technocracy

arXiv:2601.04281v1 Announce Type: new Abstract: The disclosure of the Log4Shell vulnerability in December 2021 led to an unprecedented wave of global scanning and exploitation activity. A recent study provided important initial insights, but was largely limited in duration and geography, focusing primarily on European and U.S. network telescope deployments and covering the immediate aftermath of disclosure. As a result, the longer-term evolution of exploitation behavior and its regional characteristics has remained insufficiently understood. In this paper, we present […]

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arXiv:2602.04021v1 Announce Type: cross Abstract: We present GROOVE, a semi-supervised multi-modal representation learning approach for high-content perturbation data where samples across modalities are weakly paired through shared perturbation labels but lack direct correspondence. Our primary contribution is GroupCLIP, a novel group-level contrastive loss that bridges the gap between CLIP for paired cross-modal data and SupCon for uni-modal supervised contrastive learning, addressing a fundamental gap in contrastive learning for weakly-paired settings. We integrate GroupCLIP with an on-the-fly backtranslating autoencoder […]

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Stop the memory rot TL;DR: You can keep your AI sharp by forcing it to summarize and prune what it remembers (a.k.a. compacting). Common Mistake ❌ You keep a single, long conversation open for hours. You feed the AI with every error log and every iteration of your code. Eventually, the AI starts to ignore your early instructions or hallucinate nonexistent functions. Problems Addressed 😔 Context Decay: The AI loses track of your original goals in the middle of […]

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arXiv:2604.06245v1 Announce Type: new Abstract: Impact craters are a cornerstone of planetary surface analysis. However, while most deep learning pipelines treat craters solely as a detection problem, critical scientific workflows such as catalog deduplication, cross-observation matching, and morphological analog discovery are inherently retrieval tasks. To address this, we formulate crater analysis as an instance-level image retrieval problem and introduce CraterBench-R, a curated benchmark featuring about 25,000 crater identities with multi-scale gallery views and manually verified queries spanning diverse […]

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Loss of plasticity refers to the progressive inability of a model to adapt to new tasks and poses a fundamental challenge for continual learning. While this phenomenon has been extensively studied in homogeneous neural architectures, such as multilayer perceptrons, its mechanisms in structurally heterogeneous, attention-based models such as Vision Transformers (ViTs) remain underexplored. In this work, we present a systematic investigation of loss of plasticity in ViTs, including a fine-grained diagnosis using local metrics that capture parameter diversity […]

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arXiv:2603.23517v1 Announce Type: new Abstract: Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). […]

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Vision-Language-Action (VLA) models show strong generalization for robotic control, but finetuning them with reinforcement learning (RL) is constrained by the high cost and safety risks of real-world interaction. Training VLA models in interactive world models avoids these issues but introduces several challenges, including pixel-level world modeling, multi-view consistency, and compounding errors under sparse rewards. Building on recent advances across large multimodal models and model-based RL, we propose VLA-MBPO, a practical framework to tackle these problems in VLA finetuning. […]

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arXiv:2602.12458v1 Announce Type: new Abstract: A central challenge in multi-agent reinforcement learning is enabling agents to adapt to previously unseen teammates in a zero-shot fashion. Prior work in zero-shot coordination often follows a two-stage process, first generating a diverse training pool of partner agents, and then training a best-response agent to collaborate effectively with the entire training pool. While many previous works have achieved strong performance by devising better ways to diversify the partner agent pool, there has […]

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