arXiv Computation and Language

Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning

arXiv Computation and Language

Aug 25

Modeling Claim Dependency Structure for Patent Litigation Prediction with Graph Attention Networks

arXiv:2608.21924v1 Announce Type: new Abstract: Patent litigation imposes substantial costs on firms and distorts R&D incentives, making early risk identification a practically important task. While...

By Takao Arai, Hiroyasu Inoue

benchmarks

arXiv AI

Jun 26

Patent Representation Learning via Self-supervision

arXiv:2511. 10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives.

By You Zuo (ALMAnaCH), Kim Gerdes (LISN), Eric Villemonte de La Clergerie (ALMAnaCH), Beno\^it Sagot (ALMAnaCH)

rag benchmarks safety

arXiv Machine Learning

Aug 4

Agentic Graph Token Reasoning

arXiv:2608. 00542v1 Announce Type: new Abstract: Graphs model relational data throughout science and industry, from citation networks to product co-purchase graphs.

By Zhuoyi Peng, Yi Yang

llms agents nlp fine-tuning

arXiv AI

Jul 13

PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation

arXiv:2607. 09094v1 Announce Type: cross Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research.

By Devanshu Verma, Vasudha Bhatnagar, Vikas Kumar, Balaji Ganesan

rag computer-vision benchmarks

arXiv Machine Learning

Aug 18

Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation

arXiv:2608. 16269v1 Announce Type: cross Abstract: Recent advances in neural topic models with pre-trained language models (PLMs) have achieved strong performance by leveraging general-domain pre-training, yet their topic interpretability often degrades on specialized corpora.

By Seung-Won Seo, Won Ik Cho, Yongmin Yoo

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

arXiv AI

Sep 1

REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling

arXiv:2608.29899v1 Announce Type: cross Abstract: Dense retrieval over long documents is expensive. Token-level encoders scale quadratically in sequence length, and most long-context embedding models...

By Devrim \c{C}avu\c{s}o\u{g}lu, Emre Akba\c{s}

llms rag fine-tuning benchmarks

Hugging Face Trending Papers

Jul 11

When Reasoning Hurts Legal Drafting: The Verbalization Bottleneck in Patent Claim Generation

Patent claim drafting is a challenging legal drafting task that requires technical expertise, precise linguistic control, strict adherence to formal conventions, and the preservation of complex logical relationships among claim elements. While Chain-of-Thought (CoT) prompting has been widely used to improve the reasoning capabilities of large language models (LLMs), recent evidence suggests that its benefits may be limited, or even negative, in highly structured or pattern-sensitive tasks.

llms

arXiv AI

Jun 30

PromptGNN-sim: Deep Fusion and Alignment of GNN and LLMs for Text-Attributed Graph Learning

arXiv:2606. 30291v1 Announce Type: new Abstract: Text-Attributed Graphs (TAGs) combine textual semantics with graph structure and are central to many graph learning tasks.

By Zhifei Hu, Alexandra I. Cristea

llms multimodal safety

arXiv Computation and Language

Sep 22

ChemCLIR-Bench: Benchmarking Cross-Lingual Information Retrieval in Multilingual Chemical Patents

arXiv:2609.23231v1 Announce Type: new Abstract: Cross-lingual information retrieval (CLIR) is increasingly important in multi-national industries, where critical technical evidence may exist in a dif...

By Mahdi Astaraki, Mohammad Khodadad, Reza Namazi, Mohammad Arshi Saloot, Amir Reza Behzad Moghadam, Hamidreza Mahyar, Soheila Samiee

rag benchmarks

arXiv AI

Jul 10

MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs

arXiv:2512. 12477v2 Announce Type: replace Abstract: Estimating node importance in heterogeneous knowledge graphs is a fundamental problem underlying recommendation, search, and knowledge decision systems.

By Jiawen Chen, Yanyan He, Qi Shao, Mengli Wei, Duxin Chen, Wenwu Yu, Yanlong Zhao

llms rag benchmarks safety

arXiv Computation and Language

Sep 3

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.

By Yihang Sun, Zhihan Zhu, Zhiyuan Jiang, Jingyi Ge, Zixuan Li, Jiaxuan You

llms agents benchmarks

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