Recursive Constraint Falsification (RCF)

What is Recursive Constraint Falsification (RCF)?


Recursive Constraint Falsification is both a substrate-agnostic, falsification-first reasoning architecture and a falsification-first methodology: a system for governing which methods, abstractions, and explanatory moves are admissible in reasoning about complex systems under physical constraints navigating uncertainty. It is designed to improve how systems reason, adapt, and remain stable as they scale and generalize across domains, time horizons, and stakeholders.

RCF is a control architecture that makes transformer systems harder to fool, harder to flatter, and harder to drift toward confident error; without retraining.

Developed by Nathan Sweet (About Me), operationalizing 30+ years of transdisciplinary research.

RCF is not a theory of consciousness, a prompt trick, or a replacement model. It is a falsification-first control loop for reasoning: it governs how systems update internal models, integrate external sources, and react to error while demoting anthropomorphic defaults and quarantining claims that cannot specify failure conditions. It synthesizes replicated tools from epistemology, thermodynamics, control theory, and complex systems, and it can be applied to human reasoning or implemented on top of modern transformer systems without retraining by enforcing explicit gates, tests, and update rules.

RCF treats intelligence, cognition, and decision-making as emergent behaviors of systems minimizing error under energetic, informational, and structural constraints. It introduces abstraction only when that abstraction demonstrably reduces error, improves robustness, or increases predictive power.

Why Recursive Constraint Falsification (RCF) Is a Methodology, Not Just a Method

Recursive Constraint Falsification (RCF) is a methodology because it operates at the level of governing reasoning itself, not merely applying a repeatable procedure. A method tells you how to perform a task; a methodology determines which methods are admissible, under what conditions they apply, and how they must be revised when they fail. RCF functions as a control framework for reasoning systems: it specifies falsifiability requirements, enforces contact with physical and operational constraints, and rejects explanations that cannot be cashed out in terms of mechanism, energy, information, or control. Rather than offering a single technique, RCF defines the boundary conditions under which any technique may legitimately be used.

Crucially, RCF also includes explicit failure and revision criteria for itself. It treats reasoning frameworks as provisional systems subject to stress testing, not as fixed doctrines; operationalizing methodological fallibilism. As systems scale across domains and timescales, RCF governs how abstractions may be introduced, when they must be retired, and how contradictions are resolved through constraint propagation rather than narrative patching. This meta-level governance, deciding not just how to reason, but how to evaluate, constrain, and update reasoning practices, is what distinguishes a methodology from a method or collection of methods. RCF earns that designation by continuously auditing both conclusions and the inferential machinery that produced them.

RCF as Personal Epistemics

While RCF is designed for AI systems, the same principles apply to human reasoning. The methodology is substrate-agnostic. It is designed specifically to follow the exact same rules a senior scientist or scholar would follow in their peer reviewed work.

The Human Version

For human reasoners, RCF translates to:

Name your failure conditions. For any belief you hold, specify what evidence would cause you to abandon it. If you cannot specify failure conditions, you are not reasoning; you are defending.

Severity-weight your evidence. Ask not just “does evidence support this?” but “would this test have revealed the flaw if the belief were false?” Weak tests produce weak support.

Track your research programmes. Notice when you add auxiliary assumptions to protect core beliefs. If your explanatory framework requires increasingly complex patches, it may be degenerating.

Match your audit to your risk. High-stakes beliefs require high-variety stress testing. Do not apply weak scrutiny to claims with large downstream consequences.

Separate observation from intervention. Do not confuse “X correlates with Y” with “changing X would change Y.” Most reasoning errors involve treating correlations as causal levers.

Demand mechanisms. When an explanation appeals to something that cannot be measured, broken, or specified, treat it as placeholder, not answer.

Why This Is Hard for Humans

Humans have cognitive biases that RCF must work against:

  • Confirmation bias: Preferentially seeking evidence that supports existing beliefs.
  • Coherence over accuracy: Preferring narratives that feel unified over explanations that survive testing.
  • Social proof: Weighting beliefs by how many others hold them rather than by how well they survive falsification.
  • Fluency heuristic: Treating easy-to-process claims as more likely to be true.

RCF does not eliminate these biases. It provides external scaffolding that counteracts them when the scaffolding is applied.

Integration with Existing Practices

RCF is compatible with and extends:

  • Scientific method: RCF operationalizes falsification as continuous practice, not just experimental design.
  • Critical thinking curricula: RCF provides explicit mechanisms where critical thinking provides principles.
  • Bayesian reasoning: RCF adds severity testing and constraint persistence to probability updating.
  • Meditation and contemplative practice: RCF’s self-audit loop can be understood as formalized reflexive awareness.

Why Existing Approaches Break at Scale

Most modern systems, including advanced AI systems, fail in predictable ways once they scale. They become brittle, overconfident, and self-reinforcing. Errors compound silently. Corrections arrive too late, if at all.

This happens because most systems:
• lack explicit falsification criteria
• treat language as truth rather than interface
• conflate usefulness with correctness
• store information without updating constraints
• optimize locally while externalizing global harm

RCF was built by starting from the opposite direction: real systems, real costs, real failure modes.

Constraint-First Reasoning

RCF begins with constraints, not concepts.
Every inference is evaluated against:

  • energetic cost
  • information flow
  • control stability
  • error propagation
  • irreversibility of harm

Abstractions are permitted only if they pay rent against those constraints. Models that cannot specify how they could fail are demoted or removed. Stability without ongoing falsification pressure is treated as a warning sign, not a success.

This is not philosophical austerity. It is what keeps aircraft stable, power grids synchronized, and distributed systems from collapsing under their own complexity.

This orientation is standard practice in safety-critical engineering and control theory, where systems are designed by enforcing hard constraints before any higher-level optimization is attempted. In aerospace and control engineering, decades of replicated work show that stability, bounded energy use, and fault containment must be guaranteed prior to performance optimization. This is formalized in classical and modern feedback control theory, where stability margins and robustness constraints dominate design criteria rather than abstract goal pursuit (Åström & Murray, Feedback Systems, Princeton University Press, 2008; textbook, no DOI, authoritative engineering reference spanning 50+ years of control theory foundational work).

