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HFEPX Benchmark Hub

HotpotQA Or BFCL Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 22 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. Common annotation unit: Trajectory. Frequently cited benchmark: HotpotQA. 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 Oct 10, 2025.

Papers: 22 Last published: Oct 10, 2025 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

22 / 22 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 11 papers explicitly name benchmark datasets in the sampled set.
  • 9 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

  • 18.2% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 40.9% of papers in this hub.
  • HotpotQA is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

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

Benchmark Interpretation

  • HotpotQA appears in 63.6% of hub papers (7/22); use this cohort for benchmark-matched comparisons.
  • BFCL appears in 36.4% of hub papers (4/22); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 54.5% of hub papers (6/22); compare with a secondary metric before ranking methods.
  • latency is reported in 27.3% of hub papers (3/22); 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
MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents

Apr 6, 2026

 Automatic Metrics Not reported Accuracy, Recall 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
OSCAR: Orchestrated Self-verification and Cross-path Refinement

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
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
RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA

Oct 23, 2025

 Automatic Metrics Not reported Accuracy, F1 Not reported
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs

Oct 1, 2025

 Automatic Metrics Not reported F1 Not reported
CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering

Sep 25, 2025

 Automatic Metrics Not reported Accuracy, Latency Not reported
Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions

Sep 23, 2025

 Automatic Metrics Not reported Accuracy Not reported
DSPO: Stable and Efficient Policy Optimization for Agentic Search and Reasoning

Oct 10, 2025

 Simulation Env Demonstrations Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (18.2% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (81.8% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (27.3% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 81.8% metrics).
  • Agentic evaluation appears in 81.8% of papers.

Known Gaps

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

Suggested Next Analyses

  • Stratify by benchmark (HotpotQA vs BFCL) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and latency.

Recommended Queries

Benchmark Slice: HotpotQAMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% 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 (9)
  • Simulation Env (2)

Human Feedback Mix

  • Demonstrations (2)

Top Benchmarks

  • HotpotQA (7)
  • BFCL (4)
  • ALFWorld (1)
  • CommonsenseQA (1)

Top Metrics

  • Accuracy (6)
  • Latency (3)
  • Cost (2)
  • F1 (2)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or HotpotQA Or BFCL Benchmark Papers HotpotQA Benchmark Papers HotpotQA Benchmark Papers (Last 365 Days) HotpotQA In CS.CL Papers HotpotQA Benchmark Papers (Last 300 Days) BFCL 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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