Yi-Lun Zhao

This work studies the codification readiness of implementation-facing research-method specifications, defined by whether they provide sufficient methodological information for a competent implementer or coding agent to construct the intended method without unsupported assumptions.

This work introduces ActReview, a rebuttal-guided post-training framework that connects paper-specific diagnoses to concrete, grounded revision plans, and introduces ActReview-Bench, a human-curated benchmark of 1,000 instances for evaluating diagnostic quality and revision usefulness.

Cross-attentive MLLMs already solve the attribute-binding failures that cripple embedding models—and distilling their soft multi-level ranking distributions via Rank-KL unlocks that reasoning for bi-encoders without degrading standard recall.

Standard retriever evaluations hide critical weaknesses in agentic search systems, but a new benchmark and training method exposes and addresses these flaws.

LLMs are rapidly transforming peer review, but critical gaps remain in ensuring quality, fairness, and ethical considerations across the entire workflow.

Frontier models are wasted on routine GUI tasks: a step-level cascade that adaptively invokes stronger models only when lightweight monitors detect progress stalls or semantic drift slashes compute costs without sacrificing performance.

Stop generating superficial reviews: RbtAct leverages rebuttals to train LLMs to provide actionable feedback, leading to concrete revisions and improved author uptake.