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.

In the time series domain, increasing efforts extend large language models (LLMs) to multimodal large language models (MLLMs) by incorporating temporal data for downstream temporal understanding tasks. In conventional MLLM training, models primarily rely on language signals, while temporal information provides substantially weaker supervision. This imbalance leads to insufficient learning of temporal patterns. To address this issue, we propose TimeSense, a framework with a disentangled reconstruction structure that introduces explicit temporal supervision and encourages the learning of both global trends and local temporal variations that are often underrepresented in text-dominated multimodal training. Existing QA benchmarks provide limited coverage of temporal variations and compositional temporal dependencies, making it difficult to systematically diagnose these MLLMs capabilities across diverse temporal understanding tasks. To facilitate a more fine-grained evaluation, we introduce EvalTS, a comprehensive benchmark spanning three difficulty levels (Atomic, Molecular, and Compositional) with diverse high-quality queries for evaluating temporal understanding abilities in large language and multimodal language models. Experiments on EvalTS and existing benchmarks demonstrate that TimeSense consistently outperforms prior methods and substantially improves temporal understanding performance.