5 papers
Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization
Huilin Zhou, Jian Zhao, Yilu Zhong +7
Red teaming is critical for uncovering vulnerabilities in Large Language Models (LLMs). While automated methods have improved scalability, existing approaches often rely on static…
Nice Fold or Hero Call: Learning Budget-Efficient Thinking for Adaptive Reasoning
Zhaomeng Zhou, Lan Zhang, Junyang Wang +2
Large reasoning models (LRMs) improve problem solving through extended reasoning, but often misallocate test-time compute. Existing efficiency methods reduce cost by compressing re…
Permit: Permission-Aware Representation Intervention for Controlled Generation in Large Language Models
Pengcheng Sun, Lan Zhang, Zhaopeng Zhang +2
Large language models (LLMs) are increasingly deployed in enterprise settings where they handle sensitive documents and user context, raising acute concerns over security and contr…
Monotonic Reference-Free Refinement for Autoformalization
Lan Zhang, Marco Valentino, André Freitas
While statement autoformalization has advanced rapidly, full-theorem autoformalization remains largely unexplored. Existing iterative refinement methods in statement autoformalizat…
FormalScience: Scalable Human-in-the-Loop Autoformalisation of Science with Agentic Code Generation in Lean
Jordan Meadows, Lan Zhang, Andre Freitas
Formalising informal mathematical reasoning into formally verifiable code is a significant challenge for large language models. In scientific fields such as physics, domain-specifi…