Visual Grounding

Despite recent successes, test-time scaling -- i.e., dynamically expanding the token budget during inference as needed -- remains brittle for vision-language models (VLMs). Unstructured visual reasoning chains entangle perception and reasoning, leading to long, disorganized contexts where small perceptual mistakes may cascade into completely wrong answers. Reasoning also requires expensive reinforcement learning with hand-crafted rewards. Here, we introduce SPARC (Separating Perception And Reasoning Circuits), a modular framework that explicitly decouples visual perception from reasoning. Inspired by sequential sensory-to-cognitive processing in the brain, SPARC implements a two-stage pipeline where the model first performs explicit visual search to localize question-relevant regions, then conditions its reasoning on those regions to produce the final answer. This separation enables independent test-time scaling with asymmetric compute allocation (e.g., prioritizing perceptual processing under distribution shift), and supports selective optimization (e.g., improving the perceptual stage alone when it is the bottleneck for end-to-end performance). It also accommodates compressed contexts by running global search at lower image resolutions and allocating high-resolution processing only to selected regions, thereby reducing visual token count and compute. SPARC outperforms monolithic baselines and strong visual-grounding approaches across challenging visual reasoning tasks, such as improving Qwen3VL 4B on the $V^*$ VQA benchmark by 6.7 points and surpassing "thinking with images" by 4.6 points in an OOD setting with a $200\times$ lower token budget.

Visual grounding (VG) localizes target objects in an image from natural-language expressions. In real-world perception, RGB cues often degrade under low illumination and adverse weather, making visual grounding substantially more challenging. However, existing VG benchmarks are largely RGB-only and provide limited, structured coverage of such conditions, hindering systematic robustness evaluation and cross-spectral comparison. We present RGBT-GroundBench, the first large-scale benchmark for RGB-Thermal (TIR) visual grounding in complex environments. It contains over 40K images (21,535 RGB-TIR pairs) and 38,760 object instances with referring expressions, bounding boxes, and fine-grained annotations at three levels: scene types, environmental conditions (illumination and weather), and object properties (size and occlusion). As a benchmark suite, RGBT-GroundBench provides not only curated RGB-TIR grounding annotations but also a unified evaluation protocol supporting RGB-only, TIR-only, and RGB+TIR inputs. Under this protocol, we benchmark 11 representative VG models across diverse scenes and environmental conditions. Our results show that grounding accuracy is strongly correlated with scene complexity, LoRA-based models are more robust in complex scenes, and low-illumination conditions cause significant performance degradation that has been rarely explored. Guided by these observations, we introduce RGBT-VGNet, a simple and reproducible reference baseline under the unified protocol, featuring Asymmetric Modality Adaptation, Language-Aware Visual Synergy, and Tri-Prior Fusion for reliability-aware RGB-TIR integration. Resources, annotations, code, checkpoints, and evaluation scripts have been publicly released.

Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark of textbook-style geometry problems that exposes this assumption by operationally decomposing performance into perception, reasoning, and multimodal-specific components. Each problem is derived from a symbolic specification and accompanied by visual diagrams, text-only variants, multimodal questions, and targeted perceptual probes, enabling controlled measurement of each component. Using this decomposition, we show that common training strategies are associated with systematically different capability profiles that are invisible under aggregate accuracy. Across the models we study, reinforcement learning primarily improves perceptual grounding and robustness to diagram variation, while our analysis suggests that textual SFT yields gains through reflective reasoning. In contrast, as perception and reasoning improve, a growing fraction of remaining errors fall outside these components and are categorized as multimodal-specific. These results suggest that apparent progress in multimodal reasoning reflects shifting balances among subskills rather than uniform advancement, motivating evaluation beyond scalar accuracy.

Vision-language-action (VLA) models have shown promising progress in robotic manipulation. However, directly mapping visual observations and language instructions to low-level actions often results in limited interpretability and weak robustness in complex, long-horizon tasks. To address these challenges, we employ a modular manipulation framework that separates high-level planning from low-level control. At its core is Gondola, a grounded vision-language planning model that generates structured plans with explicit pixel-level object grounding before action execution. Given multi-view observations and planning history, Gondola predicts the next-step plan as interleaved textual instructions and multi-view segmentation masks corresponding to target objects and goal locations. To train Gondola, we construct synthetic datasets that provide explicit supervision for short-horizon grounded planning, multi-view referring expression, and long-horizon compositional reasoning. By coupling grounded plan generation with a 3D-based execution policy, our framework achieves state-of-the-art performance on the challenging GemBench benchmark. The system further demonstrates promising transfer to real robots. Ablation studies confirm that pixel-level grounding and the proposed planning-oriented supervision are critical for effective high-level reasoning. Project webpage: https://cshizhe.github.io/projects/robot_gondola.html

Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correlational signals without directly validating which image cues support prediction-relevant internal features. To this end, we introduce VisionLogic, a post-hoc framework that grounds these features in visual concepts through intervention-based validation. VisionLogic first identifies compact sets of features whose contributions reproduce the model's original prediction. It then represents their activation states as predicates using class-specific thresholds. An iterative refinement procedure grounds these predicates in visual regions through ablation tests. A region is accepted when its removal deactivates the corresponding predicate, linking the feature's numerical role to visual evidence in the input. The same predicates allow us to examine how features are activated, selected, and reused across images and classes. Across CNNs and vision transformers on ImageNet-1k, we find that only a few features are selected to explain each prediction, and frequently active features are not always selected. In a large-scale human evaluation with 465 participants, VisionLogic significantly improves participants' understanding of model behavior over established methods ACE and CRAFT. Code is available at https://github.com/allengeng123/VisionLogic.