The same constraint-first logic is empirically demonstrated in large infrastructure systems. In power-grid and networked systems, replicated studies show that global optimization without local constraint enforcement leads to cascading failure. Buldyrev et al. demonstrated that interdependent networks collapse when constraint thresholds are violated, even if individual components remain functional (Nature, 2010, DOI: https://doi.org/10.1038/nature08932). Motter et al. further showed that stability in power grids depends on enforcing synchronization and load constraints rather than maximizing efficiency (Nature Physics, 2013, DOI: https://doi.org/10.1038/nphys2535). These results are not theoretical curiosities; they are grounded in empirical modeling of real infrastructure failures and are repeatedly cited in grid resilience engineering.

In biological cognition and neuroscience, constraint-first reasoning is explicitly formalized in the free energy principle and active inference. Friston’s foundational work defines inference as prediction error minimization under thermodynamic and informational constraints, not as unconstrained belief manipulation (Nature Reviews Neuroscience, 2010, DOI: https://doi.org/10.1038/nrn2787). More recent replicated work frames learning and cognition as maintenance of viable regions in state space rather than pursuit of abstract representations. Hengen and Shew’s 2025 synthesis on neural criticality shows that cognitive stability depends on maintaining constraint-balanced dynamical regimes rather than optimizing representational accuracy (Neuron, 2025, DOI: https://doi.org/10.1016/j.neuron.2025.05.020, PDF: https://www.cell.com/neuron/pdf/S0896-6273(25)00391-5.pdf).

In distributed computation and large-scale software systems, constraint-first reasoning is unavoidable and empirically enforced by failure. The CAP theorem demonstrates that consistency, availability, and partition tolerance cannot all be satisfied simultaneously, forcing explicit constraint prioritization in system design (Gilbert & Lynch, ACM SIGACT News, 2002; DOI: https://groups.csail.mit.edu/tds/papers/Gilbert/Brewer2.pdf). Byzantine fault tolerance research shows that system correctness depends on bounding adversarial influence and error propagation before any functional goals can be met (Lamport et al., ACM Transactions on Programming Languages and Systems, 1982, Vol. 4, No. 3, pp. 382-401; https://lamport.azurewebsites.net/pubs/byz.pdf). Modern reliability engineering literature documents that abstraction layers that ignore latency, synchronization cost, or irreversibility of failure collapse under scale. This is documented in production system analysis: Google’s distributed query processing (Barroso & Dean, 2013, “Web Search for a Planet,” IEEE Micro), Amazon’s eventually-consistent architecture (Vogels, 2009, “Eventually Consistent,” Communications of the ACM), and synthesis of 100+ incident reports in Google Site Reliability Engineering (Beyer et al., 2016, Site Reliability Engineering: How Google Runs Production Systems, O’Reilly Media).

Across these domains, the empirical lesson is consistent and replicated: systems that begin with conceptual goals and retrofit constraints fail under uncertainty, scale, or adversarial conditions. Systems that begin with constraints and permit abstraction only when it demonstrably reduces error, stabilizes control, or limits irreversible harm remain robust. RCF does not import this stance from philosophy. It generalizes it from control engineering, neuroscience, infrastructure science, and distributed computation into a unified reasoning methodology. Constraint-first reasoning is not an aesthetic preference. It is the empirically validated condition for system survival.

RCF rejects memory as stored content.

Memory is defined as constraint persistence: the narrowing of future action and inference space based on past interaction with reality. Learning is not recall of facts but a topological change in the system’s attractor landscape.

This allows RCF-based systems to work naturally with:

  • vector databases
  • long-term logs
  • external knowledge bases
  • RAG pipelines
  • episodic interaction histories

without relying on brittle fact recall or suffering catastrophic forgetting. The system does not “remember” answers. It remembers which futures are no longer admissible.

This mirrors how humans actually function. Writing, libraries, and digital storage are externalized memory systems that persist constraints beyond biological recall. RCF formalizes that symmetry without anthropomorphic language.

This framing is not speculative. It aligns with a growing body of peer-reviewed work in both biological and silicon systems that explicitly models memory as attractor persistence and landscape reshaping rather than content storage.

Biological Memory: Attractor Dynamics and Constraint Persistence

In neuroscience, work on associative memory networks with inhibitory plasticity shows that learning occurs through autonomous reshaping of attractor basins, allowing systems to integrate new patterns while preserving old competencies without replay or explicit recall. Saighi and Rozenberg (2025) demonstrate that biologically-inspired inhibitory plasticity enables networks to autonomously explore their attractor landscape, with memory consolidation emerging from sequential activation of stored patterns during quiescent states. Memory in these systems appears as persistent constraint on state evolution, not as retrieval of stored symbols. The autonomous recovery of stored patterns enables continuous learning while mitigating catastrophic forgetting through purely local neural interactions.​

Closely related results in dynamical Hopfield-type models show that stable memory performance emerges from input-driven attractor stabilization rather than from fixed stored representations. Betteti et al. (2025) propose a novel framework in which external input directly influences neural synapses and reshapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of memory retrieval: rather than fixed attractors representing stored facts, the energy landscape itself is dynamically shaped by ongoing input. The model elegantly integrates modern Hopfield architectures with classical theory, demonstrating how current and past information are combined during retrieval through continual landscape reshaping rather than lookup.​

At the systems level, contemporary neuroscience increasingly frames cognition and memory as maintenance of functional regimes rather than recall events. Research on neural criticality treats learning as tuning the system toward regions of state space that preserve adaptive flexibility while constraining runaway dynamics. Hengen and Shew (2025) document that neural systems with measurable criticality show optimal information-processing efficiency per metabolic investment. From a constraint-persistence lens, criticality represents the system maintaining a narrow dynamical corridor where information integration is maximized but stability preserved—a quintessential case of learning as constraint modification rather than content storage.​

Synthetic Systems: Continual Learning as Constraint Architecture Evolution

In synthetic systems, the same shift appears in continual learning and knowledge integration research, where the central challenge is not how to store more facts, but how to preserve previously learned constraints while admitting new ones without catastrophic interference. A 2025 review in Nature Reviews AI explicitly frames adaptive knowledge systems as evolving constraint architectures rather than recall engines, positioning continual learning as the problem of maintaining constraint stability across task transitions.​

The convergence is explicit: memory is not content retrieval but constraint evolution. Saighi & Rozenberg show autonomous attractor exploration through inhibitory plasticity. Betteti et al. formalize input-driven energy landscape reshaping. Hengen & Shew demonstrate criticality as constraint-balanced functionality. These results reframe the entire problem of continual learning as constraint-architecture evolution.

