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.
arXiv:2601. 02854v2 Announce Type: replace Abstract: As an agent-level reasoning and coordination paradigm, Multi-Agent Debate (MAD) orchestrates multiple agents through structured debate to improve answer quality and support complex reasoning.
arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.
arXiv:2609.22228v1 Announce Type: cross Abstract: Large language models increasingly rely on long chain-of-thought (CoT) trajectories for complex reasoning, but autoregressive generation brings subst...
arXiv:2604. 18584v2 Announce Type: replace-cross Abstract: Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity.
The paper introduces a new benchmark that evaluates large language models (LLMs) on their agentic mathematical reasoning rather than just final answers. It aligns problem‑solving behaviors with a taxonomy of reusable mathematical atomic capabilities and includes planning, action, and feedback tasks in both textual and multimodal settings. Experiments show that models with similar end‑to‑end accuracy can have very different agentic profiles, highlighting the importance of process‑level evaluation.
arXiv:2606. 05704v1 Announce Type: cross Abstract: Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems.
arXiv:2606. 15160v1 Announce Type: cross Abstract: Reasoning capabilities of multimodal large language models (MLLMs) have improved considerably in recent years.
Recent Large Language Models (LLMs) have shown impressive reasoning abilities; but they are still susceptible to hallucinations, intermediate reasoning mistakes, and unreliable reasoning results in complex mathematical reasoning problems. In this study, we introduce a critic-based heterogeneous multi-agent approach to improve the dependability of mathematical reasoning.
arXiv:2510.01719v5 Announce Type: replace Abstract: Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. W...
The paper introduces Latent Reasoning in Continuous space (LARC), a framework that replaces discrete token-based reasoning with reasoning over continuous hidden states to better align with image generation’s continuous nature. LARC is trained in two stages: curriculum supervised fine‑tuning that gradually shifts from token to latent reasoning, followed by an information‑gain reinforcement learning stage that refines latent reasoning without ground‑truth supervision. Experiments on multiple text‑to‑image benchmarks show that LARC improves generation quality, prompt adherence, and compositional fidelity, achieving state‑of‑the‑art results on GenEval and demonstrating robust test‑time scaling.
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:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.