Now he's dead, here's the full story of what happened that day... and the ghastly aftermath no one knows about Wake up and see he's the master of the dark arts: MEGYN KELLY blows the lid on the REAL Mamdani... how are they missing this? 'Screaming' Sydney Sweeney'hates' that she was caught hiding in ex-fiancé's car: Now insiders spill truth about backseat rendezvous and lingering'frustrations' Mom is an Oscar winner who has acted with Selena Gomez and Nicole Kidman, who is this nepo kid who came out last year? Bella Thorne continues swimsuit season as she works sexy bikini for Los Cabo trip with her'love' Mark Emms Experts pinpoint typical life expectancy from initial dementia diagnosis - and there's a huge variation between different subtypes Diddy's male prison protector unmasked: How disgraced mogul has repaid him... and turned to God for repentance Teachers threatened over bloody'Problem Solved' T-shirts over claims they mocked murder of Charlie Kirk Astonishing moment Miss Universe winner storms out of this year's event after pageant president reprimands Miss Mexico and tells security to remove her for not showing'respect' Mystery of the'golfer's curse' is SOLVED: Scientists pinpoint why golf balls'lip out' after appearing to enter the hole READ MORE: Golf balls are a'product of colonial exploitation', exhibition says Experts have finally solved the mystery of one of the most infuriating occurrences in golf - the dreaded lip out. The phenomenon occurs when the golf ball appears to enter the hole, only to immediately pop back out again. Scientists have finally pinpointed the physics behind the'curse', which has plagued everyone from amateur hobbyists to PGA professionals. Best of all, they've revealed the best way to avoid it - keeping your score intact.

Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a better understanding of the correct procedures for addressing a given task. Despite these advancements, CoT lacks the ability of reflection and error correction, potentially causing a model to perpetuate mistakes and errors. Therefore, inspired by the human ability for said tasks, we propose Error Reflection Prompting (ERP) to further enhance reasoning in language models. Building upon CoT, ERP is a method comprised of an incorrect answer, error recognition, and a correct answer. This process enables the model to recognize types of errors and the steps that lead to incorrect answers, allowing the model to better discern which steps to avoid and which to take. The model is able to generate the error outlines itself with automated ERP generation, allowing for error recognition and correction to be integrated into the reasoning chain and produce scalability and reliability in the process. The results demonstrate that ERP serves as a versatile supplement to conventional CoT, ultimately contributing to more robust and capable reasoning abilities along with increased interpretability in how models ultimately reach their errors.

Specialized reasoning language models (RLMs) have demonstrated that scaling test-time computation through detailed reasoning traces significantly enhances performance. Although these traces effectively facilitate knowledge distillation into smaller, instruction-tuned models, the precise nature of transferred reasoning remains unclear. In this study, we investigate to what extent distilled models internalize replicated stylistic patterns during reasoning. To this end, we systematically analyze reasoning traces, identifying structural and lexical patterns that characterize successful reasoning. We then introduce two new datasets -- a dataset of emergent reasoning traces and a synthetic dataset explicitly constructed to replicate these stylistic patterns -- to precisely examine their influence on distilled models' reasoning capabilities. We find that models trained on the synthetic traces achieve comparable performance, indicating that distilled reasoning abilities rely significantly on surface-level patterns. Surprisingly, we observe an increase in performance even when the synthetic traces are altered to lead to the wrong answer. Our findings highlight how stylistic patterns can be leveraged to efficiently enhance LM reasoning across diverse model families.

As Big Tech pours countless dollars and resources into AI, preaching the gospel of its utopia-creating brilliance, here's a reminder that algorithms can screw up. The latest evidence: You can trick Google's AI Overview (the automated answers at the top of your search queries) into explaining fictional, nonsensical idioms as if they were real. According to Google's AI Overview (via @gregjenner on Bluesky), "You can't lick a badger twice" means you can't trick or deceive someone a second time after they've been tricked once. That sounds like a logical attempt to explain the idiom -- if only it weren't poppycock. Google's Gemini-powered failure came in assuming the question referred to an established phrase rather than absurd mumbo jumbo designed to trick it.

Large language models (LLMs) can perform complex reasoning by generating intermediate thoughts under zero-shot or few-shot settings. However, zero-shot prompting always encounters low performance, and the superior performance of few-shot prompting hinges on the manual-crafted demonstrations. In this paper, we present RoSE (Reasoning with Orchestrated Streaming Experiences), a general framework for solving reasoning tasks that can self-improve without complex external efforts. To enable RoSE, we describe an architecture that extends an LLM to store all answered questions and their thoughts in a streaming experience pool then orchestrates helpful questions from the pool to assist in answering new questions. To set up a question-aware orchestration mechanism, RoSE first calculates the similarity of each question in the pool with a new test question. Since the solution to each answered question is not always correct, RoSE will sort the questions according to their similarity with the new question, and then uniformly divide them into multiple buckets. It finally extracts one question from each bucket to make these extracted questions more diverse. To make these extracted questions help RoSE answer new questions as much as possible, we introduce two other attributes of uncertainty and complexity for each question. RoSE will preferentially select the questions with low uncertainty and high complexity from each bucket. We evaluate the versatility of RoSE in various reasoning tasks, LLMs, and CoT methods.

Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. Existing works aim to enhance LLM capabilities via domain-specific adaptation or in-context learning, which require additional training on accessible model parameters, an infeasible option for black-box LLMs. To address this challenge, we introduce Matryoshika, a lightweight white-box LLM controller that guides a large-scale black-box LLM generator by decomposing complex tasks into a series of intermediate outputs. Specifically, we consider the black-box LLM as an environment, with Matryoshika serving as a policy to provide intermediate guidance through prompts for driving the black-box LLM. Matryoshika is trained to pivot the outputs of the black-box LLM aligning with preferences during iterative interaction, which enables controllable multi-turn generation and self-improvement in optimizing intermediate guidance. Empirical evaluations on three diverse tasks demonstrate that Matryoshika effectively enhances the capabilities of black-box LLMs in complex, long-horizon tasks, including reasoning, planning, and personalization. By leveraging this pioneering controller-generator framework to mitigate dependence on model parameters, Matryoshika provides a transparent and practical solution for improving black-box LLMs through controllable multi-turn generation using white-box LLMs.