论文

实验表明，OpenComputer 的硬编码验证器比 LLM-as-judge 评估更贴合人类裁定，尤其当任务成败取决于细粒度应用状态时。Experiments show that OpenComputer's hard-coded verifiers align more closely with human adjudication than LLM-as-judge evaluation, especially when success depends on fine-grained application state.

本文提出三种协议级原语以填补Model Context Protocol的空白：身份传递、自适应工具预算与结构化错误语义，并提出Structured Error Recovery Framework (SERF)，提供机器可读的失败语义以支持确定性的Agent自校正。Three protocol-level primitives are proposed to fill gaps in the Model Context Protocol: identity propagation, adaptive tool budgeting, and structured error semantics, and the Structured Error Recovery Framework (SERF), which provides machine-readable failure semantics that enable deterministic agent self-correction.

Agent Lightning v1.0 是一个轻量级的可控 Agentic RL 框架，约 3500 行代码实现，支持任意 Agent harness，并作为研究 retokenization、样本合并、优势计算、损失归一化与后端调度等挑战的实用测试平台。Agent Lightning v1.0 is presented, a lightweight framework for harnessed agentic RL implemented in approximately 3,500 lines of code that supports arbitrary agent harnesses and serves as a practical testbed for studying challenges in retokenization, sample merging, advantage calculation, loss normalization, and backend scheduling.

研究表明矛盾解析本质上是写入时并发控制，并将缺失的契约——一个在隔离性、模式与来源维度上被证明正确的写入时正确性规范——显式化，固定了每个生产启发式都默认假设、却没有任何已部署系统显式给出的保证。It is shown that contradiction resolution is write-time concurrency control and make the missing contract explicit, a write-time correctness specification, proved sound across isolation, schema, and provenance, pinning the guarantee every production heuristic assumes but no deployed system makes explicit.

本文探讨 multi-agent system，并指出当前尚未被充分解决的问题，同时探索了 multi-agent system 在区块链系统中的潜在应用，为其在真实分布式系统中的未来发展与落地提供启示。This paper explores multi-agent systems and identifies challenges that remain inadequately addressed, and explores potential applications of multi-agent systems in blockchain systems to shed light on their future development and application in real-world distributed systems.

在资源受限环境中，专业癫痫专家稀缺，使基于 LLM 的决策支持对管理纵向治疗的一线临床医生具有吸引力。此类系统必须适应当地处方实践并知道何时转诊。我们在乌干达儿科癫痫诊疗中研究该问题，基于纵向非结构化门诊记录预测抗癫痫用药方案。标准提示与医生处方取得了一定程度的一致性，但神经科医生审查显示许多错误反映的是分布失校的处方默认值而非失败。Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managing longitudinal treatment. Such systems must adapt to local prescribing practice and know when to defer. We study this problem in Ugandan pediatric epilepsy care, predicting anti-seizure medication regimens from longitudinal unstructured clinic notes. Standard prompting achieves non-trivial agreement with physician prescriptions, but neurologist review shows that many errors reflect distribution-miscalibrated prescribing defaults rather than fail

实验结果表明，所得参数集可生成可区分的个性化换道行为，同时 RAG 始终提升偏好理解效果，尤其对隐式指令效果显著，表明将基于 LLM 的自然语言交互与 Apollo 集成以支持个性化换道行为生成具有潜力。Experimental results show that the derived parameter sets generate distinguishable personalized lane-change behaviors, while RAG consistently improves preference interpretation, particularly for implicit commands, indicating the potential of integrating LLM-based natural-language interaction with Apollo to support personalized lane-change behavior generation.

本文提出 DuoMem，一种双空间蒸馏框架，可将程序化问题求解能力从大型教师模型迁移到紧凑学生模型，并适用于实时边缘部署，而这一点对教师模型而言颇具挑战。DuoMem is introduced, a dual-space distillation framework that transfers procedural problem-solving ability from a large teacher model to compact student models and is viable for real-time edge deployment, which would be challenging for the teacher.

提出 Transparent Two-Pass Execution，一种在推理时将工具执行与 schema 约束响应生成解耦的策略；实验结果表明该方法无需模型重新训练即可恢复工具调用能力，同时保持结构化输出保证。Transparent Two-Pass Execution is proposed, an inference-time strategy that decouples tool execution from schema-constrained response generation and experimental results show that this approach restores tool invocation while preserving structured output guarantees without requiring model retraining.

AOHP 的核心设计原则是将 Agent 视为 OS 中的一等公民，从而支持自适应用户界面以及对 Agent 友好的运行时环境；在任务完成度、执行成本和安全策略合规性方面均展现出明显优势。The core design principle of AOHP is to treat agents as first-class OS actors, enabling adaptive user interfaces and agent-friendly runtime environments, and shows clear advantages in task completion, execution cost, and security-policy compliance.

