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HFEPX Hub

Tool Use + Automatic Metrics (Last 30 Days)

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

Read Full Context

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 10, 2026.

Papers: 11 Last published: Mar 10, 2026 Global RSS Tag RSS
Tool UseAutomatic MetricsLast 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%

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.

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Why This Matters For Eval Research

  • 9.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% 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 81.8% of hub papers (9/11); compare with a secondary metric before ranking methods.
  • cost is reported in 36.4% of hub papers (4/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (9.1% 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 (100% 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 (36.4% benchmarks, 100% 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

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
ToolFlood: Beyond Selection -- Hiding Valid Tools from LLM Agents via Semantic Covering

Mar 14, 2026

No
Not Reported
Automatic Metrics ToolBench Success rate , Jailbreak success rate Not Reported
Sabiá-4 Technical Report

Mar 10, 2026

Yes Automatic Metrics Not Reported Accuracy , Cost 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 , Cost 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 Cost 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 , Cost 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
Schema on the Inside: A Two-Phase Fine-Tuning Method for High-Efficiency Text-to-SQL at Scale

Mar 25, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy , Precision 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. 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.

  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. 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.

  4. Sabiá-4 Technical Report

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: The models were developed through a four-stage training pipeline:.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics. Focus: BFCL / accuracy. Abstract: How much should a language agent think before taking action?

  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. ToolFlood: Beyond Selection -- Hiding Valid Tools from LLM Agents via Semantic Covering

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: ToolBench / success rate. Abstract: Large Language Model (LLM) agents increasingly use external tools.

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Autonomous tool-using agents in networked environments must decide which information source to.

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

  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (11)

Top Benchmarks

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

Top Metrics

  • Accuracy (9)
  • Cost (4)
  • Latency (2)
  • Jailbreak success rate (1)

Rater Population Mix

Quality Controls

Coverage diagnostics (sample-based): human-feedback 9.1% · benchmarks 36.4% · metrics 100.0% · quality controls 0.0%.

Top Papers

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