BFCL or HumanEval+ Benchmark Papers

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Developing .

• 11 papers explicitly name benchmark datasets in the sampled set.
• 8 papers report at least one metric term in metadata extraction.
• Start with the ranked shortlist below before reading all papers.

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

• Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning

  May 30, 2026 · Citations: 0 · Score: 7.5

  Eval: Simulation Env · Metrics: Precision

• Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models

  Apr 22, 2026 · Citations: 0 · Score: 7.5

  Eval: Automatic Metrics · Metrics: Jailbreak success rate

• Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents

  Aug 12, 2026 · Citations: 0 · Score: 6.5

  Eval: Automatic Metrics · Metrics: Cost

• Tool Calling is Linearly Readable and Steerable in Language Models

  May 8, 2026 · Citations: 0 · Score: 6.0

  Eval: Automatic Metrics · Metrics: Accuracy

• SkillX: Automatically Constructing Skill Knowledge Bases for Agents

  Apr 6, 2026 · Citations: 0 · Score: 6.0

  Eval: Automatic Metrics · Metrics: Task success

• Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

  Apr 2, 2026 · Citations: 0 · Score: 6.0

  Eval: Automatic Metrics · Metrics: Accuracy

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Top Papers On This Benchmark

• Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning

  Chishui Chen, Jiaye Lin, Te Sun, Yi Yang, Junxi Wang · May 30, 2026 · Citations: 0

  Pairwise Preference Simulation Env

  Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks.

• SPADE: Self-Play in Adaptive Synthetic Executable Environments

  Bo Liu, Simon Yu, Yiding Jiang, Ao Qu, Andrew Zhao · Aug 19, 2026 · Citations: 0

  For language agents, existing training environment pools (hand-curated, statically synthesized, or frozen-verifier) keep the goal distribution fixed as the learner scales.

• Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models

  Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis, Seshu Tirupathi, John D. Kelleher · Apr 22, 2026 · Citations: 0

  Pairwise PreferenceRed Team Automatic Metrics

  The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the capabilities of AI-powered system by invoking external functions.

• Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents

  Dylan Bouchard, Mohit Singh Chauhan · Aug 12, 2026 · Citations: 0

  Automatic Metrics

  For LLM agents, however, the unit of observation is an interactive trajectory, where the model can ask clarifying questions, call tools, update state, and make intermediate decisions whose errors propagate to the final outcome.

• Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs

  Andrea Sassella, Andrea Chizzola, Tommaso Bianchi, Luca Alessandrelli, Mark James Carman · May 8, 2026 · Citations: 0

  This report benchmarks the performance of ENGINEERING Ingegneria Informatica S.p.A.'s EngGPT2MoE-16B-A3B LLM, a 16B parameter Mixture of Experts (MoE) model with 3B active parameters.

• Tool Calling is Linearly Readable and Steerable in Language Models

  Zekun Wu, Ze Wang, Seonglae Cho, Yufei Yang, Adriano Koshiyama · May 8, 2026 · Citations: 0

  Automatic Metrics

  When a tool-calling agent picks the wrong tool, the failure is invisible until execution: the email gets sent, the meeting gets missed.

• The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check

  Qingyu Lu, Liang Ding, Kanjian Zhang, Jinxia Zhang, Dacheng Tao · Jan 19, 2026 · Citations: 0

  Automatic Metrics

  In this work, we present a comprehensive evaluation of dLLMs (e.g., LLaDA, Dream) across two distinct agentic paradigms: Embodied Agents (requiring long-horizon planning) and Tool-Calling Agents (requiring precise formatting).

• SkillX: Automatically Constructing Skill Knowledge Bases for Agents

  Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang · Apr 6, 2026 · Citations: 0

  Automatic Metrics

  Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited…

• Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions

  Junhao Su, Yuanliang Wan, Junwei Yang, Hengyu Shi, Tianyang Han · Sep 23, 2025 · Citations: 0

  Automatic Metrics

  The agent produces a short yet precise reflection: it diagnoses the failure using evidence from the previous step and then proposes a correct, executable follow-up call.

• Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

  Xuan Qi · Apr 2, 2026 · Citations: 0

  Automatic Metrics

  Chain-of-thought (CoT) reasoning is widely assumed to improve agent performance, but the relationship between reasoning length and accuracy in structured tool-use settings remains poorly understood.

• Try, Check and Retry: A Divide-and-Conquer Framework for Boosting Long-context Tool-Calling Performance of LLMs

  Kunfeng Chen, Qihuang Zhong, Juhua Liu, Bo Du, Dacheng Tao · Mar 12, 2026 · Citations: 0

  Tool-DC (TF) brings up to +25.10% average gains against the baseline on BFCL and ACEBench benchmarks, while Tool-DC (TB) enables Qwen2.5-7B to achieve comparable or even better performance than proprietary LLMs, e.g., OpenAI o3 and…

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