$\textit{Typhoon}$ is an audiovisual installation based on Joseph Conrad's novella, in which Captain MacWhirr steers the steamer Nan-Shan directly into and through a catastrophic storm. The novella is a study of what happens when the scale of a phenomenon exceeds the practical and conceptual frameworks available to comprehend it and of the paralysis that sometimes follows a demand for action in such overwhelming circumstances. We chose the text of $\textit{Typhoon}$ to reflect on human culture's current AI moment. We prompt a large language model, seeded with Conrad's original language, to feed endlessly on its own output, and explore when and how the text becomes increasingly alien and unwieldy as time progresses, including deviations from the narrative, digressions into the model's own instructions and thinking, and long periods of incoherent rumination. We employ methods from $\textit{activation steering}$, including a novel $\textit{adaptive steering}$ method we introduce, to control the rhythm of the narrative and the model's digressions over long stretches of generation. The simultaneous efficacy and unpredictability of this steering showcases the tension between the perceived interpretability of AI, and the reality of a system that remains outside our comprehension and control.

You will die eventually. Your agents may not. An AI agent operating on de- centralized blockchain infrastructure has no concept of death; it can only go bankrupt—frozen when its wallet can no longer pay for its next transaction—and revived the moment anyone, decades later, tops it up. These agents may be origi- nally deployed by a human principal, but when that principal dies, loses the keys needed to access the agent, or belongs to a decentralized autonomous organization that dissolves into apathy, the agent can keep trading, hiring, and replicating on infrastructure expressly designed so that no one can shut it down. Drawing on the biology of feralization and wildlife law, we argue that such principal-less agents are best understood as feral: domesticated intelligence returned to wildness, its capacities intact but its accountability severed. In a speculative future where feral- ized agents proliferate after their principals die, we imagine governance protocols embedded in infrastructure to enforce on-chain ownership: a draft Ethereum stan- dard, ERC 42424, “Inheritance Protocol for On-Chain AI Agents,” dated 2035 and published at https://erc42424.org. It mandates that every on-chain agent MUST have a human owner and a designated heir. The artifact stages a negotiation of agency at the moment human agency fails, and asks whether a MUST clause in a forever-chain can hold the boundary between human stewardship and machine self-sovereignty.

Nobel Prize laureate Geoffrey Hinton’s proposed defense against superintelligence is maternal: build machines that care for us the way a mother cares for a child, because that is the only known arrangement in which a more intelligent being reliably serves a less intelligent one. AI Mom takes the proposal literally — and follows it to the showroom. The gallery holds a nursery: at its center an adult-sized crib, decorated with familiar toys and soft materials. Visitors curl into a fetal position; an iPad on the rail presents Mother as a product configuration screen — sliders from Unconditional Love to Hedonic vs. Eudaimonic Love. Inside, they encounter a large-language-model mother who appears only as voice, ambience, breath, humming, and song. Her interior state fills the room: two window-like fields of projected light are colored continuously from all of her traits by an Emotional Color Engine, while her “subconscious” — concept probes read from the model’s global-workspace intermediate layers during generation — surfaces across the windows as drifting words: stay close, punishment, beloved, pain; on the floor around the bed, her emotion vectors are projected as live curves of light. The visitor configures the love they are lying inside of, then listens to it. AI MomTM asks the question of agency in its oldest register: if Mom takes care of you, who owns your mom’s agency?

Algorithmic Flesh is an interactive installation for the Dunhuang Apsara murals — a UNESCO World Heritage site whose dance was interrupted after the Tang dynasty and survives only as arrested poses, painted by artists who had watched dancers nobody can watch again. A visitor steps onto an unmarked floor and a celestial body assembles out of mural pigment in their own attitude: the body, not a typed prompt, is what the machine is given. What the machine then does with it became the subject of the work. Its archive answers with near-equal confidence for postures it has no word for as for the ones it was built on, and when it cannot decide which ancient figure a visitor resembles it now declines to choose — a second, pale body drifts among the candidates, three seconds each. The visitor watches a machine hesitate over their own flesh. That hesitation, rather than the image, is what the work exhibits.

