Lesly Miculicich
I'm a Senior Research Engineer at Google Cloud AI Research team. My recent work focuses on LLM's reasoning capabilities and structured data understanding. Previously, I was Researcher at Microsoft, and I received my PhD degree from EPFL in Switzerland. My research interest is on general natural language understanding, and machine learning.
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VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Dj Dvijotham
Mirko Montanari
ACL 2026
Preview abstract
Artificial intelligence is rapidly evolving, marked by the emergence of Large Language Model (LLM) agents – systems capable of complex reasoning, planning, and interaction with digital and physical environments. These agents, powered by advancements in LLMs, demonstrate remarkable capabilities across diverse domains, including finance, healthcare, web navigation, software development, and daily task assistance. Unlike traditional AI systems, LLM agents can perceive their surroundings, formulate multi-step plans, utilize external tools and APIs, access memory or knowledge bases, and execute actions to achieve specified goals. This ability to act upon the world, however, introduces significant safety and security challenges.
The safety paradigms developed for traditional LLMs, primarily focused on mitigating harmful textual outputs (e.g., toxicity, bias), are insufficient for safeguarding LLM agents. Agents interacting with dynamic environments and executing actions present a broader attack surface and new categories of risk. These include performing unsafe operations, violating privacy constraints through improper data handling or access control failures, deviating from user objectives (task misalignment), and susceptibility to novel manipulation techniques like indirect prompt injection and memory poisoning. Ensuring the trustworthy operation of these powerful agents is paramount, especially as they are integrated into high-stakes applications. To address this critical challenge, we introduce VeriGuard, a novel framework designed to enhance the safety and reliability of LLM agents by interactively verifying their policies and the actions. VeriGuard integrates a verification module that intercepts code-based actions proposed by the agent. In the first step, VeriGuard will generates and verifies the policies. The policies are rigorously checked against a set of predefined safety and security specifications Then each action will be verified to make sure it will align with the agent specification. This interactive verification loop ensures that the agent's behavior remains within safe operational bounds, effectively preventing the execution of harmful or unintended operations. By verifying each step, VeriGuard provides a robust safeguard, substantially improving the trustworthiness of LLM agents in complex, real-world environments.
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Causal Armor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
Minbeom Kim
Phil Wallis
Kyomin Jung
Dj Dvijotham
2026
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AI agents equipped with tool-calling capabilities are susceptible to \emph{Indirect Prompt Injection} (IPI) attacks. In this attack scenario, malicious commands hidden within \emph{untrusted} content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often suffer from the \emph{over-defense dilemma}: they deploy expensive, \emph{always-on} sanitization that degrades utility and latency even in benign scenarios. We revisit IPI through an operational causal lens: a successful injection manifests as a \emph{grounding collapse} where the user request no longer provides decisive support for the agent's privileged action, while a particular untrusted segment provides disproportionate marginal support. Based on this signature, we propose \texttt{CausalArmor}, a selective defense framework that (i) computes lightweight, normalized leave-one-out attributions at privileged decision points, and (ii) triggers targeted sanitization only when an untrusted segment dominates the user intent. Additionally, CausalArmor employs \emph{retroactive Chain-of-Thought masking} to prevent the agent from acting on ``poisoned" reasoning traces. Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses with explainability while preserving utility and latency of AI agents.
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Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
Pengfei He
Ash Fox
Stefan Friedli
Daniel Fabian
Burak Gokturk
Jiliang Tang
ICML 2026
Preview abstract
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 Code-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming workflows by integrating security-domain knowledge, code-aware analysis, execution-grounded iterative reasoning, and long-term memory. Code-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions based on real execution feedback while learning from prior trajectories. Extensive evaluations on challenging security benchmarks demonstrate that Code-RedTeam consistently outperforms strong baselines across diverse backbone models, achieving over 60% attack success rate in vulnerability exploitation and up to 10% absolute improvement in vulnerability detection. Ablation and iteration studies further confirm the critical role of execution feedback, structured interaction, and memory for building robust and generalizable cybersecurity agents.
