Code is finally public. Some of you asked for it after my earlier posts. github.com/MarcHesse/mhflocke What it is: 4,650 Izhikevich spiking neurons with R-STDP (reward-modulated spike-timing-dependent plasticity) Central Pattern Generator for innate gait Cerebellar forward model (Marr-Albus-Ito) for balance correction Competence gate: CPG fades as the SNN proves it can walk Results (Unitree Go2, MuJoCo, 10 seeds, 50k steps): Full system: 45.15 ± 0.67m PPO baseline: 12.83 ± 7.78m Zero falls GitHub: github.com/MarcHesse/mhflocke Paper: doi.org/10.5281/zenodo.19336894 Paper: aixiv.science/abs/aixiv.260301.000002 Docs: […]

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arXiv:2602.04910v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely used for code generation. However, the correctness of code generated by LLMs remains a concern. A potential remedy to this concern is to have LLMs generate formal correctness proofs along with such code. However, compared with code generation, code-proof generation requires much higher reasoning capability and has much less existing data to learn from. In this paper, we present VeruSyn, a data synthesis pipeline for Verus, a […]

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arXiv:2604.14363v1 Announce Type: new Abstract: Multimodal language models systematically underperform on visual perception tasks, yet the structure underlying this failure remains poorly understood. We propose centroid replacement, collapsing each token to its nearest K-means centroid, as a controlled probe for modal dependence. Across seven models spanning three architecture families, erasing text centroid structure costs 4$times$ more accuracy than erasing visual centroid structure, exposing a universal imbalance where language representations overshadow vision even on tasks that demand visual reasoning. […]

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arXiv:2602.14862v1 Announce Type: new Abstract: Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: improving the calibration of classifiers and tuning the stochasticity of large language models (LLMs). In both cases, temperature scaling is the most popular method for the job. Despite its popularity, a rigorous theoretical analysis of the properties of temperature scaling has remained elusive. We investigate here some of these properties. For […]

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arXiv:2604.15351v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become the dominant parameter-efficient fine-tuning method for large language models, yet standard practice applies LoRA adapters uniformly to all transformer layers regardless of their relevance to the downstream task. We introduce Aletheia, a gradient-guided layer selection method that identifies the most task-relevant layers via a lightweight gradient probe and applies LoRA adapters only to those layers with asymmetric rank allocation. Across 81 experiment rows covering 14 successful models from […]

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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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Q-learning is one of the most fundamental algorithms in reinforcement learning. We analyze constant-stepsize Q-learning through a direct stochastic switching system representation. The key observation is that the Bellman maximization error can be represented exactly by a stochastic policy. Therefore, the Q-learning error admits a switched linear conditional-mean recursion with martingale-difference noise. The intrinsic drift rate is the joint spectral radius (JSR) of the direct switching family, which can be strictly smaller than the standard row-sum rate. Using […]

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arXiv:2604.14306v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated high proficiency on English-centric medical examinations, their performance often declines when faced with non-English languages and multimodal diagnostic tasks. This study protocol describes the development of EuropeMedQA, the first comprehensive, multilingual, and multimodal medical examination dataset sourced from official regulatory exams in Italy, France, Spain, and Portugal. Following FAIR data principles and SPIRIT-AI guidelines, we describe a rigorous curation process and an automated translation pipeline for […]

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We’ve come across announcement of multiple robot vacuums from the CES 2026 event, but there was one that immediately caught the attention of many. Here, I’m talking about the Roborock’s Saros Rover. It is a robot vacuum designed for multi-storey homes. Unlike regular robot vacuums that crawl across floors to get it cleaned, the Saros Rover uses a wheel-leg mechanism that enables it to climb stairs. Interestingly, there’s even more; it can also maintain stability on uneven surfaces […]

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Human interventions are a common source of data in autonomous systems during testing. These interventions provide an important signal about where the current policy needs improvement, but are often noisy and incomplete. We define Robust Intervention Learning (RIL) as the problem of learning from intervention data while remaining robust to the quality and informativeness of the intervention signal. In the best case, interventions are precise and avoiding them is sufficient to solve the task, but in many realistic […]

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