Tong Ju | Searcharxiv

arXiv2026

Right-Handed Neutrino Production by an Axion-like Inflaton: Implications for Leptogenesis

We study heavy right-handed neutrino production induced by a derivative coupling to an axion-like inflaton and its implications for non-thermal leptogenesis. We develop a unified treatment of Majorana-fermion production during inflation and preheating. During inflation, the rolling inflaton background generates a helicity-asymmetric spectrum that can be obtained analytically in the slow-roll regime, while during preheating the oscillating background drives repeated non-adiabatic production events with a helicity-dependent effective momentum and a natural momentum cutoff. We compute the resulting abundance in both the delayed-decay and prompt-decay limits, including the effects of Pauli blocking and successive production stages. We further clarify the relation between the fermion basis used to identify non-adiabatic production and the Hamiltonian-diagonal basis used to define instantaneous occupation numbers. Applying these results to non-thermal leptogenesis, we identify parameter regions consistent with the observed baryon asymmetry.

hep-ph↗

arXiv2026

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However, the underlying retrieval databases may naturally contain, or be intentionally injected with, adversarial knowledge, which can perturb model outputs and undermine system reliability. To investigate this risk, prior studies have explored knowledge poisoning attacks in medical RAG systems. Nevertheless, most of them rely on the strong assumption that adversaries possess prior knowledge of user queries, which is unrealistic in deployments and substantially limits their practical applicability. In this paper, we propose M\textsuperscript{3}Att, a knowledge-poisoning framework designed for medical multimodal RAG systems, assuming only limited distribution knowledge of the underlying database. Our core idea is to inject covert misinformation into textual data while using paired visual data as a query-agnostic trigger to promote retrieval. We first propose a unified framework that introduces imperceptible perturbations to visual inputs to manipulate retrieval probabilities. Besides, due to the prior medical knowledge in LLMs, naively poisoned medical content with explicit factual errors can be corrected during generation. Thus, we leverage the inherent ambiguity of medical diagnosis and design a covert misinformation injection strategy that degrades diagnostic accuracy while evading model self-correction. Experiments on five LLMs and datasets demonstrate that M\textsuperscript{3}Att consistently produces clinically plausible yet incorrect generations. Codes: https://github.com/ypr17/M3Att.

cs.CR↗