Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation confirms Moran's I and modularity terms are complementary; removing either degrades both spatial residual structure and explanation stability. The framework demonstrates how spatial similarity - extended beyond geometric proximity through GWR-derived local relationships - can be embedded in interpretable models, advancing trustworthy geospatial machine learning and offering a transferable template for domain-aware explainability.

At least three artificial intelligence researchers have resigned from Meta's new superintelligence lab, just two months after CEO Mark Zuckerberg first announced the initiative. Two of the staffers have returned to OpenAI, where they both previously worked, after less than one-month stints at Meta, WIRED has confirmed. Ethan Knight worked at the ChatGPT maker earlier in his career but joined Meta from Elon Musk's xAI. A third researcher, Rishabh Agarwal, announced publicly on Monday he was leaving Meta's lab as well. He joined the tech giant in April to work on generative AI projects before switching to a role at Meta Superintelligence Labs (MSL), according to his LinkedIn profile.

MSLs are showcased in long horizon optimal control of ODEs and PDEs and as latent models for sequence generation.

Mark Zuckerberg notified Meta staff today to introduce them to the new superintelligence team. The memo, which WIRED obtained, lists names and bios for the recently hired employees, many of whom came from rival AI firms like OpenAI, Anthropic, and Google. Over the past few months, Meta CEO Mark Zuckerberg has been on a recruiting frenzy to poach some of the most sought after talent in AI. The social media giant has invested 14.3 billion in Scale AI and hired Alexandr Wang, its CEO, to run Meta's Superintelligence Labs (MSL). News of the memo was first reported by Bloomberg.

Packing and shuffling tokens is a common practice in training auto-regressive language models (LMs) to prevent overfitting and improve efficiency. Typically documents are concatenated to chunks of maximum sequence length (MSL) and then shuffled. However setting the atom size, the length for each data chunk accompanied by random shuffling, to MSL may lead to contextual incoherence due to tokens from different documents being packed into the same chunk. An alternative approach is to utilize padding, another common data packing strategy, to avoid contextual incoherence by only including one document in each shuffled chunk. To optimize both packing strategies (concatenation vs padding), we investigated the optimal atom size for shuffling and compared their performance and efficiency. We found that matching atom size to MSL optimizes performance for both packing methods (concatenation and padding), and padding yields lower final perplexity (higher performance) than concatenation at the cost of more training steps and lower compute efficiency. This trade-off informs the choice of packing methods in training language models.

This is the second of our daily digests from RoboCup2024 in Eindhoven, The Netherlands. If you missed the first digest, which gives some background to RoboCup, you can find it here. Competitions continued across all the leagues today, with participants vying for a place in Sunday's finals. The RoboCup@Work league focusses on robots in work-related scenarios, utilizing ideas and concepts from other RoboCup competitions to tackle open research challenges in industrial and service robotics. I arrived at the arena in time to catch the advanced navigation test.