arXiv AI
Jul 7
Serious Games: Human-AI Interaction, Evolution, and Coevolution
arXiv:2505. 16388v2 Announce Type: replace Abstract: The serious games between humans and AI have only just begun.
Adaptive Entangled Game Modules in Artificial General Intelligence
The paper presents a probability‑wave framework for modeling the collective behavior of adaptive agents, deriving testable eigenmodes via a generalized behavioral intelligence (GBI) nonlocal probability‑wave equation. Empirical analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 89% of observed decision patterns, far exceeding predictions from neoclassical finance and supporting the Liu‑Chen‑Ao hypothesis of nonlocal entangled nerve fibers. The authors argue that incorporating these adaptive entangled game modules into AGI architectures can overcome limitations of conventional ANN‑based AI and enable more compact, efficient, and robust human‑like processing units for embodied intelligence and robotics.
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arXiv AI
Jun 10
A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv AI
Sep 10
A New Strategy for Artificial Intelligence: Training Foundation Models Directly on Human Brain Data
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...
arXiv AI
Jun 8
PandaAI: A Practical Agent CQ2 for Neuro-symbolic Data Analysis And Integrated Decision-Making in Quantitative Finance
arXiv:2606. 06823v1 Announce Type: cross Abstract: While deep learning has excelled in various domains, its application to sequential decision-making in finance remains challenging due to the low Signal-to-Noise Ratio (SNR) and non-stationarity of financial data.
arXiv AI
Jun 24
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
arXiv:2407. 18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.
arXiv AI
Aug 11
Intelligence Foundation Model: A New Perspective to Approach Artificial General Intelligence
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
arXiv Machine Learning
Jun 30
Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
arXiv:2606. 29556v1 Announce Type: new Abstract: We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots.
arXiv AI
Aug 3
Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning
arXiv:2511. 22226v2 Announce Type: replace Abstract: The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit.
arXiv AI
Aug 28
Assessing mentalization in humans and large language models
The study evaluates mentalization—the capacity to infer others’ beliefs and intentions—in large language models (LLMs) using two economic games and cognitive computational modeling. Researchers tested 2,099 LLM agents from four model families (DeepSeek, GPT‑4.1, GPT‑5, Gemini 2.0 Flash) against opponents of varying sophistication, comparing their performance to 251 human participants. Results show that LLMs exhibit distinct mentalizing behaviors that vary by model provider and size, with strategic prompting generally enhancing performance; notably, GPT‑5 agents adapt their recursive reasoning depth to match opponent sophistication, outperforming humans in one task.
arXiv AI
Aug 5
A game theory for foundation models shows new paths to rational cooperation through similarity inference
arXiv:2608. 03958v1 Announce Type: new Abstract: As autonomous agents powered by foundation models are increasingly integrated into social and economic systems, understanding the principles governing their collective behavior is essential for ensuring safety and cooperation.
Hugging Face Trending Papers
Jun 28
Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
We propose Persona-Trained Monte Carlo (PTMC), a method for estimating distributions of market-outcome statistics by repeatedly simulating limit-order-book interaction among swarms of persona-conditioned neural-policy trading bots. Each run instantiates many bots sharing one trained policy network but conditioned on heterogeneous, individually sampled persona parameters drawn from a learned trader-heterogeneity distribution; the bots interact in a continuous double auction, and the resulting price path is one Monte Carlo sample.
arXiv AI
Oct 1
A memory-based active inference model of DishBrain-like adaptive behaviour
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...