16 citations · 40 across the 13 of their papers we have counts for
16 papers
VeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL
Piyush Jha, Cruise Song, Zhengyu Li +1
Financial Artificial Intelligence (AI) governance and model risk management (MRM) emphasize independent validation, transparency, and effective challenge of AI-generated computatio…
Towards Certificate-Driven Software Porting: A Self-Improving Agentic Harness for Scientific Program Optimization
Piyush Jha, Aishik Ghosh, Vijay Ganesh
The upgrade and rewriting of large scientific codebases has traditionally been a major challenge. While evolutionary search with large language models (LLMs) can port and accelerat…
Learning Scattering Amplitudes with Transformer Reinforcement Learning
Philip Velie, Tianji Cai, Piyush Jha +2
We introduce a transformer reinforcement learning algorithm that learns to solve high loop-level scattering amplitudes in planar N = 4 Super Yang-Mills theory. Our algorithm improv…
CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery
Piyush Jha, Jake Rudolph, Victoria Knapp-Pérez +3
Many scientific discovery problems require searching combinatorial hypothesis spaces under complex domain constraints. Reinforcement learning (RL) offers a promising approach, but…
MathConstraint: Automated Generation of Verified Combinatorial Reasoning Instances for LLMs
Viresh Pati, Zhengyu Li, Piyush Jha +3
We introduce MathConstraint, a hard, adaptive benchmark for evaluating the combinatorial reasoning capabilities of LLMs. We combine constraint satisfaction problems with rigorous s…
Abstractions-of-Thought: Intermediate Representations for LLM Reasoning in Hardware Design
Matthew DeLorenzo, Kevin Tieu, Prithwish Jana +4
Large language models (LLMs) have achieved impressive proficiency on logic and programming tasks, often rivaling expert-level performance. However, generating functionally correct…
LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMs
Piyush Jha, Arnav Arora, Vijay Ganesh
We introduce LLMStinger, a novel approach that leverages Large Language Models (LLMs) to automatically generate adversarial suffixes for jailbreak attacks. Unlike traditional metho…
RLSF: Fine-tuning LLMs via Symbolic Feedback
Piyush Jha, Prithwish Jana, Pranavkrishna Suresh +2
Large Language Models (LLMs) have transformed AI but often struggle with tasks that require domain-specific reasoning and logical alignment. Traditional fine-tuning methods do not…
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
Chunxiao Li, Charlie Liu, Jonathan Chung +3
Restart policy is an important technique used in modern Conflict-Driven Clause Learning (CDCL) solvers, wherein some parts of the solver state are erased at certain intervals durin…
Layered and Staged Monte Carlo Tree Search for SMT Strategy Synthesis
Zhengyang Lu, Stefan Siemer, Piyush Jha +3
Modern SMT solvers, such as Z3, offer user-controllable strategies, enabling users to tailor solving strategies for their unique set of instances, thus dramatically enhancing solve…
AlphaMapleSAT: An MCTS-based Cube-and-Conquer SAT Solver for Hard Combinatorial Problems
Piyush Jha, Zhengyu Li, Zhengyang Lu +3
This paper introduces AlphaMapleSAT, a Cube-and-Conquer (CnC) parallel SAT solver that integrates Monte Carlo Tree Search (MCTS) with deductive feedback to efficiently solve challe…
CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution
Prithwish Jana, Piyush Jha, Haoyang Ju +3
In this paper, we present an LLM-based code translation method and an associated tool called CoTran, that translates whole-programs from one high-level programming language to anot…
BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer
Piyush Jha, Joseph Scott, Jaya Sriram Ganeshna +2
We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as foll…
CGDTest: A Constrained Gradient Descent Algorithm for Testing Neural Networks
Vineel Nagisetty, Laura Graves, Guanting Pan +2
In this paper, we propose a new Deep Neural Network (DNN) testing algorithm called the Constrained Gradient Descent (CGD) method, and an implementation we call CGDTest aimed at exp…
An Augmented Translation Technique for low Resource language pair: Sanskrit to Hindi translation
Rashi Kumar, Piyush Jha, Vineet Sahula
Neural Machine Translation (NMT) is an ongoing technique for Machine Translation (MT) using enormous artificial neural network. It has exhibited promising outcomes and has shown in…
Common Representation Learning Using Step-based Correlation Multi-Modal CNN
Gaurav Bhatt, Piyush Jha, Balasubramanian Raman
Deep learning techniques have been successfully used in learning a common representation for multi-view data, wherein the different modalities are projected onto a common subspace.…