Generating academic paper titles requires balancing two key goals: faithfulness to the content and attractiveness to readers. Existing methods often treat title generation as a simple summarization task, producing accurate but unengaging titles. While multi-objective Direct Preference Optimization (DPO) offers a potential solution, our empirical analysis shows that combining conflicting objectives into a single reward is associated with substantial gradient interference during training. To address this problem, drawing inspiration from Pareto dominance in multi-objective optimization, we propose Multi-Objective Dominance Alignment (MODA), a data-centric training strategy tailored for academic title generation. MODA retains preference pairs with strict Pareto dominance, thereby reducing exposure to supervision with opposing reward directions. We implement MODA using dual reward models to independently score both dimensions. Across our experiments, this filtering strategy improves the observed balance between faithfulness and attractiveness, outperforms Human Author baselines, and approaches the performance of frontier models despite using an 8B backbone. Our code is publicly available at https://github.com/realyoume/title_it_right.

Evidence-intensive analytical reports are expected to be fact-dense, quantitatively correct, and supported by figures. Yet one-shot long-form generation with large language models (LLMs) frequently produces fluent but under-supported drafts: core facts are missed, numbers drift, and key visuals are absent, making the report hard to trust. We propose EviReport, an evidence-tracked report-writing workflow that improves reliability by (i) organizing corpus evidence into compact, traceable units and retrieves query-relevant subgraphs into retrieval-ready packages (ii) leveraging a reasoning-focused LLM sketches a high-level plan for full coverage, then a chat-based LLM sharpens it into a detailed hierarchical outline with explicit scope and ordering (iii) rive generation with a facts-first iterative loop: extracting verifiable facts, composing strictly from those facts, then triggering gap-aware append queries to fill missing evidence To evaluate both correctness and completeness, we introduce EviReportBench, a benchmark instantiated on data-rich indicator reports that measures factual accuracy (claim verification), factual coverage (quiz-based evaluation), and visual evidence integration (image recall). Across 8 topics, experiments show that EviReport consistently outperforms strong baselines in factual coverage (2.16×), factual accuracy (+8.9 points), and visual evidence integration (+34 points), approaching the quality of expert-written reports across multiple dimensions.