3 papers
cs.CL2026
Co-LMLM: Continuous-Query Limited Memory Language Models
Yair Feldman, Linxi Zhao, Nathan Godey +5
Limited memory language models (LMLMs) externalize factual knowledge during pretraining to a knowledge base (KB), rather than memorizing it in their weights. During generation, the…
cs.CL2026
No Mean Feat: Simple, Strong Baselines for Context Compression
Yair Feldman, Yoav Artzi
Context compression reduces Transformer inference costs by replacing lengthy inputs with shorter pre-computed representations. It carries significant benefits for retrieval-augment…
cs.CL2025
Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons
Giovanni Monea, Yair Feldman, Shankar Padmanabhan +2
The scalability of large language models for long-context reasoning is severely constrained by the linear growth of their Transformer key-value cache, which incurs significant memo…