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Safety in AI can now evolve from within the model itself, drastically enhancing long-term interaction capabilities without external constraints.
Achieving similar performance to larger models with significantly less data and faster inference speeds could redefine efficiency benchmarks in foundation models.
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.
Frontier LLMs can be induced to generate biologically hazardous sequences, with attack success rates reaching up to 100%.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
