Vadim Epstein

Part of Assembly — an ongoing exploration of synthetic narratives with agentic AI (2026).

This work reframes computer vision not as a problem of perceptual accuracy but as an act of interpretation. It asks not what a model sees but what it understands: what latent meaning it constructs beneath the visible surface, what it emphasises or suppresses, and what it projects beyond what the image straightforwardly depicts.

Process

The work runs as a closed generative loop. A vision model describes an input image in concrete visual terms; a chain of LLM agents then reads that description not for what is shown but for what it implies — latent tensions, concealed structures, unresolved contradictions. From this reading, the agents formulate a hypothesis about what remains unexpressed and propose a next development. An image generator renders that proposal as a new image, which enters the following cycle.

Each cycle passes through three distinct model types — image describer, language reasoner, and image generator — each with its own latent space, biases, artifacts, and blind spots. Description is never neutral; generation never literal. As meaning moves through these successive transformations, it drifts, refracts, and accumulates distortion. What one model foregrounds, another may dissolve; what appears as noise may return as structure. This drift is not a failure of the system but the work’s primary medium.

The process is guided by interpretive modes — revealing essential qualities, escalating dormant tensions, exposing hidden structural mechanisms. These modes do not supply external content or narrative. Instead, they shape how the system reads and misreads itself, while preventing the loop from collapsing into statistical attractors or incoherence and allowing its internal tendencies to surface.

Obscura belongs to Assembly’s broader apparatus for exploring interpretation, agency, and semantic drift. Assembly provides the recursive structure, agentic workflow, and branching process; Obscura is one linear trajectory through that system.

Motivation

While each image can be read for meaning, the real interest is in what accumulates across cycles — a recursive interpretive trajectory of the system re-reading its own projections.

Recursive generation through feedback loops has been widely explored, usually showing properties of a medium through progressive signal decay. This work enters the same lineage but with one difference: the loop passes through a reasoning layer, so what circulates is meaning rather than signal, with no external ground truth to anchor it. The drift is therefore not entropic but hermeneutic — not a loss of signal but a compounding of semantic bias. The approach is diagnostic, not decorative: it makes visible the reading every model already performs in silence.

All pieces are generated autonomously from a single source image, with no additional semantic context.

More info: wesual.art/assembly

Stock photo (c) ziggymaj