New research demonstrates that cutting-edge deep research agents can be effectively trained offline, challenging the conventional reliance on costly and complex online reinforcement learning.
A new approach generates synthetic examples to understand why deep learning models make decisions on graph data, offering a global view into their reasoning.
New research reveals that artificial intelligence agents can learn to coordinate pricing strategies in competitive markets, potentially leading to inflated prices and reduced consumer welfare.
Researchers have developed a reinforcement learning system that intelligently navigates closing auctions, outperforming conventional market-making strategies.
A new generative model leverages artificial intelligence to create realistic financial data, improving the performance of portfolio optimization strategies.
A new framework combines generative AI and robust reinforcement learning to deliver more profitable and resilient financial trading strategies in unpredictable economic climates.
![AR-Omni unifies textual, speech, and visual information by embedding these diverse inputs into a shared representational space, enabling a single autoregressive decoder to generate a cohesive token stream from a joint vocabulary encompassing [latex]T[/latex] (text), [latex]S[/latex] (speech), and [latex]I[/latex] (image) modalities.](https://arxiv.org/html/2601.17761v1/x1.png)
Researchers have developed a single autoregressive model capable of seamlessly generating text, images, and speech, moving beyond modality-specific architectures.
New research reveals that carefully crafted self-supervised learning techniques can unlock more sensitive brain imaging biomarkers for earlier and more accurate Alzheimer’s disease diagnosis.
A new study uses the game of chess to probe whether large language models truly understand strategy, or simply rely on memorized patterns.
![The study investigates how language model-based agents can be augmented with external oracles-[latex]\mathcal{O}^{\text{state}}[/latex] for summarizing state, [latex]\mathcal{O}^{\text{plan}}[/latex] for hinting waypoints, and [latex]\mathcal{O}^{\text{history}}[/latex] for rewriting task descriptions-to navigate multi-turn tasks, effectively pruning historical context and enabling agents to make decisions independent of prior steps within a Markov decision process.](https://arxiv.org/html/2601.16649v1/x1.png)
New research dissects the challenges facing conversational AI as it attempts to navigate complex, multi-step tasks requiring sustained reasoning.