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A new arXiv paper introduces EngiAI, a LangGraph-based multi-agent reference system, and EngiBench, a benchmark suite to evaluate how LLM agents handle engineering workflows, retrieval, and HPC orchestration.

DeepMind says it is expanding its Singapore work with new programs in healthcare, education, and sustainability, as part of Google’s national AI partnership with the Singapore Government.

Microsoft Research released MagenticLite plus two small models, MagenticBrain and Fara1.5, aiming to run agentic workflows across the browser and local files on a user’s machine.

A May 12 arXiv paper proposes GRAFT, mapping tools to special tokens and training on sampled trajectories to improve whether multi-step tool plans follow dependency constraints.

An arXiv paper reports a “knowing–doing gap” in tool use: models may recognize a tool is needed but still fail to perform the tool call in agent-like workflows.

Google DeepMind introduced Co‑Scientist, a multi-agent Gemini-based system for generating and refining scientific hypotheses, and says access will roll out via a research tool.

A new arXiv paper studies hidden-state trajectories during chain-of-thought and argues you must correct for response length before comparing “reasoning” behavior across tasks.

A new arXiv paper expands MathArena into a continuously maintained evaluation platform for LLM mathematical reasoning, aiming to reduce benchmark saturation and improve comparisons.

Meta researchers say tokenization changes scaling behavior and report results suggesting compute-optimal training should track data in bytes, not tokens.

Meta researchers introduce NeuralBench and NeuralBench‑EEG, a unified benchmark intended to compare brain-signal AI models across dozens of tasks and many datasets through one framework.

NIST’s CAISI says its evaluation of DeepSeek V4 Pro finds the model lags the frontier by about eight months, based on benchmarks spanning cyber, coding, science, reasoning, and math.

Meta Reality Labs released RL-R CHAT, an egocentric multimodal dataset of group conversations to support hearing-assist and speech enhancement research.

Anthropic researchers report that a small, roughly constant number of poisoned fine-tuning examples can install a backdoor in constitutional classifiers without obvious robustness losses.