aiqbench analysis
CRAFT introduces a novel framework for agentic spreadsheet manipulation that prioritizes structural integrity and template awareness over generic regeneration. By grounding error detection in specific regions and utilizing protected slots, the system significantly reduces misalignment issues common in current form-filling agents. This advancement represents a meaningful step forward in making AI agents more reliable for complex data entry tasks involving irregular layouts.
CRAFT: An Agentic Spreadsheet Form Filling System with Template Awareness
arXiv:2610.04437v1 Announce Type: new Abstract: Spreadsheet form filling requires agents to consolidate external evidence, ground values to precise cells, and preserve irregular template structure. Errors in early edits can overwrite labels or misalign fields, undermining later decisions. We propose CRAFT, a template-aware agent framework that connects reflective validation to constrained local repair. Instead of treating reflection as a free-form request to regenerate the workbook, CRAFT grounds detected errors to spreadsheet regions, restores corrupted template state when necessary, and re-grounds plausible writable slots before subsequent edits. A Rectangle-Aware Slot Grounder (RASG) proposes writable cells, while label-slot hints and protected regions constrain subsequent edits. We introduce FormFillBench, with 327 forms across Instruction-Only and Multi-File tracks. Compared with the strongest baselines, CRAFT improves pair accuracy by 8.51 and 23.38 percentage points on these tracks, respectively. Component-removal experiments support structural adjudication and slot re-grounding within the pipeline, and the framework retains its relative advantage among the methods evaluated with a second backbone. The code and benchmark FormFillBench are available at https://github.com/Glllllly/CRAFT.
Abstract:Spreadsheet form filling requires agents to consolidate external evidence, ground values to precise cells, and preserve irregular template structure. Errors in early edits can overwrite labels or misalign fields, undermining later decisions. We propose CRAFT, a template-aware agent framework that connects reflective validation to constrained local repair. Instead of treating reflection as a free-form request to regenerate the workbook, CRAFT grounds detected errors to spreadsheet regions, restores corrupted template state when necessary, and re-grounds plausible writable slots before subsequent edits. A Rectangle-Aware Slot Grounder (RASG) proposes writable cells, while label-slot hints and protected regions constrain subsequent edits. We introduce FormFillBench, with 327 forms across Instruction-Only and Multi-File tracks. Compared with the strongest baselines, CRAFT improves pair accuracy by 8.51 and 23.38 percentage points on these tracks, respectively. Component-removal experiments support structural adjudication and slot re-grounding within the pipeline, and the framework retains its relative advantage among the methods evaluated with a second backbone. The code and benchmark FormFillBench are available at this https URL.
Artificial Intelligence (cs.AI)
arXiv:2610.04437 [cs.AI]
(or arXiv:2610.04437v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.04437 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 258 words
