LLMs flop at 2% on spatial puzzles while this energy-based model solves 96% in milliseconds – proof autoregressive is broken for real reasoning.
LLMs like GPT-5.2 and Claude Opus 4.5 timeout on basic puzzles at ~2% success. Enter Kona: 96.2% solved in 313ms[1].
Kona is an energy-based model that reasons holistically over spatial tasks, efficiently revising partial solutions. Unlike autoregressive LLMs (which generate sequentially and fail hard on visuals/spatial logic), Kona scores globally and adapts on-the-fly[1]. There’s a live demo – go break it.
Developers building AR/VR agents, robotics, or vision-language tools: this exposes why chain-of-thought flops on grids/maps/puzzles. Kona’s approach could hybridize with LLMs for true multimodal reasoning, fixing the ‘vision blindspot’ in current models[1].
Vs. leading LLMs: GPT-5.2/Claude/Gemini 3 struggle due to sequential generation. Energy-based models like Kona think like humans – global optimization first. Emerging trend: diffusion/score-based hybrids might replace pure transformers for spatial tasks[1].
Action item: Play the demo[1], read the paper, experiment with energy-based fine-tunes on your vision datasets. Will this spark a post-autoregressive era, or stay niche? Prototype it.
Source: Into AI