Multimodal models such as Intern-S2 and AlphaGenome Atlas target scientific literature and genomics, while Arm, Qwen and maritime AI projects push LLMs into robotics, autonomous driving and navigation. This marks a shift from language tools to domain-specific AI for science and embodied applications.
InternLM introduces Intern-S2-397B, a 397-billion-parameter multimodal model for scientific intelligence and long-horizon agents. It learns from raw scientific literature pages without intermediate parsing, combining symbolic and visual reasoning, an...
M3-Former combines static attributes and navigation intent encoded by an LLM with dynamic features, using a dual-granularity MoE and a steering-specific loss. On a Danish AIS dataset it reduces 4-hour ADE and FDE by 4.4% and 5.1% against the stronges...
A public dataset records 558 complete AI scientific agent trajectories with thoughts, tool calls, errors, and confidence. Traditional benchmarks look only at final outputs; here comparable frontier models reveal different error profiles. Claude Opus ...
Qwen has published an open-weight model derived from a 4B checkpoint and fine-tuned for driving. The full BF16 checkpoint is 9B. It signals Chinese labs pushing toward autonomous driving with open models, with implications for local inference, data s...
DeepMind has released AlphaGenome Atlas, a public repository of precomputed predictions for nine billion possible single-letter changes to the human genome. It removes the need to write code or run the model, adds an impact score, and is available fo...
Arm brings together more than 80 partners to define common standards for robotics and physical AI. The Robotics Capability Framework introduces capability tiers similar to SAE levels, tying use cases to latency, compute placement, memory, power, and ...