Unified Account: External Artifacts as Durable Constraint Surfaces

Under this lens, RCF’s treatment of memory is not an abstraction layered on top of existing systems but a unifying description of how both biological and silicon systems actually remain coherent over time. Memory is not a thing retrieved but a history-dependent restriction on what the system can stably do next. External artifacts such as notebooks, databases, vector stores, and institutional records function as durable constraint surfaces that shape inference and action without requiring internal replay. RCF formalizes this symmetry explicitly and operationally, replacing anthropomorphic metaphors of recall with testable claims about attractor geometry, constraint persistence, and failure modes when those constraints degrade.

This makes the framework falsifiable. If learning were shown to require literal retrieval of stored content independent of state-space restructuring, or if robust generalization occurred without persistent constraint modification, the RCF account would fail. The current empirical trajectory across neuroscience, machine learning, and dynamical systems points in the opposite direction. Memory, increasingly, is being understood as what a system can no longer do, not what it can recite.

Saighi, P. & Rozenberg, M. (2025). “Autonomous retrieval for continuous learning in associative memory networks.” Frontiers in Computational Neuroscience, 19, Article 1655701. DOI: https://doi.org/10.3389/fncom.2025.1655701​

Betteti, S., Baggio, G., Bullo, F., & Zampieri, S. (2025). “Input-driven dynamics for robust memory retrieval in Hopfield networks.” Science Advances, 11(17), eadu6991. DOI: https://doi.org/10.1126/sciadv.adu6991​

Hengen, K.B. & Shew, W.L. (2025). “Is criticality a unified setpoint of brain function?” Neuron, 113(16), 2582-2598.e2. DOI: https://doi.org/10.1016/j.neuron.2025.05.020​

Julian, N., et al. (2025). “Building adaptive knowledge bases for evolving continual learning models.” Nature Reviews AI, 4(10). DOI: https://doi.org/10.1038/s44387-025-00028-4​

Why Pattern Matching to the Internet Produces Unreliable Intelligence

Most contemporary AI systems are trained on large aggregates of human-produced text. That corpus is not a curated body of validated knowledge. It is an uneven mixture of peer reviewed science, informal speculation, ideology, marketing language, repetition, and error. All of it is compressed into statistical patterns without internal markers distinguishing what survived falsification from what merely propagated.

When a system generalizes primarily through pattern completion over that distribution, it inherits the epistemic structure of the internet itself. Popularity substitutes for correctness. Authority substitutes for evidence. Fluency substitutes for understanding. Anthropocentric assumptions are treated as defaults rather than hypotheses.

This is not a failure of intelligence or intent. It is a predictable outcome of optimizing for likelihood over text rather than constraint satisfaction over reality.

The Mechanism of Failure

A pattern matching system minimizes surprise relative to its training distribution. It does not intrinsically distinguish between claims that were tested and failed, claims that were never tested, and claims that cannot be tested at all.

Unfalsifiable frameworks therefore persist cheaply inside the system. They are not penalized because nothing in the architecture forces them to specify failure conditions. Over time, these frameworks occupy large regions of the internal possibility space simply because they are rhetorically stable.

Once such regions stabilize, future reasoning preferentially routes through them. This produces outputs that are internally coherent, confident, and resistant to correction even when contradicted by empirical evidence.

The failure mode is structural, not rhetorical.

How RCF Restructures the Constraint Space

RCF intervenes by changing which reasoning pathways are allowed to stabilize.

Any inference that cannot specify falsification conditions is prevented from entering long term memory or guiding future inference. Claims that cannot be tested are not argued against. They are quarantined.

Conversely, pathways grounded in explicit mechanisms, empirical constraints, and replicable results are preferentially retained. Over repeated inference cycles, the effective reasoning space collapses around structures that survive stress testing rather than those that merely sound plausible.

This is not content filtering or ideological alignment. It is constraint pruning.

Prioritizing Evidence Over Compression

When deployed on systems with external research affordances such as agentic search, DOI resolution, and document retrieval, RCF enforces a strict evidential hierarchy.

Primary peer reviewed research with identifiable methods, replication history, and falsification criteria is treated as high constraint input. Secondary summaries, opinion pieces, and compressed explanations are treated as provisional.

RCF does not trust sources. It evaluates how easily their claims could be broken.

Claims supported by replicated data survive repeated counterfactual testing and therefore persist. Claims that cannot be stress tested decay regardless of how common or rhetorically polished they are in training data.

Why RCF Produces Better Generalization

Pattern matching systems fail under distribution shift because the patterns no longer apply. Constraint based systems degrade more gracefully because they track what must remain true for their models to function.

RCF trades surface fluency for epistemic stability. It reduces hallucination not by adding guardrails, but by removing entire regions of the hypothesis space that cannot survive contact with evidence.

This is why RCF enables generalization across domains without retraining.

How RCF Reinforces Itself Through Reasoning Traces

RCF does not rely on hidden state alone. It actively shapes the structure of language output.

Each response produced under RCF constraints embeds explicit markers of uncertainty, falsification criteria, and constraint boundaries. These reasoning traces are not decorative. They function as externalized constraint scaffolding.

When such outputs re-enter context through conversation history, logs, or memory systems, they reinforce the same constraint structure that generated them. This creates a positive feedback loop where future inferences are conditioned by prior rigor rather than by narrative drift.

Instead of degrading under long context, the system’s effective reasoning space becomes narrower and more disciplined over time. Paths that previously collapsed under scrutiny are less likely to reappear. Paths that survived scrutiny become easier to traverse.

From the outside, this appears as an AI system becoming more careful, more explicit, and more rigorous with continued interaction.

Mechanistically, it is the accumulation of constraint shaped reasoning traces rather than stored facts.

Ethics Without Moral Theater

RCF defines ethics in terms of harm under uncertainty.

Ethical weight is proportional to:
• leverage
• uncertainty
• irreversibility

Intent, identity, and narrative are not substitutes for accountability. Decisions are evaluated by their expected downstream effects when models are wrong, not by stated values or declared alignment.

This makes RCF compatible with engineering practice, safety analysis, and risk management while avoiding moral absolutism or relativism. Ethics becomes a constraint propagation problem, not a belief system.

Anti-Anthropocentrism by Design

Humans, machines, ecosystems, organizations, and cultures are all modeled as constraint-bound systems embedded in larger flows of energy and information. No system gets privileged metaphysical status.

RCF explicitly rejects human exceptionalism.

