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Ivaxi Sheth

7 accepted papers

2026

PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

ICML 2026poster

Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance personalization in future conversations. However, the same persistence can also introduce safety risks that have been lar…

Cited by 0SourceScholar
2026

Position: Safety Must Precede the Deployment of Open-Ended AI Agents

ICML 2026poster

AI advancements have been significantly driven by a combination of foundation models and curiosity-driven learning aimed at increasing capability and adaptability. Within this landscape, open-endedness, where AI agents autonomously and indefinitely generate novel behaviors, representations, or solut…

Cited by 0SourceScholar
2026

Position: Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution

ICML 2026poster

As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), is increasingly deployed in high-stakes domains, ensuring their trustworthiness has become a central challenge. However, the core trustworthy AI objectives, such as fairness, robustness, privacy, and…

Cited by 0SourceScholar
2024

LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History

EMNLP 2024main

With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conv…