Papers for

scientific communication teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

FigAct turns scientific figures into interactive visual explanations

FigAct: Turning Scientific Figures into Active Canvases for Explanation

Abstract: Scientific figures are designed to communicate information visually, yet MLLMs typically explain them by translating their visual content back into text. This requires readers to manually map the resulting explanations back to the figure. Inspired by how people present visual information, we introduce FigAct, a framework that transforms static scientific figures into question-conditioned visual presentations by acting directly on their existing graphical elements. Like a human presenter, FigAct generates a sequence of short narrations, grounds each narration in the corresponding visual evidence, and applies visual actions to guide the viewer's attention. We develop a hierarchical search strategy for efficient element localization, reducing token usage by approximately 40$\times$. We further train FigAct-8B using three task-specific rewards for grounding accuracy, search efficiency, and rendering quality. We further build a human-verified benchmark from figures in real-world scientific papers to evaluate the ability of MLLMs to generate grounded visual explanations. Our results demonstrate the effectiveness of FigAct and show that treating scientific figures as presentation canvases makes explanations clearer and easier to follow.

Mon 28 SeptArtificial Intelligence
The gist
Scientific figures usually show information visually, but AI models often explain them by turning the images back into text, which makes it hard to connect the explanation to the picture. The authors created FigAct, a system that makes these figures interactive by highlighting parts and adding short spoken notes, helping viewers focus on the important details as if a person is presenting. FigAct uses a smart search method to find figure elements efficiently and was trained to explain accurately and clearly. Tests showed FigAct helps make explanations easier to follow than just text alone.
Open → 2609.36190v1

Scientific posters improved by persistent control using semantic contracts

From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts

Abstract: Scientific poster generation distills a multimodal paper into a single-page visual artifact, forcing strict trade-offs between informational coverage and readability under a fixed spatial budget. Existing methods pass plans as transient prompts and validate individual stages in isolation. This strategy causes requirements to drift across content and layout modules, and previous checks to be silently invalidated. We introduce PosterVisor, a control framework that shifts poster generation from transient prompts to persistent control. An Orchestrator grounds rubrics in the paper and visual assets, compiling them into a Semantic-Geometric Contract (SGC) that binds claims and sources to required visuals, budgets, and spatial commitments. Only fully instantiated records become executable assertions; other usable requirements remain soft guidance. Recursive Contract Enforcement (RCE) dynamically triggers checks across stages as evidence emerges. Crucially, during repairs, RCE rechecks affected checkpoint states, preventing repair-induced regressions from propagating silently. We instantiate PosterVisor in HTML/CSS and editable PPTX generators. On the 100-paper Paper2Poster benchmark, PosterVisor-PPT improves observed mean poster-grounded QA accuracy over PosterGen (64.47% vs. 58.53%) and is preferred by human judges in 72.5% of non-tied pairwise comparisons (95% CI, 61.6-83.4%). A secondary 30-paper study also yields higher VLM Overall and PaperQuiz means. These results support rubric-compiled contracts and stage-conditioned enforcement for controllable poster synthesis.

Tue 15 SeptArtificial Intelligence
The gist
Creating scientific posters involves fitting a lot of information into one page in a clear way, which is tricky. The researchers developed PosterVisor, a system that keeps track of all requirements from a paper and its visuals in a consistent way, avoiding mistakes that happen when plans change during the process. This system checks the poster at different steps and fixes issues without causing new problems. They tested it on a set of 100 papers and found it produced better posters than previous methods, as confirmed by both automated tests and human preferences.
Open → 2609.17326v1