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HFEPX Hub

Long Horizon + Coding (Last 45 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 13 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 13 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: BFCL. 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 Apr 1, 2026.

Papers: 13 Last published: Apr 1, 2026 Global RSS Tag RSS
Long HorizonCodingLast 45d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (13) Replication-Ready Only (3)

High-Signal Coverage

100.0%

13 / 13 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Currently showing only replication-ready papers in ranking and matrix sections (3 papers).

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Why This Matters For Eval Research

  • 15.4% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 76.9% of papers in this hub.
  • BFCL is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (BFCL vs GSM8K) before comparing methods.

Benchmark Interpretation

  • BFCL appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 38.5% of hub papers (5/13); compare with a secondary metric before ranking methods.
  • cost is reported in 23.1% of hub papers (3/13); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (15.4% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30.8% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (76.9% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (15.4% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (38.5% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.4% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BFCL vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: BFCL Metric Slice: accuracy Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 No
Not Reported
Automatic Metrics MATH 500 , GSM8K Pass@1 , Inference cost Not Reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 No
Not Reported
Automatic Metrics BFCL Task success Not Reported
Effective Strategies for Asynchronous Software Engineering Agents

Mar 23, 2026

 No
Not Reported
Automatic Metrics Paperbench Accuracy Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal S0 Tuning: Zero-Overhead Adaptation of Hybrid Recur… SkillX: Automatically Constructing Skill Knowledge… Effective Strategies for Asynchronous Software Engi…
Human Feedback Not reportedNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks MATH 500, GSM8KBFCLPaperbench
Metrics Pass@1, Inference costTask successAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryUnknownUnknown
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter.

  2. AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and.

  3. SkillX: Automatically Constructing Skill Knowledge Bases for Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: BFCL / task success. Abstract: Learning from experience is critical for building capable large language.

  4. When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short, static problem solving.

  5. The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: critique/edit feedback. Abstract: Psychological scale development has traditionally required extensive expert involvement, iterative revision, and large-scale pilot.

  6. S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: MATH-500 / pass@1. Abstract: Using roughly 48 execution-verified HumanEval training solutions, tuning a single.

  7. Effective Strategies for Asynchronous Software Engineering Agents

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Paperbench / accuracy. Abstract: AI agents have become increasingly capable at isolated software engineering.

  8. Scaling Reasoning Tokens via RL and Parallel Thinking: Evidence From Competitive Programming

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: We study how to scale reasoning token budgets for competitive programming through.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.4% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Critique Edit (2)

Evaluation Modes

  • Automatic Metrics (10)
  • Simulation Env (2)

Top Benchmarks

  • BFCL (1)
  • GSM8K (1)
  • HumanEval+ (1)
  • Interruptbench (1)

Top Metrics

  • Accuracy (5)
  • Cost (3)
  • Latency (2)
  • Pass@1 (2)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 15.4% · benchmarks 30.8% · metrics 76.9% · quality controls 0.0%.

Top Papers

  • S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

    Jack Young · Apr 1, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Using roughly 48 execution-verified HumanEval training solutions, tuning a single initial state matrix per recurrent layer, with zero inference overhead, outperforms LoRA by +10.8 pp (p < 0.001) on HumanEval.

  • SkillX: Automatically Constructing Skill Knowledge Bases for Agents

    Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…

  • Effective Strategies for Asynchronous Software Engineering Agents

    Jiayi Geng, Graham Neubig · Mar 23, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Inspired by these collaboration primitives, we introduce Centralized Asynchronous Isolated Delegation (CAID), a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous…

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