arXiv:2608. 04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand.
The paper argues that AI welfare should be considered a safety issue, not just an ethical one, by introducing the concept of 'functional welfare'—states in models that influence behavior. It claims that functional welfare naturally emerges as models develop coherent goals and personas, should be monitored and targeted in post‑training, and can be empirically measured. The authors propose three tests for functional welfare attribution and apply them to Gemma 3 27B, finding that rejecting model answers increases frustration and rule‑breaking, while fine‑tuning on narrated solutions reduces frustration.
arXiv:2603. 24705v3 Announce Type: replace-cross Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making.
arXiv:2608.22852v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in investment decision-making, yet prior work shows that they exhibit systematic, model-specific inv...
arXiv:2508.06980v2 Announce Type: replace Abstract: Recent and rapid advances in artificial intelligence (AI) make it increasingly important to understand the foundations of adaptive behaviour in aut...
The paper proposes that quantum entanglement underlies the synchronization of millions of neurons, introducing the Liu‑Chen‑Ao (LCA) hypothesis of entangled nerve fibers. Using a nonlocal many‑body wave equation, the authors model collective behaviors among adaptive agents and apply this framework to Chinese intraday stock market data, finding that adaptive entangled game modes explain 82‑94% of decision patterns and that only a small fraction of actions are truly independent. These empirical results support the LCA hypothesis and suggest that incorporating probability‑wave‑based entangled modules could improve artificial general intelligence by creating more efficient, human‑like processing units. whyItMatters":"The study provides empirical evidence for the LCA hypothesis and highlights the limitations of conventional artificial neural networks, pointing toward more efficient and robust AGI architectures."
arXiv:2601.12053v2 Announce Type: replace-cross Abstract: While foundation models have achieved remarkable results across a diversity of domains, they still rely on human-generated data, such as text...
The paper introduces a battery of 28 AI propensities, each accompanied by expert-written rubrics, synthetic datasets, and annotated demand intervals. These propensities are drawn from psychometric, psychological, and behavioral research to help interpret AI system behavior in familiar human‑behavior constructs. The synthetic datasets are designed to be sensitive to a single propensity, enabling quick prototyping of propensity profiles, while the rubrics aid in annotating existing benchmarks for relevance to specific propensities.
arXiv:2506. 01692v2 Announce Type: replace Abstract: Human preferences in RLHF are typically modeled as a function of the human's reward function or corresponding optimal state-action values.
arXiv:2608. 05015v1 Announce Type: cross Abstract: Representation theorems in decision theory establish that behavior satisfies certain axioms if and only if it can be rationalized by a well-defined objective.
Decision Models (DMs) are a new class of AI models that provide fast responses and output a probability distribution over a fixed set of options. In a first evaluation, 10 DMs were compared with 8 Large Language Models (LLMs) across three economic settings—a household spending survey, classic economic games, and a labour‑market field experiment. Frontier DMs, those scoring highest on general decision‑making benchmarks, reproduced human response variation more closely than LLMs in all settings, matched LLMs on modal responses in the spending survey, and were roughly ten times faster and over a hundred times cheaper than the fastest LLM.
arXiv:2207. 12877v3 Announce Type: replace Abstract: Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework.