arXiv AI June 30, 2026 By Ruiyu Zhang, Lin Nie, Xin Zhao

Metric Aggregation Divergence: A Hidden Validity Threat in Agent-Based Policy Optimization and a Contractual Remedy

arXiv:2606. 29038v1 Announce Type: cross Abstract: Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi-objective evolutionary algorithm (ABM+MOEA) independently re-implement how an outcome metric is extracted from simulation trajectories.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

agents

arXiv AI

Aug 10

Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints

arXiv:2608. 06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measurable demographic bias.

By Paul-Peter Arslan

llms agents benchmarks safety

arXiv AI

Aug 5

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

arXiv:2608. 02685v1 Announce Type: cross Abstract: Coding-agent benchmarks increasingly cover long-horizon, end-to-end, and interactive development, but typically retain one requested outcome or a fixed change sequence.

By Zetong Xiong, Qiao Zhao, Jun Zhang, Xueying Lyu, Zhi Li, Yixiang Tu, Xiaowen Yang, Yunjie Zhang, Yufeng Wang, Zhe Zhang, Kaize Yu, Hanwen Du, Zhongkai Sun, Zhuoxin Liu, Zekun Lin, Jianwen Yang, Ruining Chen, Ying Zhang, Tingxuan Pan, Ke Chen, Shubin Han, Chuanhao Sun, Yehua Yang

agents benchmarks safety

arXiv Machine Learning

Jul 2

EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems

arXiv:2607. 00297v1 Announce Type: new Abstract: When LLM agents use evaluator feedback to adapt their behavior in closed loops, evaluator biases propagate through the agent's strategy distribution -- a phenomenon known as evaluator preference coupling.

By Zewen Liu

llms agents

arXiv AI

Jul 7

When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

arXiv:2607. 03386v1 Announce Type: new Abstract: Agentic AI systems are increasingly used to edit, refine, and repair decision policies, but evaluating these edits is difficult when per-state expert action labels are unavailable.

By Peiying Zhu, Sidi Chang

llms agents benchmarks safety

arXiv Machine Learning

Jul 14

Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness

arXiv:2607. 10202v1 Announce Type: new Abstract: Cross-model comparisons read divergence in value dispositions as evidence that language models hold individuated values.

By Hong-In Won, Jinseok Jang, Hyoseop Kim

llms agents

arXiv AI

Jul 9

Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety

arXiv:2607. 07097v1 Announce Type: new Abstract: Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline effect.

By Lifei Liu, Haoran Yu, Xiaochong Jiang, Su Wang, Pin Qian, Yihang Chen

llms agents benchmarks safety

Related stories

Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints

BulkPR-Bench: Benchmarking Queue-Level Governance of Interacting Pull Requests

EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems

When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

Two Confounds in Cross-Model Value Comparison: Response Determinism and the Access Harness

Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety