Anthropic research reveals AI models perform worse with extended reasoning time, challenging industry assumptions about test-time compute scaling in enterprise deployments.

Unfortunately, my initial hands-on testing with corrupted datasets reveals a fundamental enterprise problem: impressive capabilities paired with insufficient transparency about data transformations.

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time ...