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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.
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...
This work proposes method that unifies textual reasoning and graph message passing within a masked diffusion language model, a language model with bidirectional attention and generative decoding that outperforms graph neural networks, graph transformers, and LLM-based baselines on all three TAG benchmarks across two ta...
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...
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