Benchmarking the Generality of Vision-Language-Action Models

• URL: http://arxiv.org/abs/2512.11315v1
• Date: Fri, 12 Dec 2025 06:31:52 GMT
• Title: Benchmarking the Generality of Vision-Language-Action Models
• Abstract summary: Generalist multimodal agents are expected to unify perception, language, and control.<n>We introduce MultiNet v1.0, a unified benchmark for measuring the cross domain generality of vision language models.
• Score: 0.9751538234290761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalist multimodal agents are expected to unify perception, language, and control - operating robustly across diverse real world domains. However, current evaluation practices remain fragmented across isolated benchmarks, making it difficult to assess whether today's foundation models truly generalize beyond their training distributions. We introduce MultiNet v1.0, a unified benchmark for measuring the cross domain generality of vision language models (VLMs) and vision language action models (VLAs) across six foundational capability regimes. Visual grounding, spatial reasoning, tool use, physical commonsense, multi agent coordination, and continuous robot control. Evaluating GPT 5, Pi0, and Magma, we find that no model demonstrates consistent generality. All exhibit substantial degradation on unseen domains, unfamiliar modalities, or cross domain task shifts despite strong performance within their training distributions.These failures manifest as modality misalignment, output format instability, and catastrophic knowledge degradation under domain transfer.Our findings reveal a persistent gap between the aspiration of generalist intelligence and the actual capabilities of current foundation models.MultiNet v1.0 provides a standardized evaluation substrate for diagnosing these gaps and guiding the development of future generalist agents.Code, data, and leaderboards are publicly available.

