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Models fine-tuned with LongCrafter data achieve unprecedented performance on long-context tasks, particularly in high-difficulty scenarios.
Implicit activation steering enables LLMs to autonomously execute tasks with a memory system that outperforms traditional explicit instruction methods.
Small models can outperform larger counterparts in task planning by leveraging autonomous experience exploration and hindsight training.
Catastrophic collapses in tool-use performance can be mitigated by strategically interleaving supervised fine-tuning with reinforcement learning.
CoT reasoning boosts verbal reasoning but falters in visual tasks, revealing a critical gap in multimodal AI capabilities.
Agentic environments are not just backdrops for LLMs; they are pivotal in shaping agent evolution and capabilities.
Instance-level experiential knowledge boosts LLM tool use performance significantly more than abstract knowledge, reshaping how we approach knowledge integration in AI.
Forget hand-crafted KG traversal policies: GraphWalker uses automatically synthesized trajectories to train agents that achieve SOTA performance and generalize to unseen reasoning paths.
Squeezing the most out of your MLLM's visual budget is now possible: ResAdapt learns to allocate visual tokens intelligently *before* encoding, boosting performance by 15% while processing 16x more frames at the same cost.
