Robustness to malicious attacks is crucial for practical decentralized signal processing and machine learning systems. A typical example of such attacks is label poisoning, meaning that some agents possess corrupted local labels and share models trained on these poisoned data. To defend against malicious attacks, existing works often focus on designing robust aggregators; meanwhile, the weighted mean aggregator is typically considered a simple, vulnerable baseline. This paper analyzes the robustness of decentralized gradient descent under label poisoning attacks, […]

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arXiv:2601.13698v2 Announce Type: replace-cross Abstract: Fairness and privacy are two vital pillars of trustworthy machine learning. Despite extensive research on these individual topics, their relationship has received significantly less attention. In this paper, we utilize an information-theoretic measure Chernoff Information to characterize the fundamental trade-off between fairness, privacy, and accuracy, as induced by the input data distribution. We first propose Chernoff Difference, a notion of data fairness, along with its noisy variant, Noisy Chernoff Difference, which allows us […]

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7B Models: Cheap, Fast… and Brutally Honest About Your Prompting If you’ve deployed a 7B model locally (or on a modest GPU), you already know the trade: Pros low cost low latency easy to self-host Cons patchy world knowledge weaker long-chain reasoning worse instruction-following unstable formatting (“JSON… but not really”) The biggest mistake is expecting 7B models to behave like frontier models. They won’t. But you can get surprisingly high-quality output if you treat prompting like systems design, […]

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arXiv:2604.07515v1 Announce Type: new Abstract: We develop the first theoretically-efficient algorithm for maintaining the maximal independent set (MIS) of a graph in the parallel batch-dynamic setting. In this setting, a graph is updated with batches of edge insertions/deletions, and for each batch a parallel algorithm updates the maximal independent set to agree with the new graph. A batch-dynamic algorithm is considered efficient if it is work efficient (i.e., does no more asymptotic work than applying the updates sequentially) […]

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When people talk about marketplaces, they usually talk about demand – growth, users, and marketing channels.  But in practice, marketplaces live or die by supply – and by how early you turn supply into a system rather than a manual operation. I learned this the hard way while building and scaling a two-sided marketplace in the local services space. Over several years, my team and I grew the platform from an early proof-of-concept to tens of thousands of […]

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Anthropic are the only major AI lab to publish the system prompts for their user-facing chat systems. Their system prompt archive now dates all the way back to Claude 3 in July 2024 and it’s always interesting to see how the system prompt evolves as they publish new models. Opus 4.7 shipped the other day (April 16, 2026) with a Claude.ai system prompt update since Opus 4.6 (February 5, 2026). I had Claude Code take the Markdown version […]

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Foundry Digital announced a US based Zcash mining pool launching in April, expanding institutional mining infrastructure beyond Bitcoin for the first time as The Block reported. Electric Coin Company separately raised $25 million for Zcash wallet development. The crypto news confirms that infrastructure investment continues even through the deepest fear. While most tokens bleed and meme coins lose 80% from their highs, this crypto news cycle features one presale that defies the bearish narrative entirely. Pepeto sits at […]

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Table of Links Abstract and 1. Introduction Methods Quantitative Results and Creativity Support Index Qualitative Results from Focus Group Discussions Discussion Mitigations and Conclusion and Acknowledgments Ethical Guidance References A. Related Work on Computational Humour, AI and Comedy B. Participant Questionaire C. Focus C FOCUS GROUP QUESTIONS C.1 Qualitative questions about the specific writing task • Did you find any of the generated outputs helpful? If so, could you recall one output that was usable and explain in […]

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arXiv:2603.22368v1 Announce Type: new Abstract: Visualizations help communicate data insights, but deceptive data representations can distort their interpretation and propagate misinformation. While recent Vision Language Models (VLMs) perform well on many chart understanding tasks, their ability to detect misleading visualizations, especially when deception arises from subtle reasoning errors in captions, remains poorly understood. Here, we evaluate VLMs on misleading visualization-caption pairs grounded in a fine-grained taxonomy of reasoning errors (e.g., Cherry-picking, Causal inference) and visualization design errors (e.g., […]

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