Large Language Models (LLMs) often struggle to maintain their original performance when faced with semantically coherent but task-irrelevant contextual information. Although prior studies have explored this issue using fixed-template or retrieval-based distractions, such static methods show limited effectiveness against contemporary models. To address this problem, we propose a dynamic distraction generation framework based on tree search, where the generation process is guided by model behavior. Without modifying the original question or answer, the method efficiently produces challenging adaptive distractions across multiple datasets, enabling systematic stress testing of LLMs' contextual robustness. Experiments on four benchmarks demonstrate that the generated distractions lead to an average performance drop of over 45% for mainstream models. Further comparisons of mitigation strategies show that prompt-based optimization methods yield limited gains, whereas post-training approaches (e.g., DPO) significantly enhance the model's contextual robustness. The results indicate that these issues do not stem from knowledge deficits in LLMs, but from a fundamental inability to maintain consistent reasoning under contextual distraction, posing a major challenge to the reliability of LLMs in real-world applications.

Large Language Models (LLMs) often struggle to maintain their original performance when faced with semantically coherent but task-irrelevant contextual information. Although prior studies have explored this issue using fixed-template or retrieval-based distractions, such static methods show limited effectiveness against contemporary models. To address this problem, we propose a dynamic distraction generation framework based on tree search, where the generation process is guided by model behavior. Without modifying the original question or answer, the method efficiently produces challenging adaptive distractions across multiple datasets, enabling systematic stress testing of LLMs' contextual robustness. Experiments on four benchmarks demonstrate that the generated distractions lead to an average performance drop of over 45\% for mainstream models. Further comparisons of mitigation strategies show that prompt-based optimization methods yield limited gains, whereas post-training approaches (e.g., DPO) significantly enhance the model's contextual robustness. The results indicate that these issues do not stem from knowledge deficits in LLMs, but from a fundamental inability to maintain consistent reasoning under contextual distraction, posing a major challenge to the reliability of LLMs in real-world applications.

Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: . In real-world reasoning scenarios, much of the available information is irrelevant, and effective deductive inference requires identifying and ignoring such distractions. We propose a framework for assessing LM reasoning efficiency through the lens of logic programming, introducing a simple method to align proofs written in natural language---as generated by an LM---with shortest proofs found by executing the logic program. Efficiency is quantified by measuring how well a model avoids unnecessary inference. Empirically, we construct a dataset of math word problems injected with various number of irrelevant axioms that vary in semantic overlap with the goal theorem. We find that current LMs show marked accuracy declines under such conditions---even with minimal, domain-consistent distractions---and the proofs they generate frequently exhibit detours through irrelevant inferences.

Here are our recommendations for apps that help you stay focused on the task at hand. And with attention spans crumbling in the TikTok era, we now have an entire category of apps dedicated to helping you stick to what you're supposed to be doing. These apps all work more or less in the same way, giving you a straightforward method of tracking how long you're spending on a task, and offering some sort of incentive to keep going for the allotted amount of time. Sometimes you get a few extra features as well, like the ability to block access to other apps. In the interest of trying to write this specific article without switching between browser tabs and apps every two minutes, I gave three of the best focus tools a try.

Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningless content, at the cost of neglecting important environment dynamics. While existing approaches attempt to solve this problem, we highlight its continuing impact on leading MBRL methods ---including DreamerV3 and DreamerPro--- with a novel environment where background distractions are intricate, predictable, and useless for planning future actions. To address this challenge we develop a method for focusing the capacity of the world model through a synergy of a pretrained segmentation model, a task-aware reconstruction loss, and adversarial learning. Our method outperforms a variety of other approaches designed to reduce the impact of distractors, and is an advance towards robust model-based reinforcement learning.

Fomi watches you work, then scolds you when your attention wanders. It's helpful, but there are privacy issues to consider. I've tested a lot of software tools over the years designed to block distractions and keep you focused. None of them work perfectly, mostly because of context. Reddit, for example, is something I should generally avoid during the workday, so I tend to block it--this is a good decision for me overall.

Apple could unveil Gemini-powered Siri in Feb. These open-fit earbuds actually make a difference with background noise. Rarely does a set of open-fit earbuds actually impress me. I tend to find them underwhelming because overall sound quality is subpar compared to the more "traditional" in-ear models. The first time I used the Shokz OpenFit Pro ($249.95)

Can You Reclaim Your Mind? To feel mentally alive, you have to do more than defeat distraction. Looking back over the columns I've written in 2025, I can see that a lot of them, broadly construed, have been about reclaiming one's mind. I wrote about living in the present, picturing the future, and exploring one's memories; about reading, learning, and making the most of one's spare time; and about whether artificial intelligence will end up expanding our thinking or limiting it . The shared subject was resistance to the forces, malevolent or inertial, that can render us mentally exhausted and scattered.