February 2026

In recommendation systems, representing user-item interactions as a bipartite network is a fundamental approach that provides a structured way to model relationships between users and items, allowing for efficient predictions via network science. Collaborative filtering is one of the most widely used and actively researched techniques for recommendation systems, its rationale is to predict user preferences based on shared patterns in user interactions, and vice versa. Memory-based collaborative filtering relies on directly analyzing user-item interactions to provide recommendations […]

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Background/Objectives: The rapid advancement of artificial intelligence (AI) has had a notable impact in the healthcare field, particularly in the realm of assessment and diagnosis. One specific area where the integration of AI technologies shows promise is the evaluation of progressive neurological disorders (PNDs). PNDs are characterized by a progressive decline in neurological function, resulting in changes in cognition, movement, and communication. PNDs pose significant challenges in terms of early detection and categorization. Speech and voice changes are […]

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Reinforcement learning with verifiable rewards (RLVR) has become a trending paradigm for training reasoning large language models (LLMs). However, due to the autoregressive decoding nature of LLMs, the rollout process becomes the efficiency bottleneck of RL training, consisting of up to 70% of the total training time. In this work, we propose Quantized Reinforcement Learning (QuRL) that uses a quantized actor for accelerating the rollout. We address two challenges in QuRL. First, we propose Adaptive Clipping Range (ACR) […]

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Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed. Learning from such signals is challenging, as LMs must implicitly infer how observed failures should translate into behavioral changes for future iterations. We introduce Experiential Reinforcement Learning (ERL), a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process. Given a task, the model generates an initial […]

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As Generative AI shifts from simple retrieval to autonomous action, enterprises face the engineering challenge of scaling scattered proofs of concept into robust, secure systems. This article deconstructs the architecture of a production-grade AI Agent Platform, positioning it as an internal Platform-as-a-Service (PaaS) that supports both code-first engineers and low-code integrators. By breaking the system down into seven logical containers—Interaction, Development, Core, Foundation, Information, Observability, and Trust—the guide outlines a blueprint for a “factory” capable of serving, securing, […]

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Tokenization is a hardcoded compression step which remains in the training pipeline of Large Language Models (LLMs), despite a general trend towards architectures becoming increasingly end-to-end. Prior work has shown promising results at scale in bringing this compression step inside the LLMs’ architecture with heuristics to draw token boundaries, and also attempts to learn these token boundaries with straight-through estimates, which treat the problem of drawing discrete token boundaries as a continuous one. We show that these token […]

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Automated Machine Learning (AutoML) has revolutionized the development of data-driven solutions; however, traditional frameworks often function as "black boxes", lacking the flexibility and transparency required for complex, real-world engineering tasks. Recent Large Language Model (LLM)-based agents have shifted toward code-driven approaches. However, they frequently suffer from hallucinated logic and logic entanglement, where monolithic code generation leads to unrecoverable runtime failures. In this paper, we present iML, a novel multi-agent framework designed to shift AutoML from black-box prompting to […]

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Code generation has progressed more reliably than reinforcement learning, largely because code has an information structure that makes it learnable. Code provides dense, local, verifiable feedback at every token, whereas most reinforcement learning problems do not. This difference in feedback quality is not binary but graded. We propose a five-level hierarchy of learnability based on information structure and argue that the ceiling on ML progress depends less on model size than on whether a task is learnable at […]

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Someone has to prompt the Claudes, talk to customers, coordinate with other teams, decide what to build next. Engineering is changing and great engineers are more important than ever. — Boris Cherny, Claude Code creator, on why Anthropic are still hiring developers Tags: careers, anthropic, ai, claude-code, llms, coding-agents, ai-assisted-programming, generative-ai

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The last three posts have been about Stan Wagon’s algorithm for finding x and y satisfying x² + y² = p where p is an odd prime. The first post in the series gives Gauss’ formula for a solution, but shows why it is impractical for large p. The bottom of this post introduces Wagon’s algorithm and says that it requires two things: finding a quadratic non-residue mod p and a variation on the Euclidean algorithm. The next […]

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