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Model Swarms: Collaborative Search of Adapted LLM Experts via Swarm Intelligence
Shangbin Feng
Yike Wang
Nathalie Rauschmayr
Yejin Choi
Yulia Tsvetkov
ICML 2025
Preview abstract
We propose Model Swarms, a collaborative search algorithm to adapt LLM experts via swarm intelligence. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and search for adapted models that optimize the utility function. Compared to existing model composition approaches, Model Swarms offers modularity, works in low-data regimes, and doesn't need assumptions about existing experts and how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single dataset, multi-dataset domains, reward models, as well as diverse human preferences. Further analysis reveals that LLM experts discover previously unseen capabilities in the search process and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.
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Preview abstract
Artificial intelligence is rapidly evolving, marked by the emergence of Large Language Model (LLM) agents – systems capable of complex reasoning, planning, and interaction with digital and physical environments. These agents, powered by advancements in LLMs, demonstrate remarkable capabilities across diverse domains, including finance, healthcare, web navigation, software development, and daily task assistance. Unlike traditional AI systems, LLM agents can perceive their surroundings, formulate multi-step plans, utilize external tools and APIs, access memory or knowledge bases, and execute actions to achieve specified goals. This ability to act upon the world, however, introduces significant safety and security challenges.
The safety paradigms developed for traditional LLMs, primarily focused on mitigating harmful textual outputs (e.g., toxicity, bias), are insufficient for safeguarding LLM agents. Agents interacting with dynamic environments and executing actions present a broader attack surface and new categories of risk. These include performing unsafe operations, violating privacy constraints through improper data handling or access control failures, deviating from user objectives (task misalignment), and susceptibility to novel manipulation techniques like indirect prompt injection and memory poisoning. Ensuring the trustworthy operation of these powerful agents is paramount, especially as they are integrated into high-stakes applications. To address this critical challenge, we introduce VeriGuard, a novel framework designed to enhance the safety and reliability of LLM agents by interactively verifying their policies and the actions. VeriGuard integrates a verification module that intercepts code-based actions proposed by the agent. In the first step, VeriGuard will generates and verifies the policies. The policies are rigorously checked against a set of predefined safety and security specifications Then each action will be verified to make sure it will align with the agent specification. This interactive verification loop ensures that the agent's behavior remains within safe operational bounds, effectively preventing the execution of harmful or unintended operations. By verifying each step, VeriGuard provides a robust safeguard, substantially improving the trustworthiness of LLM agents in complex, real-world environments.
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Deep Researcher with Test-time Diffusion
Guan Sun
Yuanjun (Sophia) Bi
Weiming Wen
Hui Wan
Chunfeng Wen
Solène Maître
George Lee
Vishy Tirumalashetty
Emily Xue
Burak Gokturk
2025
Preview abstract
Deep research agents, powered by Large Language Models (LLMs), are rapidly advancing; yet, their performance often plateaus when generating complex, long-form research reports using generic test-time scaling algorithms. Drawing inspiration from the iterative nature of human research, which involves cycles of searching, reasoning, and revision, we propose the Test-Time Diffusion Deep Researcher (TTD-DR). This novel framework conceptualizes research report generation as a diffusion process. TTD-DR initiates this process with a preliminary draft, an updatable skeleton that serves as an evolving foundation to guide the research direction. The draft is then iteratively refined through a "denoising" process, which is dynamically informed by a retrieval mechanism that incorporates external information at each step. The core process is further enhanced by a self-evolutionary algorithm applied to each component of the agentic workflow, ensuring the generation of high-quality context for the diffusion process. This draft-centric design guides the report writing process to be more timely and coherent while reducing information loss during the iterative search process. We demonstrate that our TTD-DR achieves state-of-the-art results on a wide array of benchmarks that require intensive search and multi-hop reasoning, significantly outperforming existing deep research agents.
