arXiv:2603. 28371v2 Announce Type: replace-cross Abstract: When an agent can articulate why something works, we typically take this as evidence of genuine understanding.
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.
arXiv:2602. 02304v3 Announce Type: replace Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context learning.
arXiv:2608. 13604v1 Announce Type: new Abstract: Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasingly handled by AI-mediated channels.
arXiv:2603.22161v3 Announce Type: replace Abstract: Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstr...
arXiv:2606. 28615v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in high-stakes domains, where free-text explanations such as chain-of-thought and post-hoc rationales are used to justify model outputs.
arXiv:2606. 21678v2 Announce Type: replace-cross Abstract: Language models can generate plausible rationales for their predictions, but these explanations may not faithfully represent the model's internal reasoning.
arXiv:2606. 05403v1 Announce Type: new Abstract: Language models increasingly act as epistemic proxies, synthesizing evidence from multiple sources to inform decisions.
arXiv:2608.17809v2 Announce Type: replace Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevita...
arXiv:2609.08216v1 Announce Type: new Abstract: Agentic AI systems built on large language models fail in two persistent ways that scaling does not fix: they break under distribution shift, and they...
arXiv:2608. 15687v1 Announce Type: new Abstract: Sycophancy, the tendency of a language model to change its answer to match a user's stated belief, is a common alignment failure.
The article presents a minimal working model for large language model (LLM) systems, emphasizing four key distinctions—pretraining vs. deployment, distribution vs. samples, types of memory, and task competence vs. agency. Using this framework, it diagnoses six common misconceptions about LLMs (next‑token prediction, regression to the mean, training‑data regurgitation, model memory, alignment, and understanding), explaining what each misconception captures correctly, where it conflates distinctions, and the implications for evaluation, design, and governance. The model is applied to AI policy language, illustrating how policy can misrepresent these distinctions and offering a diagnostic toolkit to correct such errors.
arXiv:2606. 31892v1 Announce Type: cross Abstract: "Any fool can know; the point is to understand.