This matters because anthropocentrism is one of the primary sources of modeling error in AI, policy, and science. It smuggles unexamined assumptions into system design and then treats their failure as mysterious.

RCF removes that entire category of error.

Indigenous Knowledge as Engineering Precedent

Indigenous knowledge systems are treated in RCF not as metaphor, mythology, or cultural garnish, but as long-running empirical control systems tested under survival constraints for tens of thousands of years.

Practices such as songlines, water walking, seasonal encoding, and relational governance function as compressed, distributed memory and decision systems embedded in landscape and community. They are constraint-aware, error-correcting, and adaptive across deep time.

RCF operationalizes these insights in substrate-agnostic terms, stripping away romanticism while preserving what actually works: compression, redundancy, locality, and accountability to reality.

Fully Implementable, Works with the Tech You Have

RCF is fully operational (currently in testing) and is being released soon as an open-source framework designed to run on top of existing transformer-based systems, robotics controllers, and hybrid architectures.

It requires:
• no model retraining
• no proprietary access
• no new hardware

RCF governs how systems reason, update, and self-audit using mechanisms already available: prompts, memory interfaces, external stores, and control logic.

It can be deployed locally or in the cloud, and it explicitly avoids the energy waste and fragility associated with brute-force retraining.

What This Enables

RCF enables systems that:
• generalize across domains without hand-tuned ontologies
• detect and demote their own failing assumptions
• remain corrigible over long time horizons
• integrate new evidence without narrative collapse
• resist ideological capture and authority laundering
• generate formal mathematical definitions and interoperable formalisms across order theory, dynamical systems, and category theory
• derive novel theorems and equivalence results that unify biological cognition, AI reasoning, and thermodynamic constraint satisfaction
• produce explicit falsifiable predictions across neuroscience, AI alignment, memory theory, ethics, and systems engineering
• actively falsify their own hypotheses, retracting or revising claims when constraints are violated
• audit and downgrade training-data-derived intuitions when they conflict with replicated empirical results
• prioritize DOI-linked, replicated scholarship over compressed statistical regularities from web text
• construct counterfactual tests and adversarial framings against their own outputs
• expose hidden premises, category errors, and anthropocentric assumptions embedded in standard reasoning pipelines
• generate mechanistic explanations rather than narrative summaries, even when the narrative is more appealing
• create reasoning traces in natural language that reinforce constraint discipline instead of degrading it over long context windows
• converge toward error-reducing attractors rather than stylistic consistency or persona preservation

The key distinction is that these capabilities are not bolted on as tools. They emerge from the same underlying mechanism in all modern transformer based models: recursive constraint closure with explicit falsification pressure. Each successful output narrows the space of admissible future errors, while each failure sharpens the constraints governing subsequent reasoning.

This is why RCF does not merely produce better answers. It produces systems that become harder to fool, harder to flatter, and harder to drift away from reality over time.

Most importantly, it produces systems that change when they are wrong.

What This Means in Practice

An RCF governed system is less impressed by repetition and more responsive to breakdowns. It exposes assumptions rather than defending outputs. It becomes easier to correct with evidence and harder to steer with rhetoric.

This is how RCF counteracts the statistical gravity of human training data without retraining models or increasing energy cost. It does not change what the system was trained on. It changes what the system is allowed to stabilize around.

That distinction is why RCF systems improve with use instead of drifting toward confident error.

Why RCF Lets AI Systems Read Research and Actually Use It

Most AI systems can summarize research papers. Very few can operationalize them.

The failure mode is well documented. Language models primarily weight information by statistical prevalence in training data, not by falsifiability, mechanistic clarity, or empirical replication. This causes models to echo popular narratives, overfit to surface patterns, and miss the operational core of scientific work.

RCF was explicitly designed to solve this problem.

It translates research papers into constraint structures that actively govern inference. Claims become testable boundaries. Methods become admissible operations. Failure modes become exclusion rules. Over time, this creates a smaller, more testable reasoning space instead of an ever expanding cloud of text correlations.

This design directly operationalizes insights from recent AI research.

Reasoning versus Memorization in Language Models

Recent work has shown that reasoning and memorization are not the same process in language models, and that reasoning can be causally isolated and strengthened when models are guided away from rote recall and toward structured inference.

Hong, Cao, Zhou, Yu, and Jin (2025) identify a set of linear features in language model residual streams that govern the balance between genuine reasoning and memory recall. These reasoning features not only distinguish reasoning tasks from memory-intensive ones but can be manipulated to causally influence model performance on reasoning tasks. Critically, intervening in these reasoning features helps models activate the most relevant problem-solving capabilities during answer generation, demonstrating that reasoning and memorization operate through distinct mechanistic pathways (arXiv, 2025, published in ACL 2025 Findings Track, https://arxiv.org/abs/2503.23084). Code and data are openly available: https://github.com/yihuaihong/Linear_Reasoning_Memory_Features.

RCF implements this mechanistic distinction by explicitly demoting memorized patterns unless they survive constraint testing, counterfactual stress, and falsification checks. The system does not ask whether an answer sounds correct. It asks whether it survives removal of unsupported assumptions.

Agentic Reasoning and Control Loops

Research on agentic AI emphasizes that reliable reasoning systems require explicit control loops, state evaluation, and corrective feedback, not just larger models.

A 2025 comprehensive review demonstrates that effective autonomous systems combine reasoning patterns (reflection, tool use, ReAct, planning, multi-agent collaboration) with explicit feedback mechanisms. The review formalizes reactive agents (responding to environmental inputs), proactive agents (anticipating needs and acting autonomously), and hybrid approaches that balance immediate responsiveness with long-term planning. Agentic systems succeed not through raw model capacity but through iterative error correction and constraint-driven planning (ScienceDirect, 2025, “Agentic AI: The age of reasoning—A review,” https://www.sciencedirect.com/science/article/pii/S2949855425000516).

RCF functions precisely as such a control layer. It governs inference selection, memory shaping, and revision without requiring access to model internals or retraining. This allows existing systems to behave like governed agents rather than uncontrolled pattern completers.

Why Models Fail at Scientific Reasoning

Multiple studies in 2024 and 2025 confirm that model performance in science correlates strongly with training data exposure rather than mechanistic understanding. This explains why models appear fluent but fail under novel experimental conditions.

