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HFEPX Hub

CS.AI + Multi Agent Papers

Updated from current HFEPX corpus (Feb 27, 2026). 28 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: Lawbench. 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: 28 Last published: Feb 25, 2026 Global RSS Tag RSS
Cs.AIMulti Agent

Research Narrative

Grounded narrative Model: deterministic-grounded

Updated from current HFEPX corpus (Feb 27, 2026). This page covers 28 papers centered on CS.AI + Multi Agent Papers. Common evaluation modes include Automatic Metrics, Simulation Env, with benchmark emphasis on Lawbench, LiveCodeBench. Metric concentration includes accuracy, cost, and the agentic footprint highlights Multi Agent, Long Horizon. Use the anchored takeaways below to compare protocol choices, quality-control patterns, and evidence depth before allocating new eval budget.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Lawbench appears as a recurring benchmark anchor in this page.
  • 1 papers (3.6%) mention Lawbench.
  • Most common evaluation modes: Simulation Env.

Metric Interpretation

  • accuracy is a common reported metric and should be paired with protocol context before ranking methods.
  • 5 papers (17.9%) mention accuracy.
  • Most common evaluation modes: Automatic Metrics.

Researcher Checklist

  • Papers with explicit human feedback: Coverage is a replication risk (25% vs 45% target).
  • Papers reporting quality controls: Coverage is a replication risk (7.1% vs 30% target).
  • Papers naming benchmarks/datasets: Coverage is a replication risk (14.3% vs 35% target).
  • Papers naming evaluation metrics: Coverage is usable but incomplete (32.1% vs 35% target).
  • Papers with known rater population: Coverage is a replication risk (17.9% vs 35% target).
  • Papers with known annotation unit: Coverage is usable but incomplete (25% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (25% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (7.1% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (14.3% vs 35% target).

Papers naming evaluation metrics

Coverage is usable but incomplete (32.1% vs 35% target).

Papers with known rater population

Coverage is a replication risk (17.9% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (25% vs 35% target).

Suggested Reading Order

  1. 1. Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

    Start with this anchor paper for scope and protocol framing. Covers Automatic Metrics.

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

    Covers Automatic Metrics.

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

    Covers Automatic Metrics.

  4. 4. A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives

    Covers Automatic Metrics.

  5. 5. SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

    Covers Automatic Metrics. Includes human-feedback signal: Expert Verification.

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

    Covers Simulation Env.

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

    Covers Simulation Env.

  8. 8. Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination

    Covers Automatic Metrics. Includes human-feedback signal: Demonstrations.

Known Limitations

  • Narrative synthesis is grounded in metadata and abstracts only; full-paper method details may be missing.
  • Extraction fields are conservative and can under-report implicit protocol details.
  • Cross-page comparisons should control for benchmark and metric mismatch.

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=16

0 papers use both Human Eval and Automatic Metrics.

llm_as_judge vs automatic_metrics

both=0, left_only=2, right_only=16

0 papers use both Llm As Judge and Automatic Metrics.

Top Papers

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