arXiv Computation and Language
Benchmarking Patent Drafting from Inventor-Style Disclosures
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Related stories
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.
arXiv AI
Sep 15
Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents
arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We...
arXiv AI
Jul 22
Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data
arXiv:2606. 16952v2 Announce Type: replace-cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.
arXiv Computation and Language
Oct 16
QuasiLeak-Bench: Evaluating LLM-Based Privacy Detectors on Policy-Conditioned Quasi-Evidence Group Recovery
QuasiLeak-Bench is a new benchmark that evaluates large language model (LLM) based privacy detectors on their ability to recover quasi‑evidence groups—sets of weak quasi‑identifiers that jointly reveal private attributes—while respecting sender‑specified disclosure policies. The benchmark uses a reverse‑construction pipeline to generate documents containing verified quasi‑evidence groups and applies automated quality control to produce reliable ground truth at scale. Experiments show that current privacy detectors perform poorly on this task, but a fine‑tuned 4B LLM can outperform the best prompted models, indicating the task is learnable.
arXiv AI
Jun 16
Phantoms and Disclosures: a Causal Framework for Auditing Synthetic Data
arXiv:2606. 16952v1 Announce Type: cross Abstract: The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alternative to sensitive real-world datasets.
arXiv AI
Aug 19
QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents
QuantumNovelty is an open‑source, skill‑orchestrating language agent that both creates quantum‑computing artifacts—such as papers, ansatz candidates, and patent drafts—and evaluates them through simulated referee and patent‑examiner panels. Its core innovation is an audit‑and‑falsify layer of deterministic gates (Pareto domination, numerical recomputation, Wilson intervals, and cross‑vendor consensus) that restricts claims to those that survive rigorous checks, with every model call logged for transparency. In initial tests on a planted adversarial corpus and a small real‑world deployment, the system successfully flagged all overclaims without false positives and produced panels that were more conservative than typical public acceptance rates.