OpenAI's GPT-5.4, an agentic coding model, is now generally available in GitHub Copilot. The model was tested on real-world software development scenarios and demonstrated improved coding capabilities.
OpenAI has launched GPT-5.4, its latest model with native computer use capabilities that allow it to operate computers and complete tasks across applications. The release represents a step toward autonomous AI agents that can handle complex jobs independently. The model includes advancements in reasoning, coding, and professional work with spreadsheets, documents, and presentations.
Researchers introduce AMA-Bench, a benchmark for evaluating long-horizon memory in LLM-based autonomous agents using real-world trajectories and synthetic scaling. Existing memory systems underperform due to lack of causality and reliance on lossy similarity-based retrieval. The proposed AMA-Agent system with causality graphs and tool-augmented retrieval achieves 57.22% accuracy, outperforming baselines by 11.16 percentage points.
Researchers introduce RAPO (Retrieval-Augmented Policy Optimization), a reinforcement learning framework that improves LLM agent reasoning by incorporating off-policy retrieval signals during training. The method achieves an average 5.0% performance gain across fourteen datasets and delivers 1.2x faster training efficiency compared to existing agentic RL approaches.
A new study demonstrates that code agents can autonomously evolve existing math problems into more complex, solvable variations through systematic exploration. The multi-agent framework addresses a critical bottleneck in training advanced LLMs toward IMO-level mathematical reasoning by providing a scalable mechanism for synthesizing high-difficulty problems.
Researchers have introduced VideoTemp-o3, a unified framework that addresses limitations in long-video understanding by combining temporal grounding and question-answering in a single agentic system. The approach uses a unified masking mechanism during training and reinforcement learning with dedicated reward signals to improve video segment localization and reduce hallucinations.
American International Group (AIG) has deployed an agentic AI system with an orchestration layer, reporting faster-than-expected productivity gains in underwriting and portfolio management. The deployment demonstrates measurable improvements in throughput and workflow efficiency, according to recent investor disclosures.