We lifted 8 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| tsinghua-fib-lab/agentsocietychallenge | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| calculate_price | Ran | tsinghua-fib-lab/agentsocietychallenge/GTsimulation/ModGTAgent.py code served (permissive licence) · get_code("cdc4ce4d79504560") |
| check_required_files | Ran | tsinghua-fib-lab/agentsocietychallenge/data_process.py code served (permissive licence) · get_code("7d558df069ffb851") |
| filter_data | Ran | tsinghua-fib-lab/agentsocietychallenge/data_process.py code served (permissive licence) · get_code("ff075553db5b65e2") |
| load_data | Ran | tsinghua-fib-lab/agentsocietychallenge/data_process.py code served (permissive licence) · get_code("c7134037e50f3c8c") |
| sanitize_input | Ran | tsinghua-fib-lab/agentsocietychallenge/GTsimulation/ModGTAgent.py code served (permissive licence) · get_code("fa222ac81ae15470") |
| count_tokens | Not yet run | tsinghua-fib-lab/agentsocietychallenge/GTsimulation/ModGTAgent.py code served (permissive licence) · get_code("766aece7c07a8cae") |
| llm_response | Not yet run | tsinghua-fib-lab/agentsocietychallenge/GTsimulation/RecGTAgent.py code served (permissive licence) · get_code("685037a23906602e") |
| num_tokens_from_string | Not yet run | tsinghua-fib-lab/agentsocietychallenge/GTsimulation/RecGTAgent.py code served (permissive licence) · get_code("24ed49c54abe8af9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The AgentSociety Challenge is the first competition in the Web Conference that aims to explore the potential of Large Language Model (LLM) agents in modeling user behavior and enhancing recommender systems on web platforms. The Challenge consists of two tracks: the User Modeling Track and the Recommendation Track. Participants are tasked to utilize a combined dataset from Yelp, Amazon, and Goodreads, along with an interactive environment simulator, to develop innovative LLM agents. The Challenge has attracted 295 teams across the globe and received over 1,400 submissions in total over the course of 37 official competition days. The participants have achieved 21.9% and 20.3% performance improvement for Track 1 and Track 2 in the Development Phase, and 9.1% and 15.9% in the Final Phase, representing a significant accomplishment. This paper discusses the detailed designs of the Challenge, analyzes the outcomes, and highlights the most successful LLM agent designs. To support further research and development, we have open-sourced the benchmark environment at https://tsinghua-fib-lab.github.io/AgentSocietyChallenge.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2502.18754")
get_code_for_paper("2502.18754")
have("2502.18754")
Connect an agent — have() is free.