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HFEPX Metric Hub

Inference Cost In CS.AI Papers

Updated from current HFEPX corpus (Apr 9, 2026). 14 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 14 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Llm As Judge. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: Yc-Bench. Common metric signal: cost. 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 Apr 6, 2026.

Papers: 14 Last published: Apr 6, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Medium .

Metric Coverage

35.7%

5 sampled papers include metric names.

Benchmark Anchoring

7.1%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 14 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 14.3% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 28.6% of papers in this hub.
  • Yc-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven 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 multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Metric Interpretation

  • cost is reported in 100% of hub papers (14/14); compare with a secondary metric before ranking methods.
  • inference cost is reported in 100% of hub papers (14/14); compare with a secondary metric before ranking methods.

Benchmark Context

  • Yc-Bench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
$\texttt{YC-Bench}$: Benchmarking AI Agents for Long-Term Planning and Consistent Execution

Apr 1, 2026

 Cost, Inference cost Yc Bench Automatic Metrics Not reported
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Apr 6, 2026

 Cost, Inference cost Not reported Automatic Metrics Not reported
CAMEL: Confidence-Gated Reflection for Reward Modeling

Feb 24, 2026

 Accuracy, Cost Not reported Automatic Metrics Not reported
Luna-2: Scalable Single-Token Evaluation with Small Language Models

Feb 20, 2026

 Accuracy, Latency Not reported Llm As Judge, Automatic Metrics Not reported
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation

Jun 4, 2025

 Cost Not reported Simulation Env Not reported
GAIN: Multiplicative Modulation for Domain Adaptation

Apr 6, 2026

 Not reported Not reported Not reported Not reported
VISion On Request: Enhanced VLLM efficiency with sparse, dynamically selected, vision-language interactions

Mar 24, 2026

 Not reported Not reported Not reported Not reported
ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models

Mar 22, 2026

 Not reported Not reported Not reported Not reported
Post-Training Local LLM Agents for Linux Privilege Escalation with Verifiable Rewards

Mar 18, 2026

 Not reported Not reported Not reported Not reported
Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization

Mar 18, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14.3% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (7.1% 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).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (14.3% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (14.3% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Track metric sensitivity by reporting both cost and inference cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: Yc-BenchMetric Slice: costRecent 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)

Top Metrics

  • Cost (14)
  • Inference cost (14)
  • Accuracy (4)
  • Latency (3)

Evaluation Modes

  • Automatic Metrics (4)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • Yc Bench (1)

Agentic Mix

  • Long Horizon (1)
  • Web Browsing (1)

Top Papers Reporting This Metric

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