February 2026

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 […]

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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]

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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]

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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]

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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]

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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]

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Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary data. Online updates often induce catastrophic forgetting, while methods that improve stability frequently increase latency, memory footprint, or dense computation in ways that do not scale well to long contexts. We introduce TRC$^{2}$ (Thalamically Routed Cortical Columns), a decoder-only backbone that addresses continual learning at the architectural level. TRC$^{2}$ combines sparse thalamic routing over cortical columns with mechanisms […]

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Policy steering is an emerging way to adapt robot behaviors at deployment-time: a learned verifier analyzes low-level action samples proposed by a pre-trained policy (e.g., diffusion policy) and selects only those aligned with the task. While Vision-Language Models (VLMs) are promising general-purpose verifiers due to their reasoning capabilities, existing frameworks often assume these models are well-calibrated. In practice, the overconfident judgment from VLM can degrade the steering performance under both high-level semantic uncertainty in task specifications and low-level […]

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Executive Summary & Motivation Project Title: Hamilton-SMT: A Formalized Population-Based Training Framework for Verified Multi-Agent Evolution Category: Foundational ML & Algorithms / Computing Systems and Parallel AI Keywords: MARL, PBT, SMT-Solving, Lean 4, JAX, Formal Verification submitted by /u/Regular_Run3923 [link] [comments]

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