activity
20242026
most citedGuiding LLM Temporal Logic Generation with Explicit Separation of Data and Control

3 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.HC2026

Decision-Oriented Programming with Aporia

Saketh Ram Kasibatla, Raven Rothkopf, Hila Peleg +4

AI agents help developers express computational intent abstractly, but at a cost: developers cede decision-making authority to agents, often without realizing that important design…

cs.HC2025

The Command Line GUIde: Graphical Interfaces from Man Pages via AI

Saketh Ram Kasibatla, Kiran Medleri Hiremath, Raven Rothkopf +3

Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980's and lives on in modern desktop operating systems. The com…

cs.HC2025

HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding

Emmanuel Anaya González, Raven Rothkopf, Sorin Lerner +1

While AI programming tools hold the promise of increasing programmers' capabilities and productivity to a remarkable degree, they often exclude users from essential decision-making…

cs.SE2024★ 1 cited

Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesis

William Murphy, Nikolaus Holzer, Feitong Qiao +4

In the past few years, Large Language Models (LLMs) have exploded in usefulness and popularity for code generation tasks. However, LLMs still struggle with accuracy and are unsuita…

cs.LG2024★ 3 cited

Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control

William Murphy, Nikolaus Holzer, Nathan Koenig +4

Temporal logics are powerful tools that are widely used for the synthesis and verification of reactive systems. The recent progress on Large Language Models (LLMs) has the potentia…

cs.PL2024

Rose: Composable Autodiff for the Interactive Web

Sam Estep, Wode Ni, Raven Rothkopf +1

Reverse-mode automatic differentiation (autodiff) has been popularized by deep learning, but its ability to compute gradients is also valuable for interactive use cases such as bid…

cs.AI2024

Procedural Adherence and Interpretability Through Neuro-Symbolic Generative Agents

Raven Rothkopf, Hannah Tongxin Zeng, Mark Santolucito

The surge in popularity of large language models (LLMs) has opened doors for new approaches to the creation of interactive agents. However, managing and interpreting the temporal b…