HFEPX Hub
Updated from current HFEPX corpus (Feb 27, 2026). 5 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Common annotation unit: Freeform. Frequent quality control: Adjudication. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.
Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 5 papers for CS.MA + General Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on multiple benchmark families and metric focus on accuracy, success rate. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.
automatic metrics appears in 80% of papers in this hub.
Evidence: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models , From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
multi-agent setups appears in 80% of papers, indicating agentic evaluation demand.
Evidence: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems , From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
accuracy is a repeated reporting metric here, enabling more consistent cross-paper score interpretation.
Evidence: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models , Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Most common quality-control signal is adjudication (20% of papers).
Evidence: From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems , Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Rater context is mostly unspecified rater pools, and annotation is commonly Freeform; use this to scope replication staffing.
Evidence: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models , Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Track metric sensitivity by reporting both accuracy and success rate.
Evidence: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models , Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Papers with explicit human feedback
Coverage is a replication risk (0% vs 45% target).
Papers reporting quality controls
Coverage is usable but incomplete (20% vs 30% target).
Papers naming benchmarks/datasets
Coverage is a replication risk (0% vs 35% target).
Papers naming evaluation metrics
Coverage is strong (40% vs 35% target).
Papers with known rater population
Coverage is a replication risk (0% vs 35% target).
Papers with known annotation unit
Coverage is a replication risk (20% vs 35% target).
1. Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
Start here for detailed protocol reporting, including rater and quality-control evidence.
2. Training Generalizable Collaborative Agents via Strategic Risk Aversion
Start here for detailed protocol reporting, including rater and quality-control evidence.
3. Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Start here for detailed protocol reporting, including rater and quality-control evidence.
4. Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems
Adds simulation environments for broader coverage within this hub.
Adds automatic metrics for broader coverage within this hub.
automatic_metrics vs simulation_env
both=0, left_only=4, right_only=1
0 papers use both Automatic Metrics and Simulation Env.
Metric Brief
accuracy
Coverage: 2 papers (40%)
2 papers (40%) mention accuracy.
Examples: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
Metric Brief
success rate
Coverage: 1 papers (20%)
1 papers (20%) mention success rate.
Examples: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
Most Cited In This Hub
Fast path to methods with the strongest citation traction in this scope.
Papers: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Most Recent
Fast path to latest protocol changes and newly published evaluation setups.
Papers: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Best Protocol Detail
Papers with explicit rater/unit metadata and quality-control signals for reproducibility.
Papers: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Training Generalizable Collaborative Agents via Strategic Risk Aversion , Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Tomoya Kawabe, Rin Takano · Feb 25, 2026 · Citations: 0
We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner.
Chengrui Qu, Yizhou Zhang, Nicholas Lanzetti, Eric Mazumdar · Feb 25, 2026 · Citations: 0
Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals.
Sasha Robinson, Kerem Oktar, Katherine M. Collins, Ilia Sucholutsky, Kelsey R. Allen · Feb 24, 2026 · Citations: 0
With increasing integration of Large Language Models (LLMs) into areas of high-stakes human decision-making, it is important to understand the risks they introduce as advisors.
Mason Nakamura, Abhinav Kumar, Saswat Das, Sahar Abdelnabi, Saaduddin Mahmud · Feb 16, 2026 · Citations: 0
Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks.
Brendan Gho, Suman Muppavarapu, Afnan Shaik, Tyson Tsay, Atharva Mohan · Nov 18, 2025 · Citations: 0
As foundation models are increasingly deployed as interacting agents in multi-agent systems, their collective behavior raises new challenges for trustworthiness, transparency, and accountability.