本研究收集了使用主流 AI Agent 开发的大量真实应用语料，并设计了结合 Agent 辅助代码审计与人工验证的漏洞分析框架，揭示出与传统软件开发流程中常见的漏洞模式不同的反复出现的漏洞规律。This study collects a large corpus of real-world applications developed using popular AI agents and designs a vulnerability analysis framework that combines agent-assisted code auditing with human validation, and reveals recurring vulnerability patterns that differ from those commonly observed in conventional software development workflows.

提出一种多 Agent 框架，通过以确定性编排约束替代 "LLM-as-a-judge" 路由，解决可能在到达患者前未被发现的过早诊断交接与静默临床幻觉问题；观察到 OLDCARTS 完整度与语义熵之间存在统计显著的负相关，提示结构化信息采集与诊断不确定性降低相关。A multi-agent framework that addresses premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient by replacing ``LLM-as-a-judge''routing with deterministic orchestration constraints is proposed and observes a statistically significant negative correlation between OLDCARTS completeness and semantic entropy, suggesting that structured information gathering is associated with reduced diagnostic uncertainty.

本文引入 trace-economic underwriting，将工具调用 trace 映射为客户风险敞口与可索赔损失，并以此表示用于定价、控制与风险转移，使用确定性经济标签而非 LLM 评判器。T trace-economic underwriting is introduced, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer, and uses deterministic economic labels rather than an LLM judge.

PalmClaw 是一个开源 Agent 框架，原生运行于手机端，直接在设备上管理 session、memory、Skill、工具以及 agent loop，使 Agent 能够直接调用移动端能力，同时保证每一步操作的显式与可控。PalmClaw is an open-source agent framework that runs natively on mobile phones and manages the sessions, memory, skills, tools, and agent loop directly on the device, allowing agents to use mobile capabilities directly while keeping each action explicit and controlled.

SPEAR 是一个 Python 库，可通过模块化插件架构连接任意 Unreal Engine 应用并对其进行编程化控制；同时引入一种表达力强的高层编程模型，使用户能够以任意数据依赖关系指定复杂的 UE 工作图，并在单个 UE 帧内确定性执行这些图。SPEAR is a Python library that can connect to, and programmatically control, any Unreal Engine application via a modular plugin architecture, and introduces an expressive high-level programming model that enables users to specify complex graphs of UE work with arbitrary data dependencies among work items, and to execute these graphs deterministically within a single UE frame.

本文设计了一种自适应随机谈判策略，同时保证行为差分隐私、报价序列的几乎处处收敛以及较高的谈判效用，并证明在获得强隐私保证的同时不会带来显著的性能损失。This paper designs an adaptive stochastic negotiation policy that jointly guarantees behavioral differential privacy, almost-sure convergence of the offer sequence, and high negotiation utility, and demonstrates that strong privacy guarantees can be achieved without significant loss of performance.

本文提出 FlashRT，一种 Agent Harness，引导编码 Agent 将开发者编写的简易参考实现提升为优化的多 GPU 部署，并可灵活权衡时延与吞吐量等目标指标，证明在专家优化尚不成熟的平台上，由 Agent 驱动的优化具有更高的可扩展性。FlashRT is presented, an agent harness that guides coding agents to lift simple developer-written reference implementations into optimized multi-GPU deployments that flexibly weigh target metrics like latency and throughput, demonstrating that agent-driven optimization can be more scalable on platforms with less mature expert optimization.

本文提出 HACO，一种运行时控制方案，将每次角色请求视为在候选 agent 实例上的可靠性约束选择问题，每个候选实例耦合了角色类型、LLM 与具体执行环境。HACO is proposed, a runtime control scheme that treats each role request as a reliability-constrained selection problem over candidate agent instances, each coupling a role type, an LLM, and a concrete execution environment.

本文提出一个框架，将静态的单轮任务转化为动态多轮对话，其中用户意图在多轮间持续演化，同时保留每个任务原有的评估协议，使现有基准能够在无需新增标注的情况下作为受控测试平台被复用。This work introduces a framework that transforms static, single-turn tasks into dynamic multi-turn conversations in which the user's intent evolves across turns, while preserving each task's original evaluation protocol, enabling existing benchmarks to be reused as controlled testbeds without new annotation.

提出 Multi-Head Latent Control，一种轻量级层，读取冻结 LLM 或 VLM 的隐状态轨迹以生成部署时的控制信号，从而支持从部分生成的提前交接，并在多模型系统中实现更准确的干预决策。Multi-Head Latent Control is introduced, a lightweight layer that reads hidden-state trajectories from a frozen LLM or VLM to produce deployment-time control signals, enabling early handoff from partial generations and more accurate intervention decisions in multi-model systems.