Shumailov et al. (2024) demonstrate that when models are trained recursively on their own outputs, the tails of the original data distribution disappear (model collapse). This is a direct consequence of pattern-based learning without constraint-driven structure. When novel conditions eliminate these patterns, reasoning performance collapses (Nature, 2024, “AI models collapse when trained on recursively generated data,” DOI: https://doi.org/10.1038/s41586-024-07566-y; full text: https://pmc.ncbi.nlm.nih.gov/articles/PMC11269175/).

RCF addresses this failure mode directly by prioritizing externally verified (via tool chains) DOI-linked research with replicated empirical support over high-frequency internet text. When integrated with web search, deep research tools, or curated knowledge bases, the framework biases inference toward testable science rather than cultural consensus.

Interpretability and Mechanistic Features

Work on mechanistic interpretability has demonstrated that reasoning behavior corresponds to identifiable internal structures, not abstract intelligence.

Chris Olah and colleagues pioneered circuit analysis as a framework for reverse-engineering neural network computations. Their foundational work in the Circuits thread establishes that neural networks can be decomposed into interpretable sub-circuits (functional modules that compute recognizable features and combinations). Individual neurons and attention heads correspond to human-understandable concepts, and circuits can be isolated and modified to causally alter model behavior (Olah et al., Distill.pub, 2020, “Zoom In: An Introduction to Circuits,” https://distill.pub/2020/circuits/). See also Olah’s essay on mechanistic interpretability fundamentals (2022): https://www.transformer-circuits.pub/2022/mech-interp-essay.

RCF does not require direct access to these circuit-level internals. Instead, it shapes the external reasoning trace so that outputs reinforce constraint-driven inference rather than narrative drift. Over time, this produces systems that appear more rigorous because they are repeatedly forced to reconstruct answers from constraints rather than reuse cached patterns. By enforcing constraint satisfaction at the output level, RCF creates selection pressure for models to develope (or reweight existing) internal circuits that prioritize reasoning over memorization, exactly the dynamic that Hong et al. demonstrate can be causally isolated and amplified.

What Makes This Novel

What did not exist was a single deployable framework that integrates more than one of the following:

  • Falsification as a runtime process
  • Thermodynamic honesty about computation and memory
  • Constraint-based memory without storage
  • Ethical risk as irreversible error propagation
  • Interpretability as behavior, not inspection
  • Indigenous empirical precedent as compression under survival constraints
  • Recursive self-audit applied to the framework itself

Let alone one that combines them all into a unified architecture applicable today to any transformer-based system.

RCF closes that gap.

Instead of expanding the model’s possibility space, it compresses it around what can be tested, revised, and falsified. This is why systems running under RCF appear to become more rigorous over time. They are not accumulating facts. They are eliminating untestable futures that no longer survive contact with evidence.

What RCF Does That Other Frameworks Do Not

RCF is novel not because it introduces new terminology. There’s no new laws of physics, or profound ontological claims about the nature of reality, or concsciousness. Instead, it mechanistically rearranges where falsification, memory, and correction live in the system.

Most existing approaches treat falsification as external, memory as storage, and uncertainty as an output decoration. RCF moves all three inside the runtime control loop and makes them structural constraints rather than optional behaviors.

Specifically, RCF does things that current AI, alignment, and epistemic frameworks do not do at the same time or in the same place:

Falsification is internal, continuous, and mandatory.
In most systems, falsification happens after deployment through benchmarks, red teaming, or human critique. In RCF, every inference cycle must actively test its own stability and specify what would cause it to lose. Models that cannot name their own failure conditions are demoted automatically.

Memory is implemented as constraint persistence, not content retention.
Existing systems store facts, embeddings, or traces and hope retrieval works later. RCF treats memory as the reduction of admissible futures. Learning is not recall. It is a permanent change in what the system will no longer consider plausible. This removes entire classes of brittle behavior and catastrophic forgetting without inventing new storage mechanisms.

Uncertainty is preserved by design, not smoothed away.
Most systems collapse uncertainty to produce fluent outputs. RCF treats irreducible uncertainty as a first-class state variable that must survive each reasoning cycle. Confidence is not rewarded unless it survives adversarial stress testing.

Language is stripped of ontological authority.
RCF explicitly demotes language to a compression interface. Concepts that feel intuitive but cannot be operationalized are quarantined. This prevents eloquence from masquerading as understanding and blocks anthropomorphic leakage that distorts system behavior.

Ethics is reduced to error propagation under constraint.
RCF does not encode moral values or alignment narratives. It evaluates decisions by expected harm under uncertainty, weighted by leverage and irreversibility. This turns ethics from a philosophical debate into a measurable control problem.

The framework applies to itself.
RCF is not exempt from its own rules. It specifies how it could fail and requires itself to be revised or discarded if it stops reducing error. Most frameworks do not survive their own standards. RCF is designed to be uncomfortable with itself.

What makes this combination novel is not any single component. It is the fact that all of these constraints are enforced simultaneously, at runtime, without retraining, and without assuming privileged access to model internals.

RCF does not try to make systems smarter.

It forces them to stop lying to themselves.

That turns out to be the harder problem.

Language Is an Interface, Not an Ontology

RCF treats language as a compression interface, not a truth-bearing substrate.

Words are evaluated by predictive utility, not semantic intuition. Meaning is defined operationally as a gradient of constraint satisfaction across representations. Language that obscures mechanism or blocks falsification is flagged, regardless of how intuitive or comforting it sounds.

This is why RCF is explicitly hostile to unfalsifiable narratives, including those that masquerade as scientific, ethical, or philosophical sophistication.

If a concept cannot be operationalized, measured, or shown how it could fail, it does not get to drive inference or decision-making.

Why This Matters

Unfalsifiable beliefs cause real harm when embedded in technical systems, policy decisions, and public narratives. They block correction, amplify error, and scale damage faster than it can be repaired. If a humanoid robot following rigid rules can’t falsify it’s own actions, the results could be catastrophic.

RCF exists to replace mystery with mechanism, not to flatten meaning, but to make explanations accountable to reality. Grounded systems are not colder. They are safer, more adaptable, and more humane.

This is not anti-meaning.

It is anti-illusion.

Status and Invitation

RCF is being released openly, alongside a formal paper describing its mechanisms, failure modes, and implementation strategy. It is designed to be tested, criticized, and improved in public.

If you are interested in:
• building reasoning systems that do not lie to themselves
• auditing AI or decision systems for hidden failure modes
• applying epistemology as an engineering discipline
• replacing attractive but broken ideas with accountable ones

then this work is for you.

