"Actualities seem to float in a wider sea of possibilities from out of which they were chosen; and somewhere, indeterminism says, such possibilities exist, and form part of the truth."
LLMs and World Models, Part 1
How do Large Language Models Make Sense of Their “Worlds”?
Large language models are cultural technologies. What might that mean?
Four different perspectives
Model Collapse Ends AI Hype
suzgunmirac/belief-in-the-machine
Belief in the Machine: Investigating Epistemological Blind Spots of Language Models
Epistemological Fault Lines Between Human and Artificial Intelligence
Large language models (LLMs) are widely described as artificial intelligence, yet their epistemic profile diverges sharply from human cognition. Here we show that the apparent alignment between...
An Observation on Generalization
PoliSim@CHI 2026
Large Language Models are rapidly evolving from text generators into reasoning systems that can act as autonomous agents. When placed in social contexts, these agents display emergent behaviors such as forming coalitions, spreading information, and making collective decisions.
The Return of Language-Oriented Programming | Middle of Nowhere
I’ve been wondering what LLMs mean for language design and implementation. Some believe that, because language models are obviously trained on existing content, they are inherently less capable of assisting users with new programming languages. Intuitively this makes sense. However:
Artificial intelligence is a familiar-looking monster, say Henry Farrell and Cosma Shalizi
The academics argue that large language models have much older cousins in markets and bureaucracies
Recursive Language Models: the paradigm of 2026
How we plan to manage extremely long contexts
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The Philosophy of Language Models
ABSTRACT The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human‐like linguistic and cognitive capacities, whereas others ascribe it to shallow pattern matching. These disputes stem from deep‐seated philosophical disagreements about the nature of language and cognition. We provide an opinionated survey of these disagreements across core topics in the philosophy of mind and language, including syntactic competence, compositionality, linguistic meaning, representation, attitudes, reasoning, agency, and consciousness. We contend that progress on these issues requires not only clarity about background philosophical commitments but also, in many cases, close engagement with emerging empirical evidence.
Understanding Understanding: A Pragmatic Framework Motivated by...
Motivated by the rapid ascent of Large Language Models (LLMs) and debates about the extent to which they possess human-level qualities, we propose a framework for testing whether any agent (be it...
Forcing Generative Models to Degenerate Ones: The Power of Data...
Growing applications of large language models (LLMs) trained by a third party raise serious concerns on the security vulnerability of LLMs.It has been demonstrated that malicious actors can...
What comes next with open models
Markets, capabilities, cope, and bewilderment in the industrialization of language models.
How Large Language Models Actually Work
Large language models are not the problem
If a Large Language Model (LLM) can replicate your scientific contribution, the problem is not the LLM. What does it say about our field that so much of the anxiety about AI comes down to the fear that a machine could do what we do? Perhaps it says we should be doing something better.
