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Benchmark Hub

Retrieval Benchmark Papers With Cost

Updated from current HFEPX corpus (Feb 27, 2026). 10 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. Common annotation unit: Freeform. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. 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 24, 2026.

Papers: 10 Last published: Feb 24, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 10 papers for Retrieval Benchmark Papers With Cost. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, ALFWorld and metric focus on cost, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 100% of hub papers (10/10); use this cohort for benchmark-matched comparisons.
  • ALFWorld appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 100% of hub papers (10/10); compare with a secondary metric before ranking methods.
  • accuracy is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (0% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (10% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (100% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (0% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (20% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (0% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (10% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (100% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (100% vs 35% target).

Papers with known rater population

Coverage is a replication risk (0% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (20% vs 35% target).

Suggested Reading Order

  1. 1. Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Structured Prompt Language: Declarative Context Management for LLMs

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Cross-lingual Matryoshka Representation Learning across Speech and Text

    Adds automatic metrics for broader coverage within this hub.

  5. 5. Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents

    Adds simulation environments for broader coverage within this hub.

  6. 6. Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Embodied Task Planning via Graph-Informed Action Generation with Large Language Model

    Adds simulation environments for broader coverage within this hub.

  8. 8. Fast-weight Product Key Memory

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 10% 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 Links

automatic_metrics vs simulation_env

both=0, left_only=8, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 10 papers (100%)

10 papers (100%) mention Retrieval.

Examples: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration , Structured Prompt Language: Declarative Context Management for LLMs

Benchmark Brief

ALFWorld

Coverage: 1 papers (10%)

1 papers (10%) mention ALFWorld.

Examples: Embodied Task Planning via Graph-Informed Action Generation with Large Language Model

Benchmark Brief

MMLU

Coverage: 1 papers (10%)

1 papers (10%) mention MMLU.

Examples: KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration

Metric Brief

cost

Coverage: 10 papers (100%)

10 papers (100%) mention cost.

Examples: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration , Structured Prompt Language: Declarative Context Management for LLMs

Metric Brief

accuracy

Coverage: 2 papers (20%)

2 papers (20%) mention accuracy.

Examples: Cross-lingual Matryoshka Representation Learning across Speech and Text , Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering

Metric Brief

jailbreak success rate

Coverage: 2 papers (20%)

2 papers (20%) mention jailbreak success rate.

Examples: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , Cross-lingual Matryoshka Representation Learning across Speech and Text

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration , Structured Prompt Language: Declarative Context Management for LLMs

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration , Structured Prompt Language: Declarative Context Management for LLMs

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG , KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration , Structured Prompt Language: Declarative Context Management for LLMs

Top Papers On This Benchmark

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