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8,948 articles found

Together Compiler: Continual Compilation for AI Inference
ArXiv AI (cs.AI)
Industry News1m

Together Compiler: Continual Compilation for AI Inference

arXiv:2611.14681v1 Announce Type: new Abstract: Today's AI workloads are rapidly changing: new models are released more often, model architectures are increasingly diverse, and traffic distributions change frequently. The challenge we face at Together AI is continual optimization

Nov 14, 2026
SynthCorp: A Platform for Creating Benchmarks for Data Agents
ArXiv AI (cs.AI)
AI Agents1m

SynthCorp: A Platform for Creating Benchmarks for Data Agents

arXiv:2611.14765v1 Announce Type: new Abstract: Current benchmarks for LLM-driven data agents saturate quickly and cannot target specific capabilities. We present SynthCorp, a platform that creates customizable data estates and benchmarks by simulating how business events produce

Nov 14, 2026
When Should Semantic Priors Enter Partially-Identified Causal Discovery?
ArXiv AI (cs.AI)
Industry News1m

When Should Semantic Priors Enter Partially-Identified Causal Discovery?

arXiv:2611.14736v1 Announce Type: new Abstract: Observational causal discovery often leaves causal di- rections unresolved or unstable. Large language mod- els (LLMs) can provide semantic information to resolve these directions, but incorrect proposals can exclude the true causal

Nov 14, 2026
Cleaning an Agent's History Hurts When the World Still Holds the Error
ArXiv AI (cs.AI)
AI Agents1m

Cleaning an Agent's History Hurts When the World Still Holds the Error

arXiv:2611.14654v1 Announce Type: new Abstract: Tool-using language-model agents accumulate histories of their own mistakes, and a system that compacts, repairs or hands off such a history decides what it says about those mistakes while their effects stay in the world. Prior work

Nov 14, 2026
Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1
AWS Machine Learning
Industry News1m

Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

GS Caltex connects Amazon SageMaker Canvas to Amazon SageMaker Pipelines so process engineers' no-code models run in production. This post shows how ML engineers reuse validated preprocessing flows and models, cutting deployment from about a week to a few hours and running batch

Nov 13, 2026