Papers with human-level intelligence
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models (2024.naacl-demo)
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| Challenge: | Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. |
| Approach: | They propose a toolkit to simplify the finetuning of general foundation models. |
| Outcome: | The proposed toolkit simplifies the domain- and task-aware finetuning of general foundation models with limited computing resources. |
CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation (2025.emnlp-industry)
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| Challenge: | Existing models rely on rigid, hand-crafted rules to model nuanced behavior in urban environments. |
| Approach: | They propose an urban simulator that generates realistic daily schedules using a recursive value-driven approach that balances mandatory activities, personal habits, and situational factors. |
| Outcome: | The proposed urban simulator exhibits closer alignment with real humans than previous work. |
Revealing the Barriers of Language Agents in Planning (2025.naacl-long)
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| Challenge: | Existing studies show language agents lack human-level planning abilities . limitations and mechanisms to address them remain insufficiently understood . |
| Approach: | They apply a feature attribution study to identify key factors hindering agent planning . they identify the limited role of constraints and diminishing influence of questions . |
| Outcome: | The proposed model achieves 15.6% on a real-world planning benchmark. |