MMShopBench: A Real-Log Benchmark for Multimodal, Multi-Turn Shopping Agents
arXiv:2606.17698v1 Announce Type: new Abstract: As LLM-based shopping agents enter production, existing benchmarks fail to capture how a shopper's requirements arrive: stated
arXiv:2610.21074v1 Announce Type: new Abstract: Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most
arXiv:2508.21475v3 Announce Type: replace Abstract: Existing multimodal browsing benchmarks often fail to require genuine multimodal reasoning, as many tasks can be solved with
arXiv:2605.08828v1 Announce Type: new Abstract: Large language model agents increasingly operate through environment-facing scaffolds that expose files, web pages, APIs, and logs.
arXiv:2608.11616v1 Announce Type: new Abstract: Agentic systems powered by large language models (LLMs) have opened new opportunities for business ideation. Yet existing
arXiv:2610.21109v1 Announce Type: new Abstract: When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon
arXiv:2610.00025v1 Announce Type: new Abstract: Agent harnesses increasingly want to run small language models (SLMs) on the microtasks around a frontier large language model
arXiv:2604.22436v1 Announce Type: new Abstract: The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of
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