arXiv:2608. 09934v1 Announce Type: cross Abstract: Large language model (LLM) agents improve task performance by decomposing problems into role-specialized behaviors.
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The paper investigates how to improve large language model (LLM) based autonomous web agents by filtering irrelevant webpage content. The authors reproduce baseline models on the WebArena benchmark and identify failure modes caused by raw HTML input. They propose DeBERTa‑ and T5‑based retrieval models that rank HTML elements by relevance, fine‑tuned on Mind2Web data, and demonstrate that the DeBERTa model raises the LLaMA‑2‑70B agent’s success rate from 1.97% to 2.96%. Additionally, a zero‑shot ColBERT retriever achieves recall of 0.52 on Mind2Web and 0.47 on WebArena.
arXiv:2606. 06462v1 Announce Type: new Abstract: Benchmarks are fundamental for evaluating and advancing LLMs and MLLMs by providing standardized and explicit measures of performance.
arXiv:2603.18272v2 Announce Type: replace Abstract: While large language models (LLMs) have advanced the development of general-purpose agents, robust generalization to unseen tasks remains challengi...
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.