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HFEPX Hub

Long Horizon Or Web Browsing Papers

Updated from current HFEPX corpus (Feb 27, 2026). 89 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. 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: 89 Last published: Feb 25, 2026 Global RSS Tag RSS
Long HorizonWeb Browsing

Research Narrative

Grounded narrative Model: deterministic-grounded

Updated from current HFEPX corpus (Feb 27, 2026). This page covers 89 papers centered on Long Horizon Or Web Browsing Papers. Common evaluation modes include Automatic Metrics, Simulation Env, with benchmark emphasis on Retrieval, MATH. Metric concentration includes accuracy, cost, and the agentic footprint highlights Long Horizon, Web Browsing. 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

  • Retrieval appears as a recurring benchmark anchor in this page.
  • 13 papers (14.6%) mention Retrieval.
  • Most common evaluation modes: Automatic Metrics, Simulation Env.

Metric Interpretation

  • accuracy is a common reported metric and should be paired with protocol context before ranking methods.
  • 18 papers (20.2%) mention accuracy.
  • Most common evaluation modes: Automatic Metrics, Simulation Env.

Researcher Checklist

  • Papers with explicit human feedback: Coverage is a replication risk (15.7% vs 45% target).
  • Papers reporting quality controls: Coverage is a replication risk (1.1% vs 30% target).
  • Papers naming benchmarks/datasets: Coverage is strong (39.3% vs 35% target).
  • Papers naming evaluation metrics: Coverage is strong (52.8% vs 35% target).
  • Papers with known rater population: Coverage is a replication risk (10.1% vs 35% target).
  • Papers with known annotation unit: Coverage is usable but incomplete (34.8% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (15.7% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (1.1% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (39.3% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (52.8% vs 35% target).

Papers with known rater population

Coverage is a replication risk (10.1% vs 35% target).

Papers with known annotation unit

Coverage is usable but incomplete (34.8% vs 35% target).

Suggested Reading Order

  1. 1. GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

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

  2. 2. SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

    Covers Automatic Metrics.

  3. 3. D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

    Covers Automatic Metrics.

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

    Covers Automatic Metrics.

  5. 5. Self-Correcting VLA: Online Action Refinement via Sparse World Imagination

    Covers Simulation Env.

  6. 6. Structurally Aligned Subtask-Level Memory for Software Engineering Agents

    Covers Automatic Metrics.

  7. 7. ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

    Covers Simulation Env.

  8. 8. LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies

    Covers Simulation Env.

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 automatic_metrics

both=1, left_only=1, right_only=65

1 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=2, left_only=64, right_only=23

2 papers use both Automatic Metrics and Simulation Env.

simulation_env vs human_eval

both=1, left_only=24, right_only=1

1 papers use both Simulation Env and Human Eval.

Top Papers

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