5 papers
DRC-Aid: Design-Rule Correction via Agentic Framework utilizing Inference-Time Large Language Models
Anushka Mukherjee, Kang He, Kaushik Roy
Resolving Design Rule Violations (DRVs) in layouts entails an iterative loop of geometric edits and verification. We present DRC-Aid, a closed-loop agentic framework that automates…
SWE-Adept: An LLM-Based Agentic Framework for Deep Codebase Analysis and Structured Issue Resolution
Kang He, Kaushik Roy
Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-level software engineering (SWE), which de…
LogicTree: Structured Proof Exploration for Coherent and Rigorous Logical Reasoning with Large Language Models
Kang He, Kaushik Roy
Large language models (LLMs) have achieved remarkable multi-step reasoning capabilities across various domains. However, LLMs still face distinct challenges in complex logical reas…
Panoptic Diffusion Models: co-generation of images and segmentation maps
Yinghan Long, Kaushik Roy
Recently, diffusion models have demonstrated impressive capabilities in text-guided and image-conditioned image generation. However, existing diffusion models cannot simultaneously…
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models
Kang He, Yinghan Long, Kaushik Roy
Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings. In t…