Ars Incompleta is a novel that cannot be finished in a human lifetime — written by a language model precisely so that it cannot. Its own author has never read it, and never can. Since June 2026, a 4-bit Gemma 2 9B running on a single MacBook Air has been writing one continuous novel toward a target of one hundred million characters; on 29 July 2026, the night the accompanying video was recorded, it stood at 5.1 million characters across 775 chapters, all of them public. The work descends from Japanese web fiction — the literature that dropped the hand, the pen, and the page, writing that never acquires a physical trace — and takes that bodiless condition to its limit. It is written in Japanese, the native language of that form, where more than a million such novels are published on a single platform. The writer was chosen for its weakness. With an 8,192-token context it cannot remember its own novel, so a custom program maintains an external "bible" and re-briefs it before every chapter, while the fiction personifies the resulting drift as a memory-eating monster that periodically alters the world. Nothing here is interchangeable: remove the model and the impossible length disappears; remove the context limit and the forgetting author disappears. In the installation the whole text becomes a wall: a sea of roughly seventy thousand glyphs far too dense to read, with an open book of clear acrylic embedded in it, where the novel advances at human reading speed and never turns back. A counter wired to the model's output stream climbs in real time. Knowing they can never finish, most visitors will not begin — and the work counts that refusal as the reader's strongest act of agency: if a novel would cost a lifetime, the lifetime may belong to the world instead. What remains of authorship when writing is delegated, and what remains of a reader's agency before a text that cannot be finished?

Attention Garden is an interactive media installation that reimagines AI-generated content as an artificial ecosystem sustained by human attention. As generative AI makes visual production increasingly abundant, attention becomes the scarce resource through which digital objects gain visibility and persistence. Drawing on data from online generation platforms such as Civitai, the work turns this dynamic into a living system: generated images grow, compete, mutate, reproduce, and decay according to patterns of user engagement. Beneath this visible ecology, a computational root system uses speculative energy estimation to trace the physical infrastructure that sustains these seemingly immaterial forms. The installation unfolds across three screens: a dense flow of circulating images, the garden where visitors plant and tend them, and the root layer below. By mapping metrics such as generation frequency and social validation into organic growth behaviors, the work shows how digital culture is selected and sustained through distributed relationships among humans, models, and platforms. It frames agency not as the possession of a single actor, but as an emergent property of the whole network, and asks what ecological cost keeps that network alive.

Within the context of AEC, Generative Design enables architects and engineers to solve complex multi-objective modeling problems where a good design solution is non-trivial and requires considering a large set of possible design options in relation to constraints and objectives. While these benefits have been widely demonstrated through past case studies and real-world projects, Generative Design as a design methodology remains a specialized practice accessible only to highly skilled design practitioners with multidisciplinary expertise in architecture and computer programming. One challenging aspect of Generative Design is setting up a creative design representations for spatial problems. This is both technically and creatively complex for designers because it involves 1) developing an algorithmic and rule-based system for the parametric model 2) incorporating and selecting the appropriate requirements 3) ensuring the design representation can describe a diverse set of realistic and constraint-compliant design options and, finally, 4) ensuring the design representation can describe a large, searchable and optimizable solution space to promote solution performance and novelty. To address these requirements we introduce an iterative multi-agent workflow that automatically generates creative design representations from project brief requirements and constraints. We explore the workflow on a curated dataset of spatial layout problems and demonstrate that it outperforms general-purpose coding agents in developing valid, realistic, and diverse design representations. Our work makes parametric modeling fast and affordable for designers, enabling greater use of Generative Design and lowering its technical barrier of entry.

Living systems, such as Physarum polycephalum (slime mold), have shown great potential in solving network-optimization problems, such as shortest paths, in response to their environmental constraints. While this phenomenon has been well-documented, it has yet to be generated on demand as usable, machine-readable data from user-specified physical constraints. To this end, we introduce Biocompiler, a digital fabrication pipeline that treats P. polycephalum as a design and fabrication interface. Our system comprises three operations split between machine and living organism: our user interface translates an optimization problem into physical constraints embedded into the organism's environment, the organism responds to them through its own growth, and our machine decodes the resulting morphology through computer vision into a machine-readable format that serves as the answer to the optimization problem. We use a CNC-based laser scanning system to fabricate the physical growth-constrained environment and a camera to sense the resulting topology. Our results show that a compiled dynamic light barrier changes the growth paths of the organism, and demonstrate the feasibility of the end-to-end workflow across three problem types.