Related papers

• Towards Generalizable Visually Grounded Exploration of Household Devices [74.66117923766632]
  Key bottleneck of realizing general autonomous embodied agents lies in Generalizable Visually Grounded Exploration.<n>We introduce VGEBench, a benchmark designed to evaluate the generalizable visually grounded exploration capabilities of Vision-Language Models.<n>Unlike static datasets, we construct a Logic-Driven State Machine framework. This framework simulates multi-turn interaction loops, compelling agents to achieve goals by active visual perception and feedback-driven correction.
  arXiv  Detail & Related papers  (2026-09-01T07:42:45Z)
• AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models [15.610649996654876]
  We introduce AnyGroundBench, a benchmark designed to shift the STVG evaluation from static zero-shot testing to rigorous domain adaptation.<n>We target five specialized domains (animal, industry, sports, surgery, public security)<n>We extensively evaluate 15 state-of-the-art VLMs, assessing their zero-shot generalization and In-Context Learning (ICL) capabilities.
  arXiv  Detail & Related papers  (2026-07-02T14:52:45Z)
• SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks [73.92333717662558]
  SpatialWorld is a benchmark for evaluating the interactive spatial understanding of multimodal agents in complex real-world tasks.<n>It features 760 human-annotated tasks across diverse domains (e.g., household routines, travel, social collaboration)<n>For reliable evaluation, each task includes a human-validated initial state, a reference trajectory, and a terminal-state verifier.
  arXiv  Detail & Related papers  (2026-06-08T15:51:51Z)
• ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation [66.02142169323521]
  Vision-Language-Action models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence.<n>Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise.<n>We introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution.
  arXiv  Detail & Related papers  (2026-03-30T15:06:41Z)
• MA-VLCM: A Vision Language Critic Model for Value Estimation of Policies in Multi-Agent Team Settings [2.9017174137438815]
  Multi-agent reinforcement learning (MARL) commonly relies on a centralized critic to estimate the value function.<n>Large vision-language-action models (VLAs) trained on internet-scale data exhibit strong multimodal reasoning and zero-shot generalization capabilities.<n>We propose Multi-Agent Vision-Language-Critic Models (MA-VLCM), a framework that replaces the learned centralized critic in MARL with a pretrained vision-language model.
  arXiv  Detail & Related papers  (2026-03-16T15:29:41Z)
• On the Out-of-Distribution Generalization of Reasoning in Multimodal LLMs for Simple Visual Planning Tasks [56.98385132295952]
  We evaluate how well chain-of-thought approaches generalize on a simple planning task.<n>We find that reasoning traces which combine multiple text formats yield the best (and non-trivial) OOD generalization.<n> purely text-based models consistently outperform those utilizing image-based inputs.
  arXiv  Detail & Related papers  (2026-02-17T09:51:40Z)
• LIBERO-X: Robustness Litmus for Vision-Language-Action Models [32.29541801424534]
  This work systematically rethinks VLA benchmarking from both evaluation and data perspectives.<n>We introduce LIBERO-X, a benchmark featuring a hierarchical evaluation protocol with progressive difficulty levels targeting three core capabilities.<n> Experiments with representative VLA models reveal significant performance drops under cumulative perturbations.
  arXiv  Detail & Related papers  (2026-02-06T09:59:12Z)
• Understanding Degradation with Vision Language Model [56.09241449206817]
  Understanding visual degradations is a critical yet challenging problem in computer vision.<n>We introduce DU-VLM, a multimodal chain-of-thought model trained with supervised fine-tuning and reinforcement learning.<n>We also introduce textbfDU-110k, a large-scale dataset comprising 110,000 clean-degraded pairs with grounded physical annotations.
  arXiv  Detail & Related papers  (2026-02-04T13:51:15Z)
• VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents [130.70999337445468]
  Key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, is shift from textual states to complex visual observations.<n>We ask: Can VLM agents construct internal world models through explicit visual state reasoning?<n>We architecturally enforce and reward the agent's reasoning process via reinforcement learning (RL)<n>We find that the agent's reasoning into State Estimation and Transition Modeling is critical for success.
  arXiv  Detail & Related papers  (2025-10-19T16:05:07Z)
• From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models [5.660635614478238]
  Vision-Language-Action (VLA) models promise to produce versatile, "generalist" robot policies.<n>Traditional imitation learning benchmarks are unsuitable due to the lack of language instructions.<n>We introduce a unified suite of 50 simulation-based tasks across 10 subcategories spanning language instruction, vision, and objects.
  arXiv  Detail & Related papers  (2025-06-11T16:52:18Z)
• From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons [85.99268361356832]
  We introduce a process of adapting an MLLM to a Generalist Embodied Agent (GEA)<n>GEA is a single unified model capable of grounding itself across varied domains through a multi-embodiment action tokenizer.<n>Our findings reveal the importance of training with cross-domain data and online RL for building generalist agents.
  arXiv  Detail & Related papers  (2024-12-11T15:06:25Z)
• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs)<n>MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts.<n>It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• Unsupervised Model Diagnosis [49.36194740479798]
  This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance. Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
  arXiv  Detail & Related papers  (2024-10-08T17:59:03Z)
• GenRL: Multimodal-foundation world models for generalization in embodied agents [12.263162194821787]
  Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. Current foundation vision-language models (VLMs) require fine-tuning or other adaptations to be adopted in embodied contexts. Lack of multimodal data in such domains represents an obstacle to developing foundation models for embodied applications.
  arXiv  Detail & Related papers  (2024-06-26T03:41:48Z)
• Unveiling the Generalization Power of Fine-Tuned Large Language Models [81.70754292058258]
  We investigate whether fine-tuning affects the intrinsic generalization ability intrinsic to Large Language Models (LLMs) Our main findings reveal that models fine-tuned on generation and classification tasks exhibit dissimilar behaviors in generalizing to different domains and tasks. We observe that integrating the in-context learning strategy during fine-tuning on generation tasks can enhance the model's generalization ability.
  arXiv  Detail & Related papers  (2024-03-14T08:18:59Z)
• Deep Multimodal Fusion for Generalizable Person Re-identification [15.250738959921872]
  DMF is a Deep Multimodal Fusion network for the general scenarios on person re-identification task. Rich semantic knowledge is introduced to assist in feature representation learning during the pre-training stage. A realistic dataset is adopted to fine-tine the pre-trained model for distribution alignment with real-world.
  arXiv  Detail & Related papers  (2022-11-02T07:42:48Z)
• INDIGO: Intrinsic Multimodality for Domain Generalization [26.344372409315177]
  We study how multimodal information can be leveraged in an "intrinsic" way to make systems generalize under unseen domains. We propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO)
  arXiv  Detail & Related papers  (2022-06-13T05:41:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.