Success Rate Metric Papers

Research Utility Snapshot

Top Papers Reporting This Metric

• Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning

  Tomoya Kawabe, Rin Takano · Feb 25, 2026

  Automatic Metrics General

  We present a hierarchical multi-agent LLM-based planner with prompt optimization: an upper layer decomposes tasks and assigns them to lower-layer agents, which generate PDDL problems solved by a classical planner.

• Self-Correcting VLA: Online Action Refinement via Sparse World Imagination

  Chenyv Liu, Wentao Tan, Lei Zhu, Fengling Li, Jingjing Li · Feb 25, 2026

  Simulation Env Coding

  Reinforcement learning enhances physical grounding through exploration yet typically relies on external reward signals that remain isolated from the agent's internal states.

• LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies

  Yue Yang, Shuo Cheng, Yu Fang, Homanga Bharadhwaj, Mingyu Ding · Feb 25, 2026

  Simulation Env General

  We introduce a 21-task simulation benchmark consisting of two challenging suites: LIBERO-Long++ and Ultra-Long.

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

  Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026

  Automatic Metrics General

  Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.

• AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs

  Che Wang, Jiaming Zhang, Ziqi Zhang, Zijie Wang, Yinghui Wang · Feb 24, 2026

  Simulation Env General

  The integration of external data services (e.g., Model Context Protocol, MCP) has made large language model-based agents increasingly powerful for complex task execution.

• MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs

  Chun Yan Ryan Kan, Tommy Tran, Vedant Yadav, Ava Cai, Kevin Zhu · Feb 21, 2026

  Automatic Metrics General

  Defending LLMs against adversarial jailbreak attacks remains an open challenge.

• Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

  Jyotin Goel, Souvik Maji, Pratik Mazumder · Feb 19, 2026

  Automatic Metrics General

  Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and worsen under adversarial updates.

• Evolutionary System Prompt Learning for Reinforcement Learning in LLMs

  Lunjun Zhang, Ryan Chen, Bradly C. Stadie · Feb 16, 2026

  Automatic Metrics Coding

  Building agentic systems that can autonomously self-improve from experience is a longstanding goal of AI.

• What Matters For Safety Alignment?

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

  Automatic Metrics General

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

• Reasoning Up the Instruction Ladder for Controllable Language Models

  Zishuo Zheng, Vidhisha Balachandran, Chan Young Park, Faeze Brahman, Sachin Kumar · Oct 30, 2025

  Automatic Metrics General

  Our finetuned models achieve consistent improvements on instruction following and instruction hierarchy benchmarks, achieving roughly a 20% improvement on the IHEval conflict setup.

• EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

  Mohammad Hossein Samaei, Faryad Darabi Sahneh, Lee W. Cohnstaedt, Caterina Scoglio · Sep 24, 2025

  Llm As JudgeSimulation Env General

  We introduce EpidemIQs, a novel multi-agent LLM framework that integrates user inputs and autonomously conducts literature review, analytical derivation, network modeling, mechanistic modeling, stochastic simulations, data visualization and

• CoAct-1: Computer-using Multi-Agent System with Coding Actions

  Linxin Song, Yutong Dai, Viraj Prabhu, Jieyu Zhang, Taiwei Shi · Aug 5, 2025

  Automatic Metrics General

  Autonomous agents that operate computers via Graphical User Interfaces (GUIs) often struggle with efficiency and reliability on complex, long-horizon tasks.

• When Style Breaks Safety: Defending LLMs Against Superficial Style Alignment

  Yuxin Xiao, Sana Tonekaboni, Walter Gerych, Vinith Suriyakumar, Marzyeh Ghassemi · Jun 9, 2025

  Automatic Metrics General

  In this work, we seek to understand whether style patterns compromise LLM safety, how superficial style alignment increases model vulnerability, and how best to mitigate these risks during alignment.

• Measuring AI Ability to Complete Long Software Tasks

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

  Automatic Metrics General

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

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