arXiv AI

Stabilising Generative Models of Attitude Change

arXiv:2604. 19791v3 Announce Type: replace Abstract: Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories.

Hugging Face Trending Papers
Jul 20

Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives

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 AI
Sep 16

Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling

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 AI
Aug 20

Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers

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