arXiv AI
MMEnterprise-Bench: Evaluating Enterprise Agents over Evolving Multimodal Records
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 30
Hybrid Retriever Evolution for Multimodal Document Reasoning Agents
arXiv:2606. 29648v1 Announce Type: cross Abstract: Different retrievers, including lexical, semantic, and multimodal approaches, provide highly complementary strengths for multimodal document understanding, yet most systems combine them through fixed pipelines that cannot adapt to the demands of individual reasoning steps.
arXiv AI
Oct 7
DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents
arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management...
arXiv AI
Sep 17
MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents
MIRAGE is a controlled study that examines how multimodal personal agents use historical evidence when conversation state changes. The study keeps evidence, questions, and scoring constant while varying only the conversation state, then checks if agents can determine answerability, recover the correct source, and answer from it. Results across seven multimodal backbones show distinct failure regimes before and after compaction, heavy reliance on context continuity by open-weight models, and mixed effects of retrieval pressure on source attribution.
arXiv Computation and Language
Sep 23
ICDAR2026 Competition on Multimodal Reasoning over Documents in Multiple Domains
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 AI
Jun 4
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
arXiv:2605. 29861v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into long-form reports.
arXiv Computation and Language
Oct 9
When History Helps and Hurts: Selective History Use across Multimodal Turns
The paper introduces ReTurn, a benchmark of 7,000 multimodal tasks designed to evaluate how models selectively use conversational history. ReTurn includes pairs that test task-carrying history (Reconfirm/Reground) and evidence-carrying history (Retrieve/Rebind), each preserving the same target question, media, and answer. Experiments on 13 vision‑language and audio‑language models show a drop in open‑ended accuracy from 93.7% to 72.3% when history is involved, revealing that high question recall does not guarantee effective task application and that competing media can misdirect responses.