The lump of cognition fallacy

The extended mind as the advance of civilization

The misunderstood limits of folk science: an illusion of explanatory depth

People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion—an illusion of explanatory depth. The illusion is f...

Anthropic has caught up to OpenAI in image understanding

But neither one is all that good.

Horismos: Self-representation and the Derived Constitutional Boundary in Enriched Cognitive Systems

We present a theory of self-representing cognitive systems grounded in $$([0,\infty ],+)$$([0,∞],+)-enriched category theory and the Yoneda lemma. The central object is a self-representing $$([0,\infty ],+)$$([0,∞],+)-enriched category $$\mathcal{C}$$C—a Lawvere metric space whose objects are complete epistemic architectures, whose hom-values record directed informational upgrade costs, and which is separated, closed under internal homs, and bilaterally Cauchy complete—together with a contractive cognitive endofunctor $$F:\mathcal{C}\rightarrow \mathcal{C}$$F:C→Cmodelling iterative self-improvement. We establish eight results in a single logical arc. The Horizon Theorem shows that the Yoneda embedding $$\varphi (A)=\mathcal{C}(-,A)$$φ(A)=C(-,A)is never essentially surjective: $$\mathcal{C}$$C sits strictly inside its own free Cauchy completion $$\mathcal{P}(\mathcal{C})$$P(C), with the non-representable presheaves forming a topologically dense family, proved via a reflexivity argument. The Lawvere–Banach Attractor Theorem shows that every contractive endofunctor on a bilaterally complete, separated $$([0,\infty ],+)$$([0,∞],+)-enriched category converges to a unique fixed point $$\mathbf {\Omega }$$Ωat a geometric rate. The Boundary Derivation Theorem shows that $$\mathbf {\Omega }$$Ωis the minimal F-invariant substructure of $$\mathcal{C}$$C, with all of $$\mathcal{C}$$Cas its basin of attraction—the constitutional boundary, derived rather than postulated. The Horizon Expansion Theorem shows that each strictly ascending self-modification produces a new, quantitatively distinct non-representable witness. Beyond these four central results, we prove that Kleene and Bourbaki–Witt conditions yield only non-expansiveness when metrised, that contractive endofunctors form a monoid, and that the Yoneda horizon admits an observable diagnostic stabilising in finite time. The architectural section derives structural corrigibility and the alignment-incompleteness duality among five implications. The organising duality is exact: the non-surjectivity of $$\varphi $$φ and the existence of $$\mathbf {\Omega }$$Ωare two faces of the same $$([0,\infty ],+)$$([0,∞],+)-enriched structure. $$\mathbf {\Omega }$$Ωinhabits the space between them—not as a postulate, but as a proof. We argue that the eight theorems constitute universal laws of contractive cognitive systems: a stable constitutional boundary is not an engineering design choice but a topological inevitability for any reliably self-improving agent operating within a self-representing enriched metric space. The postulate becomes a theorem. The boundary is not imposed. It emerges.

Reify This

The authors contend that contemporary efforts to render AI systems interpretable rest on a mistake: reification, the process of treating abstractions and statistical artifacts as if they were concrete realities.…

La Singularidad Reflexiva: Derivas Identitarias en la Topología Probabilística de Modelos Generativos

Introspección asistida por entropía

The World Doesn’t Exist: William Gibson, William Blake, and Navigating an Algorithmic Hall of Mirrors | ODDCRITIC

Scrolling the TL revealed distorted realities, shaped by algorithms and biases.

Study: Sycophantic AI can undermine human judgment

Subjects who interacted with AI tools were more likely to think they were right, less likely to resolve conflicts.

People Who Can't Visualize Anything Are Challenging a 300-Year-Old Theory of Thought

Mental imagery might not be as central to complex human thought as we believed, philosophers say.

AI, Artifice, And Authenticity

The era of badly-automated homogenized engagement slop is upon us. I desperately want to believe there's a renaissance for authenticity on the other end.

The Octotypic Mind

Carcinization, Cognitive Prosthetics, and the Shape of Intelligence After AI

OpenAI admits AI hallucinations are mathematically inevitable, not just engineering flaws

In a landmark study, OpenAI researchers reveal that large language models will always produce plausible but false outputs, even with perfect data, due to fundamental statistical and computational limits.

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.

Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!

Intermediate token generation (ITG), where a model produces output before the solution, has become a standard method to improve the performance of language models on reasoning tasks. These intermediate tokens have been called \say{reasoning traces} or even \say{thinking traces} -- implicitly anthropomorphizing the traces, and implying that these traces resemble steps a human might take when solving a challenging problem, and as such can provide an interpretable window into the operation of the model's thinking process to the end user. In this position paper, we present evidence that this anthropomorphization isn't a harmless metaphor, and instead is quite dangerous -- it confuses the nature of these models and how to use them effectively, and leads to questionable research. We call on the community to avoid such anthropomorphization of intermediate tokens.

The Triadic Mind: How Language Reveals the Limits of Human Cognition

Languages are the most complex symbolic systems humans have ever created. Yet children acquire them effortlessly, without formal…