Code-Driven Planning in Grid Worlds with Large Language Models

We propose an iterative programmatic planning (IPP) framework for solving grid-based tasks by synthesizing interpretable agent policies expressed in code using large language models (LLMs). Instead of relying on traditional search or reinforcement learning, our approach uses code generation as policy synthesis, where the LLM outputs executable programs that map environment states to action sequences. Our proposed architecture incorporates several prompting strategies, including direct code generation, pseudocode-conditioned refinement, and curriculum-based prompting, but also includes an iterative refinement mechanism that updates code based on task performance feedback. We evaluate our approach using six leading LLMs and two challenging grid-based benchmarks (GRASP and MiniGrid). Our IPP framework demonstrates improvements over direct code generation ranging from 10\% to as much as 10x across five of the six models and establishes a new state-of-the-art result for GRASP. IPP is found to significantly outperform direct elicitation of a solution from GPT-o3-mini (by 63\% on MiniGrid to 116\% on GRASP), demonstrating the viability of the overall approach. Computational costs of all code generation approaches are similar. While code generation has a higher initial prompting cost compared to direct solution elicitation (\$0.08 per task vs. \$0.002 per instance for GPT-o3-mini), the code can be reused for any number of instances, making the amortized cost significantly lower (by 400x on GPT-o3-mini across the complete GRASP benchmark).

Backdoor Defense in Diffusion Models via Spatial Attention Unlearning

Text-to-image diffusion models are increasingly vulnerable to backdoor attacks, where malicious modifications to the training data cause the model to generate unintended outputs when specific triggers are present. While classification models have seen extensive development of defense mechanisms, generative models remain largely unprotected due to their high-dimensional output space, which complicates the detection and mitigation of subtle perturbations. Defense strategies for diffusion models, in particular, remain under-explored. In this work, we propose Spatial Attention Unlearning (SAU), a novel technique for mitigating backdoor attacks in diffusion models. SAU leverages latent space manipulation and spatial attention mechanisms to isolate and remove the latent representation of backdoor triggers, ensuring precise and efficient removal of malicious effects. We evaluate SAU across various types of backdoor attacks, including pixel-based and style-based triggers, and demonstrate its effectiveness in achieving 100% trigger removal accuracy. Furthermore, SAU achieves a CLIP score of 0.7023, outperforming existing methods while preserving the model's ability to generate high-quality, semantically aligned images. Our results show that SAU is a robust, scalable, and practical solution for securing text-to-image diffusion models against backdoor attacks.