142 citations · 224 across the 7 of their papers we have counts for
10 papers
Mining Agent Skills from Production Traces
Yue Ran Kang, Colton Mikolajczyk, Chhaya Methani +4
Agent skills that record procedural instructions are increasingly mined from execution traces rather than curated by hand. Skill-mining pipelines often use known task outcomes or f…
Is Bash All You Need? An Empirical Study of Tool Interfaces for Enterprise Digital Worker Agents
Hazel Mak, Susheel Suresh, Sahil Bhatnagar +3
In this study, we examine whether a general shell can outperform specialized tools on enterprise tasks. Shell-based agents have shown strong results in coding, but enterprise work…
Grounding Agent Memory: Environment-Probing Curation for Enterprise Agents
Susheel Suresh, Hazel Mak, Sahil Bhatnagar +2
Persistent memory is entering production-oriented agent platforms to help long-horizon agents accumulate experience across sessions. Yet a post-task curator agent restricted to com…
AgenticRAG: Agentic Retrieval for Enterprise Knowledge Bases
Susheel Suresh, Hazel Mak, Shangpo Chou +2
We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG pipelines place significant burden of grounding on the s…
OCTAL: Graph Representation Learning for LTL Model Checking
Prasita Mukherjee, Haoteng Yin
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, suffer from the sta…
Federated Graph Representation Learning using Self-Supervision
Susheel Suresh, Danny Godbout, Arko Mukherjee +3
Federated graph representation learning (FedGRL) brings the benefits of distributed training to graph structured data while simultaneously addressing some privacy and compliance co…
OCTAL: Graph Representation Learning for LTL Model Checking
Prasita Mukherjee, Haoteng Yin, Susheel Suresh +1
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification. Pure symbolic approaches while popular, still suffer from t…
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Susheel Suresh, Vinith Budde, Jennifer Neville +2
Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon…
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Susheel Suresh, Pan Li, Cong Hao +1
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning…
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
Susheel Suresh, Jennifer Neville
The problem of knowledge graph (KG) reasoning has been widely explored by traditional rule-based systems and more recently by knowledge graph embedding methods. While logical rules…