Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification.
arXiv:2608.29610v1 Announce Type: new
Abstract: The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency. W...
By Chenghao Yang
The paper introduces the Self‑Emergence Agent Architecture (SEAA), a framework that combines a Hidden Markov Model for behavioral inertia, a reflexive metacognition loop that updates the HMM, and a social environment where agents compare behaviors. This closed loop enables agents to develop distinct, stable personalities and social structures without external prompts. Experiments with both a language‑model‑free prototype and hosted LLMs demonstrate spontaneous symmetry breaking and the emergence of consensus hubs and outliers.
By Xiaoyang Liu
arXiv:2607. 15883v1 Announce Type: cross Abstract: Large language models are broadly capable, yet in sustained one-to-one conversation they still read as flat: competent, responsive, and somehow not quite the presence of a mind.
By Sebastian Cochinescu
arXiv:2609.22362v1 Announce Type: new
Abstract: Debates about whether artificial systems can feel are often forced between two unsatisfactory positions: behavioral equivalence is treated as sufficien...
By Marzieh Zare
arXiv:2609.34187v2 Announce Type: replace-cross
Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they c...
By Julia Witte Zimmerman, Calla G. Beauregard, Tabia Tanzin Prama, Parisa Suchdev, Kathryn Cramer, Elisabeth Kollrack
arXiv:2607. 10248v1 Announce Type: cross Abstract: Language builds discourse contexts other than the actual: a painting, a belief, a memory, a hypothetical.
By Oliver Steele, Jiangtao Wen, Yuxing Han
arXiv:2507.19364v3 Announce Type: replace
Abstract: The integration of Large Language Models (LLMs) into social simulation has generated considerable enthusiasm, but also raises substantial methodolo...
By Patrick Taillandier, Jean Daniel Zucker, Arnaud Grignard, Benoit Gaudou, Nghi Quang Huynh, Haojia Kong, Alexis Drogoul
arXiv:2609.37497v1 Announce Type: new
Abstract: Modern transformer models excel at capturing semantic relationships through sentence embeddings, yet their ability to perform pragmatic reasoning remai...
By Stefania Butnaru, Claudiu Creanga
The study turns the speculative field of astrolinguistics into an experiment by using two large language models with deliberately incompatible constructed lexicons as informants. A scripted orchestrator translates between the two category systems, and a protocol combining cross‑situational elimination, predictive probes, active scene selection, and a stricter recovery round successfully prevents the ‘kangaroo effect’—the silent attachment of a word to the wrong referent—in over 400 simulated and live runs. When informant noise is introduced, the protocol remains robust up to 2% per‑word noise and largely abstains rather than errs at higher noise levels, while a generate‑and‑test loop allows recovery of words outside the scripted hypothesis space, achieving full coverage as the rule‑proposing LLM’s capability increases.
whyItMatters":"The protocol demonstrates that experimental astrolinguistics can reliably avoid mistranslations and recover unknown terms, showing that correctness is governed by the protocol while coverage depends on the instruments used."
By Francesco Cordella, Mauro Cappelli
arXiv:2607. 28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve generalisability across different domains.
By Zhibo Hu, Chen Wang, Yanfeng Shu, Hye-young Paik, Liming Dong, Liming Zhu
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...
By Arun Vignesh Malarkkan, Xinyuan Wang, Yanjie Fu