activity
20162026
most citedTweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

134 citations · 185 across the 17 of their papers we have counts for

collaborators

21 papers

cs.PL2026

VHDL-REPOBENCH: A Repository-Level Benchmark for Evaluating Large Language Models on VHDL Design Generation

Prashanth Vijayaraghavan, Akul Malhotra, Ashutosh Jadhav +2

Large Language Models (LLMs) are increasingly applied in hardware design automation, demonstrating strong potential in generating and understanding hardware description languages.…

cs.CL2026

ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards

Prashanth Vijayaraghavan, Charles Mackin, Luyao Shi +6

Large language models (LLMs) have shown promise in code summarization, yet their effectiveness for Hardware Description Languages (HDLs) like VHDL and Verilog remains underexplored…

cs.AI2026

StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +2

Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Veri…

cs.CL2026

SYMDIREC: A Neuro-Symbolic Divide-Retrieve-Conquer Framework for Enhanced RTL Synthesis and Summarization

Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi +5

Register-Transfer Level (RTL) synthesis and summarization are central to hardware design automation but remain challenging for Large Language Models (LLMs) due to rigid HDL syntax,…

cs.CL2026

CODMAS: A Dialectic Multi-Agent Collaborative Framework for Structured RTL Optimization

Che-Ming Chang, Prashanth Vijayaraghavan, Ashutosh Jadhav +4

Optimizing Register Transfer Level (RTL) code is a critical step in Electronic Design Automation (EDA) for improving power, performance, and area (PPA). We present CODMAS (Collabor…

cs.CL2025★ 7 cited

Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization

Prashanth Vijayaraghavan, Apoorva Nitsure, Charles Mackin +9

Large Language Models (LLMs) have become widely used across diverse NLP tasks and domains, demonstrating their adaptability and effectiveness. In the realm of Electronic Design Aut…