研究表明，当冻结模型与外部记忆配合、且该记忆将每个 episode 提炼为可检索的自然语言规则时，反馈信号足以支撑持续学习。It is shown that feedback is a sufficient signal for continual learning when the frozen model is paired with an external memory that distils each episode into retrievable natural-language rules when the frozen model is paired with an external memory.

将文件系统的默认设置转化为 agent memory 的设计空间，证明模型并非塑造 store 形态的唯一杠杆：仅调整工具集即可以与更换模型相当的力度重塑 store。The study turns the filesystem default from an assumption into a design space for agent memory, and turns the model is not the only lever over a store's shape: changing the tool set alone reshapes the store as strongly as swapping the model.

本综述旨在厘清自进化 coding agent 的概念边界，为设计更具适应性、可靠性与软件感知能力的 agentic 系统奠定基础。This survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.

一个持久化执行状态、使运行可被中断、能在崩溃后存活并继续的框架，必须为已经触发的副作用界定"恢复"的含义。五种广泛部署的 Agent 工作流框架给出了不同答案，且均未公开可机器验证的契约，其行为甚至违背了它们自己声明的片段。RESUME CONTRACT 针对持久化 API 陈述了六项性质（前缀延续、副作用恰好一次、分支确定性、检查点有效性、消费一次、恢复确定性），并附加分支意图与活性义务。TLA+ 模型对参考语义进行了检验……A framework that persists execution state so a run can be interrupted, survive a crash, and continue must decide what a resume means for effects that already fired. Five widely deployed agent workflow frameworks answer differently, none exposes a machine-checkable contract, and behavior violates even the fragments they state. The RESUME CONTRACT states six properties over the persistence API (prefix continuation, effect exactly-once, fork determinism, checkpoint validity, consume-once, recovery determinism), plus fork-intent and liveness obligations. A TLA+ model checks a reference semantics e

将软件侧密钥替换为硬件受限的密钥，通过厂商中立的 PKCS#11 接口访问，并由包含会话身份、作用域边界、语义验证、污点追踪和硬件执行边界的五层 Zero-Trust 执行栈提供保护。This work replaces software-resident keys with hardware-confined keys accessible through a vendor-neutral PKCS#11 interface, enabled by a surrounding five-layer Zero-Trust enforcement stack comprising session identity, scope bounds, semantic validation, taint tracking, and the hardware execution boundary.

将一个确定性、无模型的流水线编译进 Agent 记忆：该流水线将本地采集流切分为类型化的活动帧与有界事件片段，携带应用、站点、时间、输入量以及回指原始行的证据指针，全程无模型参与。A deterministic, zero-model pipeline is compiled into agent memory with a deterministic, zero-model pipeline that segments a local capture stream into typed activity frames, bounded episodes carrying application, site, timing, input volume, and evidence pointers back to the raw rows, with no model in the loop.

所提架构通过提供模块化、面向实现的具身认知能力 IVA 部署框架，弥合高层智能体推理模型与实时具身执行之间的鸿沟，助力在复杂交互虚拟环境中构建可扩展、自适应且可解释的智能体。The proposed architecture contributes by providing a modular, implementation-oriented framework for the deployment of embodied, cognitive-capable IVAs and bridges the gap between high-level agent reasoning models with real-time embodied execution, for scalable, adaptive, and explainable agents in complex interactive virtual environments.

VeriForge 是一种混合主动写作系统，通过划分认知劳动，使系统在领域发现上承担主动权，而作者保留对叙事合成的完全主动权；在受控的冷启动写作任务中，专家评审者认为其产出在领域扎根方面更强。VeriForge is a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.

SKILLER 是一个由自然语言驱动的强化学习框架，旨在为小模型自动生成执行器特定的 skills，使用强模型作为 actor 和 critic，将小模型 Agent 系统视为环境，并通过自然语言完全传递所有强化学习信号。SKILLER is a natural-language-driven reinforcement learning framework designed to automatically generate executor-specific skills for small models, which employs a strong model as the actor and critic, treats the small-model agent system as the environment, and propagates all reinforcement learning signals entirely via natural language.

提出一种 chunked-prefill 策略，可缓解由此带来的内存容量惩罚，在 32 GiB 共享内存上将允许的上下文宽度扩展 $2.7 \times$，但即便降低了内存开销，仍需打补丁才能使 Nanbeige4.2-3B 可用。A chunked-prefill strategy is introduced which alleviates the incurred memory-capacity penalty, extending allowable context width by $2.7 \times$ on 32~GiB shared memory, however, even with the reduced memory overhead, it is shown that patches are required to render Nanbeige4.2-3B usable.