In classical AI, perception relies on learning state-based representations, while planning -- temporal reasoning over action sequences -- is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both perceptual and temporal structure. We show that standard temporal contrastive learning, despite its popularity, often fails to capture temporal structure due to its reliance on spurious features. To address this, we introduce Contrastive Representations for Temporal Reasoning (CRTR), a method that uses a negative sampling scheme to provably remove these spurious features and facilitate temporal reasoning. CRTR achieves strong results on domains with complex temporal structure, such as Sokoban and Rubik's Cube. In particular, for the Rubik's Cube, CRTR learns representations that generalize across all initial states and allow it to solve the puzzle using fewer search steps than BestFS -- though with longer solutions. To our knowledge, this is the first method that efficiently solves arbitrary Cube states using only learned representations, without relying on an external search algorithm.

In classical AI, perception relies on learning state-based representations, while planning --- temporal reasoning over action sequences --- is typically achieved through search. We study whether such reasoning can instead emerge from representations that capture both perceptual and temporal structure. We show that standard temporal contrastive learning, despite its popularity, often fails to capture temporal structure due to its reliance on spurious features. To address this, we introduce Contrastive Representations for Temporal Reasoning (CRTR), a method that uses a negative sampling scheme to provably remove these spurious features and facilitate temporal reasoning. CRTR achieves strong results on domains with complex temporal structure, such as Sokoban and Rubik's Cube. In particular, for the Rubik's Cube, CRTR learns representations that generalize across all initial states and allow it to solve the puzzle using fewer search steps than BestFS -- though with longer solutions. To our knowledge, this is the first method that efficiently solves arbitrary Cube states using only learned representations, without relying on an external search algorithm.

Jemele Hill says she feels'terribly sad' for Karmelo Anthony because his lawyer was white Five of the most unhinged fan theories that make'The Sopranos' a re-watchable masterpiece'Whalefall' trailer is here to add getting swallowed by a sperm whale while SCUBA diving to your list of fears Christopher Nolan's'The Odyssey' uncorks a Trojan Horse popcorn bucket that stores the goods in its crotch New trailer released for upcoming post-apocalyptic thriller'The Dog Stars' with Jacob Elordi'House of the Dragon' Season 3 premiere runtime and details revealed for hit HBO series You're not getting away with watering your grass with your'crank' out on Sheriff Grady Judd's watch Taylor Sheridan's hit CIA/military series'Lioness' gets official season release date on Paramount+ It wasn't on his shopping list, but a man managed to accidentally shoot himself in the groin at Walmart anyway Trump's Iran deal announcement sends markets skyrocketing, oil prices tumble Trump's Iran deal will not change regime's terror behavior, expert warns Paul Mauro: Crockett's weapon argument lacks'basic algebraic logic' Trump says Iran agreement documents are in'final shape,' signing soon Former Navy lieutenant commander says Iran doesn't'have a whole lot to work with' Massive national sporting events fuel market of'illicit trafficking,' says ex-DOJ prosector Doug Burgum praises Trump's leadership on rolling back regulations Iranian oil operations face'nuclear option' as US blockade traps ships Mike Pompeo: A piece of paper is'largely worthless' to the Iranian regime Trump says Iran will sign a deal'by this weekend' A solid WEEK after election night, progressive Nithya Raman has suddenly surged into the lead in LA--leaving voters completely flabbergasted. Few things amaze me like people who can solve a Rubik's Cube. Sure, lots of things amaze me more -- mountains, elaborate water features, how my dog sits on the couch and watches like he's super into it -- but it's a very specific kind of amazement that's like, Man, that's wild; I could never do that... nor do I really care to. But I like that other people are super into it to the point that there's now a Guinness World Record cottage industry of people solving them under different circumstances, and we've got a new top dog when it comes to solving a bunch of them while skydiving. A Rubik's Cube, the ultimate test of dexterity and spinning colored blocks.

A.1 MCTS-kSubS algorithm In Algorithm 4 we present a general MCTS solver based on AlphaZero. Solver repeatedly queries the planner for a list of actions and executes them one by one. Baseline planner returns only a single action at a time, whereas MCTS-kSubS gives around kactions - to reach the desired subgoal (number of actions depends on a subgoal distance, which not always equals k in practice). MCTS-kSubS operates on a high-level subgoal graph: nodes are subgoals proposed by the generator (see Algorithm 3) and edges - lists of actions informing how to move from one subgoal to another (computed by the low-level conditional policy in Algorithm 2). The graph structure is represented by treevariable. For every subgoal, it keeps up to C3 best nearby subgoals (according to generator scores) along with a mentioned list of actions and sum of rewards to obtain while moving from the parent to the child subgoal. Most of MCTS implementation is shared between MCTS-kSubS and AlphaZero baseline, as we can treat the behavioral-cloning policy as a subgoal generator with k = 1. MCTS-kSubS and the baseline are encapsulated in GEN_CHILDREN function (Algorithms 5 and 6).

Technology Robots Brothers build a robot to solve Rubik's cubes in record-setting time The robot completed the puzzle in just 45.3 seconds, breaking its own record of 55 seconds made just moments earlier. The Revenger set a world record. Breakthroughs, discoveries, and DIY tips sent six days a week. A pair of brothers in the U.K. have officially broken the Guinness World Record for the fastest time solving a four-by-four Rubik's Cube with a robot. Their DIY machine, which the brothers call The Revenger, completed the puzzle in only 45.3 seconds.

In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in non-acyclic environments in relation to shortest paths. We prove that, if the total flow is minimized, forward and backward policies traverse the environment graph exclusively along shortest paths between the initial and terminal states. Building on this result, we show that the pathfinding problem in an arbitrary graph can be solved by training a non-acyclic GFlowNet with flow regularization. We experimentally demonstrate the performance of our method in pathfinding in permutation environments and in solving Rubik's Cubes. For the latter problem, our approach shows competitive results with state-of-the-art machine learning approaches designed specifically for this task in terms of the solution length, while requiring smaller search budget at test-time.