The paper audits self‑evolving financial agents—SkillOpt, Agent Workflow Memory (AWM), and ReasoningBank—by evaluating their performance, security drift, and execution‑interface mismatches in simulated e‑banking scenarios. It shows that while utility improves after evolution, exposure to malicious content and unauthorized state changes also rise, and that AWM’s text‑action envelope can disrupt tool execution, highlighting the need for comprehensive auditing beyond accuracy metrics.
By Jialong Li, Jialing Zhu
arXiv:2607. 00871v1 Announce Type: new Abstract: Self-evolving agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated.
By Biswa Sengupta
EvolveTrade is a self‑evolving framework that treats the system prompt of a tool‑using LLM trading agent as a text‑parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and portfolio feedback while keeping the backbone LLM fixed, allowing the agent to refine its information‑acquisition and portfolio‑construction procedures over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed‑policy baselines, with behavioral analyses indicating increased code‑mediated analysis and regime‑relevant computations.
By Sehee Kim, Yumin Choi, Minki Kang, Sung Ju Hwang
PerMIT is a memory architecture that provides provable integrity guarantees for persistent memory used by large language model agents. It limits trust to user‑typed inputs and values confirmed at a gate, mediating untrusted environment‑derived values through a finite interface while preserving multi‑turn planning and tool choice. Two machine‑checked integrity theorems in Lean 4 ensure that only gate‑confirmed data can influence sensitive actions, and community endorsements can accumulate to reduce verification effort as the user base grows.
By Tanqiu Jiang, Min Bai, Sadat Shahriar, Wanyu Du, Sandesh Swamy
arXiv:2609.07065v1 Announce Type: new
Abstract: Agent-to-agent (A2A) alpha discovery is slowed by repeated feedback cycles between mining and evaluation agents, whose hand-offs, in contemporary LLM m...
By Yuqi Li, Siyuan Liu, Bingjun Liu
The paper investigates how adversarial signals can infiltrate large‑language‑model (LLM) based multi‑agent trading systems through the agents’ communication channels. By restricting the attacker to realistic inputs—source data and prompts—it studies role‑specific attacks on four functional roles (Analyst, Researcher, Trader, Risk Manager) and evaluates four communication topologies under data‑ and agent‑level attacks. Experiments across multiple assets, backbones, and target directions show that no architecture is inherently robust, highlighting the need for safer designs in agentic trading systems.
By CheolWon Na, Hao Ni, Lukasz Szpruch, Zhangyang Wang, Dhagash Mehta, Saurabh Nagrecha, Alejandro Lopez-Lira, Chanyeol Choi, Yongjae Lee, Jee-Hyong Lee