Skip to content

Beyond Instruction-Driven Editing: Source-Grounded Problem Discovery with User-Governed Repair for Scientific Posters

Sep 2026 · 0 citations · 56 references
Computer Science

TL;DR

This work introduces PROS (Proactive Refinement Of Scientific Posters), which separates epistemic initiative from behavioral authority: the system can surface source-grounded candidate problems, while users decide which become repair goals and whether resulting changes are committed.

Abstract

Interactive editors usually assume that users already know what to change. Yet an important interaction state comes earlier: a user may recognize that an artifact is not working without knowing what intervention to request. We call this the articulation gap. We introduce PROS (Proactive Refinement Of Scientific Posters), which separates epistemic initiative from behavioral authority: the system can surface source-grounded candidate problems, while users decide which become repair goals and whether resulting changes are committed. Accepted issues hand off to native-object PPTX editing with validation and reversible preview. We also introduce PROS-Bench, a source-linked collection of 120 papers and 320 editable PPTX posters, including a 120-poster matched primary core and a separate conference representation challenge. On the primary core, PROS achieves a mean VLM-rated stage-balanced diagnosis quality score of 67.2 on a 0-100 scale and 87.6% operator-verified target resolution among accepted diagnoses. Temporally blinded automated scoring yields a +22.7-point paper-macro accepted-target uplift, yet 14.8% of assessable accepted targets decline. This divergence shows why problem discovery, local resolution, and realized outcome should be evaluated separately. More broadly, intelligent editors can support problem discovery before a concrete edit request exists without taking authority over consequential change.

View source

Similar papers

#artificial intelligence Preprint Aug 2026

SEPO: Evidence-Grounded Prompt Optimization via Structural Editing

This paper introduces SEPO (Structural, Evidence-grounded Prompt Optimization), a multi-trajectory prompt optimiser centred on edit-effect lineage feedback that makes prompt optimisation addressable, attributable, and actionable.

Xiao-Yu Ma, Hao-Yue Liu, Yiwen Li et al. · 1 citation
Preprint Sep 2026

SIVIA-RSI: Source-Grounded Adaptation of Diagramming Skills

Scientific method diagrams express computations through entities, dependencies, and conditional routes. Although generated figures can be improved through repeated editing, it is less clear whether experience from one paper improves the first figure of another. We present \sys, a framework for source-grounded adaptatio...

Feng Yuan, Yi-Fan Gao, Hao-Yue Li et al. · 0 citations
Review Open access Sep 2026

CITAE: A Passage-Verifiable Retrieval-Augmented Generation Platform for Researchers and Thesis Writers

CITAE is an open-source web application that integrates the complete scientific-literature workflow—discovery, read- ing, verification, organisation and citation—into a single system, and makes AI grounding visible to the user rather than hidden inside the model.

Richar Andre Vilca-Solorzano, Dina Maribel Yana-Yucra, Fred Torres-Cruz et al. · 0 citations
#explainable ai Book Open access Oct 2026

ECHO: Explainable Co-editing with Human-in-the-loop Operations for Presentation Slide Refinement

Authoring and refining presentation slides is time-consuming in academic and professional settings. Although generative AI lowers the barrier to creating initial drafts, its black-box, one-way workflow often limits fine-grained control. A formative study with 10 frequent presentation authors identified trial-and-error...

Yu Fu, Yong-Qi Kang, Yu-Jia Zhou et al. · 0 citations
Preprint Sep 2026

Omni2Web: Benchmarking Audiovisual Website Development

Screen-recorded web editing requests contain weak deictic expressions such as ``this''and ``there,''whose referents depend on speech, cursor trajectories, page state, and edit history. Such requests require intent recovery beyond the explicit specifications assumed by many existing web-editing benchmarks. We introduce...

Ming-Hao Han, Zhenghao Xing, Xi-Ze Cheng et al. · 1 citation · ⚡1
Book Open access Aug 2026

“Let Me Take That Back”: Designing and Testing Deletion Interfaces for Human-AI Interaction

When interacting with large language models (LLMs), users have limited visibility and control over how their personal information is retained, particularly after incorporation into model training. Although some deployed LLMs provide options to opt-out of training, these mechanisms do not offer guarantees about the dele...

Justin Szczepaniak, Verena Zimmermann, Karola Marky · 0 citations

Related blog posts

MIT News · Artificial Intelligence Oct 7, 2026

Discovering the value of humanistic inquiry

Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

MIT News · Artificial Intelligence Sep 30, 2026

This game-playing AI is the new champ at Stratego

Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.