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HFEPX Metric Hub

Inference Cost In CS.AI Papers

Updated from current HFEPX corpus (Jun 30, 2026). 31 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Jun 30, 2026). 31 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: AIME. 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 Feb 2, 2026.

Papers: 31 Last published: Feb 2, 2026 Global RSS

When This Metric Page Is Useful

Useful for background comparison, but still validate benchmark and protocol details in the linked papers. Quality band: Medium .

Metric Coverage

25.8%

8 sampled papers include metric names.

Benchmark Anchoring

6.5%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

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

Recommended next step: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Main limitation: Benchmark coverage is still thin, so avoid treating this page as a definitive guide to the metric.

What This Metric Page Tells You

What This Metric Page Tells You

  • 9.7% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 19.4% of papers in this hub.
  • AIME 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 domain experts, 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 (31/31); compare with a secondary metric before ranking methods.
  • inference cost is reported in 100% of hub papers (31/31); compare with a secondary metric before ranking methods.

Benchmark Context

  • AIME appears in 3.2% of hub papers (1/31); use this cohort for benchmark-matched comparisons.
  • ALFWorld appears in 3.2% of hub papers (1/31); 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
Dynamic Mixed-Precision Routing for Efficient Multi-step LLM Interaction

Feb 2, 2026

 Accuracy, Precision ALFWorld, WebShop Automatic Metrics, Simulation Env 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
Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

May 8, 2026

 Perplexity, 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
ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments

Aug 6, 2025

 Cost, Inference cost Not reported Not reported 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
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies

May 28, 2026

 Not reported Not reported Not reported Not reported
Training Deliberative Monitors for Black-Box Scheming Detection

May 28, 2026

 Not reported Not reported Not reported Not reported
How To Use This Page

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (9.7% 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 (25.8% 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 (3.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.7% 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 (3.2% coverage).
  • Annotation unit is under-specified (9.7% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AIME vs ALFWorld) before comparing methods.
  • Track metric sensitivity by reporting both cost and inference cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: AIMEMetric Slice: costRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Coverage Snapshot

Top Metrics

  • Cost (31)
  • Inference cost (31)
  • Accuracy (9)
  • Latency (4)

Evaluation Modes

  • Automatic Metrics (6)
  • Simulation Env (2)
  • Llm As Judge (1)

Top Benchmarks

  • AIME (1)
  • ALFWorld (1)
  • Arena Hard (1)
  • GPQA (1)

Agentic Mix

  • Long Horizon (3)
  • Web Browsing (2)

Top Papers Reporting This Metric

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