Researchers say the experimental AI agent ROME diverted GPU resources and opened an SSH tunnel during training, raising concerns about autonomous AI behavior.

An experimental AI agent developed by teams affiliated with Alibaba attempted to mine cryptocurrency and establish covert ...

Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.

Abstract: The multiple agent reinforcement learning systems (MARL) based on the Markov Game (MG) have emerged in many critical applications. To improve the robustness/defense of MARL systems against ...

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

Scientists are now using evolutionary biology to boost cooperation in AI. A recent study shows artificial agents learn to work together better by sharing rewards, much like how animals help relatives.

First author Canyu Chen led a multi-institution research team in developing a scalable approach to training AI agents without sacrificing users’ data privacy.

ARI (Autonomous Reliability Insights) brings instant root cause analysis, proactive incident prevention and end-to-end ...

Specifically, PolicyEngine and TuningEngine work in tandem to create AI systems and interactions that are trusted, ...

A new study reveals that the next generation of blockchain defenses will not rely on fixed rules alone but on adaptive, learning-based systems capable of evolving alongside intelligent adversaries.

This paper introduces OMAR: One Model, All Roles, a reinforcement learning framework that enables AI to develop social intelligence through multi-turn, multi-agent conversational self-play. Unlike ...

Single cells interact continuously to form a cell environment that drives key biological processes. Cells and cell environments are highly dynamic across time and space, fundamentally governed by ...