Project Fiction
Active · primaryHow do we transform what we already know into something new, without materially new external information?
Defining and measuring innovative transformation. Foundational questions remain under stress-testing.
As frontier AI acquires increasingly sophisticated cognitive capabilities, Hermes Labs investigates how those capabilities can be deliberately engineered. We isolate human faculties, reproduce their functions in artificial systems, and experiment with their recombination to discover new possibilities for artificial minds.
How do we transform what we already know into something new, without materially new external information?
Defining and measuring innovative transformation. Foundational questions remain under stress-testing.
How do we construct, manipulate, and reason about possibilities that are not presently given?
Developing the capability definition and ways to evaluate it.
When can spontaneous, internally generated thought become productive exploration?
Forming the concept; its boundaries and computational interpretation remain open.
How can affect regulate attention, valuation, memory, motivation, and action in artificial systems?
Defining a measurable regulatory function. No implementation or result is established.
Research questions and working targets—not demonstrated capabilities.
Human cognition presents us with an extraordinary collection of capabilities. We invent, imagine, judge, anticipate, remember, and reorganize what we know into possibilities we had never previously considered.
Each capability invites a difficult question: what operations make it possible?
Hermes Labs approaches this question experimentally. We investigate particular cognitive functions, develop testable accounts of their operation, and attempt to reconstruct them in artificial systems. Our longer ambition is to understand these capabilities sufficiently to alter their computational organization and investigate new architectures of cognition.
Frontier AI makes the relationship between task performance and cognitive organization especially consequential. Behavioral competence, mechanism identification, functional reproduction, and controllable engineering are different achievements. Our research asks how evidence can connect them.
Experimental Cognitive Engineering is our methodological orientation: operationally define a capability, investigate explanations, attempt artificial reproduction, and use controlled interventions to study its organization and possible combinations.
This work joins established traditions in computational cognitive science, cognitive architectures, interpretability, modular learning, artificial life, and computational creativity. Prior-art context ↗
Seven stages express an intended, iterative progression. Findings can send us back to a definition, a mechanism, or an experimental condition.
What exactly are we trying to reproduce?
Identify a particular cognitive capability, establish its boundaries, and distinguish the phenomenon from neighboring functions that may contribute to it.
A capability must first become a recognizable object of investigation. In invention, novel ideas, useful solutions, remembered knowledge, analogies, and changes in problem representation may arise from overlapping operations.
Isolation constructs a research question precise enough to investigate. Studying a faculty separately is a methodological choice; its independence from other functions remains a hypothesis.
Distinguish the phenomenon from the mechanism proposed to explain it. State which neighboring phenomena the investigation must discriminate.
How can we recognize and investigate the capability?
Translate the phenomenon into measurable functions, competing explanations, and experimental conditions capable of distinguishing among them.
An intellectually compelling definition begins an investigation. Construct validity asks whether the task and its measurements actually capture the intended capability. Independent evaluation, explicit information boundaries, controls, and failure criteria make that question testable.
Investigate existing theories before selecting an engineering approach. Retrieval, analogy, search, representation change, or their coordination may explain the behavior. Rival hypotheses should predict observations that can distinguish them; the operational definition must remain independent of the favored mechanism.
Define evaluators independently of the signals or mechanisms being tested. Record alternatives, controls, justified success and failure criteria, and the decision each outcome would support.
Can the capability be instantiated artificially?
Construct computational systems that exhibit the operationally defined functions, and evaluate their behavior against independent standards.
The scientific question determines the engineering approach: an existing model, a computational architecture, targeted training, a structured inference procedure, or a new mechanism may be appropriate.
Functional reproduction requires reliable behavior across the specified conditions, appropriate baselines, and held-out evaluation. Mechanistic reproduction makes a stronger claim about correspondence with the human process and requires separate evidence. One successful output establishes neither.
Retain reproducible inputs, versions, resource budgets, outputs, and limitations. State the scope of functional success separately from evidence for human-mechanism correspondence.
What changes when we intervene on the machinery?
Alter candidate computational mechanisms, representations, resources, or interactions and measure the resulting changes in capability.
Artificial systems permit interventions on memory organization, information access, selection procedures, representations, and component interactions. Removing, constraining, amplifying, or reorganizing an element can expose dependencies and tradeoffs.
Controlled comparisons and ablations test predicted consequences. A behavioral difference can have several causes, including collateral changes in compute or information; the design must establish which causal interpretation is warranted.
Specify the intervention, matched comparisons, expected effects, and alternative causes. Interpret a causal effect within the tested system and conditions.
What becomes possible when investigated capabilities interact?
Bring cognitive functions or computational mechanisms into new relationships and study the consequences of their interaction.
Imagination may influence planning; memory may shape invention; affect may alter attention and valuation. These relationships motivate experiments rather than assumptions of interchangeable capabilities.
Compare components alone and in combination. Different arrangements may produce complementarity, interference, unexpected dependencies, or functions absent from the individual components. Recombination investigates these interaction effects.
Keep component contributions identifiable and test gains and interference against appropriate individual and combined baselines.
Can investigated capabilities form a coherent cognitive architecture?
Integrate compatible computational capabilities into larger artificial systems and investigate whether their organization supports sustained cognitive function.
Composition concerns system-level coordination: shared interfaces, compatible states, memory access, and resolution of competing outputs. It builds on investigated interactions while asking whether the resulting organization works as a whole.
