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HFEPX Benchmark Hub

DROP Or BFCL Or WebArena Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 65 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 65 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Gold Questions. Frequently cited benchmark: DROP. 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 Mar 22, 2026.

Papers: 65 Last published: Mar 22, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: High .

Analysis blocks are computed from the loaded sample (60 of 65 papers).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

12

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

1.7%

1 papers report calibration/adjudication/IAA controls.

  • 17 papers explicitly name benchmark datasets in the sampled set.
  • 12 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 10.8% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 15.4% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is gold-question checks (1.5% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • DROP appears in 69.2% of hub papers (45/65); use this cohort for benchmark-matched comparisons.
  • BFCL appears in 15.4% of hub papers (10/65); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 32.3% of hub papers (21/65); compare with a secondary metric before ranking methods.
  • cost is reported in 16.9% of hub papers (11/65); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

 Human Eval, Llm As Judge Demonstrations Precision, Pass@1 Not reported
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

 Automatic Metrics Expert Verification Accuracy Gold Questions
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data

Mar 16, 2026

 Automatic Metrics Expert Verification Accuracy, Auroc Not reported
Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Aug 28, 2025

 Automatic Metrics Red Team Accuracy Not reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

 Llm As Judge, Automatic Metrics Not reported Accuracy, Faithfulness Not reported
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Apr 2, 2026

 Automatic Metrics Not reported Accuracy Not reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 Automatic Metrics Not reported Task success Not reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

 Simulation Env Critique Edit Not reported Not reported
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

 Automatic Metrics Not reported Accuracy Not reported
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check

Jan 19, 2026

 Automatic Metrics Not reported Precision, Latency Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (10.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.5% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (64.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3.1% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.2% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 64.6% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 1.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.1% coverage).
  • Annotation unit is under-specified (9.2% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (DROP vs BFCL) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Judge vs Human AgreementBenchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 1.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.1% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (10)
  • Simulation Env (6)
  • Llm As Judge (2)
  • Human Eval (1)

Human Feedback Mix

  • Demonstrations (2)
  • Expert Verification (2)
  • Critique Edit (1)
  • Pairwise Preference (1)

Top Benchmarks

  • DROP (45)
  • BFCL (10)
  • WebArena (10)
  • ToolBench (3)

Top Metrics

  • Accuracy (21)
  • Cost (11)
  • Precision (5)
  • Latency (4)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or BFCL Benchmark Papers DROP Or WebArena Benchmark Papers DROP Or HumanEval+ Or BFCL Benchmark Papers DROP Or BFCL Or LongBench Benchmark Papers DROP Or HumanEval+ Or WebArena Benchmark Papers DROP Or LongBench Or WebArena Benchmark Papers GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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