11 citations · 19 across the 26 of their papers we have counts for
11 papers · 1 filter
FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth +1
Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering u…
Share-Borne AI Virus: Memory-Hopping Attacks Across LLM Agents
Sidharth Pulipaka, Ansh Sharma, Stanislau Hlebik +4
Large language models are increasingly deployed as stateful assistants that retain information across interactions and use tools to read, modify, and create persistent artifacts. A…
Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return
Arham Sethi, Arsen Kenzhebayev, Saanvi Paturi +3
Tool-augmented language models are evaluated on whether they reach the right answer, not on whether they report honestly when a tool fails to supply one. We isolate this post-failu…
When Tools Get in the Way: The Effect of Unnecessary Tool Availability on LLM Answering
Saanvi Paturi, Arsen Kenzhebayev, Arham Sethi +3
Large language models (LLMs) are increasingly deployed with external tools that extend what they can do beyond their own knowledge. Tools help on tasks that need external informati…
Mitigating Over-Personalization in LLMs via Structured Memory
Hakeem Hannoon, Andrew Zhao, Mihir Narayan +2
Conversational assistants increasingly rely on persistent long-term memory to personalize responses across sessions. However, when stored user information is reintroduced into the…
Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution
Ruta Binkyte, Ivaxi Sheth, Zhijing Jin +3
As artificial intelligence (AI), including machine learning (ML) models and foundation models (FMs), are increasingly deployed in high-stakes domains, ensuring their trustworthines…
Hidden in Memory: Sleeper Memory Poisoning in LLM Agents
Sidharth Pulipaka, Stanislau Hlebik, Leonidas Raghav +4
Large language models are increasingly augmented with persistent memory, allowing assistants to store user-specific information across sessions for personalization and continuity.…
Cross-Lingual LLM-Judge Transfer via Evaluation Decomposition
Ivaxi Sheth, Zeno Jonke, Amin Mantrach +1
As large language models are increasingly deployed across diverse real-world applications, extending automated evaluation beyond English has become a critical challenge. Existing e…
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
Ivaxi Sheth, Zhijing Jin, Bryan Wilder +2
In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of the endogenous variable. Identi…
PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
Sidharth Pulipaka, Oliver Chen, Manas Sharma +3
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 p…
Funny or Persuasive, but Not Both: Evaluating Fine-Grained Multi-Concept Control in LLMs
Arya Labroo, Ivaxi Sheth, Vyas Raina +2
Large Language Models (LLMs) offer strong generative capabilities, but many applications require explicit and \textit{fine-grained} control over specific textual concepts, such as…