DeALOG is a decentralized multi‑agent framework designed for multimodal question answering. It employs specialized agents—Table, Context, Visual, Summarizing, and Verification—that communicate via a shared natural‑language log, serving as persistent memory. This log‑based coordination enables collaborative error detection and verification without central control, leading to competitive performance on several benchmark datasets.
Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering. However, most existing systems maintain a single growing context that mixes retrieval traces, observations, and intermediate reasoning.
The ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains introduced a new Visual Question Answering benchmark that tests reasoning over documents from eight distinct domains such as business reports, scientific papers, and engineering drawings. Twenty valid submissions from eight teams were evaluated, featuring approaches ranging from zero‑shot vision‑language models to multi‑agent ensembles and fine‑tuned multimodal systems. Results indicate that the most effective systems employ structured evidence extraction, retrieval, verification, and orchestration across multiple components rather than single‑pass prompting.
arXiv:2606. 05749v1 Announce Type: cross Abstract: Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering.
arXiv:2609.08025v1 Announce Type: new Abstract: Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms su...
MAMath-Bench is a new benchmark that evaluates multimodal mathematical reasoning in multi‑agent systems (MASs). It contains 1,003 problems across 11 categories, multiple difficulty levels, and modalities such as text, images, and audio, with fine‑grained milestone annotations for correctness, reasoning‑process reliability, and robustness to adversarial perturbations. The study finds that while collaboration can improve intermediate reasoning coverage, it does not consistently boost final‑answer accuracy and can increase redundancy, and current systems remain vulnerable to subtle adversarial errors.
DocPilot is an agentic framework for long visual document understanding that orchestrates evidence seeking through page-level navigation and fine-grained localization, integrating visual inspection, code-based numerical reasoning, and cross-modal reasoning. It employs curriculum-based agentic training to enable small language models to learn complex document solving, progressing from single-ability learning to adaptive orchestration. Experiments on benchmarks such as MMLongBench-Doc, DocBench, FinRAGBench-V, and MADQA show that DocPilot achieves strong gains with frontier VLM backbones, and its 4B variant competes with much larger models, demonstrating effectiveness and generalization across diverse long-document reasoning tasks.
The paper introduces SMART‑Med, a local multi‑agent system for medical reasoning that learns when to collaborate instead of always engaging all agents. By using an uncertainty‑driven routing mechanism, it selectively recruits expert agents only for uncertain queries, thereby converting dense collaboration into query‑adaptive coordination. The authors also present a new multimodal dataset, MedVR‑3K, and demonstrate that SMART‑Med matches or surpasses existing multi‑agent approaches while cutting inference time by over 3.8× and token usage by 72%, with a 6.7% average accuracy gain across four VQA benchmarks.
arXiv:2508.13186v2 Announce Type: replace-cross Abstract: AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However...
DeAR (Decentralized Agentic Reasoning) is a new framework that replaces centralized protocols with autonomous peer‑to‑peer collaboration for agentic reasoning. It introduces three key mechanisms: decentralized capability grounding for agent specialization, thought map navigation for targeted peer interactions, and topology updates for adaptive error correction. Across nine multimodal reasoning and text‑based QA benchmarks, DeAR consistently outperforms recent baseline methods, demonstrating that decentralized and adaptive collaboration improves accuracy in knowledge‑intensive reasoning tasks.
arXiv:2505. 23399v2 Announce Type: replace Abstract: We propose GAM-Agent, a game-theoretic multi-agent framework for enhancing vision-language reasoning.
arXiv:2607. 09623v1 Announce Type: cross Abstract: We present our submission to the QANTA 2026 shared challenge at the ICML 2026 Workshop on Efficient Multimodal Question Answering (EMM-QA).
arXiv:2606. 12106v1 Announce Type: cross Abstract: This paper presents our solution to the 2026 SoccerNet VQA Challenge.