Papers with schema generation

5 papers
Text2Mem: A Unified Memory Operation Language for Memory Operating System (2026.findings-acl)

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Challenge: Existing memory frameworks lack a formal, executable specification for memory control.
Approach: They propose a unified memory operation language that standardizes translation of natural-language instructions into reliable execution.
Outcome: The proposed language standardizes translation of natural-language instructions into reliable execution.
Reference-Free Schema Generation for Literature Review Tables via Multi-Faceted Rewards (2026.acl-srw)

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Challenge: Literature review systems generate literature review tables by inferring schemas and values from documents.
Approach: They propose to use schema generation as a reinforcement learning problem to determine which dimensions to compare a set of papers.
Outcome: The proposed model improves over the untuned model across intrinsic, reference-based, and LLM-judge metrics and remains competitive with supervised fine-tune models at 5 the parameter count on structural and diversity dimensions.
Position-aware Automatic Circuit Discovery (2025.acl-long)

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Challenge: Existing circuit analysis methods treat component positions as equally relevant across input positions, limiting their ability to capture cross-positional interactions or mechanisms that vary across positions.
Approach: They propose to extend edge attribution patching to differentiate between token positions and introduce a dataset schema to enable position-aware circuit discovery in datasets with variable length examples.
Outcome: The proposed approach enables position-aware circuit discovery even on tasks with variable length examples.
AttributeForge: An Agentic LLM Framework for Automated Product Schema Modeling (2025.emnlp-industry)

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Challenge: e-commerce platforms are producing only tens of attributes per month for schema modeling . authors present a framework to automate end-to-end product schema modeling using Large Language Models .
Approach: They introduce a framework to automate end-to-end product schema modeling using Large Language Models.
Outcome: The proposed framework achieves an 88 increase in modeling throughput while delivering superior quality.
Intent-aware Schema Generation and Refinement for Literature Review Tables (2025.findings-emnlp)

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Challenge: ambiguity in reference-based evaluations and lack of editing/refinement methods have slow progress on schema generation.
Approach: They propose a method for augmenting unannotated table corpora with synthesized intents . they propose prompted workflows and fine-tuned models to improve schema generation .
Outcome: The proposed approach significantly improves baseline performance in reconstructing reference schemas.

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