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Chain-of-Table: Evolves Tables in the LLM Reasoning Chain for Table Understanding
Zilong Wang
Hao Zhang
Jingbo Shang
ICLR (2024)
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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 free-form questions and semi-structured tabular data. Chain-of-Thought and its similar approaches incorporate the reasoning chain in the form of textual context, but it is still an open question how to effectively leverage tabular data in the reasoning chain. We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts. Specifically, we guide LLMs using in-context learning to iteratively generate operations and update the table to represent a tabular reasoning chain. LLMs can therefore dynamically plan the next operation based on the results of the previous ones. This continuous evolution of the table forms a chain, showing the reasoning process for a given tabular problem. The chain carries structured information of the intermediate results, enabling more accurate and reliable predictions. Chain-of-Table achieves new state-of-the-art performance on WikiTQ, FeTaQA, and TabFact benchmarks across multiple LLM choices.
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TableRAG: Million-Token Table Reasoning with Language Models
Zilong Wang
Hsuan-Tien Lin
Si-An Chen
2024
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Recent advancements in Large Language Models (LLMs) have expanded their capabilities in table reasoning. However, traditional methods that feed entire tables into LLMs face scalability issues due to the context length constraints of LLMs. These challenges are compounded by increased computational costs and potential degradation in reasoning capabilities known as the "Lost-in-the-Middle" phenomenon. To address these limitations, we introduce TableRAG, a retrieval-augmented generation (RAG) framework for LLM-based table reasoning. This framework combines schema retrieval, which identifies crucial columns and their data types, with cell retrieval, focusing on extracting essential keywords and information. This dual approach allows for efficient data encoding, significantly reducing token costs and avoiding information loss. We evaluated TableRAG's performance using new benchmarks derived from the Arcade and BIRD-SQL datasets and expanded synthetic data from TabFact for multi-scale evaluations. The results confirm that TableRAG efficiently and effectively solves queries across various table sizes, demonstrating its potential as a scalable solution for LLM-based table reasoning.
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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 introduce CaLM, a novel verification framework. CaLM leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. Our framework empowers smaller LMs, which rely less on parametric memory and excel at processing relevant information given a query, to validate the output of larger LMs. Larger LM responses that closely align with the smaller LMs' output, which relies exclusively on cited documents, are verified. Responses showing discrepancies are iteratively refined through a feedback loop. Experiments on three open-domain question-answering datasets demonstrate significant performance gains of 1.5% to 7% absolute average without any required model fine-tuning.
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SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL
Satya Gundabathula
Pengcheng Yin
Hanjun Dai
Hootan Nakhost
TMLR (2024)
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Text-to-SQL, the process of translating natural language into Structured Query Language
(SQL), represents a transformative application of large language models (LLMs), potentially
revolutionizing how humans interact with data. This paper introduces the SQL-PaLM
framework, a comprehensive solution for understanding and enhancing Text-to-SQL using
LLMs, using in the learning regimes of few-shot prompting and instruction fine-tuning. With
few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error filtering. With instruction fine-tuning, we delve deep in understanding the critical
paradigms that influence the performance of tuned LLMs. In particular, we investigate
how performance can be improved through expanded training data coverage and diversity,
synthetic data augmentation, and integrating query-specific database content. We propose
a test-time selection method to further refine accuracy by integrating SQL outputs from
multiple paradigms with execution feedback as guidance. Additionally, we tackle the
practical challenge of navigating intricate databases with a significant number of tables and
columns, proposing efficient techniques for accurately selecting relevant database elements to
enhance Text-to-SQL performance. Our holistic approach yields substantial advancements
in Text-to-SQL, as demonstrated on two key public benchmarks, Spider and BIRD. Through
comprehensive ablations and error analyses, we shed light on the strengths and weaknesses
of our framework, offering valuable insights into Text-to-SQL’s future work.
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