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SelfCompact reveals that language models can autonomously manage context decay, achieving up to 18.1 points improvement in performance while cutting token costs by 30-70%.
Realistic user simulation is now possible: Pare offers a framework that moves beyond flat tool-calling APIs to model stateful user interactions, enabling better evaluation of proactive agents.
Forget painstakingly collecting user data – PersonaTrace lets you bootstrap realistic digital footprints with LLMs, and models trained on this synthetic data actually generalize better to real-world tasks.
LLM agents can learn to solve tasks previously beyond their reach by exploring high-level language strategies instead of low-level actions, leading to more efficient and effective reinforcement learning.