Evaluate the integrated architecture across sustained tasks, changing conditions, and component failures. Individually functional parts do not guarantee coherent cognition. This is a future-facing engineering objective for Hermes Labs.
Evaluate system-level reliability, coordination and attribution, retaining ablations and resource-matched comparisons. No complete synthetic mind is established by current Hermes work.
What other architectures of cognition might become possible?
Investigate how constructed cognitive systems can adapt, undergo systematic modification, or develop new functional configurations.
An evaluated architecture becomes a new experimental object. Successive designs can alter its components and organizational principles, asking which configurations remain coherent and which acquire useful functions.
Learning, deliberate redesign, computational search, and evolutionary computation are different processes. Each implementation must specify which process produces change and how it is evaluated. Biological evolution and developmental learning require their own models and evidence.
Use Evolve as an experimental horizon. Define the actual adaptation or modification procedure; evaluate retained and lost functions without treating design trajectories as forecasts of biological evolution.
A cognitive capability becomes scientifically useful when we can distinguish its observation from our explanation of it. Our investigations therefore separate the phenomenon, its measurement, the mechanisms proposed to explain it, and the effects of interventions upon those mechanisms.
Experiments must accommodate competing explanations. Independent evaluators establish what an observed result warrants; records retain conditions, baselines, deviations, and limitations. A result that defeats a favored hypothesis can reveal what the previous explanation failed to account for.
A working instrument demonstrates behavior within its tested contract. Claims about cognition require evidence connecting that instrument to the proposed capability. Human judgments, model behavior, mechanistic attribution, and practical usefulness each carry distinct evidentiary obligations.
Project Fiction investigates how cognitive systems transform available information into novel, actionable possibilities. A new combination may be useless; a useful solution may have been retrieved. A changed problem representation may reveal an opportunity, or introduce a more convenient objective.
Controlled informational environments let us examine these distinctions. Independent measures of novelty and effectiveness remain a design obligation; existing apparatus has narrower scope. Search, analogy, and representational transformation remain candidate explanations to distinguish.
An apparent advantage of the constructed reformulation strategy disappeared under an identical-order search-only comparison.
In PT-001’s deterministic rehearsal, search alone reproduced the strategy’s outcomes across the tested conditions. Improved outcomes therefore could not identify a distinct cognitive mechanism of problem transformation: the tested operationalization did not support that attribution.
Local measurement rehearsal · 23 October 2026 · No human or frontier-model inference observations.
Completed local measurement rehearsal · 23 October 2026 · Constructed deterministic strategies; no human or model-inference observations.
Can these outcome measures distinguish a benefit of changing problem formulation from a benefit of objective optimization or search?
Four finite resource-allocation worlds specify legal actions and explicit value criteria. Feasibility and value are scored; novelty is deliberately unscored. The value criteria are assumptions of the toy task.
The local rehearsal compared constructed policies and an identical-order search-only ablation: 4,372 bounded sequences and 480 strategy records. Candidate-evaluation ceilings were matched; total compute was not established as equal.
The search-only ablation reproduced the reformulation strategy’s candidates and final allocation in every tested world, permutation, and budget. Better outcomes did not identify a distinct formulation mechanism.
This operationalization cannot attribute the gains specifically to reframing. Further investigation needs independent formulation evidence and stronger objective/search controls. Whether cognitive problem transformation contributes remains open.
CWR supplies complementary offline evaluation apparatus; three empirical designs remain proposed.
Explore Project Fiction ↗Hermes appears at the threshold. The messenger crosses between worlds, carrying meanings from one domain into another. The trickster disturbs familiar arrangements, exploiting the possibilities that exist where established boundaries meet.
A boundary separates established arrangements from possibilities they cannot yet accommodate. The Hermean function is to cross that boundary, transform the relationships among what already exists, and make new possibilities available for investigation.
A faculty takes its form within an inherited biological organization. Its relationships with memory, attention, embodiment, and other capacities help determine what it can do. Attempting to reconstruct its function in another substrate asks which of those relationships are necessary, and which belong to the particular arrangement in which we first encountered it.
The crossing is consequential because it can change the transformations available to us. If a function survives translation, its new implementation may expose dependencies we can vary, interfaces we can reconnect, or constraints we can loosen. Fidelity becomes an experimental question, and the inherited organization becomes one arrangement to investigate among others.
We call this the Hermean Function. Translation tests what can be carried across a boundary; transformation investigates what becomes possible when the relationships on either side can change. Recombination and composition extend that inquiry toward unfamiliar cognitive architectures whose viability must be discovered through construction and experiment.
This philosophical interpretation draws on Lewis Hyde’s Trickster Makes This World. The computational questions developed here belong to Hermes Labs.
The human mind provides our first great field of investigation. It need not exhaust the possibilities of cognition.
Biological evolution produced human cognition under particular constraints. Artificial cognitive engineering offers a way to investigate which constraints a function requires, which are contingent on its inherited organization, and what other arrangements might be viable.
As particular capabilities become experimentally intelligible, new questions emerge about their compatibility and transformation. Our ambition is to understand them sufficiently to support deliberate construction, intervention, and integration—and eventually to investigate architectures substantially different from those familiar in human cognition.
The architectures we inherit provide a beginning for investigating architectures we might construct.
We intend to find out experimentally.
Method account · 24 October 2026 · Canonical method record