The paper discusses the trustworthiness of agentic AI systems built on large language models, highlighting new security and operational risks such as indirect prompt injection, memory contamination, and cross‑session data leakage. It categorizes failure modes, reviews mitigation strategies—including instruction hierarchies, context isolation, and constrained tool use—and introduces the Trustworthy Agent Development Lifecycle (TADL), a six‑phase framework for specification, design, training, evaluation, deployment, and monitoring. The authors note that TADL has not yet been empirically validated but offers a structured foundation for developing more secure and accountable agentic systems, and they call for improved benchmarks and future research priorities.
By Fayeq Jeelani Syed, Rehan Ahmad, Ali Al Bataineh, Aakriti Adhikari
The paper proposes rethinking bias in AI as a diagnostic tool rather than merely a flaw to be minimized. It introduces a multidimensional framework that examines bias across origin, lifecycle emergence, technical causes, and validation methods, covering 30 bias types, 16 verification methods, and 20 countermeasures for both traditional and generative AI. The authors present a hierarchical evidence framework distinguishing internal and external validity, and advocate for Ethics by Design principles to embed bias verification throughout the AI development lifecycle.
By Samira Maghool, Paolo Ceravolo
arXiv:2610.27610v1 Announce Type: cross
Abstract: Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements. Evaluat...
By Zihan Chen, Di Zhu, Lei Nico Zheng
The paper introduces the concept of Evolutionary Safety for recursive self-improving AI, focusing on how safety properties evolve as an AI system and its successors change. It identifies key risks such as intent drift, error accumulation, and safety-property erosion, and presents a taxonomy covering agent state, model state, evaluation, environment, and update mechanisms. The authors propose methods for discovering and evaluating evolutionary risks, and outline governance principles for modification, selection, authorization, provenance, and recovery, while highlighting open problems for maintaining safety in persistent, adaptive, and recursively self-improving systems.
By Chang Gong, Jingping Bi, Di Yao, Xinjian Liang, Chao Xiang, Ruijie Guo
arXiv:2606. 04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents.
By Yiqi Wang, Jiaqi Zhang, Taotao Cai, Zirui Liu, Qingqiang Sun, Zequn Sun, Zhangkai Wu, Mingkai Zhang, Yanming Zhu
arXiv:2609.22961v1 Announce Type: cross
Abstract: Agentic systems increasingly invoke tools, services, data, and other agents across organizational boundaries, yet a relying party cannot assess a del...
By Huafu Li, Jia Xia
Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements. Evaluating this promise requires separating product claims...
arXiv:2610.18813v1 Announce Type: new
Abstract: Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model we...
By Chuyang Wei, Haoxiang Guan, Minghang Zhu, Kexin Zhou, Jiyan He, Xiren Zhou, Huanhuan Chen, Yifei Shen
The paper introduces the Self‑Evolving Agent Harness, a framework that allows an agent’s software scaffolding—prompts, memory, workflows, and executable components—to evolve while keeping the underlying model weights unchanged. It formalizes a feedback‑driven process where accepted revisions persist across runs, separating update targets from proposal and admission mechanisms, and distinguishes changes to interpreted state from revisions to tools and code. The authors analyze evolutionary search, provide generalization and retention bounds under PAC‑learning assumptions, and discuss the impact of persistent harness revisions on knowledge sharing, organizational workflows, and broader implications for scientific reliability, safety, and accountability.
arXiv:2607. 16130v1 Announce Type: cross Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way.
By Andrea Ferrario
The paper introduces the Alignment Flywheel, a governance‑centric hybrid multi‑agent system (MAS) that separates decision generation from safety governance. It defines a Proposer that generates candidate trajectories, a Safety Oracle stack that evaluates safety, and an Enforcement layer that applies risk policies at runtime. A governance MAS oversees monitoring, red‑teaming, verification, and versioned release management, enabling patch‑local fixes to safety failures without retraining the Proposer. The architecture is implementation‑agnostic and is demonstrated in two scenarios: a learned spatial Oracle and a clinical GenAI proxy. The authors provide open‑source code at https://github.com/decide-ugent/Alignment-Flywheel.
By Elias Malomgr\'e, Pieter Simoens
arXiv:2607. 07663v1 Announce Type: new Abstract: AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself.
By Mingguang Chen, Licheng Wang, Bo Qu