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Ant Group
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OASIF achieves up to 16.9 percentage points improvement in instruction-following success rates for LLMs facing commercial-grade obfuscation, redefining the limits of automated binary analysis.
GGR transforms the landscape of open-set semi-supervised learning by ensuring that auxiliary gradients enhance rather than conflict with supervised updates.
Collaboration boosts contextual optimization: sharing beliefs across heterogeneous clients slashes regret in real-world design tasks.
Stop training black-box reward models: VL-MDR offers a transparent alternative that surfaces *why* a VLM is getting a certain reward, opening the door to more targeted alignment.
