JC-AI Newsletter #13

Two weeks have passed, and it is time to present a new collection of readings that may shape developments, utilization or ideas in the field of artificial intelligence in 2026.

While significant activity characterizes the AI field, many unresolved research, design, and implementation challenges continue to impact progress. Future advancement depends heavily on understanding the nature of these challenges to approach probabilistic problems from the appropriate directions. This JC-AI newsletter features insightful interviews with key figures in the field, enabling readers to ask the right questions and compare visions of an 'uncertain future' against current capabilities to maintain a grounded perspective.

article: Deep Researcher with Sequential Plan Reflection and Candidates Crossover (Deep Researcher Reflect Evolve)
authors: Saurav Prateek
date: 2026-01-28
desc.: This paper introduces Deep Researcher, a novel architecture that shifts the paradigm from latency-optimized parallel scaling to an accuracy-driven sequential refinement model. Within the development of Deep Research Agents (DRAs), two primary paradigms are considered, Parallel Scaling and Sequential Refinement. The Deep Researcher agent achieved an overall score of 46.21 on the Research Bench, demonstrating superior performance compared to existing agents, including Claude Researcher, Nvidia AIQ Research Assistant, Perplexity Research, Kimi Researcher, and Grok Deep Search. While these improvements are good, the field requires further research to address remaining challenges.
category: research

article: Manipulation in Prediction Markets: An Agent-based Modeling Experiment
authors: Bridget Smart, Ebba Mark, Anne Bastian, Josefina Waugh (University of Oxford)
date: 2026-01-28
desc.: The paper investigates the utilization of agentic systems in the economic field and their impact on prediction. First, the paper evaluates an agent-based model of a prediction market in which bettors with heterogeneous expertise, noisy private information, variable learning rates, and budgets observe the evolution of public opinion on a binary election outcome to inform their betting strategies in the market. The agentic system exhibits stability across experiments. The second area relates to experiments on how "whale" agents, a highly resourced minority with biased information, may distort market prices and for how long. The paper discusses interesting simulation results on how biased information may change the market from a long-term perspective.
category: research

article: Beyond Accuracy: A Cognitive Load Framework for Mapping the Capability Boundaries of Tool-use Agents
authors: Qihao Wang, Yue Hu, Mingzhe Lu, Jiayue Wu, Yanbing Liu, Yuanmin Tang
date: 2026-01-28
desc.: While LLMs' ability to use external tools enables powerful real-world applications, current benchmarks focus on final accuracy rather than revealing the cognitive bottlenecks that limit their true capabilities. This paper presents a framework based on Cognitive Load Theory that aims to decompose tasks into two components: Intrinsic Load and Extraneous Load. The paper discusses performance inconsistencies as cognitive load increases, and demonstrates how the proposed framework enables the identification of capability boundaries in the examined examples.
category: research

article: Build a Prompt Learning Loop - SallyAnn DeLucia & Fuad Ali, Arize
authors: AI Engineer, Sally Ann Delucia, Fuad Alli (Arize)
date: 2026-01-06
desc.: This talk aims to provide ideas on how it is possible to improve LLM responses by using feedback loops. It's important to view this talk through the lens of current research results regarding the LLM hallucination phenomenon and other factors. The main reason to keep current research results in mind is to avoid ending up in an infinite loop of failure/error.
category: youtube

article: Stanford CS230 | Autumn 2025 | Lecture 8: Agents, Prompts, and RAG
authors: Stanford Online
date: 2025-11-11
desc.: For more information about Stanford’s Artificial Intelligence professional and graduate programs
category: youtube, tutorial

article: Developer Experience in the Age of AI Coding Agents – Max Kanat-Alexander, Capital One
authors: AiEngineer, Max Kanat-Alexander
date: 2025-12-23
desc.: It feels like every two weeks, the world of software engineering is being turned on its head. Are there any principles we can rely on that will continue to hold true, and that can help us prepare for the future, no matter what happens? Max uses research, data, and his 20+ years working in enterprise Developer Experience teams to talk through what we can do now that will prepare us for an agentic future, no matter what that future holds.
category: youtube, opinion

