RecRM-Bench is a new large-scale benchmark dataset and framework for multi-dimensional reward modeling in agentic recommender systems, spanning instruction following, factual consistency, query-item relevance, and user behavior prediction.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 2representative citing papers
A new SLT framework uses latent thoughts as a middle reasoning layer and plan-then-ground decoding to improve coherence and faithfulness in gloss-free sign language translation.
citing papers explorer
-
RecRM-Bench: Benchmarking Multidimensional Reward Modeling for Agentic Recommender Systems
RecRM-Bench is a new large-scale benchmark dataset and framework for multi-dimensional reward modeling in agentic recommender systems, spanning instruction following, factual consistency, query-item relevance, and user behavior prediction.
-
Think in Latent Thoughts: A New Paradigm for Gloss-Free Sign Language Translation
A new SLT framework uses latent thoughts as a middle reasoning layer and plan-then-ground decoding to improve coherence and faithfulness in gloss-free sign language translation.