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HFEPX Hub

Automatic Metrics + Coding + Tool Use Papers

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: agent-diff-bench. 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
Automatic MetricsCodingTool Use

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

  • 9.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • agent-diff-bench 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 (agent-diff-bench vs Longmemeval) before comparing methods.

Benchmark Interpretation

  • agent-diff-bench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • Longmemeval appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 45.5% of hub papers (5/11); 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 (agent-diff-bench vs Longmemeval) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: agent-diff-bench 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
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
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Feb 12, 2026

 No
Not Reported
Automatic Metrics Zoombench Latency Not Reported
Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation

Feb 11, 2026

 No
Not Reported
Automatic Metrics Agent Diff Bench Task success Not Reported
LightMem: Lightweight and Efficient Memory-Augmented Generation

Oct 21, 2025

 No
Not Reported
Automatic Metrics Longmemeval Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal ToolFlood: Beyond Selection -- Hiding Valid Tools f… Zooming without Zooming: Region-to-Image Distillati… Agent-Diff: Benchmarking LLM Agents on Enterprise A…
Human Feedback Not reportedNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks ToolBenchZoombenchAgent Diff Bench
Metrics Success rate, Jailbreak success rateLatencyTask success
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit UnknownUnknownUnknown
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. The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: cost. Abstract: Tool use enables large language models (LLMs) to access external information, invoke software.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics. Focus: ToolBench / success rate. Abstract: Large Language Model (LLM) agents increasingly use external tools.

  6. Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Zoombench / latency. Abstract: Multimodal Large Language Models (MLLMs) excel at broad visual understanding.

  7. Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: agent-diff-bench / task success. Abstract: We present Agent-Diff, a novel benchmarking framework for evaluating.

  8. LightMem: Lightweight and Efficient Memory-Augmented Generation

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Longmemeval / accuracy. Abstract: Despite their remarkable capabilities, Large Language Models (LLMs) struggle 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

  • Agent Diff Bench (1)
  • Longmemeval (1)
  • ToolBench (1)
  • Zoombench (1)

Top Metrics

  • Accuracy (5)
  • Cost (4)
  • F1 (1)
  • 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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