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HFEPX Hub

Tool Use + General (Last 90 Days)

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. 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 Mar 23, 2026.

Papers: 11 Last published: Mar 23, 2026 Global RSS Tag RSS
Tool UseGeneralLast 90d

Researcher Quick Triage

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

All Sampled Papers (11) Replication-Ready Only (4)

High-Signal Coverage

100.0%

11 / 11 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 4 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 (4 papers).

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Why This Matters For Eval Research

  • 18.2% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 81.8% 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 unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (BFCL vs Finmcp-Bench) before comparing methods.

Benchmark Interpretation

  • BFCL appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • Finmcp-Bench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 54.5% of hub papers (6/11); compare with a secondary metric before ranking methods.
  • cost is reported in 18.2% of hub papers (2/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher 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 (36.4% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (63.6% vs 35% target).

  • Gap: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (45.5% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (36.4% benchmarks, 63.6% metrics).
  • 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 (0% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BFCL vs Finmcp-Bench) 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
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Apr 2, 2026

 No
Not Reported
Automatic Metrics BFCL Accuracy Not Reported
Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency

Apr 6, 2026

 No
Not Reported
Automatic Metrics Full Duplex Bench Accuracy , Pass@1 Not Reported
FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol

Mar 26, 2026

 No
Not Reported
Automatic Metrics Finmcp Bench Accuracy Not Reported
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

 No
Not Reported
Automatic Metrics MMLU Accuracy , Cost Not Reported

Protocol Diff (Top Papers)

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

Signal Brief Is Better: Non-Monotonic Chain-of-Thought Bud… Full-Duplex-Bench-v3: Benchmarking Tool Use for Ful… FinMCP-Bench: Benchmarking LLM Agents for Real-Worl…
Human Feedback Not reportedNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks BFCLFull Duplex BenchFinmcp Bench
Metrics AccuracyAccuracy, Pass@1Accuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit FreeformUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Full-Duplex-Bench-v3: Benchmarking Tool Use for Full-Duplex Voice Agents Under Real-World Disfluency

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: full-duplex-bench / accuracy. Abstract: We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language models.

  2. Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: BFCL / accuracy. Abstract: How much should a language agent think before taking action?

  3. Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Autonomous tool-using agents in networked environments must decide which information source to query.

  4. AgenticRec: End-to-End Tool-Integrated Policy Optimization for Ranking-Oriented Recommender Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: pairwise preferences. Abstract: However, existing recommender agents typically suffer from a disconnect between intermediate reasoning and final.

  5. XSkill: Continual Learning from Experience and Skills in Multimodal Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: critique/edit feedback. Abstract: Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still.

  6. FinMCP-Bench: Benchmarking LLM Agents for Real-World Financial Tool Use under the Model Context Protocol

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Finmcp-Bench / accuracy. Abstract: This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large.

  7. Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: MMLU / accuracy. Abstract: Large Language Models (LLMs) have revolutionized inference across diverse natural.

  8. Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Applying large, proprietary API-based language models to text-to-SQL tasks poses a significant.

Known Limitations

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)

Research Utility Snapshot

Human Feedback Mix

  • Critique Edit (1)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (9)

Top Benchmarks

  • BFCL (1)
  • Finmcp Bench (1)
  • Full Duplex Bench (1)
  • MMLU (1)

Top Metrics

  • Accuracy (6)
  • Cost (2)
  • Latency (2)
  • F1 (1)

Rater Population Mix

Quality Controls

Coverage diagnostics (sample-based): human-feedback 18.2% · benchmarks 36.4% · metrics 63.6% · quality controls 0.0%.

Top Papers

Related Hubs

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