This page shows the most relevant public items for AI Engineering, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Blog • Feb 8, 2026 • Medium • Mark S. Davidson
The framework wars of 2025 have settled, leaving LangGraph and AutoGen as the dominant orchestration layers for enterprise AI Engineering. Davidson provides a brutal, battle-tested comparison of th...
Blog • Nov 20, 2025 • Language Models Newsletter • Chip Huyen
This article explores the engineering challenges involved in designing production AI systems. It discusses topics such as system architecture, monitoring, data pipelines, and evaluation. The author...
Blog • Feb 20, 2026 • Medium • ZeroDay_Scribe
As Agentic AI systems gain persistent memory and direct API access, traditional prompt injection defenses are no longer sufficient. This highly technical post introduces the concept of 'State Poiso...
Paper • Feb 28, 2026 • arXiv • Virginia Dignum, Frank Dignum
The rapid popularization of the term 'Agentic AI' has led to semantic confusion, often blurring the lines between simple generative loops and true software agency. This position paper critically ex...
Paper • Mar 2, 2026 • arXiv • Anonymous
Extracting structured event arguments from massive, unstructured document repositories is highly error-prone using single-pass LLMs. This paper proposes a collaborative multi-agent architecture fea...
Paper • Mar 4, 2026 • arXiv • Zhiming Xue, Yujue Wang
Predictive models in logistics often act as black boxes, making them difficult for supply chain managers to trust. This paper integrates Temporal Graph Attention Networks with an LLM-driven reasoni...
Paper • Mar 4, 2026 • arXiv • Minjune Hwang, Yigit Korkmaz, Daniel Seita, Erdem Bıyık
Reward learning from human preferences often suffers from spurious correlations, leading agents to develop brittle and misaligned behaviors. The authors present a framework that integrates causal i...
Blog • Mar 5, 2026 • Medium • Marcus Sterling
For the past year, web automation agents have relied heavily on parsing HTML DOM structures, making them notoriously brittle whenever a website updates its layout. The release of OpenClaw 3.0 this ...
Paper • Mar 4, 2026 • arXiv • Yong Liu, Xingjian Su, Shiyu Wang, Haoran Zhang
While text and image foundation models have scaled massively, time-series forecasting has lagged behind due to architectural constraints. Timer-S1 introduces a billion-parameter foundation model sp...
Paper • Feb 28, 2026 • arXiv • Qiang Wang, Xinyuan Gao, SongLin Dong, Jizhou Han, Jiangyang Li, Yuhang He, Yihong Gong
Generating highly detailed, temporally accurate video captions requires models to understand complex spatial and temporal dynamics. VDC-Agent introduces an autonomous framework where the captioning...
Paper • Feb 28, 2026 • arXiv • Rui Li, Zeyu Zhang, Xiaohe Bo, Quanyu Dai, Chaozhuo Li, Feng Wen, Xu Chen
Scaling multi-agent swarms using static, centralized orchestrators frequently leads to communication bottlenecks and single points of failure. This paper approaches agentic coordination through the...
Paper • Mar 7, 2026 • arXiv • Junming Liu, Yuqi Li, Shiping Wen, Zhigang Zeng, Tingwen Huang
Hit-RAG addresses a key challenge in long-context retrieval systems: attention dilution caused by large volumes of retrieved evidence. The framework introduces a multi-stage preference alignment pi...
Paper • Feb 28, 2026 • arXiv • Anonymous
Recent advancements in agentic reasoning rely heavily on self-verification loops, where models constantly double-check their own logic. However, this often leads to excessive computational waste an...
Paper • Mar 4, 2026 • arXiv • G. Madan Mohan, Veena Kiran Nambiar, Kiranmayee Janardhan
As autonomous systems proliferate, standard capability benchmarks fail to evaluate the nuanced behavioral governance required for enterprise deployment. This research introduces Design Behaviour Co...
Paper • Mar 4, 2026 • arXiv • Ratna Kandala, Niva Manchanda, Akshata Kishore Moharir, Ananth Kandala
Detecting deceptive or manipulative intent across long-term interactions is a major blind spot for standard stateless LLMs. The authors introduce EchoGuard, an agentic framework that utilizes a per...
Paper • Mar 9, 2026 • arXiv • J. Smith, A. Doe, L. Weiss
This paper presents a novel AI Engineering curriculum designed to integrate agentic tools directly into the agile software development lifecycle. It advocates for moving beyond simple AI lectures t...
Blog • Dec 22, 2025 • The New Stack • Richard MacManus
This article analyzes key AI engineering trends in 2025, highlighting the rise of vibe coding alongside agentic AI systems. It explains how developers are shifting from writing code to orchestratin...
Paper • Feb 7, 2026 • arXiv • Xingliang Hou, Yuyan Liu, Qi Sun, Haoxiu Wang, Hao Hu, Shaoyi Du, Zhiqiang Tian
IGMiRAG introduces a cognitive-inspired retrieval framework that simulates intuition-driven reasoning over hierarchical knowledge graphs. The system constructs a heterogeneous hypergraph to represe...
Paper • Feb 14, 2026 • arXiv • Rui Yu, Tianyi Wang, Ruixia Liu, Yinglong Wang
PT-RAG proposes a structure-aware retrieval framework designed specifically for long academic documents. Traditional RAG pipelines flatten documents into chunks, which destroys the natural hierarch...
Blog • Dec 22, 2025 • Latent Space • Swyx
This article analyzes how AI agents are reshaping the software industry. It explores the shift from traditional applications toward autonomous systems capable of planning and executing tasks. The a...
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