LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval (2026.findings-acl)
Copied to clipboard
| Challenge: | Document editing requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. |
| Approach: | a framework that constructs lightweight dependency graphs captures semantic relationships and structural hierarchies across document elements is proposed for agentic document editing . a scaLing agentic agentic framework is based on a dependency graph framework that captures dependencies and refactors function dependencies. |
| Outcome: | a new framework achieves 76 consistency versus 56 baseline while reducing token usage by 85 . the framework is based on a framework that captures semantic relationships and structural hierarchies across document elements . it can be used to improve document consistency, but it also reduces token costs and latency . |
Similar Papers
Discourse Graph Guided Document Translation with Large Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Recent agentic machine translation systems mitigate context window constraints but require substantial computational resources and are sensitive to memory retrieval strategies. |
| Approach: | They propose a framework that explicitly models inter-chunk relationships through structured discourse graphs and selectively conditions each translation segment on relevant graph neighbourhoods rather than sequential or exhaustive context. |
| Outcome: | The proposed framework surpasses strong baselines in translation quality and terminology consistency while incurring significantly lower token overhead. |
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Existing Document-level machine translation systems struggle to handle discourse-level phenomena such as pronoun resolution, lexical cohesion, and ellipsis. |
| Approach: | They propose a graph-based document-level machine translation framework that leverages Large Language Models to model translation flow and discourse structure. |
| Outcome: | The proposed framework outperforms commercial and closed systems in eight languages and six domains. |
A Neural Edge-Editing Approach for Document-Level Relation Graph Extraction (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to extract relation information from documents do not capture interactions between entities. |
| Approach: | They propose an iterative approach to extract relation information from a document . they initialize relation graphs using a pretrained transformer model and a graph convolutional neural network model . |
| Outcome: | The proposed method extracts relation information from a document using a rule-based system and empty graphs. |
Scaling Graph-Based Dependency Parsing with Arc Vectorization and Attention-Based Refinement (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing graph-based dependency parsers use a standard two-pipeline approach that only scores arcs and labels . |
| Approach: | They propose a graph-based dependency parsing architecture that explicitly constructs vectors from which both arcs and labels are scored. |
| Outcome: | The proposed model outperforms state-of-the-art models on PTB and UD in accuracy and efficiency. |
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)
Copied to clipboard
| Challenge: | Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence. |
| Approach: | They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance . |
| Outcome: | The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures. |
GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026.acl-long)
Copied to clipboard
Zhaofen Wu, Hanrong Zhang, Fulin Lin, Wujiang Xu, Xinran Xu, Yankai Chen, Henry Peng Zou, Shaowen Chen, Weizhi Zhang, Xue Liu, Philip S. Yu, Hongwei Wang
| Challenge: | Current unified stream-based memory systems facilitate context updates but remain vulnerable to interference from transient noise. |
| Approach: | They propose a hierarchical Graph-based Agentic Memory framework that explicitly decouples memory encoding from consolidation to resolve conflict between rapid context perception and stable knowledge retention. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on LoCoMo and LongDialQA. |
Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (2025.acl-long)
Copied to clipboard
| Challenge: | Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks. |
| Approach: | They introduce a framework that enhances large language model reasoning by integrating external tool-using agents. |
| Outcome: | The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research. |
See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have explored textual graph descriptions and visual modalities for VLMs to understand graphs. |
| Approach: | They propose a unified framework that enhances both scalability and modality coordination in graph understanding by integrating textual and visual modalities. |
| Outcome: | GraphVista scales to large graphs, 200 larger than those used in existing benchmarks, and consistently outperforms existing textual, visual, and fusion-based methods. |
From What to Why: Improving Relation Extraction with Rationale Graph (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing neural relation extraction models are limited by entity type and textual context. |
| Approach: | They propose a novel RAtionale Graph to organize co-occurrence constraints among entity types, triggers and relations in a holistic graph view. |
| Outcome: | The proposed method outperforms baselines significantly and achieves state-of-the-art performance on document-level and sentence-level RE benchmarks. |
MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches rely on semantic similarity over monolithic memory stores, entangling temporal, causal, and entity information. |
| Approach: | They propose a multi-graph agentic memory architecture that represents each memory item across orthogonal semantic, temporal, causal, and entity graphs. |
| Outcome: | Experiments on LoCoMo and LongMemEval show that MAGMA outperforms state-of-the-art models in long-horizon reasoning task. |