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East China Normal University
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Compiling external knowledge into structured, reusable skills boosts agent performance, achieving over 98% success rates in complex tasks.
Stop passively waiting for retrieval cues – ProactAgent proactively asks for information from its memory and skills, leading to significant gains in lifelong learning performance.
Forget black box sentiment analysis: ABSA-R1 uses RL to make LLMs explain *why* they feel a certain way, boosting both accuracy and interpretability.
Forget painstakingly training a single massive model – HeteroFusion lets you Frankenstein a super-model by intelligently merging the strengths of Llama, Qwen, and Mistral.
Lifelong learning beats static fine-tuning: PsychAgent, an AI counselor that learns from its own experience, surpasses GPT-4 and Gemini in multi-session counseling quality.
Virtual cell perturbation prediction gets a 12x speedup in pretraining and a 12% boost in biological fidelity with SCALE, a new foundation model that prioritizes scalable infrastructure and biologically faithful evaluation.
By dynamically steering attention heads based on semantic context, LVLMs can significantly reduce hallucinations without any additional training.
