3 citations · 5 across the 19 of their papers we have counts for
19 papers
Antares: Foundation Models for Agentic Vulnerability Localization
Supriti Vijay, Aman Priyanshu, Didier Chapoteau +8
Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We prese…
Neural Networks Provably Learn Spectral Representations for Group Composition
Jianliang He, Leda Wang, Fengzhuo Zhang +2
Understanding how structured internal structure emerges during neural network training is central to the study of deep learning. We investigate this phenomenon through the group co…
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model
Siyu Chen, Beining Wu, Miao Lu +2
In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian…
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking
Jianliang He, Leda Wang, Siyu Chen +1
We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task. Our work provides a full mechanistic interpretation of the l…
Llama-3.1-FoundationAI-SecurityLLM-Reasoning-8B Technical Report
Zhuoran Yang, Ed Li, Jianliang He +18
We present Foundation-Sec-8B-Reasoning, the first open-source native reasoning model for cybersecurity. Built upon our previously released Foundation-Sec-8B base model (derived fro…
Unlocking Out-of-Distribution Generalization in Transformers via Recursive Latent Space Reasoning
Awni Altabaa, Siyu Chen, John Lafferty +1
Systematic, compositional generalization beyond the training distribution remains a core challenge in machine learning -- and a critical bottleneck for the emergent reasoning abili…
Taming Polysemanticity in LLMs: Provable Feature Recovery via Sparse Autoencoders
Siyu Chen, Heejune Sheen, Xuyuan Xiong +2
We study the challenge of achieving theoretically grounded feature recovery using Sparse Autoencoders (SAEs) for the interpretation of Large Language Models. Existing SAE training…
Quantile-Optimal Policy Learning under Unmeasured Confounding
Zhongren Chen, Siyu Chen, Zhengling Qi +2
We study quantile-optimal policy learning where the goal is to find a policy whose reward distribution has the largest -quantile for some . We focus on the offline…
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention
Jianliang He, Xintian Pan, Siyu Chen +1
We study how multi-head softmax attention models are trained to perform in-context learning on linear data. Through extensive empirical experiments and rigorous theoretical analysi…
DrugImproverGPT: A Large Language Model for Drug Optimization with Fine-Tuning via Structured Policy Optimization
Xuefeng Liu, Songhao Jiang, Siyu Chen +4
Finetuning a Large Language Model (LLM) is crucial for generating results towards specific objectives. This research delves into the realm of drug optimization and introduce a nove…
Active Advantage-Aligned Online Reinforcement Learning with Offline Data
Xuefeng Liu, Hung T. C. Le, Siyu Chen +4
Online reinforcement learning (RL) enhances policies through direct interactions with the environment, but faces challenges related to sample efficiency. In contrast, offline RL le…
Enhancing Long Video Generation Consistency without Tuning
Xingyao Li, Fengzhuo Zhang, Jiachun Pan +3
Despite the considerable progress achieved in the long video generation problem, there is still significant room to improve the consistency of the generated videos, particularly in…
Unveiling Induction Heads: Provable Training Dynamics and Feature Learning in Transformers
Siyu Chen, Heejune Sheen, Tianhao Wang +1
In-context learning (ICL) is a cornerstone of large language model (LLM) functionality, yet its theoretical foundations remain elusive due to the complexity of transformer architec…
Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
Xinyang Hu, Fengzhuo Zhang, Siyu Chen +1
Chain-of-Thought (CoT) prompting and its variants have gained popularity as effective methods for solving multi-step reasoning problems using pretrained large language models (LLMs…
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
Jianliang He, Siyu Chen, Fengzhuo Zhang +1
In this work, from a theoretical lens, we aim to understand why large language model (LLM) empowered agents are able to solve decision-making problems in the physical world. To thi…
Sample-efficient Learning of Infinite-horizon Average-reward MDPs with General Function Approximation
Jianliang He, Han Zhong, Zhuoran Yang
We study infinite-horizon average-reward Markov decision processes (AMDPs) in the context of general function approximation. Specifically, we propose a novel algorithmic framework…
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
Awni Altabaa, Zhuoran Yang
In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other. Classical…
Training Dynamics of Multi-Head Softmax Attention for In-Context Learning: Emergence, Convergence, and Optimality
Siyu Chen, Heejune Sheen, Tianhao Wang +1
We study the dynamics of gradient flow for training a multi-head softmax attention model for in-context learning of multi-task linear regression. We establish the global convergenc…