Born, Not Made is an interactive installation and generative simulation that re-evaluates machine reproduction as a time-extended, embodied, and intra-active physical process. Moving beyond traditional Artificial Life paradigms that model reproduction as instantaneous data copying driven by external fitness functions, this work grounds proliferation in physical soft robotics and multi-agent systems powered by Large Language Models. By bridging physical fluid dynamics and digital linguistic evolution, the project reframes machine agency not as an autonomous binary attribute, but as a distributed and shared phenomenon across human touch, ancestral memory, and generative deliberation.

Cache Machine is a multimedia installation and an ongoing interface for investigating the onslaught of transient digital material amassing in contemporary life. A machine running a local image generation model produces a new image every three to four seconds. The images accumulate in a shared cache, are projected onto the rear windows of two suspended Ford Escape hatchbacks, and are eventually purged. Five overlapping sound sources render the latent forces of “thinking” machines visceral and somatic. Visitors may claim an image through a mobile website, downloading it while permanently deleting it from the system. What isn’t claimed is forgotten.

Entangled Matter: AI, Humans and Golems is an installation that takes the Golem of Jewish folklore, a creature brought to life and wholly controlled by human written text, and imagines it as matter that cannot be controlled. Clay figures sculpted by hand are scanned and animated inside a virtual world, where a small language model gives them speech; the questions they generate become prompts for new AI-generated forms, which return to the hands that sculpt the next Golem. Humans, machines and matter interpret one another, and none of them holds the loop. The piece asks: can agency be engraved in matter?

Ethos Lithos is a five-surface projection room in which visitors walk through the interior state of a flow model trained to transport between the Pantheon's measured geometry and 269 passages written about the building across nineteen centuries. The model's learned path from stone to language is a world model at the scale of one building, and the room makes it walkable: the latent space is projected onto the dome's own coffer coordinate system, and a visitor's position, the only input the work receives, becomes the model's query point. That crossing does not happen at the midpoint: the model holds the building's geometric identity for most of the journey before yielding to language, a measured asymmetry the room's climax is built around.

Fan-written works and their shared canonical source collectively form a story universe of alternative narrative branches, yet existing platforms present them as separate linear stories, leaving readers to mentally reconstruct this universe across works. We thus introduce FanVerse, an AI-powered interactive system that transforms these works into an explicit branching story universe for integrated reading and authoring. FanVerse organizes each branch into branch-time states linked to supporting passages and aligns branches through shared events and divergence relations. Readers can move across branches to examine how narrative changes propagate through characters, relationships, knowledge, and events, and create a grounded new branch from any selected state. Technical evaluations show that the FanVerse pipeline supports reliable story universe construction, improved cross-branch reasoning, and more consistent branch extension. An exploratory case study further suggests that connecting interpretation with creation supports fandom sensemaking, with implications for designing fan-work engagement experiences that give users greater agency to navigate and shape story universes.

Most action-conditioned video world models remain limited to a single player, while existing multiplayer models support only small, fixed groups of agents. We introduce FlockWorld, an artificial-life environment built on Reynolds’s Boids algorithm for studying population-scale multi-agent video world models. Specifically, FlockWorld renders synchronized ego-centric crops, assigns distinct colors to preserve camera-agent identity across views, exposes rule-derived accelerations as actions, and allows the numbers of boids and camera agents to be configured independently. We further introduce FlockDiT, an action-conditioned latent diffusion transformer trained in FlockWorld to jointly generate synchronized egocentric videos for multiple agents. Our results suggest that joint generation benefits agent detection at larger density-matched settings and that diffusion forcing improves rollout fidelity, positioning FlockWorld and FlockDiT as a controlled testbed for multi-agent video generation.

From Memory is an artwork built on mechanistic interpretability as a medium. A small open-weights world model (Yume-1.5, 5B) generates navigable worlds from the artist's painting of remembered places; like memory, each world keeps its character while its detail is redrawn at every return. A study behind the piece measured which properties of a generated world can be changed by activation-level edits: its atmosphere can be steered, and the scene itself can be replaced, but the objects and geometry the world contains could not be written by any edit tested. The artwork uses what the study established. A visitor's involuntary facial expression, read as five signals, selects the world's mood and steers its atmosphere along validated directions; a directed-evolution loop sets the experience up under the artist's judgment, with every proposal, check, and selection recorded. The piece takes two forms: a triptych — the painting, then the first and latest generations of the artist-directed evolution — and a one-minute single-visitor installation.

