As developers, we often need to tweak screenshots – blur out sensitive info, add annotations, or even change the text. But have you ever wanted to replace a word in a screenshot, and have it look exactly like the original? Same font, same size, same color, same style? I did, and that’s why I built Screenshot Editor – a free, open-source web app that does exactly that. In this article, I’ll walk you through the journey, the challenges, and the […]

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arXiv:2603.15723v1 Announce Type: new Abstract: Large language models are increasingly deployed in settings where relevant information is embedded within long and noisy contexts. Despite this, robustness to growing context length remains poorly understood across different question answering tasks. In this work, we present a controlled empirical study of context-length robustness in large language models using two widely used benchmarks: SQuAD and HotpotQA. We evaluate model accuracy as a function of total context length by systematically increasing the amount […]

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Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 4. Differentiable Structural Information In this section, we first establish a new […]

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Hi everyone! I am an independent researcher working on Reviser, a language model that generates through cursor-relative edit actions on a mutable canvas. It is autoregressive over edit-history actions rather than final text order, which lets it revise its response while keeping decoding efficiency close to standard autoregressive transformers. My goal is to submit the paper to a conference such as ACL, EMNLP, ICML, or a similar venue, and I would really appreciate technical feedback on things like: […]

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Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-scale reinforcement learning (RL) problems. Existing principled approaches leverage structural priors on the MDP by constructing state representations as linear combinations of the state-graph Laplacian eigenvectors. When the transition graph is unknown or the state space is prohibitively large, the graph spectral features can be estimated directly via sample trajectories. In this work, we prove an upper bound on […]

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arXiv:2602.09131v1 Announce Type: new Abstract: While the CHERI instruction-set architecture extensions for capabilities enable strong spatial memory safety, CHERI lacks built-in temporal safety, particularly for heap allocations. Prior attempts to augment CHERI with temporal safety fall short in terms of scalability, memory overhead, and incomplete security guarantees due to periodical sweeps of the system’s memory to individually revoke stale capabilities. We address these limitations by introducing colored capabilities that add a controlled form of indirection to CHERI’s capability […]

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arXiv:2601.22365v1 Announce Type: new Abstract: The Gilbert-Pollak Conjecture citep{gilbert1968steiner}, also known as the Steiner Ratio Conjecture, states that for any finite point set in the Euclidean plane, the Steiner minimum tree has length at least $sqrt{3}/2 approx 0.866$ times that of the Euclidean minimum spanning tree (the Steiner ratio). A sequence of improvements through the 1980s culminated in a lower bound of $0.824$, with no substantial progress reported over the past three decades. Recent advances in LLMs have […]

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arXiv:2604.06198v1 Announce Type: new Abstract: The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure on electricity systems. This study introduces an AI-energy coupling framework that combines large language models (LLMs)-based analysis of corporate, policy, and media data with quantitative energy-system modeling to forecast the electricity footprint of AI-driven data centers from 2025 to 2030. Results show that the new AI infrastructure is highly concentrated in North America, Western […]

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arXiv:2601.07535v1 Announce Type: new Abstract: Clustering with bandit feedback refers to the problem of partitioning a set of items, where the clustering algorithm can sequentially query the items to receive noisy observations. The problem is formally posed as the task of partitioning the arms of an N-armed stochastic bandit according to their underlying distributions, grouping two arms together if and only if they share the same distribution, using samples collected sequentially and adaptively. This setting has gained attention […]

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