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Moondream 3.1 is a vision language model with mixture-of-experts architecture (9B total parameters, 2B active), delivering state-of-the-art visual reasoning, detection, pointing, and captioning, deployable locally via the Photon inference engine or through the Moondream Cloud API.
This paper shows that state-of-the-art vision-language models fail at length generalization in visual reasoning due to 'global shortcuts', and demonstrates that combining local foveated perception with recurrence enables robust out-of-distribution generalization.
MentalThink introduces a visual-symbolic reasoning paradigm for multimodal LLMs that uses SVG code as intermediate visual representations for multi-turn reasoning. The two-stage training with SFT and RL achieves strong performance on spatial reasoning benchmarks.
PixelEyes proposes a multi-turn visual reasoning agent that decouples perception and reasoning using mask-guided search and semantic-region breadth-first search, introducing a new benchmark (Pinpoint-Bench) and dataset (PixelEyes-6K) to improve localization in visual evidence seeking.
V-Zero is a novel label-free framework for fine-grained visual reasoning that uses contrastive evidence gating and on-policy distillation to improve performance without annotated answer labels, achieving faster training than traditional methods.
VeriEvol is a novel framework for scaling reinforcement learning in visual mathematical reasoning by ensuring reliable reward labels through a two-axis approach separating prompt difficulty from answer reliability, using evolutionary operators and hypothesis-testing verification. It achieves significant accuracy gains on a five-benchmark visual-math suite.
This paper introduces visually grounded thinking, a method for vision-language models to interleave natural-language reasoning with explicit visual evidence grounding using points or boxes. A scalable synthesis pipeline and grounding-aware reinforcement learning improve reasoning accuracy, enabling a 4B model to match or surpass a 27B model on spatial and counting benchmarks.
Visual Para-Thinker++ proposes a single-policy multi-agent framework for visual reasoning that uses role-conditioned agents (Main, Worker, Summary) and dedicated training methods to reduce hallucinations and improve efficiency, outperforming baselines on hallucination-sensitive benchmarks.
This paper identifies that failures in visual reasoning often stem from breakdowns in dynamic cross-modal coordination between visual and textual evidence during chain-of-thought generation. It introduces DyCo-RL, a reinforcement learning framework that rewards effective cross-modal coordination, leading to improved reasoning performance.
Introduces Efficient Operator Search (EOS), a unified differentiable framework that generalizes token reduction methods (pruning, merging, pooling, adaptive reweighting) into a shared operator space, automatically searching for optimal operator compositions under budget constraints. The method achieves competitive results across benchmarks and reveals consistent operator patterns.
VAMPS is a new benchmark of 1,168 multimodal bilingual math problems designed to evaluate whether LLMs can benefit from constructing and reasoning over graphs/visualizations. Key finding: direct analytical solving surprisingly outperforms tool-enabled visual solving even on problems where plotting is a natural strategy.
TRON introduces a scalable online environment for visual reasoning reinforcement learning that generates unlimited diverse training instances with verifiable answers, showing consistent performance improvements across multiple multimodal benchmarks.
VisualThink-VLA introduces a visual intermediate reasoning framework for vision-language-action policies that preserves spatial precision and dramatically reduces latency compared to text-based reasoning, achieving sub-second inference and state-of-the-art success rates on robot manipulation benchmarks.
This paper proposes MARS, a mono-anchored multi-source reasoning framework that uses dynamic anchors to quantify information gain and regulate modality interactions during reinforcement learning with verifiable rewards, achieving 3.2% and 4.9% performance gains on GRPO and DAPO across diverse datasets.
ETCHR is a novel image editing approach that decouples visual reasoning from image generation, using a two-stage training process (Reasoning Imitation and Reasoning Enhancement) to improve multimodal language model performance across five visual reasoning tasks. It achieves consistent gains of 4-5% Pass@1 on models like Qwen3-VL-8B, Gemini-3.1-Flash-Lite, and Kimi K2.5.
ATLAS presents a visual reasoning framework that combines agentic operations and latent representations using functional tokens, enabling efficient training via next-token prediction and reinforcement learning while avoiding intermediate image generation.
This paper introduces RIS, a framework for spatial-semantic grounded latent visual reasoning in Multimodal Large Language Models to overcome information bottlenecks. It proposes anchoring latent tokens to spatial and semantic evidence, showing improvements on benchmarks like V* and HRBench.
This paper introduces On-Policy Data Evolution (ODE) and a visual-native agent harness to improve multimodal deep search agents. By enabling reusable visual evidence and closed-loop data generation, ODE significantly boosts the performance of Qwen3-VL agents across multiple benchmarks, surpassing Gemini 2.5 Pro.
This paper identifies and formalizes 'recorruption' in multimodal RAG, where adding accurate context causes models to abandon correct predictions due to attentional collapse (visual blindness and positional bias). The authors propose BAIR, a parameter-free inference-time framework that restores visual saliency and penalizes textual distractors, improving reliability across medical, fairness, and geospatial benchmarks.
Introduces ToolsRL, a two-stage reinforcement learning framework that teaches multimodal LLMs to use simple visual tools for complex visual reasoning tasks.