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Lesly Miculicich

7 accepted papers

2026

CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution

Minbeom Kim, Mihir Parmar, Phillip Wallis, Lesly Miculicich, Kyomin Jung, Krishnamurthy Dvijotham +2
ICML 2026poster

AI agents equipped with tool-calling capabilities are susceptible to *Indirect Prompt Injection* (IPI) attacks. In this attack scenario, malicious commands hidden within *untrusted* content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often su…

Cited by 0Source ↗Scholar ↗
2026

Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents

Pengfei He, Ash Fox, Lesly Miculicich, Stefan Friedli, Daniel Fabian, Burak Gokturk +4
ICML 2026poster

Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Co-RedTeam, a security-aware mul…

Cited by 0Source ↗Scholar ↗
2025

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

Shangbin Feng, Zifeng Wang, Yike Wang, Sayna Ebrahimi, Hamid Palangi, Lesly Miculicich +6
ICML 2025poster

We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM exp…

Cited by 6Source ↗PDF ↗Scholar ↗
2024

CaLM: Contrasting Large and Small Language Models to Verify Grounded Generation

I-Hung Hsu, Zifeng Wang, Long Le, Lesly Miculicich, Nanyun Peng, Chen-Yu Lee +1
ACL 2024findings

Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existing methods, by either feeding LMs with raw or preprocessed materials, remain prone to errors. To address this, we introd…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2024

Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding

Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang +5
ICLR 2024poster

Table-based reasoning with large language models (LLMs) is a promising direction to tackle many table understanding tasks, such as table-based question answering and fact verification. Compared with generic reasoning, table-based reasoning requires the extraction of underlying semantics from both fr…

Cited by 107Source ↗PDF ↗Scholar ↗
2024

TableRAG: Million-Token Table Understanding with Language Models

Si-An Chen, Lesly Miculicich, Julian Martin Eisenschlos, Zifeng Wang, Zilong Wang, Yanfei Chen +4
NeurIPS 2024poster

Recent advancements in language models (LMs) have notably enhanced their ability to reason with tabular data, primarily through program-aided mechanisms that manipulate and analyze tables. However, these methods often require the entire table as input, leading to scalability challenges due to the po…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