7 papers
Scale-QLoRA: Code-Invariant Adapter Merging for Native 4-bit Microscaling LLMs
Tung-Ling Li, Jiale Huang, Lee-Chi Wang +1
Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's per-forward overhead and leaves a single standalone checkpoint any serv…
Breaking Safety at the Token Boundary: How BPE Tokenization Creates Exploitable Gaps in LLM Alignment
Tung-Ling Li, Hongliang Liu, Yuhao Wu
Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural mechanism: BPE tokenization f…
The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills
Hongliang Liu, Yuhao Wu, Tung-Ling Li
AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Gov…
Behavioral Integrity Verification for AI Agent Skills
Yuhao Wu, Tung-Ling Li, Hongliang Liu
Agent skills extend LLM agents with privileged third-party capabilities such as filesystem access, credentials, network calls, and shell execution. Existing safety work catches mal…
Perturbation Probing: A Two-Pass-per-Prompt Diagnostic for FFN Behavioral Circuits in Aligned LLMs
Hongliang Liu, Tung-Ling Li, Yuhao Wu
Perturbation probing generates task-specific causal hypotheses for FFN neurons in large language models using two forward passes per prompt and no backpropagation, followed by a on…
AdvJudge-Zero: Binary Decision Flips in LLM-as-a-Judge via Adversarial Control Tokens
Tung-Ling Li, Yuhao Wu, Hongliang Liu
LLM-as-a-Judge systems supply the reward signal in modern RLHF and RLVR pipelines, but their binary verdict reduces to a single linear readout F_gap on one hidden state. We show th…