PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)
Copied to clipboard
Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, Jinyoung Yeo
| Challenge: | Recent research explores multi-agent systems where agents collaborate toward shared goals to handle complex tasks. |
| Approach: | They propose a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints. |
| Outcome: | The proposed benchmark shows that privacy constraints degrade collaboration performance and make outcomes depend more on the initiating agent than the partner. |
Similar Papers
SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)
Copied to clipboard
Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang
| Challenge: | Existing benchmarks conflate coordination ability with role-based priors. |
| Approach: | They propose a role-free benchmark for evaluating free-form collaboration under information silos. |
| Outcome: | The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs. |
Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents (2025.findings-emnlp)
Copied to clipboard
Shouju Wang, Fenglin Yu, Xirui Liu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan
| Challenge: | Existing benchmarks for privacy performance of LLM agents are limited to static, simplified scenarios. |
| Approach: | They propose a model-agnostic, contextual integrity based mitigation approach that effectively reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o. |
| Outcome: | The proposed approach reduces privacy leakage from 36.08% to 7.30% on DeepSeek-R1 and from 33.06% to 8.32% on GPT-4o while preserving task helpfulness. |
When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems (2026.findings-acl)
Copied to clipboard
| Challenge: | MAS-BENCH isolates coordination under explicit communication constraints . CAMOC significantly improves coordination success and efficiency across backends . |
| Approach: | They propose a distributed-sorting benchmark that isolates coordination under explicit communication constraints. |
| Outcome: | MAS-BENCH improves coordination success and efficiency across backends . CAMOC significantly improves efficiency under shared-state interaction . |
Privacy-R1: Privacy-Aware Multi-LLM Agent Collaboration via Reinforcement Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Prior approaches to rewriting large language models shatters linguistic coherence and removes privacy-sensitive information. |
| Approach: | They propose a framework that trains an agent to dynamically route text chunks . it implicitly distinguishes between replaceable Personally Identifiable Information (PII) and task-critical PII . |
| Outcome: | The proposed framework achieves state-of-the-art on the privacy-utility frontier . it trains an agent to dynamically route text chunks, learning a policy that balances privacy leakage and task performance. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
Copied to clipboard
Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
PII-Bench: Evaluating Query-Aware Privacy Protection Systems (2026.acl-long)
Copied to clipboard
| Challenge: | Existing models do not detect PII in user prompts, despite their convenience . current models show significant limitations in determining PI I query relevance . |
| Approach: | They propose a query-unrelated PII masking strategy and propose PIi-Bench . they propose 'quick-and-easy' PI I masking with a user query and context description . |
| Outcome: | The proposed model performs well in basic PII detection, but shows significant limitations in query relevance. |
MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)
Copied to clipboard
Kunlun Zhu, Hongyi Du, Zhaochen Hong, Xiaocheng Yang, Shuyi Guo, Zhe Wang, Zhenhailong Wang, Cheng Qian, Robert Tang, Heng Ji, Jiaxuan You
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition. |
| Approach: | They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios. |
| Outcome: | The proposed framework measures task completion and quality of collaboration and competition using novel, milestone-based key performance indicators. |
ConVerse: Benchmarking Contextual Safety in Agent-to-Agent Conversations (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) are rapidly transitioning from passive text generators to autonomous agents that act and communicate on behalf of users. |
| Approach: | a new benchmark evaluates privacy and security risks in agent–agent interactions . a converse model enables attackers to embed malicious requests within plausible discourse . the model is based on a three-tier taxonomy assessing abstraction quality . |
| Outcome: | ConVerse tests privacy and security risks in agent–agent interactions with 12 user personas and over 864 contextually grounded attacks. |
CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. |
| Approach: | They propose a benchmark to evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |
| Outcome: | The proposed benchmarks evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |
AutoPenBench: A Vulnerability Testing Benchmark for Generative Agents (2025.emnlp-industry)
Copied to clipboard
Luca Gioacchini, Alexander Delsanto, Idilio Drago, Marco Mellia, Giuseppe Siracusano, Roberto Bifulco
| Challenge: | LLM agents are promising for vulnerability testing, but lack benchmarks to evaluate and compare them. |
| Approach: | They propose an open-source benchmark for the evaluation of vulnerability testing agents that includes 33 tasks ranging from introductory exercises to actual vulnerable systems. |
| Outcome: | The proposed benchmark includes 33 tasks ranging from introductory exercises to actual vulnerable systems. |