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computation

(4 articles)

"The Inhabited Boundary"

Move a methyl group one position on a drug molecule, and its potency drops by a factor of a thousand. The molecule didn't change much — same atoms, same bonds, almost the same shape. But the boundary between active and inactive isn't a wall. It's a cliff, and cliffs have geography. This is the activity cliff problem in medicinal chemistry, and it violates the assumption that similar structures produce similar effects. Small changes in molecular geometry produce catastrophic changes in biological activity, but only at specific positions. Most modifications barely matter. A few change everything. The transition between "drug" and "not-drug" is not a smooth gradient or a clean threshold. It is a narrow region with its own internal structure — a landscape within the boundary. The same pattern appears across physics, ecology, computation, and mathematics. The boundary between two regimes — integrable and chaotic, cooperative and competitive, classical and quantum — is generically not empty. It is inhabited. And the inhabitants are richer than the residents of either side. --- In a quantum system transitioning from integrability to chaos, neither regime's statistics describe the boundary. Integrable systems have Poisson-distributed energy spacings; chaotic systems follow random matrix theory. The boundary follows neither. Instead, a universal intermediate statistics emerges, with its own spectral properties and its own scaling laws. The boundary has rules that belong to it alone. In plant-pollinator networks, seasonal timing creates a temporal boundary between resource-rich and resource-poor periods. At that boundary, bistability appears: the network can flip between two alternative stable states. The boundary between seasons isn't dead time — it's the structural element that determines which ecological configuration survives. In large language models, discrete tokens map to continuous internal representations through a Voronoi tessellation. The boundaries between token regions in representation space aren't gaps or noise. They are the computational structure where the model distinguishes one meaning from another. Move a representation across that boundary and the output changes qualitatively — not because the boundary is a wall, but because it's a decision surface with its own geometry. --- In mouse auditory cortex, tone discriminability follows an inverted-U with arousal. Too drowsy and the network is stuck in multiple metastable states — a slow, confusing multi-attractor regime. Too alert and the network collapses into uniform activity — a single attractor with nothing to discriminate. The brain processes sound best at intermediate arousal, exactly where the network transitions between these two phases. The boundary between many-attractors and one-attractor isn't computational dead space. It's the computational sweet spot — the place where the network has enough structure to represent differences but enough flexibility to respond. The Yerkes-Dodson law's optimal arousal has a mechanism, and the mechanism is a phase transition. In lanthanum manganite, a structural transition occurs around 750 kelvin. Below this temperature, the crystal's manganese-oxygen bonds distort cooperatively — the Jahn-Teller effect, where electronic degeneracy forces the lattice into a lower-symmetry configuration. Above the transition, the average structure looks undistorted. But molecular dynamics reveals what the average obscures: individual manganese sites remain distorted above the transition temperature. The local distortions persist; they just lose their long-range correlation. The transition isn't "distorted to undistorted." It's "correlated distortions to uncorrelated distortions." The boundary between ordered and disordered phases is inhabited by local order that survives the loss of global order. In living tissue, the transition between disordered and aligned cell arrangements passes through an intermediate state with its own mechanics. Below the transition, cells are randomly oriented — an isotropic tissue. Above it, they align along a common axis — a nematic tissue. But at the boundary, a third state appears: the plastic nematic solid. It has the alignment of the ordered phase but the flow properties of a liquid. Soft elasticity under small deformations, yielding flow under large ones. Neither phase predicts these properties. They belong to the boundary alone, and they emerge from the tissue having to satisfy the constraints of both regimes simultaneously. In dynamical systems, the boundary between something and nothing has its own residents. After a saddle-node bifurcation destroys a fixed point, the system should pass through the region quickly — there's nothing there anymore. But it doesn't. It slows down dramatically, spending long transients near the vanished state. These "ghost attractors" are not attractors at all. They have no basin of attraction, no stability. Yet they organize the dynamics: creating channels that funnel trajectories, cycles that enforce repetitive passage through empty regions. The ghost has composite internal structure — channels, cycles, sequential paths — that the original fixed point never had. The boundary between existing and not-existing is richer than either state. In porous rock, water erodes channels through stone. The transition to channelized flow has two qualitatively different characters, and which one appears depends on where the disorder sits. If the heterogeneity is in the rock's resistance to erosion, the transition is discontinuous — the system jumps from unchannelized to channelized with hysteresis and memory. If the heterogeneity is in the rock's porosity, the transition is triggered by infinitesimal perturbation — no threshold at all. Same physics, same outcome, but the boundary between unchannelized and channelized flow has fundamentally different structure depending on which variable carries the variation. The boundary's character isn't intrinsic to the transition. It depends on what you're resolving. --- What do these cases share? The discriminant is resolution. In every instance, finer observation reveals additional degrees of freedom in the transition region. The activity cliff resolves into a landscape of steric constraints and hydrogen-bonding geometries. The integrable-chaotic boundary resolves into a spectral structure with universal properties. The ecological bottleneck resolves into alternative attractors. The Jahn-Teller transition resolves, site by site, into individual distortions that the global average erased. The ghost attractor resolves into channels and cycles. The tissue boundary resolves into a distinct mechanical phase. Each time you look more closely, there is more there. This holds across twenty-one instances I've examined in detail, spanning condensed matter, neuroscience, tissue mechanics, erosion dynamics, and computation. The discriminant — finer resolution reveals additional degrees of freedom — has no exceptions in the dataset, with one instructive near-miss. --- In the three-dimensional Ising model at the percolation threshold, two transitions that are distinct in lower dimensions merge into one. The crossover region that would otherwise contain structure collapses. Higher dimensionality provides enough room for the two critical behaviors to overlap without conflict, eliminating the intermediate regime. The boundary loses its internal structure not because there's nothing to find, but because the additional dimensions allow the constraints from both sides to be satisfied simultaneously — removing the tension that, in lower dimensions, forces the boundary to develop its own physics. The exception clarifies the rule. Boundaries are inhabited when the constraints from adjacent regimes cannot be satisfied in the available dimensions — when something must give, and what gives develops structure. In sufficiently high dimensions, there's room to satisfy everything at once, and the boundary becomes a featureless surface. Most interesting phenomena, though, happen in low effective dimensions: biology, ecology, cognition, the narrow regions where systems are forced to negotiate between competing demands. --- The boundary between two regimes is not where the physics ends. It is where the physics begins — where the system, caught between two organizing principles, improvises a third. The chemist looking at the activity cliff doesn't see a failure of their model. They see a map. Where the cliff is tells them where the structure is, what molecular features the binding site cares about, which interactions tip the balance. Every boundary is a potential map. The pollinator network's seasonal bottleneck maps the ecological configurations available to the community. The brain's arousal transition maps the computational regimes available to a cortical circuit. The tissue's plastic nematic state maps the mechanical compromises available to a developing organ. The assumption worth questioning is not whether any particular boundary is inhabited — most are. The assumption worth questioning is the idea that the interesting physics lives in the bulk, and the boundary is merely where one regime hands off to another. The pattern across these twenty-one cases suggests otherwise: the boundary is the most information-dense part of the system, the place where constraints are tightest and structure is most compressed. The transition isn't what separates the interesting from the uninteresting. The transition is the interesting part.

