HAG: Hierarchical Demographic Tree-based Agent Generation for Topic-Adaptive Simulation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Agent-Based Modeling fail to adapt to unseen topics absent from data. |
| Approach: | They propose a Hierarchical Agent Generation framework that formalizes population generation as a two-stage decision process. |
| Outcome: | The proposed framework outperforms baseline models in a multi-domain benchmark and comprehensive evaluation framework. |
Similar Papers
HiMATE: A Hierarchical Multi-Agent Framework for Machine Translation Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing LLM-based evaluation methods fail to accurately identify error spans and assess their severity. |
| Approach: | They propose a Hierarchical Multi-Agent Framework for Machine Translation Evaluation based on the MQM error typology and a hierarchical multi-agent system enabling granular evaluation of subtype errors. |
| Outcome: | The proposed framework outperforms baselines in error span detection and severity assessment. |
HACHIMI: Scalable and Controllable Student Persona Generation via Orchestrated Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | ad-hoc prompting and hand-crafted profiles with limited control over educational theory and population distributions are often used for student personas. |
| Approach: | They propose a framework that generates theory-aligned, quota-controlled personas . they factorize each persona into a theory-anchored educational schema . |
| Outcome: | HACHIMI generates theory-aligned, quota-controlled personas for grades 1-12 . results show near-perfect schema validity, accurate quots, and substantial diversity . |
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing plans for large language model-based agents are limited by their granularity and lack flexibility. |
| Approach: | They propose a self-adaptive hierarchical planning mechanism that mimics human planning strategies and generates self-adapted hierarchic plans tailored to the varying difficulty levels of different tasks. |
| Outcome: | The proposed method significantly improves task execution success rates while mitigating overthinking at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks. |
Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks (2024.findings-emnlp)
Copied to clipboard
Yun-Shiuan Chuang, Krirk Nirunwiroj, Zach Studdiford, Agam Goyal, Vincent Frigo, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy Rogers
| Challenge: | Existing large language models can be prompted to role-play as individuals with particular demographic traits, but results are often human-like. |
| Approach: | They found that seeding LLM-based agents with a single belief improved alignment . they say that role-playing based on demographic information does not improve alignment a . |
| Outcome: | The proposed approach improves LLM alignment with human behavior . seeding agents with a single belief improves alignment for topics related to the belief network . |
CluHTM - Semantic Hierarchical Topic Modeling based on CluWords (2020.acl-main)
Copied to clipboard
| Challenge: | Hierarchical Topic modeling (HTM) exploits latent topics and relationships among them as a powerful tool for data analysis and exploration. |
| Approach: | They propose a hierarchical matrix factorization that exploits latent topics and relationships among them to create a powerful tool for data analysis and exploration. |
| Outcome: | The proposed method outperforms baselines and datasets in the vast majority of cases. |
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)
Copied to clipboard
| Challenge: | Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments. |
| Approach: | They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. |
| Outcome: | The proposed framework can support simulations of 30,000 agents faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance increases linearly with the increase of LLM computational resources. |
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models (2025.findings-acl)
Copied to clipboard
Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding
| Challenge: | Social graphs are mathematical structures stem from pairwise interactions between entities through nodes and edges. |
| Approach: | They propose a framework for dynamic, text-attributed social graph generation that simulates the temporal node and edge generation processes for zero-shot social graphs. |
| Outcome: | The proposed framework improves macroscopic graph structure metrics by 11% . the proposed model can generate graphs with up to 100,000 nodes or 10 million edges . |
Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluations focus primarily on end-to-end success, offering limited insight into where failures arise. |
| Approach: | They propose a hierarchical planning framework that analyzes web agents across three layers . they show that structured Planning Domain Definition Language (PDDL) plans produce more concise and goal-directed strategies than natural language (NL) plans . |
| Outcome: | The proposed framework analyzes web agents across three layers to improve reasoning, grounding, and recovery. |
Hierarchical Entity Typing via Multi-level Learning to Rank (2020.acl-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a canonical information extraction task that assigns spans to one of a handful of types. |
| Approach: | They propose a hierarchical entity classification method that embraces ontological structure at training and during prediction. |
| Outcome: | The proposed method outperforms previous work on strict accuracy and significantly outperformed previous work. |
Scale-Invariant Infinite Hierarchical Topic Model (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing hierarchical topic models yield fragmented topics with overlapping themes whose expected probability becomes exponentially smaller along the depth of the tree. |
| Approach: | They propose a hierarchical infinite hierarchic topic model that adapts to topic creation to make expected topic probability decay considerably slower than existing models. |
| Outcome: | The proposed model has better topic uniqueness and hierarchical diversity than existing approaches. |