Food4All: An Agentic Framework and Benchmark for Food Resource Navigation with Adaptive User Understanding
arXiv:2510.18289v3 Announce Type: replace Abstract: Food assistance referral requires conversational agents to translate underspecified, often noisy help-seeking dialogues into locally valid resource recommendations. We present Food4All, an agentic food-resource referral framework and benchmark grounded in 686 structured Indiana food resources. Food4All couples a food-specific search tool with 300 multi-turn evaluation tasks spanning single food needs, composite cases with access or document constraints, and five non-ideal user interaction traits: unreasonable demands, rambling responses, impa
The proliferation of conversational AI agents necessitates robust benchmarks for real-world, complex problem-solving, moving beyond simple task completion.
This development in AI agents addresses critical challenges in handling underspecified human language and diverse user behaviors in sensitive referral contexts, improving practical utility.
AI conversational agents are evolving from basic information retrieval to more adaptive, understanding, and robust systems capable of navigating complex human interactions and resource constraints.
- · AI agent developers
- · social services organizations
- · underserved communities
- · linguistic AI researchers
- · manual referral systems
- · AI systems lacking adaptive understanding
- · organizations with poor data on local resources
More effective and accessible AI-driven social support and referral services become possible.
Increased reliance on and trust in AI agents for sensitive and complex human needs, potentially reducing administrative overhead for social services.
The benchmark could become a standard for evaluating AI agent performance in real-world human-centric applications, accelerating agentic AI deployment across various sectors.
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Read at arXiv cs.CL