February 2026

The Formal Regulator Tier (SMT-Solving)

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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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Proposed Solution

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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

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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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Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns

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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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Meta AI Open Sources GCM for Better GPU Cluster Monitoring to Ensure High Performance AI Training and Hardware Reliability

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While the tech folks obsesses over the latest Llama checkpoints, a much grittier battle is being fought in the basements of data centers. As AI models scale to trillions of parameters, the clusters required to train them have become some of the most complex—and fragile—machines on the planet. Meta AI Research team just released GCM (GPU Cluster Monitoring), a specialized toolkit designed to solve the ‘silent killer’ of AI progress: hardware instability at scale. GCM is a blueprint […]

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Liquid AI’s New LFM2-24B-A2B Hybrid Architecture Blends Attention with Convolutions to Solve the Scaling Bottlenecks of Modern LLMs

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The generative AI race has long been a game of ‘bigger is better.’ But as the industry hits the limits of power consumption and memory bottlenecks, the conversation is shifting from raw parameter counts to architectural efficiency. Liquid AI team is leading this charge with the release of LFM2-24B-A2B, a 24-billion parameter model that redefines what we should expect from edge-capable AI. https://www.liquid.ai/blog/lfm2-24b-a2b The ‘A2B’ Architecture: A 1:3 Ratio for Efficiency The ‘A2B’ in the model’s name stands […]

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When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering

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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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