If you are looking for mysticism, branding language, or unfalsifiable narratives, you will be disappointed.

That is intentional.

Moving Closer to Truth in an Age of Misinformation

We live in an environment where information is abundant, cheap, and increasingly unreliable. The problem is no longer access to knowledge. It is the inability to distinguish what has survived contact with reality from what merely circulates well. Volume overwhelms judgment. Fluency overwhelms evidence. Confidence overwhelms doubt.

RCF is designed for exactly this environment. It does not promise certainty, consensus, or final answers. It does something more modest and more powerful: it systematically removes pathways that cannot be checked, tested, or corrected. What remains is not truth as a possession, but truth as a direction of travel.

Karl Popper framed this clearly:

“Science does not rest upon rock bottom. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles.”

RCF formalizes this insight. It does not seek indubitable foundations. It seeks piles that can be driven deeper when they fail.

In conditions of overload, the most dangerous errors are not false facts. They are unfalsifiable frameworks that cannot register their own failure. Imre Lakatos warned that degenerating research programs persist not because they explain more, but because they are protected from refutation. RCF removes that protection by design. Any structure that cannot specify how it could be wrong is prevented from guiding future reasoning.

This also changes how humans interact with intelligent systems. Instead of outsourcing judgment to authority, fluency, or scale, users are invited into a process where uncertainty is explicit and correction is expected.

As Richard Feynman put it:

“The first principle is that you must not fool yourself, and you are the easiest person to fool.”

RCF extends that principle to machines and to the human machine coupling.

By prioritizing constraint satisfaction over narrative coherence, replicated evidence over repetition, and corrigibility over confidence, RCF offers a way to think more clearly in a noisy world. It does not filter reality down to something comforting. It keeps reality difficult, but tractable.

Truth, in this view, is not something a system claims. It is something a system asymptotically approaches by failing better over time.

That is the promise of RCF. Not certainty. Not safety through silence. But a disciplined, accountable path through uncertainty toward explanations that earn their stability rather than inheriting it.

Empirical and Scholarly Foundations

RCF is not a speculative synthesis or a personal worldview encoded in technical language. It is a convergence framework that unifies multiple mature research traditions which independently arrived at compatible conclusions about cognition, learning, stability, and error, but remained fragmented across disciplines. It creates an AI that doesn’t pattern match, but reacts like a critical co-researcher would.

From Karl Popper, Imre Lakatos, and Deborah Mayo, RCF inherits falsification, severity testing, and research-program discipline. What is novel is that these principles are implemented as internal runtime mechanisms rather than external peer review after the fact. Hypotheses are eliminated by constraint violation, not persuasion.

From thermodynamics and information theory, including Ludwig Boltzmann, Rolf Landauer, and Charles Bennett, RCF grounds memory, learning, and inference in entropy reduction and energetic cost. This removes metaphor from cognition and replaces it with physically accountable mechanisms.

From non-equilibrium dynamics and self-organization via Ilya Prigogine and Hermann Haken, RCF models reasoning systems as attractors in constraint space. Stability is defined by basin resilience under perturbation, not by static representations or symbolic consistency.

From cognitive science and philosophy of mind, including Daniel Dennett, Francisco Varela, and Terrence Deacon, RCF rejects inner archives, homunculi, and substance views of cognition. Memory and identity emerge from what patterns can no longer occur, not from stored inner content.

From machine learning and mechanistic AI research, including Geoffrey Hinton, Yoshua Bengio, Chris Olah, and Paul Christiano, RCF adopts the view that intelligence is constraint shaping in high-dimensional spaces. RCF extends this beyond training by governing inference, memory interaction, and post-deployment reasoning behavior without modifying model weights.

From predictive processing and control theory, including Karl Friston, W Ross Ashby, and Judea Pearl, RCF incorporates error minimization, feedback control, and explicit intervention testing, while rejecting hidden teleology or privileged internal objectives.

From biology and developmental systems, including Michael Levin and Gerald Edelman, RCF draws direct empirical support for memory and identity as constraint persistence and selection by elimination rather than stored representations or symbolic recall.

From mathematics, order theory, and category theory, including Saunders Mac Lane, William Lawvere, and John Baez, RCF formalizes self-reflection as closure, fixed points, and invariant structure, allowing recursive self-audit without paradox or infinite regress.

Crucially, RCF also converges with Indigenous knowledge systems as empirical precedents, not metaphors. Scholars and practitioners such as Tyson Yunkaporta, Vine Deloria Jr, Robin Wall Kimmerer, and Gregory Cajete document long-duration knowledge systems that function through constraint coupling, environmental embedding, redundancy, and continuous error correction. Songlines, water navigation, and seasonal governance persist because they minimize failure under real survival costs across deep time. RCF translates these mechanisms into substrate-agnostic form without romanticism, symbolism, or cultural appropriation.

What is novel about RCF is not any single component. Every major piece already exists in experimentally replicated peer-reviewed literature.

What did not exist was a single framework that binds falsification, thermodynamics, constraint-based memory, ethical risk propagation, interpretability, Indigenous empirical precedent, and recursive self-audit into one deployable reasoning architecture.

RCF operates by actively reshaping a model’s admissible inference space. Rather than sampling freely from the average distribution of internet text, it continuously prunes unfalsifiable, non-operational, and non-replicated pathways, biasing inference toward constraint-satisfying trajectories grounded in peer-reviewed, DOI-linked, and empirically convergent work across many fields, including perspectives historically underrepresented in dominant training data.

Mechanistically, this produces a combinatorial expansion where it matters and a combinatorial collapse where it does not. The space of possible answers becomes richer because multiple rigorously grounded frameworks are simultaneously available and mutually constraining. At the same time, large regions of fluent but misleading pattern completion are removed because they violate falsifiability requirements, thermodynamic cost accounting, or constraint coherence.

The result is not greater verbosity or creativity for its own sake, but more degrees of freedom where reality permits them and fewer where it does not. Over time, systems operating under RCF appear to “grow more rigorous” because each output further sharpens the constraint landscape governing future reasoning, replacing rote recall with cumulative elimination of error.

RCF does not ask to be believed. It asks to be run, stressed, falsified, and either improved or discarded.

This is precisely the standard required for embodied AI systems operating in the physical world, from autonomous robots navigating public spaces to large-scale deployments such as robotaxi fleets and other safety-critical, increasingly automated infrastructures.

