arXiv:2603.10452v1 Announce Type: new Abstract: Isotonic regression (IR) is shape-constrained regression to maintain a univariate fitting curve non-decreasing, which has numerous applications including single-index models and probability calibration. When it comes to multi-output regression, the classical IR is no longer applicable because the monotonicity is not readily extendable. We consider a novel multi-output regression problem where a regression function is emph{cyclically monotone}. Roughly speaking, a cyclically monotone function is the gradient of some convex potential. Whereas enforcing cyclic […]

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arXiv:2601.15505v1 Announce Type: new Abstract: The quantum hashing bound guarantees that rates up to $1-H(p_I, p_X, p_Y, p_Z)$ are achievable for memoryless Pauli channels, but it is not generally tight. A known way to improve achievable rates for certain asymmetric Pauli channels is to apply a small inner stabilizer code to a few channel uses, decode, and treat the resulting logical noise as an induced Pauli channel; reapplying the hashing argument to this induced channel can beat the […]

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arXiv:2601.16376v1 Announce Type: new Abstract: On-demand Polymer discovery is essential for various industries, ranging from biomedical to reinforcement materials. Experiments with polymers have a long trial-and-error process, leading to long procedures and extensive resources. For these processes, machine learning has accelerated scientific discovery at the property prediction and latent space search fronts. However, laboratory researchers cannot readily access codes and these models to extract individual structures and properties due to infrastructure limitations. We present a closed-loop polymer structure-property […]

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arXiv:2603.20904v2 Announce Type: replace-cross Abstract: The proposed algorithm seeks to provide a novel data-driven framework for the discovery of stochastic differential equations (SDEs) by application of the Weak-formulation to stochastic SINDy. This Weak formulation of the algorithm provides a noise-robust methodology that avoids traditional noisy derivative computation using finite differences. An additional novelty is the adoption of spatial Gaussian test functions in place of temporal test functions, wherein, the use of the kernel weight $K_j(X_{t_n})$ guarantees unbiasedness in […]

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arXiv:2602.07078v1 Announce Type: new Abstract: Reinforcement Learning (RL) for Large Language Models (LLMs) often suffers from training collapse in long-horizon tasks due to exploding gradient variance. To mitigate this, a baseline is commonly introduced for advantage computation; however, traditional value models remain difficult to optimize, and standard group-based baselines overlook sequence heterogeneity. Although classic optimal baseline theory can achieve global variance reduction, it neglects token heterogeneity and requires prohibitive gradient-based computation. In this work, we derive the Optimal […]

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Being modeled as a single-label classification task for a long time, recent work has argued that Arabic Dialect Identification (ADI) should be framed as a multi-label classification task. However, ADI remains constrained by the availability of single-label datasets, with no large-scale multi-label resources available for training. By analyzing models trained on single-label ADI data, we show that the main difficulty in repurposing such datasets for Multi-Label Arabic Dialect Identification (MLADI) lies in the selection of negative samples, as […]

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Key Highlights: As 2025 is coming to an end, more and more online platforms have released their specific year-in-review. Here I’m talking about Spotify Wrapped, Google’s Year in Search, Snapchat Recap, and more. OpenAI, meanwhile, is an AI company, but it isn’t shying away from joining the bandwagon and has released its own version called “Your Year with ChatGPT.” ChatGPT users can get personalized year in review, but it’s limited to select regions now As the name suggests, […]

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arXiv:2602.23023v1 Announce Type: cross Abstract: We study the fundamental problem of clustering $n$ points into $K$ groups drawn from a mixture of isotropic Gaussians in $mathbb{R}^d$. Specifically, we investigate the requisite minimal distance $Delta$ between mean vectors to partially recover the underlying partition. While the minimax-optimal threshold for $Delta$ is well-established, a significant gap exists between this information-theoretic limit and the performance of known polynomial-time procedures. Although this gap was recently characterized in the high-dimensional regime ($n leq […]

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AI made code generation cheap. But it also made silent breakage cheap. Large diffs. Subtle logic removals. Relaxed validations. Expensive cloud calls. Secrets slipping into logs. Most of this doesn’t get caught while generating code; it gets discovered in PR review, or worse, in production. That’s exactly where git-lrc changes the game. What developers actually gain Review at the right moment. Every staged diff is reviewed before it gets committed. No extra dashboard. No context switching. Faster understanding of AI-generated diffs. […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Why Everyone is Panic-Buying Mac Minis for OpenClaw / Moltbot / Clawdbot? By @alexisrozhkov [ 5 Min read ] the reality is more nuanced than the hype suggests. Read More. 12 Must-Have Financial Market APIs for Real-Time Insights in 2026 By @apilayer [ 20 […]

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