arXiv AI
Jul 3
BaRA: Budget-constrained and Reliable Web Data Collection Agent
arXiv:2607. 00007v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based web agents automate web navigation and data collection.
BaRA: BFS-and-Reflection Web Data Collection Agent
arXiv:2607. 00007v1 Announce Type: cross Abstract: Large language model (LLM)-based web agents reduce manual scripting for web data collection, yet on live websites, they often miss relevant pages, return incomplete multimodal outputs, or return media URLs that are not directly downloadable.
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arXiv AI
Sep 25
WebArxiv: A Reproducible Benchmark for Evaluating Multimodal Web Agents on arXiv Tasks
WebArxiv is a reproducible benchmark designed to evaluate multimodal web agents on arXiv-related tasks. It consists of 510 static, time‑invariant tasks that require multi‑constraint paper retrieval, fine‑grained content extraction, and cross‑paper comparison, each with a deterministic ground truth. The benchmark highlights challenges for foundation‑model agents, such as over‑reliance on fixed interaction histories, and introduces a lightweight dynamic‑memory mechanism to improve adaptive retrieval and reasoning.
arXiv Computation and Language
Sep 24
Improving LLM-based Autonomous Web Agents with Filtering
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.
Hugging Face Trending Papers
Aug 25
BrowserForge: Scaling Web Episode via Parallel Browser Sandboxes
Web agents that act from rendered pixels avoid the fragility and heavy token cost of reading a page's HTML or accessibility tree, but training them depends on large amounts of high-quality interaction...
arXiv AI
Aug 11
Beyond Pixels: Exploring DOM Downsampling for LLM-Based Web Agents
arXiv:2508. 04412v3 Announce Type: replace Abstract: The advent of large language models (LLMs) has sparked an evolution of autonomous web browsing agents: given a web browsing task and serialised user interface (UI) state, an LLM is expected to suggest input actions that incrementally solve the given task.
arXiv AI
Aug 6
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Research Agents
arXiv:2608. 02751v2 Announce Type: replace-cross Abstract: Existing deep-research agents use a Search--Visit workflow that retrieves whole webpages without considering the structure they expose through titles, headings, sections, and metadata.
arXiv AI
Aug 5
Search, Inspect, Fetch: Exploiting Boolean Retrieval for Deep-Research Agents
arXiv:2608. 02751v1 Announce Type: cross Abstract: Existing deep-research agents use a search-visit workflow that retrieves and reads whole pages, without considering the addressable structure that web sources expose through titles, headings, sections, and metadata.
arXiv Computation and Language
Aug 31
Search, Inspect, Fetch: Exploiting Structure-Aware Boolean Retrieval for Deep-Search Agents
The paper introduces Sieve, a search‑inspect‑fetch framework that leverages a Boolean Query Language (BQL) to target specific webpage fields, rank candidates, present structure‑rich result cards, and fetch only selected sections. Compared to traditional Search‑Visit agents, Sieve achieves higher accuracy across three QA collections while reducing token usage by 20.7–50.6%. Boolean filtering consistently improves performance for all tested rankers and remains effective across different retrievers and agent backbones.
arXiv AI
Jul 10
PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs
arXiv:2607. 08269v1 Announce Type: new Abstract: Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML.
arXiv AI
Jun 17
Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns
arXiv:2606. 17645v1 Announce Type: new Abstract: Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action.
arXiv AI
Aug 11
WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
arXiv:2506. 01952v2 Announce Type: replace-cross Abstract: Powered by large language models (LLMs), web browsing agents operate graphical user interfaces in a human-like manner, offering a transparent and general framework for automating web-based tasks.
arXiv AI
Jul 31
SimpleWikiSearch: A Clean Offline Wikipedia Environment for Agentic Search
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.