How KTern.AI built agentic AI for SAP on Amazon Bedrock AgentCore
KTern.AI has transitioned its SaaS platform into an agentic AI system for SAP software by leveraging Amazon Bedrock AgentCore and the Strands Agents SDK.
KTern.AI has transitioned its SaaS platform into an agentic AI system for SAP software by leveraging Amazon Bedrock AgentCore and the Strands Agents SDK.
Monolith plans to file proposals to host a data center at its carbon black manufacturing plant in Nebraska.
This paper explores the transition of large language model-driven theorem provers from solving well-defined problems to assisting with frontier mathematical research and open conjectures.
A new compete-then-collaborate framework ranks frontier AI models to build a verifiable curriculum for training smaller student models in coding tasks.
South Korea's Kospi index fell five percent, with major AI memory chipmakers SK Hynix and Samsung Electronics experiencing sharp declines of nearly ten and over six percent, respectively.
Chinese memory manufacturer ChangXin Memory Technologies is launching a Shanghai initial public offering aiming to raise at least 4.3 billion dollars amid rising demand for memory chips.
Researchers have found that the step-by-step reasoning processes in advanced language models introduce vulnerabilities that attackers can exploit to slow down systems.
Chinese regulators are balancing the security risks of open-weight artificial intelligence models against the strategic need for open-source innovation to maintain competitiveness with the United States.
Chinese parents are increasingly utilizing artificial intelligence tools to help their children navigate the complex university admissions process and select degree programs.
SpaceX shares declined following the cancellation of a scheduled Starship rocket test flight.
Researchers introduced Latent Personality Alignment, a method that aligns language model safety using a small set of harm-agnostic statements instead of large datasets of harmful prompts.
Researchers have introduced ReCoLoRA, a framework that uses spectrum-aware recursive consolidation to enable continual fine-tuning of large language models without losing performance on prior tasks.
This study investigates the cognitive gap in large language models where newly memorized facts during fine-tuning fail to generalize to downstream reasoning tasks.
ResonatorLM is proposed as an alternative language modeling architecture that uses causal resonant field mixing to improve long-context processing efficiency.
Researchers developed video-SALMONN-R3, a framework that improves video large language models by first localizing relevant segments and then re-analyzing them at higher resolutions.
A new study demonstrates that consistency and agreement among language models are unreliable indicators of accuracy when evaluating AI outputs.
Peer-Predictive Self-Training is a collaborative, label-free fine-tuning framework where multiple language models use aggregated responses to mutually improve their reasoning capabilities.
A new subword tokenizer called Thunder-Tok reduces token fertility and inference costs for large language models while maintaining downstream performance.
This study analyzes how explainable artificial intelligence methods can facilitate the safety certification and regulatory compliance of machine learning models.
The OpenCoF framework introduces a method for models to perform logical reasoning through temporally connected video frames, termed Chain-of-Frame reasoning.
Researchers investigate whether training policies to predict both actions and environmental changes using World Action Models improves robot manipulation learning from egocentric human videos.
AWS has launched the Claude apps gateway, a self-hosted control plane allowing organizations to manage access, costs, and policies for Claude Code and Claude Desktop.
A new framework for Genetic Network Programming aims to improve agentic AI by balancing exploration and exploitation within graph-based decision structures.
A new neuro-symbolic method called Latent Grammar Flow enables the discovery of interpretable differential equations from data.