Update README

README.md

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 As current Multimodal Large Language Models (MLLMs) rapidly saturate canonical visual reasoning benchmarks, a key question emerges: do these strong scores genuinely reflect robust visual understanding? In our paper, [*The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space*](https://arxiv.org/abs/2605.09883), we identify a pervasive vulnerability: **the Cartesian Shortcut**.
 
-Visual reasoning benchmarks prevalently build on orthogonal grid-based layouts that can be readily discretized into explicit textual coordinates. Models systematically exploit this property, heavily leveraging text-based deductive reasoning to assist visual problem-solving. To systematically dismantle this shortcut, we introduce **Polaris-Bench**, which re-formulates 53 visual reasoning tasks in Polar coordinate space with paired Cartesian counterparts as reference, while preserving consistent logical constraints and task semantics. Comprehensive evaluation across 14 state-of-the-art MLLMs reveals that frontier models achieving 70-83% on Cartesian layouts collapse to 31-39% on Polar equivalents, with degradation persisting even under complete logical equivalence. These findings expose a critical deficiency in current MLLMs: the lack of topology-invariant visual reasoning.
+Models frequently discretize the orthogonal grid-based layouts prevalent in visual reasoning benchmarks into explicit textual coordinates, offloading reasoning from visual perception to text-based deduction. To re-evaluate visual reasoning when this shortcut is unavailable, we introduce **Polaris-Bench**, which re-formulates 53 visual reasoning tasks in Polar coordinate space with paired Cartesian counterparts that preserve task semantics, disrupting the orthogonal structure that models exploit. Comprehensive evaluation across 14 state-of-the-art MLLMs reveals that frontier models achieving 69–83% on Cartesian layouts collapse to 31–39% on Polar equivalents. Moreover, thinking gains largely vanish on Polar layouts, prompting interventions fail to close the gap, and comparable drops arise on other non-orthogonal layouts. These findings reveal that current MLLMs’ visual reasoning performance is strongly coupled to orthogonal grid structure, a fragility that is consistent across model families but far smaller in humans, who maintain 88.8% accuracy on Polar layouts.
 
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@@ -77,11 +77,11 @@ Polaris-Bench covers 53 tasks organized into 5 cognitive categories:
 
 | Category | Count | Tasks |
 | :--- | :---: | :--- |
-| **Visual Pattern Matching** | 11 | pattern_completion, pattern_prediction, layer_completion, shape_completion, shape_fitting, jigsaw_matching, odd_piece_out, fragment_matching, anomaly_detection, template_matching, impossible_shape |
-| **Spatial Transformation & Geometry** | 13 | grid_rotation, pivot_rotation, mirror_reflection, grid_folding, rotation_center, rotation_matching, area_balancing, minimum_flips, wall_follower, letter_collection, turn_counting, pipe_lengths, word_search |
-| **Navigation & Routing** | 14 | maze, shortest_path, bounded_path_finding, wrapping_path_finding, bounded_diagonal_paths, wrapping_diagonal_paths, bounded_knight_paths, knight_paths, checkpoint_paths, monotonic_path, rule_based_navigation, absolute_navigation, egocentric_navigation, wrapping_navigation |
-| **Combinatorics & Probability** | 9 | path_counting, lattice_paths, area_counting, edge_counting, uncut_cells, maximum_collection, longest_path, largest_number_path, curve_length |
-| **Algorithmic Logic & Simulation** | 6 | sudoku, four_color, n_queens, random_walk, collision_detection, bouncing_point |
+| **Visual Pattern Matching** | 11 | pattern_completion, shape_fitting, layer_completion, fragment_matching, template_matching, jigsaw_matching, shape_completion, odd_piece_out, anomaly_detection, letter_collection, pattern_prediction |
+| **Spatial Transformation & Geometry** | 13 | rotation_matching, mirror_reflection, grid_rotation, rotation_center, pivot_rotation, impossible_shape, grid_folding, four_color, area_counting, pipe_lengths, uncut_cells, area_balancing, curve_length |
+| **Navigation & Routing** | 14 | maze, shortest_path, longest_path, bounded_path_finding, wrapping_path_finding, wrapping_navigation, egocentric_navigation, absolute_navigation, wall_follower, rule_based_navigation, monotonic_path, turn_counting, word_search, largest_number_path |
+| **Combinatorics & Probability** | 9 | path_counting, bounded_diagonal_paths, bounded_knight_paths, checkpoint_paths, lattice_paths, wrapping_diagonal_paths, knight_paths, edge_counting, random_walk |
+| **Algorithmic Logic & Simulation** | 6 | n_queens, sudoku, minimum_flips, maximum_collection, collision_detection, bouncing_point |
 
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