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HFEPX Hub

Automatic Metrics + Coding + Long Horizon Papers

Updated from current HFEPX corpus (Feb 27, 2026). 20 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. 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 26, 2026.

Papers: 20 Last published: Feb 26, 2026 Global RSS Tag RSS
Automatic MetricsCodingLong Horizon

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 20 papers for Automatic Metrics + Coding + Long Horizon Papers. Dominant protocol signals include automatic metrics, with frequent benchmark focus on Retrieval, SWE-bench and metric focus on accuracy, cost. 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 15% of hub papers (3/20); use this cohort for benchmark-matched comparisons.
  • SWE-bench appears in 10% of hub papers (2/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 35% of hub papers (7/20); compare with a secondary metric before ranking methods.
  • cost is reported in 35% of hub papers (7/20); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (15% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (0% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (25% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (75% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (15% vs 35% target).
  • Maintain strength on Papers with known annotation unit. Coverage is strong (45% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (15% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

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

Papers naming evaluation metrics

Coverage is strong (75% vs 35% target).

Papers with known rater population

Coverage is a replication risk (15% vs 35% target).

Papers with known annotation unit

Coverage is strong (45% vs 35% target).

Suggested Reading Order

  1. 1. Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

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

  2. 2. How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?

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

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

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

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

    Adds automatic metrics for broader coverage within this hub.

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

    Adds automatic metrics for broader coverage within this hub.

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

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Classroom Final Exam: An Instructor-Tested Reasoning Benchmark

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

Top Papers

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