一种新设计的长文本智能体模型 PARSER

在 896K 令牌的多跳问答任务上，4B 版本的 PARSER 相比最强基线提升了 5.7 个百分点。

9B 版本在性能上超过了 DeepSeek-V4-Pro。该模型通过控制实验验证了其在证据位置、顺序和距离变化下的鲁棒性，并显著降低了推理延迟。

This is a brilliant paper. It's of the cleanest long-context agent designs I have seen in the past couple of months. Sequential memory agents read chunks one after another while maintaining a compact memory state. This behavior ties reasoning depth to document traversal and makes accuracy sensitive to where the evidence sits. It also makes latency grow linearly with document length. PARSER decouples the two. A bank of lightweight subagents, each bound to a single chunk, reads the whole document in parallel. A lead agent reasons through iterative scatter-gather rounds, broadcasting a query to all subagents, aggregating the returned evidence, and forming a deeper follow-up query conditioned on what it has found. All the learnable behavior is build into the lead agent, which is trained with RL. The subagents stay frozen off-the-shelf models. On multi-hop QA from 7K to 896K tokens, a 4B PARSER beats the strongest sequential memory baseline by 5.7 points on average and 12.0 points at 896K. At 9B it passes DeepSeek-V4-Pro by 6.3 points. Controlled experiments show it holds up under perturbations to evidence position, order and distance, which cause large accuracy swings in sequential methods, while cutting inference latency by up to 11x. Paper: academy.dair.ai/papers/parser-…📊 33 ⚡

本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：PARSER、长文本、多跳问答、推理模型，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

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