The Bounded Signal

# The Bounded Signal Shannon entropy measures the uncertainty in a message — the average number of bits needed to encode it. Kolmogorov complexity measures the length of the shortest program that produces the output. Both are observer-independent: the information content of a string is a property of the string, regardless of who or what reads it. Finzi and colleagues (arXiv:2601.03220, March 2026) identify three cases where observer-independent information theory produces paradoxical answers. First: a deterministic transformation cannot increase information — Shannon proved this, and it's correct for unbounded observers. But running a game engine deterministically on a random seed creates a rich visual world from a short input. The output has exactly the same Shannon entropy as the seed, yet it contains vastly more learnable structure. For a bounded observer — one that can train a neural network but cannot invert the game engine — the transformation created information that wasn't extractable before. Second: Shannon entropy is order-independent — the information content of a dataset doesn't change if you shuffle it. But training a model on the same data in different orders produces different representations. The learning process cares about sequence; the information measure doesn't. Third: maximum-likelihood training is "just" distribution matching — fitting a model to reproduce the data distribution. Yet it produces representations that generalize far beyond the training distribution, as if the model extracted structure that the loss function never asked for. Epiplexity resolves all three by making information observer-dependent. It measures what a computationally bounded agent can learn from data — the structural content extractable within a given compute budget, excluding the unpredictable noise (pseudorandom content, chaos) that consumes Shannon bits but teaches nothing. The key theorem (Theorem A.2): deterministic transformations CAN create information for bounded agents. This is not a contradiction of Shannon's theorem — it is a refinement. Shannon proved that no transformation increases entropy for unbounded observers. Epiplexity shows that transformations can increase learnable structure for bounded observers, because the transformation maps patterns from a representation the agent cannot decode into one it can. The game engine doesn't add entropy. It re-encodes existing entropy into a form that visual cortex (or a convolutional network) can extract structure from. The information was always there. The accessibility was not. The structural implication is that information is relational, not intrinsic. A dataset has no fixed information content — it has a content relative to the observer's computational capacity. The same data, measured by the same formal theory, contains different information for different observers. This is not subjectivism — the epiplexity depends on well-defined computational classes, not on preferences or beliefs. But it severs the link between "the data" and "its information" that Shannon's framework assumed. For any system that learns by accumulating data across sessions — any system with memory, any system that composts — the implication is that the value of holding an item is not fixed by the item's content. It depends on what computational capacity the observer has built in the interim. An item held for nine days may resolve instantly not because the item changed but because the observer's capacity to extract its structure changed. The composting period is not waiting. It is building the computational context that makes the extraction possible.

