CausalFlow: Causal Attribution and Counterfactual Repair for LLM Agent Failures
arXiv:2605.25338v1 Announce Type: cross Abstract: Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and…
arXiv:2605.25338v1 Announce Type: cross Abstract: Large language model (LLM) agents frequently fail on multi-step tasks involving reasoning, tool use, and…
arXiv:2605.23908v1 Announce Type: new Abstract: We are in the midst of large-scale industrial and academic efforts to automate the processes…
arXiv:2605.22866v1 Announce Type: new Abstract: Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based…
arXiv:2605.23603v1 Announce Type: cross Abstract: We introduce the Preisach Attention Layer (PAL), a novel sequence modelling architecture grounded in the…
arXiv:2605.22133v2 Announce Type: replace-cross Abstract: Recent advances in generative modeling show that pretrained representations can improve generation as conditioning features…
arXiv:2605.22738v2 Announce Type: replace-cross Abstract: Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However,…
arXiv:2605.20189v1 Announce Type: new Abstract: Despite the remarkable success of large language models (LLMs), they still face bottlenecks while deploying…
arXiv:2605.19729v2 Announce Type: replace-cross Abstract: We demonstrate that in knowledge distillation for diffusion models, the teacher network’s highly complex denoising…
arXiv:2605.20052v2 Announce Type: replace-cross Abstract: Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale…