Turning Mistakes into Strengths: Teaching AI to Recover from Errors
![The Fission-GRPO framework operates through iterative refinement-initially optimizing a policy [latex]\pi_{\theta}[/latex] across a query distribution [latex]\mathcal{D}[/latex], then isolating error trajectories via a diagnostic simulator [latex]\mathcal{S}_{\phi}[/latex], and finally employing a multiplicative resampling process-governed by a factor [latex]G^{\prime}[/latex]-to steer the policy toward successful recovery paths, embodying a system designed not to prevent decay, but to adaptively reconfigure itself within it.](https://arxiv.org/html/2601.15625v1/x2.png)
New research demonstrates a method for improving the reliability of AI agents by transforming failed actions into valuable learning opportunities.
Science
Beyond Synapses: Hypergraphs Unlock Efficient Neuromorphic Computing
A new approach to modeling spiking neural networks using hypergraphs promises to dramatically improve how these networks are deployed on specialized neuromorphic hardware.
Chatbots for All: Scaling AI Support to Small Businesses
This industry case study details a practical and secure approach to deploying AI-powered chatbots for small businesses using distributed, cloud-native technologies.
Seeing Beneath the Surface: AI Spots Skin Cancer with Greater Accuracy
A new machine vision system, built using a custom convolutional neural network, is showing promise in the early detection of skin lesions.
The Limits of Memory: Why Dense Networks Struggle to Recall
![As semantic density increases-measured by ρ-neural accuracy rapidly declines, evidenced by a sharp decrease in N50N\_{50}, which validates the Orthogonality Constraint by demonstrating that higher densities lead to increased key overlap and subsequent interference; achieving values below [latex]\rho < 0.3[/latex] proved unattainable with realistic fact structures.](https://arxiv.org/html/2601.15313v1/fig_density_vs_collapse.png)
New research reveals a fundamental constraint on how neural networks store information, explaining why they falter when faced with complex, overlapping memories.
Spotting Trouble in the Stream: How AI Learns from Past Events
A new framework uses artificial intelligence to predict and mitigate risks in live streaming by analyzing patterns of behavior across multiple sessions.
Mapping the Genome’s Geography: A New Foundation for Spatial Transcriptomics
Researchers have developed a lightweight model to better understand gene expression within the physical context of tissues, paving the way for more accurate biological insights.
Graphs Without the Gossip: Smarter Federated Learning with Diffusion
![The model distinguishes itself from traditional Federated Graph Neural Networks by establishing a communication structure-indicated by sequential exchanges [latex]❶[/latex], [latex]❷[/latex], and [latex]❸[/latex]-between server and clients, fundamentally altering the flow of information during the learning process.](https://arxiv.org/html/2601.15722v1/x1.png)
A new approach drastically cuts communication costs in federated graph learning, enabling more practical and privacy-preserving analysis of complex network data.
Hidden Risks in AI Emergency Triage
New research reveals that artificial intelligence systems used to prioritize patients in emergency rooms can exhibit subtle biases, potentially leading to unequal care.
Beyond Prediction: Building More Reliable AI Features
A new framework focuses on quantifying uncertainty directly in the learned representations, leading to more stable, calibrated, and robust AI models.