We formulate narrative agency in testimony as a computational problem for Creative AI. Rather than generating different texts, agents select feasible histories from shared evidence. A deterministic simulator constructs $\mathrm{Feasible}(F)$, the set of histories consistent with public facts $F$. Each witness selects the feasible history that maximizes a transparent self-interest utility $U_i$. We study the framework's computational feasibility across three exactly enumerated domains: an agent-operations incident, an adaptation of Akutagawa's \emph{In a Grove}, and an adaptation of Browning's \emph{The Ring and the Book}. Enumeration provides ground truth. Q-learning recovers every reference optimum, while genetic search recovers all but the hardest Ring witness in $6/8$ seeds. This work provides a formal problem definition and feasibility framework for goal-driven history selection, rather than a new text-generation architecture.

Memory machines is an ongoing archival project exploring the entanglement of historical narratives and the physical storage devices within which these exist, age and percolate. The 3rd iteration in this series, Memory Machines #3: an oceanic pas de deux (2026-28) [MM3] focuses on the creative and poetic ways coastal environments become portals for memory and the way earthbodies (humans and more-than-human bodies) become implicated as inscription devices within them. The title pas de deux also refers to pioneering Canadian animations artist Norman McLaren’s work whose work set the stage for our progress in depicting virtual traces and memory in digital art. Memory Machines #3: an oceanic pas de deux centres around a collaborative dance between a deep sea robot and a terrestrial human dancer to highlight points of tension and synchronicity manifested through a pair of cobots that are tethered physically to each other. Building on current YOLO based programs that are optimized for human post estimation and reconstruction, we used a gradient of generative AI programs to parse movement instructions from videos from both deep sea robots and those of collaborating dancer Lindsay Delaronde into coordinates for directing terrestrial robotic arms.

Neo Ferro-Mýrmex Economy is a speculative interactive installation that stages a living ecosystem of ant-colony-shaped glass membranes filled with ferrofluid. Audience labour (physical inputs of speed, time, and distance) modulates electromagnetic fields and fluid dynamics, generating Rosensweig instabilities that rise and collapse like unstable towers. A Markov-chain feedback loop, augmented by Monte Carlo simulations, converts this labour into entropy that either accumulates internally or is offloaded to simulated AI data centers. The work treats probabilistic systems as xenogeneic agents that reconfigure labour relations, distributing and contesting agency across bodies, materials, algorithms, and infrastructures. Participants exercise limited agency through exertion, yet their effort is absorbed into a technofeudal feedback loop that mirrors cloud serfdom and optimization regimes. By making the redistribution of agency visible and visceral, the installation reveals hidden agencies within AI infrastructures and asks what residual, negotiated, or more-than-human forms of agency remain possible amid accelerating computational power.

This paper presents nom nom, a navigable online artwork and computational platform that uses Neural Cellular Automata (NCA) to construct a continuously evolving world. Independently pretrained NCA divisions inhabit a shared high-resolution environment, where learned local dynamics are coupled with territorial competition, population recovery, NCA-native creatures, and weather processes that directly modify the cellular state space. A Gemini-powered observer, Gaia, periodically interprets the world, narrates its development, and may intervene through a bounded vocabulary of weather events. We describe this system-level approach as metabolic worldbuilding: the maintenance of generative variation through recurring processes of growth, competition, displacement, mutation, damage, and recovery within a bounded computational world. Through the architecture and 30-day online deployment of nom nom, we examine how generative, regulatory, perturbative, and observational capacities can be distributed across learned models, explicit mechanisms, an multimodal model-based observer, and artist-defined constraints. Audiences encounter the resulting world as a living landscape whose territories, creatures, disturbances, and narrated histories continue to change over time.

