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Mario Fritz

13 papers hereh-index 452 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author11

Across the 11 of 13 papers where every author was matched, so the position is known.

fields
  • cs.AI6
  • cs.CL3
  • cs.AR1
  • cs.CR1
  • cs.CY1
  • cs.LG1
same name
  • Mario Fritz — 116 papers, h 70
  • Mario Fritz — 11 papers, h 8
  • Mario Fritz — 11 papers, h 4
  • Mario Fritz — 6 papers, h 4
  • Mario Fritz — 5 papers, h 2
  • Mario Fritz — 5 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedTrustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

1 citations · 3 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CY2025

Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

Sai Suresh Macharla Vasu, Ivaxi Sheth, Hui-Po Wang +2

The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automati…

cs.AR2025

ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocols

Arnav Sheth, Ivaxi Sheth, Mario Fritz

Recent advances in large language models (LLMs) have demonstrated strong performance in generating code for general-purpose programming languages. However, their potential for hard…

cs.LG2025

Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models

Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3

Ensuring trustworthiness in machine learning (ML) systems is crucial as they become increasingly embedded in high-stakes domains. This paper advocates for integrating causal method…

cs.AI2025

Safety Must Precede the Deployment of Open-Ended AI

Ivaxi Sheth, Jan Wehner, Sahar Abdelnabi +2

AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this land…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.