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LLM judge

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2

RESEARCH · 1 SOURCE · Apple Machine Learning Research

RISED: Rubric-guided data selection and self-distillation for multi-environment LLM agents

Authors introduce RISED, a method that uses a shared rubric vocabulary and an LLM judge to tag rollouts across diverse interactive environments; rubric profiles guide online data selection while positive rubrics provide privileged context for on-policy self-distillation and negative rubrics steer future rollout generation away from recurring failures. The paper reports that, across model backbones, RISED achieves the highest mean pass rate across environments and ranks first or second in each environment, and includes rubric-based analyses of behavioural change.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents

The paper introduces Vibe Patenting, an end-to-end patent-drafting testbed where a separately-invoked LLM judge provides structured feedback to iteratively revise AI-generated patent drafts. The authors report that judge-guided revision consistently improves judge-assessed quality (with unguided revision saturating), enables low-reasoning agents to approach higher-reasoning agents, that stronger models, more reasoning, and domain-specific agentic workflows yield further gains, and that validation against a professional patent attorney shows meaningful but metric-dependent agreement and systematic calibration differences.

7.0