Reinforcement learning with verifiable rewards (e.g., GRPO) significantly advances LLM reasoning. However, in complex tasks, GRPO frequently encounters the "zero-advantage problem": when all rollouts for a query fail, the relative advantage collapses to zero, wasting computational budget. While resampling with more budget is a common remedy, the static sampling policy inherently constrains reasoning exploration, limiting the success rate. In this paper, we propose Lorem Perturbation for Exploration (LoPE), a simple training framework to break this exploration bottleneck. Motivated by the intuition that task-irrelevant prompt perturbations can shift the output distribution, LoPE prepends sequences stochastically assembled from the Lorem Ipsum vocabulary to the prompts before resampling, forcing the model to explore broader reasoning pathways. Experiments across 1.7B, 4B, and 7B models show LoPE significantly outperforms resampling with original prompts. Our findings, including the effectiveness of other low-perplexity Latin-based random sequences, establish LoPE as a strong baseline for broadening LLM exploration.

Multimodal large language models (MLLMs) have shown strong performance in perception and question-answering, but many clinical decisions require reasoning across observations collected over time rather than from a single input. We study this problem in longitudinal modeling, where evidence evolves across visits. We introduce LUMINA (LongitUdinal MultImodal reasoNing Agent), a single-agent framework that incrementally builds an auditable evidence state before making predictions. Instead of predicting directly from all observations, LUMINA organizes current evidence, compares observations across prior visits, maintains longitudinal memory, optionally retrieves relevant cross-subject summaries, and verifies whether the accumulated evidence supports the final decision. We evaluate LUMINA on two subject-level benchmarks: Alzheimer’s disease classification and 36-month progression forecasting from observed visit prefixes. Across nine MLLM backbones, LUMINA consistently outperforms MLLM baselines and an adapted medical reasoning agent, achieving 13.8%–18.5% relative balanced accuracy gains on classification and 16.8%–24.8% gains on progression forecasting over the strongest non-agent baseline. Additional evaluations preserve the same method ordering on Parkinsonian-state classification and breast cancer treatment-response prediction.