Challenge: Existing benchmarks focus on product search tasks, but ignore potential risks.
Approach: They propose a data generation pipeline that leverages webpage content and interactive elements to create diverse, functionality-grounded user queries.
Outcome: The proposed framework assesses the performance and safety of web agents under dynamic, real-world e-commerce environments.

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Challenge: Current language models and retrieval-augmented LMs are limited in their ability to perform tasks on the web.
Approach: They propose a benchmark to evaluate language agents built on top of language models . they propose 'AssistantBench' which includes 214 tasks that can be automatically evaluated .
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Benchmarking Web Agent Safety under E-commerce Deceptive Interfaces (2026.acl-long)

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Challenge: Existing web agents are highly susceptible to multiple classes of deceptive interfaces, but they are not designed to mitigate these failures.
Approach: They propose a lightweight plugin framework that allows controlled injection of deceptive interface patterns into existing web environments.
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Towards Reliable Agents: Benchmarking Customized LLM-Based Retrieval-Augmented Generation Frameworks with Deployment Validation (2025.naacl-industry)

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Challenge: Existing benchmarks for general-purpose RAG systems, such as CRAG, RGB, MultiHop-RAG, and CRUD-RAGG, are limited and lack a benchmark specifically tailored to evaluate frameworks.
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A Survey on Evaluation of LLM-based Agents (2026.findings-acl)

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Challenge: This paper provides the first comprehensive survey of evaluation methods for LLM-based agents . LLMs are static, having fixed knowledge, and confined to text-to-text interaction.
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MobileBench-OL: A Comprehensive Chinese Benchmark for Evaluating Mobile GUI Agents in Real-World Environment (2026.findings-acl)

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Challenge: Recent advances in mobile Graphical User Interface (GUI) agents highlight the growing need for comprehensive evaluation benchmarks.
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AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)

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Challenge: Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios.
Approach: They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios.
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DeepResearch Retail: Benchmarking Tool-Augmented Deep Research in the E-Commerce Domain (2026.acl-industry)

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Challenge: Existing DR systems are largely web-centric and do not incorporate structured, domain-specific, and personalized information accessible through internal API tools.
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ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)

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Challenge: Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering.
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ECom-Bench: Can LLM Agent Resolve Real-World E-commerce Customer Support Issues? (2025.emnlp-industry)

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Challenge: ECom-Bench is a benchmark framework for evaluating LLM agent with multimodal capabilities in e-commerce customer support domain.
Approach: They introduce a benchmark framework for evaluating LLM agent with multimodal capabilities in the e-commerce customer support domain.
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GTA: Generating Long-horizon Tasks for Web Agents at Scale (2026.acl-long)

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Challenge: Existing benchmarks provide only coarse start–goal annotations without intermediate trajectories . Existing frameworks provide no supervision over the agent's latent decision process .
Approach: They propose a framework that integrates crawling, retrieval-based seeding, in-context generation and automated quality control to produce realistic tasks paired with executable trajectories.
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