arXiv AI
Lost in the Request: How Communication Variation Disrupts Retrieval and Action in Email Agents
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arXiv Machine Learning
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RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
arXiv:2608.27831v1 Announce Type: cross Abstract: Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues--long, structured, a...
arXiv Computation and Language
Oct 15
Measuring and Reducing Unusable Replies of LLM Agents
arXiv:2610.19659v1 Announce Type: new Abstract: People delegate multi-step work to LLM agents and read only the reply that the agent hands back. Current evaluation scores task completion and the fait...
arXiv Computation and Language
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BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
arXiv:2609.23490v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly execute multi-step workflows through tool use and interaction with users and environments. However, curr...
arXiv AI
Oct 5
Evaluating the Retrieval Robustness of Large Language Models
The paper evaluates how robust large language models (LLMs) are when using retrieval‑augmented generation (RAG) in practical settings. It investigates whether RAG always outperforms non‑RAG approaches, whether adding more retrieved documents helps, and whether the order of documents matters, using a benchmark of 1,891 samples across five datasets and three task categories. Experiments with 11 LLMs show generally high retrieval robustness, but performance varies by task and prompting strategy, indicating that adopting RAG should be considered on a case‑by‑case basis.
arXiv AI
Sep 15
Translating the Translator: Decomposing the Cost of English-Forced Inter-Agent Communication
arXiv:2609.15079v1 Announce Type: cross Abstract: Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, eve...
arXiv Machine Learning
Sep 29
Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation
The paper introduces TRACE, a fine‑tuning framework for Retrieval‑Augmented Generation (RAG) that addresses conflicts between retrieved knowledge and a model’s internal knowledge. TRACE uses multi‑agent debate traces to identify correct and incorrect candidates and answer‑shift patterns, providing fine‑grained supervision for reliable knowledge‑source selection. It also incorporates an answer‑completeness regularization mechanism to prevent empty, overly short, or prematurely terminated responses, thereby improving robustness against misleading retrieved content and enhancing answer quality.