The paper introduces MPRBench, a benchmark designed to evaluate multimodal personalized reasoning in multimodal large language models (MLLMs). It comprises 12 sub-tasks and allows for interpretable analysis of error cases. Experiments show that current MLLMs struggle with personalized reasoning due to inaccurate concept recognition and sensitivity to irrelevant personal information.
People often make decisions by holistically reasoning over heterogeneous forms of personal information. In this paper, we study the relatively underexplored capability of multimodal personalized reaso...
arXiv:2505.03654v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-spec...
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
arXiv:2610.27341v1 Announce Type: new Abstract: Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet they remain limited on tasks requiring fine-grained understanding of s...
arXiv:2608.29374v1 Announce Type: new Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...
The paper introduces LIFT, a lightweight vector‑intervention technique that transfers reasoning capability from a base large language model (LLM) to a vision‑language model (VLM) without retraining the VLM backbone. LIFT defines Reasoning Vectors as differences in hidden states between a reasoning path with an explicit trace and a solver path without it, and injects these vectors into the VLM’s language‑side activations. Experiments on two VLMs across six reasoning benchmarks show that vectors derived from the base LLM consistently outperform those derived from the aligned VLM, indicating that the base LLM is a more effective source for recovering degraded reasoning. "whyItMatters":"The study demonstrates that a simple, frozen‑backbone intervention can partially restore reasoning abilities in multimodal models, highlighting the value of leveraging the original language model’s reasoning power."
arXiv:2606. 28357v1 Announce Type: cross Abstract: Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.
arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.
The paper introduces CMPM, a Chinese Multi-Panel Meme benchmark comprising 1,214 annotated samples that capture five structural types, ordering dependencies, panel-order constraints, and optional comment context. It defines a two-layer evaluation: Task 1 tests structure typing and order-sensitive panel sequencing, while Task 2 assesses Chinese meme explanation generation using human ratings across visual, panel, humor, context, and faithfulness dimensions. Benchmarking five large vision‑language models shows that accuracy on canonical displays does not guarantee order understanding, as performance drops sharply under shuffled conditions, and that Gemini 3.1 Pro and GPT‑5.5 outperform open models in Task 2, with comment context providing only modest gains.
arXiv:2508.13680v5 Announce Type: replace-cross Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (V...
arXiv:2610.15810v1 Announce Type: cross Abstract: The introduction of long chain-of-thought (CoT) reasoning and external tools has substantially improved the performance of multimodal large language...
arXiv:2606. 15231v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios.