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HFEPX Hub

Tool Use Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 19, 2026). 16 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 19, 2026). 16 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 Mar 25, 2026.

Papers: 16 Last published: Mar 25, 2026 Global RSS Tag RSS
Tool UseLast 30d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

16 / 16 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.

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Why This Matters For Eval Research

  • 23.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 62.5% 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 Finmcp-Bench) before comparing methods.

Benchmark Interpretation

  • BFCL appears in 7.7% of hub papers (1/16); use this cohort for benchmark-matched comparisons.
  • Finmcp-Bench appears in 7.7% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 69.2% of hub papers (9/16); compare with a secondary metric before ranking methods.
  • cost is reported in 23.1% of hub papers (3/16); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (23.1% 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 (7.7% 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 (7.7% 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

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
VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

Mar 25, 2026

Yes Simulation Env Vehiclemembench Not Reported Not Reported
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
The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle

Mar 30, 2026

Yes Not Reported Not Reported Not Reported Not Reported
AgenticRec: End-to-End Tool-Integrated Policy Optimization for Ranking-Oriented Recommender Agents

Mar 23, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Apr 9, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents

Mar 31, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration

Mar 24, 2026

No
Not Reported
Automatic Metrics Not Reported Not Reported Not Reported
The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

Apr 8, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

Apr 7, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
HippoCamp: Benchmarking Contextual Agents on Personal Computers

Apr 1, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal VehicleMemBench: An Executable Benchmark for Multi-… Brief Is Better: Non-Monotonic Chain-of-Thought Bud… Full-Duplex-Bench-v3: Benchmarking Tool Use for Ful…
Human Feedback Pairwise PreferenceNot reportedNot reported
Evaluation Modes Simulation EnvAutomatic MetricsAutomatic Metrics
Benchmarks VehiclemembenchBFCLFull Duplex Bench
Metrics Not reportedAccuracyAccuracy, Pass@1
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit UnknownFreeformUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: The advent of agentic multimodal models has empowered systems to actively interact with.

  2. The Detection-Extraction Gap: Models Know the Answer Before They Can Say It

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Modern reasoning models continue generating long after the answer is already determined.

  3. 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.

  4. VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + pairwise preferences. Focus: Vehiclemembench. Abstract: This evolution requires agents to continuously model multi-user preferences.

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

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: However, existing recommender agents typically suffer from a disconnect between.

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: BFCL / accuracy. Abstract: How much should a language agent think before taking action?

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: full-duplex-bench / accuracy. Abstract: We introduce Full-Duplex-Bench-v3 (FDB-v3), a benchmark for evaluating spoken language.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.7% 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

  • Pairwise Preference (2)
  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (10)
  • Simulation Env (1)

Top Benchmarks

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

Top Metrics

  • Accuracy (9)
  • Cost (3)
  • Latency (3)
  • Pass@1 (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 18.8% · benchmarks 25.0% · metrics 62.5% · quality controls 0.0%.

Top Papers

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