Why do the midterms matter? In September, a poll found that 70 percent of Democrats and 57 percent of Republicans supported limiting the construction of new data centres in the United States. But that opposition was tested in Tuesday's midterm election, with analysts examining key races to see if the public backlash would translate into votes. There were 41 ballot measures related to data centres in Tuesday's midterms, spread across the country. According to Data Centers on the Ballot, an election-monitoring project run by Redbud Consulting, voters are on track to reject data centre development in all but one of the contests.

Not everyone wants another smartwatch. These smart rings offer a more discreet way to keep an eye on your health. The best smart ring does a lot without asking much of you, passively tracking your activity, health, and sleep. Some models go even further, with meal logging, AI coaching, and cycle tracking. No bulky smartwatch banging you in the face while you're deep in REM sleep. Once the preserve of wealthy wellness buffs, top-of-the-line smart rings are becoming increasingly affordable and available. Although our top pick is still Finnish pioneer Oura's Ring 5 (from $399) I expect to see more entrants in the coming months, but for now, these are the best smart rings we've tried.

New findings shed light on what's making the parasite less treatable using the drug that's considered a first line of defense. The main drug that has been in use for decades to combat malaria in sub-Saharan Africa is running into a problem: In recent years, researchers have become increasingly concerned that parasites are becoming less sensitive to treatment. Identifying the underlying genetic factors has become an urgent priority. In an attempt to identify the reason for drug-resistant malaria, a team led by Brown University researchers conducted whole-genome analysis of 157 malaria parasite samples selected from specimens collected in Uganda between 2016 and 2024. The work, published in Nature Medicine this week, shows evidence that mutations in a gene that encodes a protein called px1, which had previously received little attention, is likely responsible for the rise in drug resistance.

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Recent reasoning-focused language models achieve high accuracy by generating lengthy intermediate reasoning paths before producing final answers. While this approach is effective in solving problems that require logical thinking, long reasoning paths significantly increase memory usage and reduce throughput of token generation, limiting the practical deployment of such models. We propose Reasoning Path Compression (RPC), a training-free method that accelerates inference by leveraging the semantic sparsity of reasoning paths. RPC periodically compresses the KV cache by retaining cache entries that receive high importance score, which are computed using a selector window composed of recently generated queries. Experiments show that RPC improves generation throughput of QwQ-32B by up to 1.60 compared to the inference with full KV cache, with an accuracy drop of 1.2% on the AIME 2024 benchmark. Our findings demonstrate that semantic sparsity in reasoning traces can be effectively exploited for compression, offering a practical path toward efficient deployment of reasoning LLMs.

Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a small set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning in vision classifiers. We make the surprising discovery that forget-set accuracy can recover from around 50\% post-unlearning to nearly 100\% with fine-tuning on just the *retain* set---i.e., zero examples of the forget set. We observe this effect across a wide variety of unlearning methods, whereas for a model retrained from scratch excluding the forget set (gold standard), the accuracy remains at 50\%. We observe that resistance to relearning attacks can be predicted by weight-space properties, specifically, $L_2$-distance and linear mode connectivity between the original and the unlearned model. Leveraging this insight, we propose a new class of methods that achieve state-of-the-art resistance to relearning attacks.

Recent studies have shown LLMs possess some ability to improve their responses when given external feedback. However, it remains unclear how effectively and thoroughly these models can incorporate extrinsic feedback. In an ideal scenario, if LLMs receive near-perfect and complete feedback, we would expect them to fully integrate the feedback and reach correct solutions. In this paper, we systematically investigate LLMs' ability to incorporate feedback by designing a controlled experimental environment. For each problem, a solver model attempts a solution, then a feedback generator with access to near-complete ground-truth answers produces targeted feedback, after which the solver tries again.

Spectral clustering is a fundamental method for graph partitioning, but its reliance on eigenvector computation limits scalability to massive graphs. Classical sparsification methods preserve spectral properties by sampling edges proportionally to their effective resistances, but require expensive preprocessing to estimate these resistances. We study whether uniform edge sampling--a simple, structure-agnostic strategy--can suffice for spectral clustering. Our main result shows that for graphs admitting a well-separated $k$-clustering, characterized by a large structure ratio $\Upsilon(k) = \lambda_{k+1} / \rho_G(k)$, uniform sampling preserves the spectral subspace used for clustering. Specifically, we prove that uniformly sampling $O(\gamma^2 n \log n / \varepsilon^2)$ edges, where $\gamma$ is the Laplacian condition number, yields a sparsifier whose top $(n-k)$-dimensional eigenspace is approximately orthogonal to the cluster indicators.

Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human preferences and values. While recent research has primarily focused on algorithmic advancements--such as reducing computational overhead or strengthening reward models to mitigate reward hacking--the critical role of prompt-data construction and its scalability has received comparatively less attention. In this paper, we address this gap by systematically exploring data-driven bottlenecks that currently hinder RLHF performance scaling, focusing specifically on the challenges posed by reward hacking and decreasing response diversity. To mitigate reward hacking, we introduce a hybrid reward system combining reasoning task verifiers (RTV) and a generative reward model (GenRM). This approach not only exhibits enhanced resistance to reward hacking, but also enables accurate assessment of responses against clearly defined ground-truth solutions. Additionally, in order to ensure response diversity and enhance learning effectiveness, we propose a novel prompt-selection method named \textbf{Pre-PPO}, explicitly identifying training prompts that are inherently challenging and thus less prone to reward hacking.