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HFEPX Hub

Automatic Metrics + Tool Use Papers

Updated from current HFEPX corpus (Feb 27, 2026). 13 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. Common metric signal: success rate. 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 26, 2026.

Papers: 13 Last published: Feb 26, 2026 Global RSS Tag RSS
Automatic MetricsTool Use

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 13 papers for Automatic Metrics + Tool Use Papers. Dominant protocol signals include automatic metrics, human evaluation, with frequent benchmark focus on Retrieval, MMLU and metric focus on success rate, 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 23.1% of hub papers (3/13); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 15.4% of hub papers (2/13); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • success rate is reported in 15.4% of hub papers (2/13); compare with a secondary metric before ranking methods.
  • accuracy is reported in 7.7% of hub papers (1/13); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (15.4% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (7.7% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (53.8% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (53.8% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (7.7% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (7.7% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (15.4% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (7.7% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (53.8% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (53.8% vs 35% target).

Papers with known rater population

Coverage is a replication risk (7.7% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (7.7% vs 35% target).

Suggested Reading Order

  1. 1. OmniGAIA: Towards Native Omni-Modal AI Agents

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

  2. 2. Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

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

  3. 3. A Benchmark for Deep Information Synthesis

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

  4. 4. PyVision-RL: Forging Open Agentic Vision Models via RL

    Adds automatic metrics for broader coverage within this hub.

  5. 5. MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents

    Adds automatic metrics for broader coverage within this hub.

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

    Adds automatic metrics for broader coverage within this hub.

  7. 7. RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution

    Adds automatic metrics for broader coverage within this hub.

  8. 8. OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.7% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

human_eval vs automatic_metrics

both=1, left_only=0, right_only=12

1 papers use both Human Eval and Automatic Metrics.

Benchmark Brief

Retrieval

Coverage: 3 papers (23.1%)

3 papers (23.1%) mention Retrieval.

Examples: A Benchmark for Deep Information Synthesis , OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering , LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Benchmark Brief

MMLU

Coverage: 2 papers (15.4%)

2 papers (15.4%) mention MMLU.

Examples: Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference , RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution

Benchmark Brief

HotpotQA

Coverage: 1 papers (7.7%)

1 papers (7.7%) mention HotpotQA.

Examples: RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution

Metric Brief

success rate

Coverage: 2 papers (15.4%)

2 papers (15.4%) mention success rate.

Examples: What Matters For Safety Alignment? , Measuring AI Ability to Complete Long Software Tasks

Metric Brief

accuracy

Coverage: 1 papers (7.7%)

1 papers (7.7%) mention accuracy.

Examples: Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Metric Brief

cost

Coverage: 1 papers (7.7%)

1 papers (7.7%) mention cost.

Examples: Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Most Cited In This Hub

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

Papers: OmniGAIA: Towards Native Omni-Modal AI Agents , Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference , A Benchmark for Deep Information Synthesis

Most Recent

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

Papers: OmniGAIA: Towards Native Omni-Modal AI Agents , Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference , A Benchmark for Deep Information Synthesis

Best Protocol Detail

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

Papers: OmniGAIA: Towards Native Omni-Modal AI Agents , Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference , A Benchmark for Deep Information Synthesis

Top Papers

  • OmniGAIA: Towards Native Omni-Modal AI Agents

    Xiaoxi Li, Wenxiang Jiao, Jiarui Jin, Shijian Wang, Guanting Dong · Feb 26, 2026 · Citations: 0

    Automatic Metrics Tool Use

    Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world.

  • Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

    Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0

    Automatic Metrics Tool Use

    Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.

  • A Benchmark for Deep Information Synthesis

    Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov · Feb 24, 2026 · Citations: 0

    Human EvalAutomatic Metrics Tool Use

    Large language model (LLM)-based agents are increasingly used to solve complex tasks involving tool use, such as web browsing, code execution, and data analysis.

  • PyVision-RL: Forging Open Agentic Vision Models via RL

    Shitian Zhao, Shaoheng Lin, Ming Li, Haoquan Zhang, Wenshuo Peng · Feb 24, 2026 · Citations: 0

    Automatic Metrics Tool Use

    Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior.

  • MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents

    Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai, Moayad Aloqaily, Ouns Bouachir · Feb 15, 2026 · Citations: 0

    Automatic Metrics Tool Use

    The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers.

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

    Lai Wei, Liangbo He, Jun Lan, Lingzhong Dong, Yutong Cai · Feb 12, 2026 · Citations: 0

    Automatic Metrics Tool Use

    To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM.

  • RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution

    Isaac Picov, Ritesh Goru · Feb 6, 2026 · Citations: 0

    Automatic Metrics Tool Use

    Explaining closed-source Large Language Model (LLM) outputs is challenging because API access prevents gradient-based attribution, while perturbation methods are costly and noisy when they depend on regenerated text.

  • OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering

    Yifan Zhu, Xinyu Mu, Tao Feng, Zhonghong Ou, Yuning Gong · Feb 3, 2026 · Citations: 0

    Automatic Metrics Tool Use

    To address these issues, we propose OmniRAG-Agent, an agentic omnimodal QA method for budgeted long audio-video reasoning.

  • STAR: Similarity-guided Teacher-Assisted Refinement for Super-Tiny Function Calling Models

    Jiliang Ni, Jiachen Pu, Zhongyi Yang, Jingfeng Luo, Conggang Hu · Feb 3, 2026 · Citations: 0

    Automatic Metrics Tool Use

    The proliferation of Large Language Models (LLMs) in function calling is pivotal for creating advanced AI agents, yet their large scale hinders widespread adoption, necessitating transferring their capabilities into smaller ones.

  • What Matters For Safety Alignment?

    Xing Li, Hui-Ling Zhen, Lihao Yin, Xianzhi Yu, Zhenhua Dong · Jan 7, 2026 · Citations: 0

    Red Team Automatic Metrics Tool Use

    This paper presents a comprehensive empirical study on the safety alignment capabilities.

  • LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

    Guozhao Mo, Wenliang Zhong, Jiawei Chen, Qianhao Yuan, Xuanang Chen · Aug 3, 2025 · Citations: 0

    Automatic Metrics Tool Use

    Unfortunately, there is still a large gap between real-world MCP usage and current evaluation: they typically assume single-server settings and directly inject tools into the model's context, bypassing the challenges of large-scale retrieva

  • Measuring AI Ability to Complete Long Software Tasks

    Thomas Kwa, Ben West, Joel Becker, Amy Deng, Katharyn Garcia · Mar 18, 2025 · Citations: 0

    Expert Verification Automatic Metrics Tool Use

    Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear.

  • Should You Use Your Large Language Model to Explore or Exploit?

    Keegan Harris, Aleksandrs Slivkins · Jan 31, 2025 · Citations: 0

    Automatic Metrics Tool Use

    We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff.

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