arXiv:2605.10073v3 Announce Type: replace Abstract: Pre-trained language models advance patent classification and retrieval by encoding claims as flat token sequences, but they overlook the dependenc...
arXiv:2511. 10657v2 Announce Type: replace-cross Abstract: We study self-supervised patent representation learning with contrastive objectives.
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.
arXiv:2607. 09094v1 Announce Type: cross Abstract: Legal precedent retrieval is a fundamental task in legal case preparation, planning, litigation strategy, and legal research.
arXiv:2606. 01632v1 Announce Type: cross Abstract: Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved problem in intellectual property economics.
ARGUS is a pipeline that builds document-level Event Knowledge Graphs (EKGs) from U.S. employment‑discrimination complaints. It uses a 5W1H-inspired schema, legal-domain models, and LLM‑based structured generation to extract fact‑bearing statements, create chunk‑level event graphs with participant, temporal, and causal structure, and merge them into comprehensive document representations. Evaluation shows that graph‑structured classifiers outperform raw and linearized baselines on claim classification, and EKG‑only retrieval improves document‑scoped QA, though overall open‑retrieval gains are limited by low first‑stage candidate recall.
Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations...
The paper introduces an expert‑annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations under I.R.C. §368, marking the first tree‑structured argument corpus in this domain. Each legal passage is labeled with one of five functional categories—Rule, Analysis, Conclusion, Background Facts, and Procedural History—and can be linked into directed support trees. Experiments demonstrate that functional labels are learnable and that supervised fine‑tuning improves within‑case retrieval, though cross‑case generalization remains weak.
arXiv:2509.00141v2 Announce Type: replace-cross Abstract: Statutory corpora and judicial decisions are growing faster than legal professionals can read them, while individual judgments often exceed t...
arXiv:2609.36550v1 Announce Type: new Abstract: Retrieval-augmented generation is widely used in professional writing, yet whether retrieval grounds revision or merely injects templates is rarely tes...
arXiv:2602. 19591v3 Announce Type: replace-cross Abstract: Small and Medium Enterprises (SMEs) constitute 99.
arXiv:2607. 19385v1 Announce Type: new Abstract: This paper tackles the problem of stock ranking and portfolio construction under realistic investment settings by jointly modeling temporal dynamics and cross-sectional dependencies.