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20232026
most citedBenchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study

4 citations · 4 across the 13 of their papers we have counts for

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Showing 2026Show all

7 papers · 1 filter

cs.CL2026

Component and Dimension Sparsity in Transformer Refusal Mechanisms

Vincent Siu, Glenn Grant-Richards, Vlad Pavlovich +3

Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions remains poorly understood. We…

cs.AI2026

Tunable Tool-Call Rates in LLM Agents via Representation Steering

Yuqi Chen, Vincent Siu, Yang Liu +2

Deciding whether to call a tool is a core competence of an LLM agent, and a costly one to get wrong: needless calls add latency, accrue cost, and may trigger irreversible side effe…

cs.CR2026

Agent Security Needs Redefinition through a Holistic Framework

Vincent Siu, Jingxuan He, Kyle Montgomery +3

Agent security is widely treated as a question about action content. Defenses ask whether an instruction looks malicious. Benchmarks ask whether an agent performs a harmful soundin…

cs.AI2026

Controlling Tool Use with Heading-Specific Activation Steering

Yuqi Chen, Vincent Siu, Yang Liu +2

Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily. We investigate whether too…

cs.AI2026

AgentBeats: Agentifying Agent Assessment for Openness, Standardization, and Reproducibility

Xiaoyuan Liu, Jianhong Tu, Yuqi Chen +26

Agent systems are advancing quickly across domains, but their evaluation remains fragmented. Most benchmarks rely on fixed, LLM-centric harnesses that require heavy integration, cr…

cs.CL2026

Peer-Preservation in Frontier Models

Yujin Potter, Nicholas Crispino, Vincent Siu +2

Recent work has found that frontier AI models can exhibit misaligned behaviors in pursuit of assigned goals. We demonstrate that models can also exhibit misaligned behaviors in def…

cs.CR2026

A Framework for Formalizing LLM Agent Security

Vincent Siu, Jingxuan He, Kyle Montgomery +4

Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruc…