Pheng-Ann Heng

Co-author of ATLAS paper, affiliated with CUHK.

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ATLAS: Agentic or Latent Visual Reasoning? One Word is Enough for Both
ATLAS resolves a core trade-off in visual reasoning by introducing functional tokens — single discrete 'words' that simultaneously serve as agentic operations and latent visual reasoning units, eliminating the need to choose between the two paradigms. Existing approaches split into two camps: agentic methods (code or tool calls) that suffer context-switching latency from external execution, and latent methods (learnable hidden embeddings) that lack cross-task generalization and resist autoregressive parallelization during training. ATLAS, proposed by Guo et al. (2026, arXiv:2605.15198), encodes each functional token with an internalized visual operation yet requires no visual supervision and remains a standard token within the tokenizer vocabulary, making it architecturally compatible with existing autoregressive language model pipelines. The framework couples the interpretability and controllability of agentic tool use with the speed and end-to-end trainability of latent reasoning, without requiring a separate image generation module or unified generative model. The paper argues this implies that discrete symbolic vocabulary tokens are a sufficient and computationally efficient substrate for visual reasoning, and that the agentic/latent dichotomy is a false choice that can be collapsed into a single token-level mechanism applicable across heterogeneous visual tasks.