We introduce InterChart, a diagnostic benchmark that evaluates how well vision-language models (VLMs) reason across multiple related charts, a task central to real-world applications such as scientific reporting, financial analysis, and public policy dashboards. Unlike prior benchmarks focusing on isolated, visually uniform charts, InterChart challenges models with diverse question types ranging from entity inference and trend correlation to numerical estimation and abstract multi-step reasoning grounded in 2–3 thematically or structurally related charts. We organize the benchmark into three tiers of increasing difficulty: (1) factual reasoning over individual charts, (2) integrative analysis across synthetically aligned chart sets, and (3) semantic inference over visually complex, real-world chart pairs. Our evaluation of state-of-the-art open- and closed-source VLMs reveals consistent and steep accuracy declines as chart complexity increases. We find that models perform better when we decompose multi-entity charts into simpler visual units, underscoring their struggles with cross-chart integration. By exposing these systematic limitations, InterChart provides a rigorous framework for advancing multimodal reasoning in complex, multi-visual environments.

Coherence in discourse is fundamental for comprehension and perception. Much research on coherence modeling has focused on better model architectures and training setups optimizing on the permuted document task, where random permutations of a coherent document are considered incoherent. However, there’s very limited work on creating “informed” synthetic incoherent samples that better represent or mimic incoherence. We source a diverse positive corpus for local coherence and propose six rule-based methods leveraging information from Constituency trees, Part-of-speech, semantic overlap and more, for “informed” negative sample synthesis for better representation of incoherence. We keep a straightforward training setup for local coherence modeling by fine-tuning popular transformer models, and aggregate local scores for global coherence. We evaluate on a battery of independent downstream tasks to assess the impact of improved negative sample quality. We assert that a step towards optimality for coherence modeling requires better negative sample synthesis in tandem with model improvements.

Existing benchmarks for visual question answering lack in visual grounding and complexity, particularly in evaluating spatial reasoning skills. We introduce FlowVQA, a novel benchmark aimed at assessing the capabilities of visual question-answering multimodal language models in reasoning with flowcharts as visual contexts. FlowVQA comprises 2,272 carefully generated and human-verified flowchart images from three distinct content sources, along with 22,413 diverse question-answer pairs, to test a spectrum of reasoning tasks, including information localization, decision-making, and logical progression. We conduct a thorough baseline evaluation on a suite of both open-source and proprietary multimodal language models using various strategies, followed by an analysis of directional bias. The results underscore the benchmark’s potential as a vital tool for advancing the field of multimodal modeling, providing a focused and challenging environment for enhancing model performance in visual and logical reasoning tasks.