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Visual evidence can now actively drive long-horizon multimodal search, transforming how agents interact with complex information.
Frontis-MA1 achieves a remarkable 71.21% Medal Average on MLE-Bench Lite, outperforming leading models and showcasing the potential of AI systems to recursively improve their own engineering processes.
Independently scaling long-term memory in language models can yield better performance with fewer parameters than simply increasing model size.
MemSFT enables LLMs to gain specialized domain knowledge without sacrificing their general performance, effectively sidestepping the alignment tax.
Forget bigger models: massive gains in document parsing accuracy are still possible through smarter data engineering alone.
LLMs can now automatically evolve and optimize GPU kernels to beat hand-tuned and proprietary models like Gemini and Claude.
Foundation models trained on audio, general time series, and brain signals can be distilled into a single, powerful encoder for scientific time series, unlocking performance gains on par with task-specific training.
Decomposing GUI agent trajectories into verifiable milestones and auditing the evidence chain yields a 10% boost in RL training performance, outperforming single-judge reward systems.
