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

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

collaborators

7 papers

cs.AI2026

Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification

Shuangjie Yao, Nikolaus Holzer, Mark Paul Santolucito +3

Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers. It's a guarantee especially valuable for code generated by la…

cs.AI2026

GIF: Locally Sound Geometric Information Flow Control for LLMs

Adam Storek, Nikolaus Holzer, Zhuo Zhang +1

Large language models increasingly mediate interactions between sensitive data, untrusted inputs, and privileged actions in agentic systems, creating security and privacy risks. Th…

cs.AI2025

TempoBench: Reasoning Execution Without Causal Attribution Is Just Simulation

Nikolaus Holzer, William Fishell, Baishakhi Ray +1

Current training paradigms, optimized for long-horizon reasoning trace execution, have made Large Language Models (LLMs) excel at pattern matching and forward simulation of reasoni…

cs.CE2025★ 1 cited

FinRL Contests: Benchmarking Data-driven Financial Reinforcement Learning Agents

Keyi Wang, Nikolaus Holzer, Ziyi Xia +5

Financial reinforcement learning (FinRL) is now a practical paradigm for financial engineering. However, applying RL strategies to real-world trading tasks remains a challenge for…

cs.CE2025★ 1 cited

Revisiting Ensemble Methods for Stock Trading and Crypto Trading Tasks at ACM ICAIF FinRL Contest 2023-2024

Nikolaus Holzer, Keyi Wang, Kairong Xiao +1

Reinforcement learning has demonstrated great potential for performing financial tasks. However, it faces two major challenges: policy instability and sampling bottlenecks. In this…

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…