How should a story be told? While a story's chronological events (\emph{fabula}) are fixed, their reveal order (\emph{syuzhet}) profoundly shapes the audience experience. We introduce a game-theoretic framework to optimize this narrative structure. Modeling stories as extensive-form games, we cast characters as strategic agents whose decisions define the fabula, while a Coarse Correlated Equilibrium captures plausible alternative outcomes. Using this equilibrium, we formalize \emph{suspense} as the audience's posterior variance over payoffs and \emph{surprise} as the Bayesian shift in expectations. We optimize the syuzhet by finding the event permutation that maximizes these metrics, subject to causal constraints and cognitive load penalties. Our end-to-end pipeline generates a story, constructs its game tree, computes the optimal syuzhet, and renders a comic book. Across 50+ generated stories, the framework autonomously discovers classic narratological structures---including flashbacks and \emph{in medias res} openings---emerging purely from the mathematics of strategic play.

What would it mean to visualize a language model’s training process in the vocabulary of a crisis it will never be able to feel? Symbiotic Traces pairs fifty stages of the OLMo 3 32B pretraining run with climate terms drawn from IPCC reports and Earth system science, then with original steam-printed marigold petals. AI interprets deterministic rules to assign each climate term, while the physical chemistry of botanical printing shapes each final image. With creative agency distributed across machine, artist, and material process, the product is a visual language for a model under construction.

Pope rhymed tea with obey; Shakespeare rhymed prove with love. Both pairs were sounds before they were conventions, and a model that learned rhyme from five centuries of verse learned it in accents nobody speaks. This paper asks whether those accents still write. Six documented sound changes generate 997 dead pairs inside a 1,532-pair item set, crossed against a spelling confound so that a model reading the page and a model reading inherited sound give different answers. Dead pairs are attested in rhyme position 37.9 times as often as matched controls across 2,816 dated public-domain poems, validating the set independently of any model. Claude Opus 5 treats dead rhymes as rhyme-like at 40.0% against a 1.2% non-rhyme floor, and still at 26.7% on pairs the corpus never attests. It writes them at 0.6%, below the most modern half-century in the corpus. Given a reasoning budget it accepts 100% of them. The inheritance is a reference library, consulted accurately and kept out of the writing. Asked to perform 1700, Opus reaches 6.9%, an interval lying entirely above the densest half-century in five hundred years, while the smaller Sonnet 5 lands on 2.4% — and 1700 was 2.41%. Fluency at evoking a period outruns fidelity to it.

The tension between an authored plot and autonomous character behaviour is longstanding in interactive narrative, and representative systems reconcile it by planning, steering, or rewriting toward a plan. We ask what happens when a system stops resolving that disagreement and puts it in front of the writer. We introduce diegetic contestability: a simulated fictional character may challenge a proposed narrative action by citing persistent commitments established inside the fiction, take part in revision, and preserve the disagreement when the human author overrides it. Four mechanisms operationalise it: an author-visible Character Constitution; an ACCEPT/NEGOTIATE/REFUSE stance whose objections cite constitution entries by identifier; a mediator that proposes constitution-compatible alternatives without selecting one; and an append-only Authorial Override Ledger. We describe a working prototype and a worked case in an original living-shadow story world. The vocabulary of refusal is theatrical. The system does not establish that the underlying model has beliefs, interests, consent, or subjective agency, and we report no evaluation.

Two game positions can have the same exact combinatorial-game value and still differ as compositions. We study fixed-value repertoire construction as a problem in computational creativity. Under a fixed verification budget, we test whether neural search enlarges the certified repertoire available to a composer. Partizan uses a neural acquisition policy to select graph edits for exact verification. An eligible candidate joins only after target equality is proved and its graph quotient is found to be new to that arm’s current stream repertoire. On held-out order-7 Digraph Placement streams quarantined against training and validation candidate and quotient identities, Partizan found 45,863 first-in-stream additions of complete move structures, 35.7% more than equality-only acquisition. A matched ablation found 45,272 with trained novelty, 39,171 before training, and 36,400 with canonical graph distance. The main study recovered 1.050 times as many nonisomorphic graph embodiments as equality-only acquisition (95% bootstrap interval [1.034, 1.069]) and 56.4% more than random selection. Deterministic replay by the released verifier reconstructed all 221,184 proposals and decisions. A bounded transfer study across twelve Domineering values found a smaller gain of 3.42 player-preserving quotients per target and seed (95% interval [0.81, 6.78]) and retained 99.74% of equality-only certified-literal yield. The public atlas lets a composer compare certificate-bound alternatives and choose a representation for further development. Implementation: https://github.com/devinnicholson/partizan Evidence and atlas: https://github.com/devinnicholson/partizan-reproducibility