A new review argues that incremental improvements to software engineering research are failing to address fundamental problems within the field’s evaluation and publication practices.
A new graph-based framework, kkNN-Graph, dramatically accelerates k-Nearest Neighbors classification by pre-computing decision boundaries and leveraging hierarchical indexing.
A new taxonomy aims to systematically categorize and address the ethical and security risks posed by increasingly sophisticated artificial intelligence systems.
![The analysis of AAPL stock between March 19, 2008, and April 22, 2024, reveals the interplay between estimated volatility [latex]\mu_{i,t}[/latex] and its idiosyncratic component [latex]exp(\varsigma_{i,t})[/latex], suggesting inherent instability within the asset's price dynamics.](https://arxiv.org/html/2601.16837v1/x31.png)
A new statistical approach offers improved methods for understanding how volatility spreads between multiple financial time series.
A new system uses artificial intelligence to deliver real-time patent recommendations, keeping financial technology innovators ahead of the curve.
New research shows that large language models, combined with a clever data retrieval technique, can accurately forecast which startups are likely to thrive, even with limited information.
As complex tasks are increasingly delegated to teams of AI agents, understanding and addressing the reasons for their failures is critical for building dependable systems.
New research reveals how safety mechanisms are encoded within large language models and demonstrates a method to pinpoint and manipulate the specific components responsible for preventing harmful outputs.
A new analysis of waveform data reveals a pattern of low-magnitude earthquakes in the Kamchatka Peninsula leading up to a significant seismic event.
![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.