lexical retriever by making the relevant email harder to find. By contrast, indirect and dialect variants remain harmful even when the relevant email is retrieved. In the agentic setting, indirect and formal requests mainly cause the agents to omit required actions, not to take more unsupported actions. These results show that a successful response is not enough to establish robustness: evaluations should vary how requests are expressed and separately measure whether agents complete the requested work.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 2 Oct 2026.
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References · 22
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
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- 2022Multi-VALUE: A Framework for Cross-Dialectal English NLPcited by 53
- 2024WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Settingcited by 53
- 2022VALUE: Understanding Dialect Disparity in NLUcited by 47
- 2023ToolTalk: Evaluating Tool-Usage in a Conversational Settingcited by 45
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- 2024Dialect prejudice predicts AI decisions about people's character, employability, and criminalitycited by 37
- 2021Evaluating the Robustness of Retrieval Pipelines with Query Variation Generatorscited by 34
- 2020On the Robustness of Language Encoders against Grammatical Errorscited by 19
- 2024ToolScan: A Benchmark for Characterizing Errors in Tool-Use LLMscited by 18
- 2024Evaluating Dialect Robustness of Language Models via Conversation Understandingcited by 14
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- 2024Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requestscited by 3
- 2025Evaluating Large language models on Understanding Korean indirect Speech actscited by 3
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- 2026Remember, Verify, or Ask? Cross-Family Evaluation of Memory Commitment in LLM Agents