The paper audits frozen decoder‑only large language models (LLMs) on geometric reasoning tasks using parametric CAD constraints. It probes hidden states for linear decodability, forced‑choice generation, activation‑level influence, and behavioral steerability, finding that pretraining improves decoding of local geometric relations but not sketch‑level DOF status. The study shows that decodable information is not always actionable: generation often fails to express it, and steering interventions do not reliably control outputs, revealing divergences among decodability, generation, activation influence, and steerability.
arXiv:2608.30751v1 Announce Type: new Abstract: Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whethe...
arXiv:2605. 15454v2 Announce Type: replace-cross Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for more steps or following a different internal trajectory.
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recognizable images. However, it is unclear whether this reflects an internal representation of 2D...
The paper investigates how incorporating physical-state (geometric) inputs influences a 0.8B hybrid language model with 6.2M trainable parameters for manipulation tasks. Training with geometry-conditioned recurrent decay gates achieves a 28.9% success rate, slightly lower than the 36.7% success when geometry increments are shuffled, and higher than the 24.4% success without explicit geometry. Robustness tests show that a state-only relative-coordinate policy retains most success under frame relabeling, whereas visual policies degrade significantly after small object displacements, indicating no clear advantage from training-time geometric alignment under the tested conditions.
arXiv:2607. 12815v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces.
Chain-of-Thought (CoT) prompting is widely used as a test-time scaling strategy for Vision-Language Models (VLMs), but it remains unclear what is extended when VLMs generate longer reasoning traces. We ask whether CoT requires continued access to image tokens, or whether it mainly operates over visual information already made available earlier in the forward pass.
arXiv:2609.22688v1 Announce Type: new Abstract: Generating parametric CAD models requires accurate geometry and stable feature dependencies. Existing methods face challenges in selecting geometric re...
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.
arXiv:2609.08602v1 Announce Type: new Abstract: Embodied planning increasingly relies on vision-language models (VLMs) to translate instructions and visual observations into executable action sequenc...
arXiv:2607. 27670v2 Announce Type: replace-cross Abstract: Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that create ambiguous ground truth in texture-repeated regions.
arXiv:2609.12606v2 Announce Type: replace Abstract: While multimodal reasoning has advanced rapidly, solving complex geometry problems critically hinges on active visual assistance, such as construct...
arXiv:2607. 05790v1 Announce Type: new Abstract: Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily.