Yu-Feng Li, Lan-Zhe Guo, Jie-Jing Shao, Zhenhua Dong, Bo-Wen Zhang, Xiao-Wen Yang, Bai-Zhi Chen, Si-Yu Han, Jing-Hao Pang, Wen-Da Wei, Guohao Cai
We lifted 15 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| LAMDASZ-ML/ChinaTravel | canonical | 0 of 15 |
| Function | Status | Where it lives |
|---|---|---|
| calc_time_delta | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py code served (permissive licence) · get_code("2ab68efe91d4bf24") |
| city_lookup | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py code served (permissive licence) · get_code("4a50071d3c3731aa") |
| city_names | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py code served (permissive licence) · get_code("83dd28641f911ace") |
| convenient_restaurant | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py code served (permissive licence) · get_code("23eac122637ede76") |
| convenient_transport | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py code served (permissive licence) · get_code("0abf81e73b933293") |
| decode_numpy_dict | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/utils.py code served (permissive licence) · get_code("9d3045c07c4343e4") |
| get_funcname_by_preference | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py code served (permissive licence) · get_code("1eb3756d467b8dbc") |
| get_rank_with_value | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py code served (permissive licence) · get_code("9da02eac7da26244") |
| is_jsonable | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/base.py code served (permissive licence) · get_code("e41eb14033474ba5") |
| load_json_file | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/utils.py code served (permissive licence) · get_code("71db62c521e435b8") |
| load_query | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py code served (permissive licence) · get_code("8331cd223183af27") |
| normalize_lang | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py code served (permissive licence) · get_code("5a627af2661e5732") |
| normalize_run_name | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py code served (permissive licence) · get_code("b79e07c34e67d9bf") |
| resolve_agent_llm_name | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py code served (permissive licence) · get_code("f52571c17760df0c") |
| resolve_llm_name | Not yet run | LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py code served (permissive licence) · get_code("bb6cbda96b51d163") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Travel planning stands out among real-world applications of Language Agents because it couples significant practical demand with a rigorous constraint-satisfaction challenge. However, existing benchmarks primarily operate on a slot-filling paradigm, restricting agents to synthetic queries with pre-defined constraint menus, which fails to capture the open-ended nature of natural language interaction, where user requirements are compositional, diverse, and often implicitly expressed. To address this gap, we introduce ChinaTravel, with four key contributions: 1) a practical sandbox aligned with the multi-day, multi-POI travel planning, 2) a compositionally generalizable domain-specific language (DSL) for scalable evaluation, covering feasibility, constraint satisfaction, and preference comparison 3) an openended dataset that integrates diverse travel requirements and implicit intent from 1154 human participants, and 4) fine-grained analysis reveal the potential of neurosymbolic agents in travel planning, achieving a 37.0% constraint satisfaction rate on human queries, a 10× improvement over purely neural models, yet highlighting significant challenges in compositional generalization. Overall, ChinaTravel provides a foundation for advancing language agents through compositional constraint validation in complex, real-world planning scenarios.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2412.13682")
get_code_for_paper("2412.13682")
have("2412.13682")
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