February 2026

Agentic Engineering Patterns > Automated tests are no longer optional when working with coding agents. The old excuses for not writing them – that they’re time consuming and expensive to constantly rewrite while a codebase is rapidly evolving – no longer hold when an agent can knock them into shape in just a few minutes. They’re also vital for ensuring AI-generated code does what it claims to do. If the code has never been executed it’s pure luck […]

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Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but […]

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The magnitude of parameter updates are considered a key factor in continual learning. However, most existing studies focus on designing diverse update strategies, while a theoretical understanding of the underlying mechanisms remains limited. Therefore, we characterize model’s forgetting from the perspective of parameter update magnitude and formalize it as knowledge degradation induced by task-specific drift in the parameter space, which has not been fully captured in previous studies due to their assumption of a unified parameter space. By […]

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Rehearsal is one of the key techniques for mitigating catastrophic forgetting and has been widely adopted in continual learning algorithms due to its simplicity and practicality. However, the theoretical understanding of how rehearsal scale influences learning dynamics remains limited. To address this gap, we formulate rehearsal-based continual learning as a multidimensional effectiveness-driven iterative optimization problem, providing a unified characterization across diverse performance metrics. Within this framework, we derive a closed-form analysis of adaptability, memorability, and generalization from the […]

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The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability, since it enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability. However, accurate EDF is a challenging problem […]

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Shem Albert Photo courtesy of Seyond Machines still struggle to see. Autonomous vehicles miscalculate distances. Delivery robots stumble on curbs. Industrial sensors fail when the weather turns harsh. Seyond addressed this gap by constructing a full spectrum of LiDAR sensors spanning from 0.01 meters to 500 meters, giving robots the visual acuity needed to operate safely among humans. Billy Evers, VP of Sales and Marketing for the Americas and APAC regions, states that Seyond offers what few others […]

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Hello, I wanted to ask how do you restrict the output vocabulary/ possible actions of VLAs. Specifically I am reading currently the research papers of RT-2 and OpenVLA. OpenVLA references RT-2 and RT-2 says nothing specifically, it just says in the fine-tuning phase: “Thus, to ensure that RT-2 outputs valid action tokens during decoding, we constrain its output vocabulary via only sampling valid action tokens when the model is prompted with a robot-action task …” So do you […]

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Long before “engineering” became a formal career path, it was already part of Will Jiang’s daily life. Growing up in China, Jiang was the student teacher whom a classmate turned to when a laptop froze minutes before class. By high school, he was maintaining his school’s network infrastructure by diagnosing connectivity issues, keeping systems online, and supporting hundreds of users. What stayed with him wasn’t just the technical challenge, but the idea that thoughtfully built systems could quietly […]

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Machine learning models often perform well during experimentation. Offline metrics improve, prototypes demonstrate potential, and early validation builds confidence across teams. In controlled environments, systems behave predictably and progress feels steady. The transition to production introduces a different set of pressures. Training jobs fail intermittently. Features arrive outside expected time windows. Historical data changes without notice. Deployments slow as teams hesitate, unsure of downstream consequences. What worked in isolation begins to strain under operational reality. The cause is […]

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Designing aligned and robust rewards for open-ended generation remains a key barrier to RL post-training. Rubrics provide structured, interpretable supervision, but scaling rubric construction is difficult: expert rubrics are costly, prompted rubrics are often superficial or inconsistent, and fixed-pool discriminative rubrics can saturate and drift, enabling reward hacking. We present SibylSense, an inference-time learning approach that adapts a frozen rubric generator through a tunable memory bank of validated rubric items. Memory is updated via verifier-based item rewards measured […]

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