5 papers
Revisiting Observation Reduction for Web Agents: Comprehensive Evaluation with a Lightweight Framework
Masafumi Enomoto, Ryoma Obara, Haochen Zhang +1
HTML observations in LLM-based web agents are extremely long, and while many reduction methods have been proposed, it remains unclear which methods reduce overall agent latency whi…
cotomi Act: Learning to Automate Work by Watching You
Masafumi Oyamada, Kunihiro Takeoka, Kosuke Akimoto +5
What if a browser agent could learn your work simply by watching you do it? We present cotomi Act, a browser-based computer-using agent that combines reliable multi-step task execu…
Read More, Think More: Revisiting Observation Reduction for Web Agents
Masafumi Enomoto, Ryoma Obara, Haochen Zhang +1
Web agents based on large language models (LLMs) rely on observations of web pages -- commonly represented as HTML -- as the basis for identifying available actions and planning su…
Can a Crow Hatch a Falcon? Lineage Matters in Predicting Large Language Model Performance
Takuya Tamura, Taro Yano, Masafumi Enomoto +1
Accurately forecasting the performance of Large Language Models (LLMs) before extensive fine-tuning or merging can substantially reduce both computational expense and development t…
On Synthesizing Data for Context Attribution in Question Answering
Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed +11
Question Answering (QA) accounts for a significant portion of LLM usage "in the wild". However, LLMs sometimes produce false or misleading responses, also known as "hallucinations"…