LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loo...
Yuyuan Feng, Zhishang Xiang, Chao Yang et al.· 0 citations
Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit infilling tasks, which require generating a middle span conditioned on both the prefix and the suffix. However, infilling is sensitive to th...
Hao-Bo Xu, Si-Rui Chen, Yuanchen Bei et al.· 0 citations
This formulation enables a systematic study of key self-improvement factors through the proposed Evo-Harness, and provides a principled understanding of how LLM agents can effectively learn on the fly.
Tian-Xin Wei, Zhan Shi, Min-hua Lin et al.· 7 citations
AFANet is introduced, a lightweight graph-based framework that models interaction trajectories through step-level semantic signals and agent-level relationships and suggests that effective agent failure attribution does not require heavy LLM reasoning and a lightweight, structured approach can achieve strong performanc...
Ting-Wei Li, Yuanchen Bei, Xiao Lin et al.· 1 citation
EvoHarness-RL is introduced, which exposes Belief, Progress, and Experience (BPE) as policy-facing harness state and reveals two key dynamics: harness annealing, where training internalizes recurring harness-use patterns into the model policy and shifts the agent from frequent harness calls toward selective external-st...
Xuying Ning, Dongqi Fu, Tianxin Wei et al.· 0 citations
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