用图像压缩辩论文本，大幅减少计算量且保留关键信息。
Cross-Modal Memory Compression for Efficient Multi-Agent Debate
- 将长文本辩论记录转为紧凑图像，通过视觉编码器续接推理。
- 压缩后输入令牌数减少92%以上，推理速度显著提升。
- 多智能体视角融合可恢复丢失信息，适合复杂推理任务。
多智能体辩论能提升推理质量并减少幻觉，但随着辩论轮次和智能体数量增加，上下文迅速膨胀。保留完整文本历史会导致令牌用量超出上下文限制，常需重复摘要，增加开销并累积信息损失。我们提出DebateOCR，一种跨模态压缩框架，用紧凑图像表示替代长文本辩论轨迹，并通过专用视觉编码器在后续轮次中使用。该设计将通常达数万至数十万令牌的对话历史压缩，输入令牌减少超92%，显著降低计算成本并加快推理，覆盖多个基准测试。我们进一步从理论角度表明，智能体间的多样性有助于恢复被省略的信息：尽管单个压缩历史会丢失细节，但聚合多个智能体的压缩视图可使集体表示以指数级高概率逼近信息瓶颈。
原文摘要 · Abstract (English)
Multi-agent debate can improve reasoning quality and reduce hallucinations, but it incurs rapidly growing context as debate rounds and agent count increase. Retaining full textual histories leads to token usage that can exceed context limits and often requires repeated summarization, adding overhead and compounding information loss. We introduce DebateOCR, a cross-modal compression framework that replaces long textual debate traces with compact image representations, which are then consumed through a dedicated vision encoder to condition subsequent rounds. This design compresses histories that commonly span tens to hundreds of thousands of tokens, cutting input tokens by more than 92% and yielding substantially lower compute cost and faster inference across multiple benchmarks. We further provide a theoretical perspective showing that diversity across agents supports recovery of omitted information: although any single compressed history may discard details, aggregating multiple agents' compressed views allows the collective representation to approach the information bottleneck with exponentially high probability.
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