{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chinatravel-a-real-world-benchmark-for","title":"ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning","arxiv_id":"2412.13682","date":"2024-12-18","proceeding":null,"authors":["Jie-Jing Shao","Xiao-Wen Yang","Bo-Wen Zhang","Baizhi Chen","Wen-Da Wei","Guohao Cai","Zhenhua Dong","Lan-Zhe Guo","Yu-Feng Li"],"abstract":"Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents. Among these, travel planning represents a prominent domain, combining academic challenges with practical value due to its complexity and market demand. However, existing benchmarks fail to reflect the diverse, real-world requirements crucial for deployment. To address this gap, we introduce ChinaTravel, a benchmark specifically designed for authentic Chinese travel planning scenarios. We collect the travel requirements from questionnaires and propose a compositionally generalizable domain-specific language that enables a scalable evaluation process, covering feasibility, constraint satisfaction, and preference comparison. Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a constraint satisfaction rate of 27.9%, significantly surpassing purely neural models at 2.6%. Moreover, we identify key challenges in real-world travel planning deployments, including open language reasoning and unseen concept composition. These findings highlight the significance of ChinaTravel as a pivotal milestone for advancing language agents in complex, real-world planning scenarios.","url_abs":"https://arxiv.org/abs/2412.13682v2","url_pdf":"https://arxiv.org/pdf/2412.13682v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chinatravel-a-real-world-benchmark-for","repo_url":"https://github.com/LAMDASZ-ML/ChinaTravel","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[{"method_slug":null,"method_name":"Travel"}],"datasets_introduced":[{"slug":"chinatravel","name":"ChinaTravel","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2412.13682","atlas_url":"https://app.syntology.ai/?focus=2412.13682","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.13682"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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