Evidence and Position-Aware Chain-of-Agents for Long-Context Reasoning

Large Language Models frequently encounter challenges when processing long-context tasks, often neglecting significant information that may be dispersed throughout extensive inputs. Traditional multi-agent frameworks, such as Chain-of-Agents, attempt to address this issue by partitioning long inputs among multiple agents. However, these approaches fail to account for the relative importance and positional vulnerability of the evidence when delegating tasks to agents. To overcome these limitations, we propose a framework that strategically assigns long-context evidence to appropriate agents based on its significance, susceptibility to positional displacement, and agent capabilities. Furthermore, a worker summary index provides non-sequential access to earlier evidence, while a scoped, non-regressive escalation mechanism corrects intermediate errors without degrading prior results. The effectiveness of the framework is assessed through rigorous evaluations on long-context question-answering and summarization tasks, utilizing established benchmarks including Qasper, QuALITY, HotpotQA, NarrativeQA, and GovReport. Performance analysis compares the proposed framework with baseline methods, such as Vanilla, CoA, Tree-of-Agents, G-Designer, and AgentRouter. Additionally, ablation studies are conducted to assess the impact of evidence importance and positional awareness on overall performance. Our findings indicate that the proposed framework provides a cohesive solution for evidence processing in long-context reasoning, significantly enhancing the retention of critical information and fostering improved multi-agent reasoning capabilities.