Beyond Readability: Building Accessible Text with People and AI
New research details a practical framework for generating easily understood text that combines the power of automated simplification with crucial human oversight.
Science
Who Decides What’s Real? A New Theory of Brain Control
![The architecture constrains policy generation to a subset of identity hypotheses defined by authority-level priors, ensuring representational belief updating does not necessitate autonomic stabilisation when an adaptive hypothesis falls outside of [latex]\mathcal{H}\_{\text{auth}}[/latex], thus formalizing a distinction between cognitive and physiological responses.](https://arxiv.org/html/2603.18888v1/alp_regulatory_admissibility_diagram.png)
New research proposes a mechanism explaining how the brain prioritizes certain beliefs – even weak ones – to govern our perceptions and actions.
Robots with Memory: Learning from the Way We Do
Researchers have developed a new robotic control system inspired by the human memory process, enabling improved long-term task performance and adaptability.
Seeing More with AI: The Future of Pathology is Multimodal
A new wave of artificial intelligence is transforming how we analyze medical images, moving beyond single-image assessments to comprehensive, data-rich diagnostics.
Shaping the Future: Flexible Robots with Tendon Control
Researchers present a complete design, fabrication, and modeling pipeline for creating highly accurate, tendon-driven continuum robots with tapered, flexible polymer spines.
Beyond Left and Right: Mapping the Ideological Landscape
A new framework proposes understanding ideology not as a single line, but as a complex network of interconnected concepts.
Robots Learn Assembly Skills with a New ‘Expert’ Approach
Researchers have developed a novel robot learning method that leverages diverse sensory input and a specialized neural network architecture to master complex assembly tasks.
Modeling Intent: A New Lens for Causal Reasoning
![The study demonstrates the computation of probability mass functions - specifically [latex]P\_{\mathcal{M}\_{fin}}(S^{\star})[/latex], [latex]P\_{\mathcal{M}\_{fin}}(S^{\star}|\textrm{do}(D=0))[/latex], and [latex]P\_{\mathcal{M}\_{fin}}(S^{\star}|\textrm{do}(P=0))[/latex] - to discern intention, effectively isolating the probabilistic influence of interventions on decision variables <i>D</i> and <i>P</i> to reveal underlying causal mechanisms.](https://arxiv.org/html/2603.18968v1/ID_3.png)
Researchers are extending traditional causal models to incorporate agent intentions, allowing for a deeper understanding of why actions are taken within complex systems.
Seeing Further: Scaling Visual Reasoning with Intelligent Agents
A new approach leverages multi-agent systems and reinforcement learning to push the boundaries of what large language models can understand from images.
Robots That Ask Questions: Bridging Ambiguity with Language and Scene Understanding
A new framework empowers robots to navigate uncertain environments by combining visual scene analysis with the reasoning power of large language models.