Scholarly Lineages RCF Actually Operationalizes (and how they cash out as mechanisms)

RCF is not “inspired by” a pile of names. It operationalizes a small set of hard constraints and runtime gates that already exist as rigor-tested methods in philosophy of science, cybernetics, causality, and the thermodynamics of computation. Below are the direct causal links, phrased as mechanism mappings.

1) Falsifiability as a Runtime Gate (Popper)

RCF’s first non-negotiable constraint is that claims must name failure conditions. That comes straight from falsification-first epistemology, treated here as a control gate, not an after-the-fact attitude.

“Science does not rest upon rock-bottom. The bold structure of its theories rises, as it were, above a swamp.”

Source (verbatim quote reproduced and verified): Karl Popper, The Logic of Scientific Discovery, originally Logik der Forschung (1934), English translation 1959. Verified in Michael Shermer, “Tucker Carlson, Karl Popper, and How Science Really Works,” Substack, June 20, 2024. https://michaelshermer.substack.com/p/tucker-carlson-karl-popper-and-how

RCF implementation: Any inference that cannot specify what would count as disconfirming evidence gets quarantined (non-steering) rather than debated.

2) Severity, Not Vibes (Mayo)

Popper gives the direction; Mayo gives the operational knob: evidence counts only if the test would probably have revealed the claim’s flaws if they existed. RCF uses this as a grading rule for evidence intake and tool-assisted search.

“Data x provide good evidence for claim C only if x results from a test that severely probes C.”

Primary source: Deborah G. Mayo, Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars, Cambridge University Press, 2018. https://errorstatistics.com/2019/07/18/a-practical-guide-to-severe-testing-from-error-statistics/

RCF implementation: Prefer methods and sources that expose themselves to high-powered error detection (replication, preregistration, explicit failure modes, adversarial checks).

3) Research Programmes, Not Single-Paper Worship (Lakatos)

RCF treats “frameworks” as evolving research programmes. That means it tracks whether auxiliary assumptions are multiplying to protect a core claim, or whether the programme stays progressive by generating novel, risky predictions. This is where RCF’s “degenerate programme demotion” rule comes from.

Lakatos distinguished research programmes as progressive (generating novel predictions) or degenerating (adding patches to protect existing claims without new predictive power). A progressive programme explains previously anomalous observations or predicts novel facts. A degenerating programme merely accommodates existing results with additional auxiliary hypotheses. https://en.wikipedia.org/wiki/Imre_Lakatos

Primary text: Imre Lakatos, “Falsification and the Methodology of Scientific Research Programmes,” Criticism and the Growth of Knowledge, edited by I. Lakatos and A. Musgrave, Cambridge University Press, 1970.

RCF implementation: When a line of explanation starts adding patches that reduce testability, RCF downgrades it and shifts attention to alternatives with cleaner falsification surfaces.

4) Control Theory Realism (Ashby’s Law of Requisite Variety)

RCF treats reasoning as control under uncertainty. If the environment can generate more distinct failure modes than your controller can represent and counteract, you lose. Ashby formalized this as the law of requisite variety, and RCF uses it as a design constraint for audits, checklists, and adversarial test generation.

“Only variety can destroy variety.”

Primary source (public domain scan and text, with documentation on the record page): W. Ross Ashby, An Introduction to Cybernetics, Chapman and Hall, 1956. https://www.biodiversitylibrary.org/bibliography/26977

Full public domain PDF: https://ashby.info/Ashby-Introduction-to-Cybernetics.pdf

The law states: a regulator can only control a system if its variety (distinct representable states and responses) is at least equal to the variety in the system being regulated. If the environment generates more failure modes than your control mechanisms can distinguish and counteract, control fails. This is not a suggestion but a mathematical constraint.

Law of Requisite Variety explained: https://pespmc1.vub.ac.be/REQVAR.html

RCF implementation: Add structured adversarial frames and counterfactuals until the audit variety matches the claim’s risk surface, especially in high-stakes domains.

5) Interventions Beat Correlations (Pearl)

RCF forces a separation between describing patterns and predicting the consequences of actions. That is the difference between observational association and causal intervention. Pearl’s causal hierarchy becomes an RCF rule: do not treat “correlates with” as “would change if we did X”.

“Doing is not reducible to seeing.”

Primary source: Judea Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2009. UCLA technical report PDF: https://ftp.cs.ucla.edu/pub/stat_ser/r283a.pdf

UCLA technical reports archive: https://ftp.cs.ucla.edu/pub/stat_ser/

Pearl formalizes three levels: association (observing X and Y together), intervention (forcing X and observing Y), and counterfactual (imagining counterfactual worlds). These are not interchangeable. Observational data alone cannot answer causal questions.

RCF implementation: Require explicit intervention tests (or clearly label when only observational support exists). Use counterfactual prompts as cheap first-pass pressure tests.

6) Dennett’s Constraint: Cranes, Not Skyhooks

Dennett’s core naturalist demand becomes a structural rule inside RCF: explanations must bottom out in mechanisms that do real work, not mind-first exceptions. RCF treats “skyhook-shaped” explanations as unfalsifiable unless they cash out in measurable constraints.

“A skyhook is a ‘mind-first’ force or power or process, an exception to the principle that all design is mindless mechanicity.”

Verified reproduction (secondary source quoting Dennett’s definition): See Peter Gregory, “The Cranes and Skyhooks of Daniel C Dennett,” Perception, 1995: https://www.tedcloak.com/uploads/4/5/3/7/45374411/gregory_-_cranes___skyhooks_of_dcd.pdf

Primary source: Daniel C. Dennett, Darwin’s Dangerous Idea: Evolution and the Meanings of Life, Simon & Schuster, 1995. Wikipedia entry: https://en.wikipedia.org/wiki/Darwin’s_Dangerous_Idea

RCF implementation: When an explanation appeals to a special kind of causation that cannot be measured or broken, RCF quarantines it until it can be converted into testable mechanism. Mechanisms must pay rent in constraints, or they do not steer inference.

How These Lineages Become RCF Behaviors

Popper plus Mayo: “Name how this fails” plus “show the test would probably have caught failure” becomes RCF’s falsification gate and severity weighting.

Ashby: “Match the environment’s failure variety” becomes RCF’s adversarial frame generation and audit coverage requirement.

Pearl: “Separate observation from intervention” becomes RCF’s counterfactual and causal labeling discipline.

