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cs.CL2026
Sliding-window beats linear attention
Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron +1
Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy: every new token costs more than the previous one, and its keys and values…
cs.CL2025
AppSelectBench: Application-Level Tool Selection Benchmark
Tianyi Chen, Michael Solodko, Sen Wang +14
Computer Using Agents (CUAs) are increasingly equipped with external tools, enabling them to perform complex and realistic tasks. For CUAs to operate effectively, application selec…
cs.CL2025
Self-reflecting Large Language Models: A Hegelian Dialectical Approach
Sara Abdali, Michael Solodko, Can Goksen +4
In this paper, we introduce a self-reflection framework for Large Language Models (LLMs) grounded in the Hegelian Dialectic, a philosophical method in which an initial proposition…