{"id":"arxiv:2512.14693","title":"Universal Reasoning Model","date":"2025-12-16","section":"Reasoning and the \"physics\" of language models","quality":null,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/","source_url":"https://arxiv.org/abs/2512.14693","text_available":true,"authors":["Zitian Gao","Lynx Chen","Yihao Xiao","He Xing"],"outgoing":[{"source":"arxiv:2512.14693","target":"arxiv:1807.03819","evidence":"id","id":"arxiv:1807.03819","title":"Universal Transformers","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1d843516d980c3404cb622a2a490edb535a3afe27a85a980335a54b5fcd2220a/"},{"source":"arxiv:2512.14693","target":"arxiv:2002.05202","evidence":"id","id":"arxiv:2002.05202","title":"GLU Variants Improve Transformer","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/e052a2974593d29355ab45c301766ac6f5545ac1bc81d605e78d3adc71e9e2e4/"},{"source":"arxiv:2512.14693","target":"arxiv:2405.15071","evidence":"id","id":"arxiv:2405.15071","title":"Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/1cd7fa83eca0b480c3bf9e2490c043b70e937777252a40b196ae7801a0620141/"},{"source":"arxiv:2512.14693","target":"arxiv:2506.21734","evidence":"id","id":"arxiv:2506.21734","title":"Hierarchical Reasoning Model","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/3604312101ff09ab9903e7d6a32c7b2b8a9a7ece6a9d145d04ea01c9d13ffa3a/"},{"source":"arxiv:2512.14693","target":"arxiv:2510.04871","evidence":"id","id":"arxiv:2510.04871","title":"Less is More: Recursive Reasoning with Tiny Networks","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/4345eebe9dc7e67f82617896772f55f0970971516995c1688b6812f9ebf054f6/"}],"incoming":[{"source":"arxiv:2604.21999","target":"arxiv:2512.14693","evidence":"id","id":"arxiv:2604.21999","title":"Universal Transformers Need Memory: Depth-State Trade-offs in Adaptive Recursive Reasoning","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/963f6bb396c25bff462a25854c82e7d92b77803fa60c891fadd9963cc56c8fea/"},{"source":"arxiv:2605.21488","target":"arxiv:2512.14693","evidence":"title","id":"arxiv:2605.21488","title":"Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/48b3beca0f016a3683df4c6c71fc4be9f13a2c12c61d4ad17d1117c6139bb11e/"},{"source":"arxiv:2606.18206","target":"arxiv:2512.14693","evidence":"title","id":"arxiv:2606.18206","title":"Fixed-Point Reasoners: Stable and Adaptive Deep Looped Transformers","url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/37ea2d83a92ece4ab3ba4434af71f291d1fdc5040676f75110210f40aad3b81a/"}],"text_parts":[{"start":0,"end":15770,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html"},{"start":15770,"end":23912,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html"}],"contents":[{"title":"Universal Reasoning Model","offset":0,"level":1,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"Abstract","offset":29,"level":6,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"1 Introduction","offset":1054,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"2 Preliminaries","offset":3024,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"2.1 Standard Transformer","offset":3044,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"2.2 Universal Transformer","offset":4890,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"2.2.1 Parameter Sharing","offset":5946,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"2.2.2 Adaptive Computation Time (ACT)","offset":6487,"level":4,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"3 Universal Reasoning Model","offset":7119,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"3.1 ConvSwiGLU","offset":7558,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"3.2 Truncated Backpropagation Through Loops","offset":9087,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4 Experiment","offset":11034,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4.1 Experiment Settings","offset":11051,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4.2 Main Results","offset":12078,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4.3 Why Universal Transformer?","offset":12737,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4.4 Short Convolution","offset":14625,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-1.html#text"},{"title":"4.5 Truncated Backpropagation Through Loops","offset":16685,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"4.6 Nonlinearity of Transformers","offset":18270,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"4.7 Muon Optimizer","offset":19615,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"5 Related Work","offset":21076,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"5.1 ARC-AGI","offset":21095,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"5.2 Universal Transformers (Looped Transformers)","offset":22240,"level":3,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"6 Conclusion","offset":23077,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"},{"title":"7 Acknowledgement","offset":23722,"level":2,"url":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/part-2.html#text"}],"text_code_points":23912,"source_markdown":"https://vinnik-dmitry07.github.io/favorite-papers/agents/papers/fcb1a389a132d64ca93fac5551d8f8b653c5e093908cb515e2a2d901524941a7/source.md"}
