If people are only using this a couple of times a week at most, and can’t think of anything to do with it on the average day, it hasn’t changed their life. OpenAI itself admits the problem, talking about a ‘capability gap’ between what the models can do and what people do with them, which seems to me like a way to avoid saying that you don’t have clear product-market fit. Hence, OpenAI’s ad project is partly just […]
Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. Relational Deep Learning (RDL) methods automate feature engineering via message passing, while classical approaches like Deep Feature Synthesis (DFS) rely on predefined non-parametric aggregators. Despite performance gains, the comparative advantages of RDL over DFS and the design principles for selecting effective architectures remain poorly understood. We present a comprehensive study that unifies RDL and DFS in a […]
The rapid growth of scientific machine learning (SciML) has accelerated discovery across diverse domains, yet designing effective SciML models remains a challenging task. In practice, building such models often requires substantial prior knowledge and manual expertise, particularly in determining which input features to use and how large the model should be. We introduce LUMOS, an end-to-end framework based on L0-regularized learning that unifies feature selection and model pruning to democratize SciML model design. By employing semi-stochastic gating and […]
Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal dynamics, assuming stationary or partially stationary causality. However, these assumptions often conflict with the complex, time-varying causal relationships observed in real-world systems. This motivates the need for methods that address fully dynamic causality, where both instantaneous and lagged dependencies evolve over time. Such a setting poses significant challenges for the efficiency and stability […]
Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning chances while trying to be unpredictable. On the other hand, returners try to predict serve directions to make good returns. The mind game between servers and returners is an important part of decision-making in professional tennis matches. To help understand the players’ serve decisions, we have developed a machine learning method for predicting professional tennis players’ first serve […]
Impact & Metrics Differentiated Contribution While AlphaProof applies formal reasoning to mathematics, Hamiltonian-SMT applies formal reasoning to Dynamic Agent Behavior. It moves MARL from a “black-box” trial-and-error craft to a rigorous, Verified-by-Design engineering discipline. Key Performance Indicators (KPIs) Adversarial Resilience: 0% contagion leakage under “Jitter-Trojan” stress tests. Convergence Rate: 3x reduction in training iterations to reach stable Nash Equilibria. Scalability: Linear scaling to 1,000+ agents via Apalache-verified distributed consensus. submitted by /u/Regular_Run3923 [link] [comments]
Automated Speciation (Bifurcation) When the Regulator returns UNSAT (identifying that performance and diversity constraints are mutually exclusive), the system triggers a Bifurcation Event. This partitions the population into specialized sub-cradles, proved by Lean 4 to be Pareto-optimal transitions. JAX-Native Parallelism Implementation utilizes JAX collective operations for O(1) scaling across multi-GPU/TPU nodes. The Symbolic Tier (Z3/Lean) runs asynchronously on CPU nodes, maintaining high-throughput JaxMARL environment rollouts. submitted by /u/Regular_Run3923 [link] [comments]
The Formal Regulator Tier (SMT-Solving) At each evolutionary step, the Z3 SMT solver acts as a “Symbolic Gateway.” Instead of standard weight copying, the Regulator solves for the Safe Impulse Vector: ∆W = argmin||Wtarget + ∆W-Wsource||2 Subject to: Lipschitz Bound: ||∆W||∞≤ L (Verified by Lean 4 to block high-jitter noise). Energy Invariant: E(Wtarget + ∆W) ≥ E(Wtarget) (Verified by TLA+ to prevent dissipative decay). submitted by /u/Regular_Run3923 [link] [comments]
We propose Hamiltonian-SMT, the first MARL framework to replace “guess-and-check” evolution with verified Policy Impulses. By modeling the population as a discrete Hamiltonian system, we enforce physical and logical conservation laws: System Energy (E): Formally represents Social Welfare (Global Reward). Momentum (P): Formally represents Behavioral Diversity. Impulse (∆W): A weight update verified by Lean 4 to be Lipschitz-continuous and energy-preserving. submitted by /u/Regular_Run3923 [link] [comments]
PROBLEM STATEMENT Large-scale Multi-Agent Reinforcement Learning (MARL) remains bottlenecked by two critical failure modes: 1) Instability & Nash Stagnation: Current Population-Based Training (PBT) relies on stochastic mutations, often leading to greedy collapse or “Heat Death” where policy diversity vanishes. 2) Adversarial Fragility: Multi-Agent populations are vulnerable to “High-Jitter” weight contagion, where a single corrugated agent can propogate destabilizing updates across league training infrastructure. submitted by /u/Regular_Run3923 [link] [comments]