NeurIPS 2026 Papers

Visual reasoning, often interleaved with intermediate visual states, has emerged as a promising direction in the field. A straightforward approach is to directly generate images via unified models during reasoning, but this is computationally expensive and architecturally non-trivial. Recent alternatives include agentic reasoning through code or tool calls, and latent reasoning with learnable hidden embeddings. However, agentic methods incur context-switching latency from external execution, while latent methods lack task generalization and are difficult to train with autoregressive parallelization. To combine their strengths while mitigating their limitations, we propose ATLAS, a framework in which a single discrete "word", termed as a functional token, serves both as an agentic operation and a latent visual reasoning unit. Each functional token is associated with an internalized visual operation, yet remains a standard token in the tokenizer vocabulary and can be generated via next-token prediction. This design avoids verbose intermediate visual content generation, while preserving compatibility with standard SFT and RL training, without architectural or methodological modifications. To further address the sparsity of functional tokens during RL, we introduce Latent-Anchored GRPO (LA-GRPO), which stabilizes the training by anchoring functional tokens with a statically weighted auxiliary objective, providing stronger gradient updates. Extensive experiments and analyses demonstrate that ATLAS achieves superior performance on challenging benchmarks while maintaining clear interpretability. We hope ATLAS offers a new paradigm inspiring future visual reasoning research.

Multimodal large language models (MLLMs) achieve impressive performance on audio-visual question answering, but correct answers do not necessarily imply perceptually grounded reasoning. Models may exploit linguistic priors or dataset shortcuts without attending to the visual regions and audio events that actually justify their predictions. Existing benchmarks predominantly evaluate final answer accuracy, providing limited insight into whether models truly “see” or “hear” the evidence required for reasoning. We introduce ANCHOR, a benchmark and evaluation framework for auditing perceptual grounding in multimodal reasoning. ANCHOR requires models to produce grounded chain-of-thought explanations that explicitly reference audio-visual evidence, including representative frames, spatial bounding boxes, and salient audio cues. To build a reliable gold standard, we combine automated annotation generation with a human-in-the-loop refinement pipeline, producing 224 human-verified videos and 975 question–answer pairs with aligned reasoning and localization annotations from 2.3K videos and 8.2K QA pairs. We further propose a metric and evaluation protocol that jointly measure answer correctness and reasoning-grounding alignment, complemented by multi-judge scoring of hallucination, logical divergence, final answer, and a token-based conciseness measurement. Experiments on nine state-of-the-art MLLMs reveal a substantial faithfulness gap: models with strong answer performance often produce explanations that are only weakly grounded in the underlying perceptual evidence. Ablation results confirm the importance of explicit grounding cues: removing bounding boxes reduces accuracy by 7.7 points, and performance drops from 57.7% with full cues to 16.1% in the audio-only setting. These findings show that answer-only evaluation is insufficient for multimodal reasoning and position ANCHOR as a new testbed for developing systems that are accurate, interpretable, and trustworthy.

Quantitatively comparing writing systems at a worldwide scale is challenging because visual similarity must be inferred without relying on predefined linguistic or genealogical assumptions, and only limited ground-truth taxonomy is available for validation. We introduce a fully data-driven framework for comparing scripts based on learned glyph representations. Using a teacher--student model, we obtain deformation-invariant glyph embeddings and aggregate them into script-level distances. To enable principled model selection in the absence of labels, we construct synthetic script datasets that simulate key aspects of script evolution, including partial inheritance, visual distortion, and inventory variation. These benchmarks are used to independently select both the script-level distance metric and the clustering method, showing that nearest-neighbour aggregation and Ward hierarchical clustering best recover controlled relatedness structure. Applying the validated pipeline to 125 historically attested writing systems yields a global visual taxonomy that recovers coherent typological families while also revealing visually driven associations beyond strict genealogy. The resulting structure is stable under controlled perturbations and shows a moderate correlation with independent geographic and temporal metadata, indicating that learned representations capture non-trivial traces of historical transmission. Beyond large-scale comparison, this approach provides a quantitative tool for exploring relationships between writing systems and has potential applications in historical analysis and the decipherment of ancient scripts by identifying visually related alphabets.