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HFEPX Hub

General + Multi Agent Papers

Updated from current HFEPX corpus (Feb 27, 2026). 19 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Adjudication. Frequently cited benchmark: Retrieval. 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.

Papers: 19 Last published: Feb 25, 2026 Global RSS Tag RSS
GeneralMulti Agent

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 19 papers for General + Multi Agent Papers. Dominant protocol signals include automatic metrics, simulation environments, LLM-as-judge, with frequent benchmark focus on Retrieval, Visualwebarena 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.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 10.5% of hub papers (2/19); use this cohort for benchmark-matched comparisons.
  • Visualwebarena appears in 5.3% of hub papers (1/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 21.1% of hub papers (4/19); compare with a secondary metric before ranking methods.
  • success rate is reported in 15.8% of hub papers (3/19); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (21.1% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (5.3% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (15.8% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (36.8% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (15.8% vs 35% target).
  • Tighten coverage on Papers with known annotation unit. Coverage is usable but incomplete (26.3% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (21.1% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (5.3% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (15.8% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (36.8% vs 35% target).

Papers with known rater population

Coverage is a replication risk (15.8% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (26.3% vs 35% target).

Suggested Reading Order

  1. 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. 2. Training Generalizable Collaborative Agents via Strategic Risk Aversion

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. The Headless Firm: How AI Reshapes Enterprise Boundaries

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

    Include a human-eval paper to anchor calibration against automated judge settings.

  5. 5. World-Model-Augmented Web Agents with Action Correction

    Include an LLM-as-judge paper to assess judge design and agreement assumptions.

  6. 6. EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

    Include an LLM-as-judge paper to assess judge design and agreement assumptions.

  7. 7. Cooperative-Competitive Team Play of Real-World Craft Robots

    Adds simulation environments for broader coverage within this hub.

  8. 8. Architecting AgentOS: From Token-Level Context to Emergent System-Level Intelligence

    Adds simulation environments for broader coverage within this hub.

Known Limitations

  • Only 5.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs llm_as_judge

both=0, left_only=1, right_only=2

0 papers use both Human Eval and Llm As Judge.

human_eval vs automatic_metrics

both=0, left_only=1, right_only=11

0 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=0, left_only=2, right_only=11

0 papers use both Llm As Judge and Automatic Metrics.

Benchmark Brief

Retrieval

Coverage: 2 papers (10.5%)

2 papers (10.5%) mention Retrieval.

Examples: Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem , Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition

Benchmark Brief

Visualwebarena

Coverage: 1 papers (5.3%)

1 papers (5.3%) mention Visualwebarena.

Examples: World-Model-Augmented Web Agents with Action Correction

Metric Brief

accuracy

Coverage: 4 papers (21.1%)

4 papers (21.1%) mention accuracy.

Examples: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , Evaluating Chain-of-Thought Reasoning through Reusability and Verifiability , The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI

Metric Brief

success rate

Coverage: 3 papers (15.8%)

3 papers (15.8%) mention success rate.

Examples: Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning , EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis , CoAct-1: Computer-using Multi-Agent System with Coding Actions

Metric Brief

cost

Coverage: 2 papers (10.5%)

2 papers (10.5%) mention cost.

Examples: The Headless Firm: How AI Reshapes Enterprise Boundaries , EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

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 , The Headless Firm: How AI Reshapes Enterprise Boundaries

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 , The Headless Firm: How AI Reshapes Enterprise Boundaries

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 , The Headless Firm: How AI Reshapes Enterprise Boundaries

Top Papers

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