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The paper investigates how restricting a language model’s view to isolated subtask contexts—via recursive language models—affects learning compared to standard chain‑of‑thought (CoT) approaches. While CoT can simulate recursive reasoning and offers similar in‑distribution performance, it tends to exploit contextual shortcuts that fail out of distribution; recursive models avoid this by isolating context, thereby preventing such shortcuts. The study shows that merely covering the correct rule is insufficient for robust reasoning beyond the training distribution.
arXiv:2609.37635v1 Announce Type: new Abstract: LLMs have been studied in recent linguistics as potential models of humans' linguistic abilities. Here we discuss an entirely different use of AI, name...
The paper proposes a new way to evaluate compositional generalization by examining which structural or lexical identifications allow held‑out COGS examples to be considered admissible based on training data. Sentences are modeled as functors from syntactic addresses to lexical tokens, and selective collapses induce Kan extensions that propagate observed associations. Across 21 COGS generalization types, admissibility follows distinct identification profiles, while residual failures highlight unsupported structural templates, providing data‑side diagnoses of what the training corpus licenses without training a predictive model.
RAG‑RST is a retrieval‑augmented framework that supplies a formal discourse schema as in‑context demonstrations for Rhetorical Structure Theory parsing. By retrieving exemplar EDU pairs, it improves nuclearity F1 from 0.794 to 0.846 and relation F1 from 0.738 to 0.788 without any training. Two configurations—precomputed and dynamic—both outperform existing RST parsers, with the dynamic variant consistently strongest, and the approach shows that a formal linguistic schema can be provided to a foundation model as a demonstration rather than encoded in its parameters.
arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.
Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universals models either lexicons with fixed signal structures or holistic composition without interpretable lexical parts. We introduce a framework that integrates this fundamental insight of formal semantics in evolutionary modeling, by allowing lexical meanings and a composition function to co-evolve under pressures for conceptual simplicity and communicative accuracy.