agentic-systems

A report proposing a unified taxonomy and an agentic literature system for LLM-driven computer graphics, analyzing trends and open challenges in human-agent collaboration and long-horizon planning.

The paper introduces Seq@K, an evaluation protocol measuring whether a language model can solve a problem within K sequential attempts given feedback after each failure, showing it reveals a materially different model-selection landscape than Pass@K across 7 benchmarks and 16 frontier models.

This paper audits evaluation artifacts and data leakage in augmented LLM systems for rare disease diagnosis, showing that candidate-space contamination and temporal misalignment artificially inflate benchmark performance, and proposes a rigorous data-isolation evaluation framework to ensure genuine clinical robustness.

A session from MIT Technology Review's EmTech Future conference exploring what it takes to build scalable, trustworthy AI foundations in highly regulated environments, featuring Jing Wang of Vanguard's Center for AI & Insights.

该论文对基于 LLM 的智能体系统的自动失败归因（AFA）进行了机制研究，发现模型存在'知道但不说'的差距：内部激活包含正确归因的信息，但无法显式输出。作者据此提出 grafting 干预方法，在多个 LLM 和基准上优于直接提示方法。

A consulting firm observes that read-only AI agents ship to production far faster than write-capable ones in regulated sectors, because governance questions around write access stall pilots, and offers practical advice on sequencing governance work and designing lightweight approval steps.

This paper theoretically and empirically explains why inference-time harnesses (scaffolds) outperform distilled student models: harnesses can compute closures that are NL-complete even from trivial AC0 primitives, a representational power no fixed-depth forward pass can replicate under standard complexity assumptions. The authors verify that enlarging the model, simplifying the task, or lengthening chain-of-thought can close this gap, including on competition math with agentic harnesses.

R²Orch 提出递归自博弈编排范式，通过内层自博弈与外层技能撤除循环，实现智能体编排的递归自我改进，在12个数据集上超越基线。

Gen-Verse 发布了 DiscoveryRSI-27B，一个 27B 指令微调语言模型，用于递归式科学假设开发、评估、修订与归因，配套 DiscoveryRSI 研究智能体框架支持生命科学研究工作流。

该报告大规模评测了 GPT-6-Astra 在 12 项机器人基准上的具身能力，指出其已跨越具身能力门槛但尚未实现实用具身智能，并探讨了 System 2/System 1 协同与真实部署中的成本、执行时间等关键挑战。

TimeRSI 提出一个围绕冻结的时间序列基础模型的自改进智能体部署系统，通过误差引导的代码迭代修订与自适应选择提升零样本预测性能，在 TIME 和 fev-bench 的 198 项任务上超越所有提交结果。

这篇关于 Evo² 的文章提出将自进化 Agent 的两条路径（修改模型权重与修改 Harness）连接起来，让 Proposer 与 Reflector 双角色进入持续训练循环，在 Polyglot 上达到 82.1%、SWE-bench Verified 上达到 48.3%，且演化出的 Harness 可直接迁移复用。

该论文提供了关于「智能体机器人」（agentic robots）的统一综述，将现有研究组织为模型、数据、环境和评测（Harness）四大功能领域的17个类别，并探讨了从机器人策略到智能体系统的演进及未来开放挑战。

推文介绍了一个关于递归自我改进(RSI)的精选研究合集，涵盖从STaR、Self-Rewarding LMs到Promptbreeder和Automated Design of Agentic Systems等关键论文，梳理了RSI从自生成训练到自我修改代码代理的演进脉络。

Dialpad researchers propose a sociolinguistic approach to authoring and measuring user persona fidelity in agentic system evaluation, showing that concrete stylistic-rate personas improve stylistic adherence over flat descriptive baselines across five customer-service agents.

SuperNav is a navigation framework that equips a pretrained multimodal LLM with an agent harness and navigation tools—without navigation-specific fine-tuning—allowing the model to interpret requests and specify visual destination points while delegating motion execution. It outperforms four baselines on instance-level, multi-object, and demand-driven tasks and is demonstrated on a real quadruped robot.

CADFather is an autonomous agentic system that reconstructs editable parametric CAD programs from 3D meshes by coordinating a vision-language assistant with complementary learned, algorithmic, and optimization tools, evaluated on benchmarks like DeepCAD, Fusion360, and CADENA-Bench without additional training.

论文提出 D-OPCD（Diffusion On-Policy Context Distillation），将文本生成图像智能体框架（harness）通过记忆、技能和迭代优化获得的增益蒸馏进扩散模型权重中，使模型在仅使用原始查询时也能保留部分收益，平均直接生成分数从 60.52 提升至 65.09，并通过 AutoSkillEvolver 实现框架与模型的持续协同进化。

Google Cloud AI Research introduces AIM (Agentic Idea Manager), an autonomous framework for idea-driven automated research that uses an Agentic Surrogate and Agentic Acquisition (inspired by Bayesian optimization) to organize and select research ideas, plus a Solution Auditor and Resource Planner to maintain idea-solution alignment and allocate experimental budgets. AIM outperforms the strongest AutoLab baseline by up to 4.9 points and reaches baseline performance up to 3.1x faster.

SCOUT introduces a two-stage agentic safety verifier that combines reasoning-intensive rubric generation with tool-intensive evidence gathering to detect unsafe behaviors in computer-use agents, improving unsafe detection on AutoElicit-Bench and reducing unsafe execution rates from 30.2% to 17.2% via test-time reflection.