In this month’s episode of Functional Futures, our guest is Erik Svedäng, a game designer who has created many board and video games. Among them is Else Heart.Break(), a puzzle video game with its own programming language. He is also the creator of Carp, a statically-typed lisp for real-time applications. In the episode, we talk about game design, game development, and how Carp enables developers to build performant games while keeping true to functional programming idioms. As always, […]

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Training a language model to automate web browsing is difficult because small mistakes cascade: if the model clicks the wrong button, the entire page changes and all subsequent actions fail. Supervised fine-tuning (SFT) teaches a model to copy human demonstrations, but it never learns how to recover from its own mistakes. We investigate whether reinforcement learning—specifically, Group Relative Policy Optimization (GRPO)—can improve a fine-tuned model by letting it practice on real web forms and learn from the outcomes. […]

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arXiv:2603.03613v1 Announce Type: new Abstract: The No Free Lunch (NFL) theorem guarantees equal average performance only under uniform sampling of a function space closed under permutation (c.u.p.). We ask when this averaging ceases to reflect what benchmarking actually reports. We study an iterative-search setting with sampling without replacement, where algorithms differ only in evaluation order. Binary objectives allow exhaustive evaluation in the fully enumerable case, and efficiency is defined by the first time the global minimum is reached. […]

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How I think about Codex Gabriel Chua (Developer Experience Engineer for APAC at OpenAI) provides his take on the confusing terminology behind the term “Codex”, which can refer to a bunch of of different things within the OpenAI ecosystem: In plain terms, Codex is OpenAI’s software engineering agent, available through multiple interfaces, and an agent is a model plus instructions and tools, wrapped in a runtime that can execute tasks on your behalf. […] At a high level, […]

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arXiv:2601.10555v1 Announce Type: cross Abstract: The causalfe package provides a Python implementation of Causal Forests with Fixed Effects (CFFE) for estimating heterogeneous treatment effects in panel data settings. Standard causal forest methods struggle with panel data because unit and time fixed effects induce spurious heterogeneity in treatment effect estimates. The CFFE approach addresses this by performing node-level residualization during tree construction, removing fixed effects within each candidate split rather than globally. This paper describes the methodology, documents the […]

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The Mechanistic Interpretability (MI) program has mapped the Transformer as a precise computational graph. We extend this graph with a conservation law and time-varying AC dynamics, viewing it as a physical circuit. We introduce Momentum Attention, a symplectic augmentation embedding physical priors via the kinematic difference operator $p_t = q_t – q_{t-1}$, implementing the symplectic shear $hat{q}_t = q_t + γp_t$ on queries and keys. We identify a fundamental Symplectic-Filter Duality: the physical shear is mathematically equivalent to […]

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arXiv:2602.03143v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has recently emerged as a practical recipe for aligning large language models with verifiable objectives. However, under sparse terminal rewards, GRPO often stalls because rollouts within a group frequently receive identical rewards, causing relative advantages to collapse and updates to vanish. We propose self-hint aligned GRPO with privileged supervision (SAGE), an on-policy reinforcement learning framework that injects privileged hints during training to reshape the rollout distribution under the […]

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arXiv:2602.18545v1 Announce Type: new Abstract: Property-based testing (PBT) is a popular technique for establishing confidence in software, where users write properties — i.e., executable specifications — that can be checked many times in a loop by a testing framework. In modern PBT frameworks, properties are usually written in shallowly embedded domain-specific languages, and their definition is tightly coupled to the way they are tested. Such frameworks often provide convenient configuration options to customize aspects of the testing process, […]

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arXiv:2603.20328v2 Announce Type: replace Abstract: Social insect colonies and ensemble machine learning methods represent two of the most successful examples of decentralized information processing in nature and computation respectively. Here we develop a rigorous mathematical framework demonstrating that ant colony decision-making and random forest learning are isomorphic under a common formalism of textbf{stochastic ensemble intelligence}. We show that the mechanisms by which genetically identical ants achieve functional differentiation — through stochastic response to local cues and positive feedback […]

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