Scaling Reasoning in AI: Challenges of Long-Context Understanding in Emerging Models

Abstract

As artificial intelligence systems take on increasingly complex reasoning tasks, their capacity to process and understand extended input sequences has become a pivotal challenge in both research and applications. This study systematically evaluates the long-context reasoning capabilities of several open-source large language models, spanning state-space architecture such as Mamba and recurrent frameworks like RWKV, across varying input lengths from 250 up to 3000 tokens. Using the FLenQA benchmark dataset, the investigation analyzes how accuracy degrades with longer inputs and identifies frequent reasoning failures, including the limitations of structured prompting strategies such as Chain-of-Thought (CoT), which, contrary to initial expectations, do not reliably alleviate declines in performance when context becomes lengthy. The research encompasses three structured reasoning tasks—Monotonic Relations, People in Rooms, and Simplified Ruletaker scenarios—and uses a controlled evaluation to compare model behavior, revealing architecture-specific trends and failure modes as context grows. Key findings highlight the importance of information positioning within extended inputs; facts placed earlier in a sequence frequently yield better results, underscoring the models’ difficulty in retaining salient content deeply buried in long contexts. While models like Mamba and RWKV theoretically support infinite context lengths, practical tests reveal significant multi-hop reasoning limitations. Collectively, these results emphasize the ongoing need for improved model architectures, adaptive learning strategies, and more robust evaluation methodologies to address the challenges of long-context understanding in next-generation AI systems.