跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+2新增论文
+2其中 AI 相关
+0已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-13 00:53 · 全站最近入库：2026-11-12 06:37各信源进度 →

统计口径与数据新鲜度

统计 2026-11-12 02:38 — 2026-11-13 02:38（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-13 00:53最近发现新增版本：2026-11-12 06:37库内论文最新更新时间：2026-11-11 08:00

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

21,675 已收录论文18,323 AI 相关6,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 3 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-13 00:50（北京时间）
新论文 · v1arXiv:2611.11886

Multi-Modal Agricultural Document Understanding at the DAGRI Task

Zhefan Wang, Da Li, Keping Bi, Nanqing Dong

原文摘要

Agricultural documents, such as prefectural technical reports and crop calendars, are critical knowledge resources. However, their complex table layouts, significant variations in document length, and dense multi-modal elements pose substantial challenges for automated understanding. To address these issues, the NTCIR-19 DAGRI (Data Analytics for aGRicultural Information) task introduced three subtasks focusing on different dimensions of document intelligence. In the Table Information Extraction (TableIE) task, we developed a multi-stage pipeline combining structure-preserving parsing via MinerU, region-adaptive segmentation for long documents, and a dual-modal (vision-text) extraction strategy for complex work schedules. This approach achieved first place with an exact match score of 0.6566. For Multi-Table Multi-Hop Question Answering (MTMH-QA) over interrelated JSON tables, we adopted an agentic search framework utilizing DCI-Agent to iteratively explore directories via bash commands, reinforced by a cross-model validation mechanism. This yielded a revised accuracy of 0.8444, initially ranked first. In the Figure and Table Question Answering (FiT-QA) task, which requires comprehending single large composite images, we implemented a dual-modal VLM pipeline enhanced by deterministic answer normalization, securing the highest BLEU score. Together, our system effectively addresses regional structural variations, dynamic cross-file evidence discovery, and multi-modal alignment, providing a robust framework for complex agricultural document understanding.

cs.IR

查看论文
arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.169ACL Anthology · ACL 2026

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding

Da Li, Yuxiao Luo, Keping Bi Chinese Academy of Sciences, Jiafeng Guo Institute of Computing Technolgy, Chinese Academy of Sciences, Wei Yuan Kuaishou- 快手科技, Biao Yang, Yan Wang, Fan Yang Kuaishou- 快手科技 等 10 位作者

原文摘要

Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enhancing performance across a range of vision-language tasks, including cross-modal retrieval, clustering, and classification. An effective embedding is expected to comprehensively preserve the semantic content of the input while simultaneously emphasizing features that are discriminative for downstream tasks. Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning, enabling the simultaneous optimization of two complementary objectives. We argue that the two aforementioned objectives can be decoupled: a comprehensive understanding of the input enables the embedding model to achieve superior performance on downstream tasks via contrastive learning. In this paper, we propose CoMa, a compressed pre-training phase, which serves as a warm-up stage for contrastive learning. Experiments demonstrate that with only a small amount of pre-training data, we can transform an MLLM into a competitive embedding model. CoMa achieves new state-of-the-art results among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness. Our project is available at https://github.com/Trustworthy-Information-Access/CoMa.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.2082ACL Anthology · ACL 2026

How Long Reasoning Chains Influence LLMs’ Judgment of Answer Factuality

Minzhu Tu, Shiyu Ni Institute of Computing Technology, Chinese Academy of Sciences, Keping Bi Chinese Academy of Sciences

原文摘要

Large language models (LLMs) are increasingly adopted as scalable judges for open-ended generation, yet how they form judgments remains insufficiently understood. Meanwhile, modern LLMs frequently produce answers accompanied by explicit reasoning, making reasoning chains a natural but understudied source of information for model-based evaluation. This work takes a first step toward understanding how exposing reasoning influences LLM-based judgment. Empirical results across factual question-answering (QA) and mathematical datasets show that the presence of reasoning substantially alters judgment behavior, with clear differences across judge capabilities. Weaker judges become more likely to accept incorrect answers when reasoning is present, suggesting over-reliance on persuasive explanations. In contrast, stronger judges exhibit more selective behavior and, in some cases, achieve higher judgment accuracy by leveraging reasoning content. Further analysis reveals that both reasoning fluency and factuality critically shape judgment outcomes. Together, these findings suggest that examining how models interpret reasoning is essential for understanding and improving LLM-based evaluation, with broader implications for the design of reliable automatic judges and evaluation protocols.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表