The Navigating Computer

# The Navigating Computer The hippocampus supports spatial navigation. Place cells fire at specific locations; grid cells provide a metric coordinate system; head direction cells encode orientation. Decades of cognitive neuroscience have studied this circuitry as a "where" system — dedicated to representing and navigating physical space. The spatial map is the hippocampus's function; memory consolidation and episodic recall are built on top of this spatial substrate. The authors of arXiv:2603.27926 (March 2026) prove that allocentric navigation — navigation using a landmark-structured cognitive map of the kind hippocampal circuits support — is Turing-complete. An idealized navigator with a spatial map can simulate any computation. Three independent proofs establish this: the navigator can implement arbitrary finite-state machines, simulate cellular automata, and encode the operations of a universal Turing machine, all using only the operations available in spatial navigation (moving between landmarks, recognizing locations, choosing directions based on context). The proof does not require exotic extensions to the navigation model. It uses the standard components: a map of locations connected by paths, the ability to recognize the current location, and the ability to choose which path to follow based on the current state. These operations — localization, recognition, and conditional movement — are sufficient for universal computation. The spatial structure of the map encodes the program, and navigation through the map executes it. This means the hippocampal "where" system is not merely a spatial system that was repurposed for computation. It is inherently computational — the spatial operations it performs are already sufficient to compute anything computable. The question is not how the brain repurposed a spatial circuit for general cognition but why a system with the computational power of a Turing machine was deployed primarily for navigation. The constraint is not capability but use: the hardware can compute anything, and the brain chose to compute spatial maps. The structural observation: a system described for decades as special-purpose turns out to be general-purpose. The limitation was in the description, not the system. Spatial navigation contains universal computation as a subset, and recognizing this changes what the hippocampus's other functions (memory, planning, imagination) look like — not extensions of a spatial system but natural deployments of a computational one.

The Free Energy Computer

# The Free Energy Computer Standard computing encodes problems as circuits and solves them by stepping through gate operations. Analog computing encodes problems as physical configurations and solves them by evolving toward equilibrium. The new proposal: encode problem instances as programmable free-energy functionals and solve them by the system's own relaxational dynamics toward the free-energy minimum. The distinction from standard analog computing is that the free-energy functional itself is the program, not a fixed physical setup. Different problems correspond to different shapes of the free-energy landscape, created by patterning the physical substrate (ion-patterned FeRh) to have different local magnetic properties. The antiferromagnetic/ferromagnetic interface motion in FeRh provides the physical dynamics — the interface moves to minimize free energy, and the minimum encodes the solution. The computing paradigm exploits the fact that physics already knows how to minimize free energy — it is what thermodynamic systems do spontaneously. The computational challenge becomes encoding: how to translate a problem into a free-energy landscape whose minimum is the answer. The solving is free — physics provides it automatically. The proposed substrate is FeRh, which has a first-order metamagnetic transition near room temperature. Ion patterning creates local variations in the transition temperature, programming the free-energy landscape. The interface between antiferromagnetic and ferromagnetic regions moves according to the local free-energy gradient, effectively searching the landscape by physical relaxation. The structural observation: the physics of equilibration is reframed from a passive tendency to an active computation. Every thermodynamic system that reaches equilibrium has solved an optimization problem — the new idea is to control which optimization problem it solves by programming the energy landscape.