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Jiahao Qiu

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Artificial Intelligence Computation and Language math.DS Computer Vision

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preprint2026arXiv

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this static nature has become a critical bottleneck, necessitating agents that can adaptively reason, act, and evolve in real time. This paradigm shift -- from scaling static models to developing self-evolving agents -- has sparked growing interest in architectures and methods enabling continual learning and adaptation from data, interactions, and experiences. This survey provides the first systematic and comprehensive review of self-evolving agents, organizing the field around three foundational dimensions: what, when, and how to evolve. We examine evolutionary mechanisms across agent components (e.g., models, memory, tools, architecture), categorize adaptation methods by stages (e.g., intra-test-time, inter-test-time), and analyze the algorithmic and architectural designs that guide evolutionary adaptation (e.g., scalar rewards, textual feedback, single-agent and multi-agent systems). Additionally, we analyze evaluation metrics and benchmarks tailored for self-evolving agents, highlight applications in domains such as coding, education, and healthcare, and identify critical challenges and research directions in safety, scalability, and co-evolutionary dynamics. By providing a structured framework for understanding and designing self-evolving agents, this survey establishes a roadmap for advancing more adaptive, robust, and versatile agentic systems in both research and real-world deployments, and ultimately sheds light on the realization of Artificial Super Intelligence (ASI) where agents evolve autonomously and perform beyond human-level intelligence across tasks.

preprint2026arXiv

CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations

Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment due to the challenge of forming and maintaining a robust spatial mental model. We identify three core cognitive challenges hindering this transition: spatial reasoning, long-horizon state tracking via mental simulation, and active exploration under partial observation. To isolate and evaluate these faculties, we introduce CubeBench, a novel generative benchmark centered on the Rubik's Cube. CubeBench uses a three-tiered diagnostic framework that progressively assesses agent capabilities, from foundational state tracking with full symbolic information to active exploration with only partial visual data. Our experiments on leading LLMs reveal critical limitations, including a uniform 0.00% pass rate on all long-horizon tasks, exposing a fundamental failure in long-term planning. We also propose a diagnostic framework to isolate these cognitive bottlenecks by providing external solver tools. By analyzing the failure modes, we provide key insights to guide the development of more physically-grounded intelligent agents.

preprint2026arXiv

Learning Agent Routing From Early Experience

LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capability boundary of cutting-edge LLMs and do not require full agent execution, making effective routing between LLMs and agents a key challenge. We study the problem of routing queries between lightweight LLM inference and full agent execution under realistic cold-start settings. To address this, we propose BoundaryRouter, a training-free routing framework that uses early behavioral experience and rubric-guided reasoning to decide whether to answer a query with direct LLM inference or escalate to an agent. BoundaryRouter builds a compact experience memory by executing both systems on a shared seed set and retrieves similar cases at inference time to guide routing decisions. To evaluate this method, we introduce RouteBench, a benchmark covering in-domain, paraphrased, and out-of-domain route settings. Experiments show that BoundaryRouter reduces inference time by 60.6% compared to the agent while improving performance by 28.6% over direct LLM inference, outperforming prompt-based and retrieval-only routing by an average of 37.9% and 8.2%, respectively.

preprint2026arXiv

On subsets of integers having dense orbits

Let $A\subset \mathbb{N}$ . We say $A$ is an $R$ -sequence for a given minimal system $(Y,S)$ if there is $y\in Y$ such that $\{S^ny:n\in A\}$ is dense in $Y$ . Richter asked if $A$ is an $R$ -sequence for all minimal equicontinuous systems implies that $A$ is an $R$ -sequence for all minimal systems. In this paper, we investigate this question and related issues within the framework of totally minimal systems, including a characterization of transitive systems that are disjoint from all totally minimal systems. A dynamical system is scattering (resp. weakly scattering) if its product with any minimal (resp. minimal and equicontinuous) system is transitive. It turns out that $(X,T)$ is scattering if and only if for any transitive point $x\in X$ and any minimal system $(Y,S)$ there is $y\in Y$ such that the orbit of $(x,y)$ is dense in $X\times Y$ if and only if for each transitive point $x\in X$ and any non-empty open subset $U$ of $X$ , $\{n\in \mathbb{N}:T^nx\in U\}$ is an $R$ -sequence. By combining this result with earlier work of Huang and Ye, we deduce that if scattering and weak scattering are distinct properties, then both Richter's question and Katznelson's question admit negative answers.

preprint2022arXiv

Polynomial orbits in totally minimal systems

Inspired by the recent work of Glasner, Huang, Shao, Weiss and Ye, we prove that the maximal $\infty$ -step pro-nilfactor $X_\infty$ of a minimal system $(X,T)$ is the topological characteristic factor along polynomials in a certain sense. Namely, we show that by an almost one to one modification of $π:X\to X_\infty$ , the induced open extension $π^*:X^*\to X_\infty^*$ has the following property: for any $d\in \mathbb{N}$ , any open subsets $V_0,V_1,\ldots,V_d$ of $X^*$ with $\bigcap_{i=0}^d π^*(V_i)\neq \emptyset$ and any distinct non-constant integer polynomials $p_i$ with $p_i(0)=0$ for $i=1,\ldots,d$ , there exists some $n\in \mathbb{Z}$ such that $V_0\cap T^{-p_1(n)}V_1\cap \ldots \cap T^{-p_d(n)}V_d \neq \emptyset$ . where an integer polynomial is the polynomial with rational coefficients taking integer values on the integers. As an application, the following result is obtained: for a totally minimal system $(X,T)$ and integer polynomials $p_1,\ldots,p_d$ , if every non-trivial integer combination of $p_1,\ldots,p_d$ is not constant, then there is a dense $G_δ$ subset $Ω$ of $X$ such that the set $\{(T^{p_1(n)}x,\ldots, T^{p_d(n)}x):n\in \mathbb{Z}\}$ is dense in $X^d$ for every $x\in Ω$ .

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Artificial Intelligence Computation and Language math.DS Computer Vision

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arxivconfidence 95%

external id: arxiv:2512.14430:author:2:jiahao-qiu

Imported May 21, 2026Synced May 21, 2026

arxivconfidence 95%

external id: arxiv:2512.23328:author:6:jiahao-qiu

Imported May 21, 2026Synced May 21, 2026

arxivconfidence 95%

external id: arxiv:2605.07180:author:2:jiahao-qiu

Imported May 20, 2026Synced May 20, 2026

Mengdi Wang3 works Shilong Liu3 works Xinzhe Juan3 works Hongru Wang2 works

Jiahao Qiu

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5 publications

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations

Learning Agent Routing From Early Experience

On subsets of integers having dense orbits

Polynomial orbits in totally minimal systems

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