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

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

The rapid rise of electric vehicles combined with breakthroughs in autonomous driving technology is reshaping the future of ...

EVA Live (Nasdaq:GOAI) has launched NeuroServer, a purpose-built AI system trained specifically for digital advertising rather than built on off-the-shelf AI models.

Abstract: This paper investigates the dependent task scheduling with service caching (DTSSC) in mobile edge computing (MEC) systems, where each task requires a specific service program for execution.

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

GLM-TTS is a high-quality text-to-speech (TTS) synthesis system based on large language models, supporting zero-shot voice cloning and streaming inference. This system adopts a two-stage architecture: ...

Tourism development in emerging destinations requires balancing economic benefits with ecological sustainability. In this study, we investigate the case of multi-attraction tourism planning in Qujing ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

RLP uses a single network (shared parameters) to (1) sample a CoT policy 𝜋 𝜃 ( 𝑐 𝑡 ∣ 𝑥 < 𝑡 ) π θ (c t ∣x <t ) and then (2) score the next token 𝑝 𝜃 ( 𝑥 𝑡 ∣ 𝑥 < 𝑡 , 𝑐 𝑡 ) p θ (x t ∣x ...