article: Token-Guard: Towards Token-Level Hallucination Control via Self-Checking Decoding
authors: Yifan Zhu, Huiqiang Rong, Haoran Luo
date: 2026-01-29
desc.: Hallucination is a recognized phenomenon in the LLM field that impacts applications such as Retrieval-Augmented Generation (RAG) and Reward Modeling (RM). This paper introduces Token-Guard, a self-checking mechanism designed to identify and control hallucinations at the token level. The experiments demonstrate improvements.
category: research

article: Reward Models Inherit Value Biases from Pretraining
authors: Brian Christian, Jessica A. F. Thompson, Elle Michelle Yang, Vincent Adam, Hannah Rose Kirk and others (University of Oxford, University Pompeu Farba)
date: 2026-01-28
desc.: Despite their importance in LLM alignment, reward models (RMs) remain under-researched. This paper provides evidence that RMs inherit biases from their base models, suggesting that the choice of an open-source model is a reflection of values as much as performance. The paper discusses limitations of experiments and offers avenues for future research.
category: research

article: Professor Geoffrey Hinton - AI and Our Future
authors: City of Hobart, Geoffrey Hinton
date: 2026-01-08
desc.: Professor Geoffrey Hinton, known as the "Godfather of AI", will discuss artificial intelligence - how it works, the risks it poses to our society, and how we might coexist with super-intelligent AI. Ideal for business leaders, creatives, researchers, educators, students and anyone curious about the future of intelligence and society.
category: opinion

article: Your MCP Server is Bad (and you should feel bad) - Jeremiah Lowin, Prefect
authors: AI Engineer, Jeremiah Lowin
date: 2026-01-12
desc.: Too many MCP servers are simply glorified REST wrappers, regurgitating APIs that were designed for SDKs rather than agents. This leads to confused LLMs, wasted tokens, and demonstrably poor performance. If you have ever pointed an MCP generator at an OpenAPI spec and called it a day, this talk is your wake-up call.
category: youtube

article: Frontier Models & AI | Sam Altman, CEO & Co-Founder, OpenAI
authors: Cisco
date: 2026-02-04
desc.: Although Sam Altman, CEO and Co-Founder of @OpenAI, explores ideas about future possibilities and potential developments, he is asked during the interview to align his vision with the current state of research and existing technological capabilities. The interview, however, does not present clear data demonstrating how Codex outperforms alternatives or what 'better' specifically means in this context. The responses to questions may appear to be non-deterministic in nature. The interview relies heavily on thoughts about an "undefined future" that would require a deterministically defined foundation. It is interesting how the interview examined frontier AI models and their implications for economies, institutions, and global systems.
category: opinion

article: How to build secure and scalable remote MCP servers
authors: Den Delimarsky (Microsoft)
date: 2025-07-25
desc.: The tutorial provides insights into how to build a reliable Model Context Protocol (MCP) server, enabling AI agents to connect to external tools. It covers several crucial areas and provides valuable resources and ideas for tackling the challenge.
category: tutorial

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Miro Wengner

Author

Miro is a Java Champion and Oracle ACE Pro with a career-long focus on enterprise AI, including machine learning, neural networks, and probabilistic algorithms. As a contributor to OpenJDK and Mission Control, he specializes in optimizing Java performance, system maintainability and AI utilization in the enterprise. Beyond his engineering work, Miro co-authored the Duke’s Choice Award-winning Robo4j project and contributes to various open-source initiatives like OpenTracing and Pi4J. A recognized JavaOne Rock Star, he actively shapes the industry through the JCP, his technical blog, and frequent speaking engagements at global conferences such as JavaOne, CodeOne, Devoxx, JCON, JavaLand etc.