Summary

This paper investigates how adjusting the performance of specific LLM capabilities influences cross-capability performance. The authors propose a novel prompting method to modulate these distinct strengths, aiming to create an optimal balance rather than forcing all capabilities to compete equally. By analyzing the underlying logic of how specific capabilities interact, the research demonstrates that fine-tuning individual tasks can directly improve the overall system. The findings indicate that careful attention to individual capability alignment is critical for enhancing the reliability of multimodal reasoning systems. The study suggests that when specific capabilities are optimized, the resulting cross-capability performance improves significantly because it leverages the strengths of each domain effectively. Ultimately, the research highlights that this specific modulating approach represents a pivotal advancement in understanding the interaction between disparate capabilities within LLM architectures.

Title

Cross Capabilities of LLMs

Description

Cross Capabilities of LLMs

Keywords

capabilities, capability, cross, individual, llms, performance, link, tasks, model, reference, benchmark, responses, models, examples, reasoning, prompting, evaluation

Categories

Technology

NS Lookup

A 185.199.109.153, A 185.199.108.153, A 185.199.111.153, A 185.199.110.153

Dates

Created 2026-02-14

Updated 2026-02-14

Summarized 2026-03-23