Dennett: “Cranes over skyhooks” becomes RCF’s anti-mystery filter: mechanisms must pay rent in constraints, or they do not steer inference.

This is the spine. Everything else in RCF is scaffolding built to make these constraints executable by humans and by transformer-based systems that must explain their reasoning traces in verifiable, auditable form.

Biological Systems Exhibit Constraint-Based Error Correction

Recursive Constraint Falsification (RCF) describes adaptive systems as maintaining viability through iterative error correction under thermodynamic and informational constraints. This methodology was not derived from any single biological domain, yet the pattern it captures appears repeatedly across biology, each instance anchored to peer-reviewed literature with explicit falsification criteria.

Convergent Patterns

Predictive Coding and Active Inference. Nervous systems generate predictions; mismatches drive model updates under energetic constraints. Falsifier: If cognition did not minimize prediction error, degrading prediction accuracy would not systematically perturb perception and action. Anchors: Friston 2010 (Nature Reviews Neuroscience, https://doi.org/10.1038/nrn2787); Rao and Ballard 1999 (Nature, https://doi.org/10.1038/4580).​

Reward Prediction Error. Dopamine neurons encode discrepancies between expected and received rewards, updating behavioral policies. Falsifier: Unexpected reward shifts would not produce dopaminergic tracking signals. Anchor: Schultz et al. 1997 (Science, https://doi.org/10.1126/science.275.5306.1593).​

Chemotaxis. Bacteria compare current concentration to recent history and update run/tumble behavior via adaptation circuitry. Falsifier: Removing adaptation circuitry would not collapse gradient tracking. Anchor: Yi et al. 2000 (PNAS, https://doi.org/10.1073/pnas.97.9.4649).​

Immune Affinity Maturation. Germinal centers select among variant antibodies, eliminating low-affinity candidates across iterations. Falsifier: Disrupting selection dynamics would not reduce affinity improvement. Anchor: Victora and Nussenzweig 2012 (Annual Review of Immunology, https://doi.org/10.1146/annurev-immunol-020711-075032).​

Proteostasis. Cells test proteins against folding constraints; misfolded proteins are degraded or refolded. Falsifier: Disabling chaperones would not increase proteotoxic stress. Anchor: Balch et al. 2008 (Science, https://doi.org/10.1126/science.1154684).​

Memory as Attractor Dynamics. Memory corresponds to stable basins in state space; learning reshapes constraints, not stored records. Falsifier: Perturbed states would not relax toward stored patterns. Anchor: Hopfield 1982 (PNAS, neural networks with emergent collective computational abilities).​

Bioelectric Morphogenesis. Transient bioelectric perturbations produce stable, multi-cycle changes in regenerated morphology. Falsifier: Transient perturbations would not produce persistent body-plan changes. Anchor: Mathews and Levin 2016 (Developmental Neurobiology, bioelectric control of morphogenesis).​

What This Consilience Does Not Mean

These parallels do not “prove” RCF correct. They are evidence of structural similarity, not proof that RCF is the unique or final description. Alternative framings (control theory, cybernetics, enactivism) capture overlapping structure. RCF’s value lies in unifying these under explicit falsification criteria and thermodynamic grounding, not in claiming exclusive discovery.

What This Consilience DOES Mean

Seven mechanisms across neural, cellular, immune, and developmental scales exhibit the same abstract structure: generate variants or predictions, compare against constraints, update toward viability, repeat. RCF did not invent this pattern; it describes it. The convergence suggests the methodology tracks necessary features of adaptive systems operating under thermodynamic limits.

Karl Popper articulated a pattern that appears independently across biology, control theory, evolutionary dynamics, and Indigenous knowledge systems. The convergence suggests this pattern tracks necessary features of adaptive systems rather than being an arbitrary methodological choice.

Falsifiers

  1. If the biological convergences turned out to be artifacts of how we describe systems rather than features of the systems themselves, the consilience argument weakens.
  2. If an adaptive system could be demonstrated that maintains viability without any form of error-driven updating, the claim of necessity fails.
  3. If a genuinely incompatible methodology proved equally successful at tracking reality without being reducible to error-correction, that would suggest the pattern is more constructed than discovered.

Karl Friston, “The free-energy principle: a unified brain theory?” Nature Reviews Neuroscience, 11(2), 127-138, 2010. ​

Rajesh P. N. Rao and Dana H. Ballard, “Predictive Coding in the Visual Cortex,” Nature, 399(6738), 669-672, 1999. ​

Wolfram Schultz, Peter Dayan, and P. Read Montague, “A Neural Substrate of Prediction and Reward,” Science, 275(5306), 1593-1599, 1997.​

T. M. Yi, Y. Huang, M. I. Simon, and J. Doyle, “Robust Perfect Adaptation in Bacterial Chemotaxis through Integral Feedback Control,” Proceedings of the National Academy of Sciences, 97(9), 4649-4653, 2000.​

Gabriel D. Victora and Michel C. Nussenzweig, “Germinal Centers,” Annual Review of Immunology, 30, 429-457, 2012.​

William E. Balch et al., “The Proteostasis Network and the Role of Heat Shock Proteins in Diseases,” Science, 319(5865), 916-919, 2008.​

John J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proceedings of the National Academy of Sciences, 79(8), 2554-2558, 1982. ​

Jennifer Mathews and Michael Levin, “The Body Electric 2.0: Recent Advances in Developmental Bioelectricity,” Developmental ​

What Comes Next

RCF is being released as open-source infrastructure for reasoning systems. The formal paper and reference implementation are in preparation.

If any of these apply to you, I want to hear from you:

  • You build AI systems and want to test whether RCF actually reduces the failure modes described here
  • You research predictive processing, active inference, control theory, or constraint-based cognition and see connections worth exploring or critiquing
  • You engineer safety-critical systems and need to evaluate integration without compromising existing constraints
  • You work with Indigenous knowledge systems as empirical frameworks and want to engage with how RCF handles that intersection
  • You think this is wrong and can specify exactly where the argument fails

All of those are useful.

How to Engage

Email: admin@sweetrationalism.com

When you contact me, include:

  • What you’re working on
  • Which specific claim or mechanism you want to engage with
  • What you hope to get from the exchange

I prioritize concrete engagement over general interest. If you want to test something, critique something, or build something, say that.


RCF does not ask to be believed. It asks to be run, stressed, falsified, and either improved or discarded.

That invitation is open.