Claw-SWE-Bench is introduced, a unified benchmark that enables researchers to systematically assess the capabilities and efficiency of general-purpose harnesses on software engineering tasks and guide the development of more capable and efficient harnesses.
Meng-Yu Zheng, Kai Han, Bo-Xun Li et al.· arXiv.org· 5 citations
QUAKE-CD represents binary change masks as grammar-constrained quadtree token sequences, making the masks compact, syntactically checkable, and deterministically decodable within an autoregressive generation space.
Xiao An, Rui-Kang Zhang, Cheng Zhong et al.· 0 citations
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We present NeoHorse-1, a family of agent-native models developed to explore this path through agentic post-training. Our system combines a heter...
NeoHorse Team, Guo-Liang Cao, Guo-Hao Dai et al.· 0 citations
This work formulate expert-cache management as a model-side algorithmic problem and proposes cache-aware post-training that jointly adapts the MoE backbone and lightweight auxiliary routers while preserving the native inference-time Top-K rule.
Zhenhe Wu, Ya-Ping Jin, Qing-Hua Xing et al.· 2 citations
The report studies singleton and multi-model routing on agentic benchmarks including DRACO and PinchBench, and argues that agentic routing is not merely cost control, but a data engine for agent-native training.
Xinchen Liu, Hang Zhou, Ying-Jie Zong et al.· arXiv.org· 1 citation· ⚡1
DIVE (Dynamic Iterative Visual Evidence Construction), a training-free framework that recasts visual-token pruning as dynamic evidence construction and builds a retained set of complementary, prompt-relevant evidence.
Cheng Zhong, Xiao An, Zijie Wang et al.· 0 citations
These results support depthwise convolution as a lightweight complement to self-attention for modeling short-range token interactions and suggest that the convolution makes repeated token IDs more sensitive to their immediate context.
Yu-Chuan Tian, Yingte Shu, Wei He et al.· arXiv.org· 0 citations
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