Chain-of-Thought significantly enhances a model’s reasoning capability, but it also comes with a considerable increase in inference costs due to long chains. With the observation that the reasoning path can be easily compressed under easy tasks but struggle on hard tasks, we explore the feasibility of elastically controlling the length of reasoning paths with only one model, thereby reducing the inference overhead of reasoning models dynamically based on task difficulty. We introduce a new tuning and inference strategy named CoT-Valve, designed to allow models to generate reasoning chains of varying lengths. To achieve this, we propose to identify a direction in the parameter space that, when manipulated, can effectively control the length of generated CoT. Moreover, we show that this property is valuable for compressing the reasoning chain. We construct datasets with chains from long to short for the same questions and explore two enhanced strategies for CoT-Valve: (1) a precise length-compressible CoT tuning method, and (2) a progressive chain length compression approach. Our experiments show that CoT-Valve successfully enables controllability and compressibility of the chain and shows better performance than the prompt-based control. We applied this method to QwQ-32B-Preview, reducing reasoning chains on GSM8K from 741 to 225 tokens with a minor performance drop (95.07% to 94.92%) and on AIME from 6827 to 4629 tokens, with only one additional incorrect answer.

Recent years have witnessed rapid advancements in Large Language Models (LLMs). Nevertheless, it remains unclear whether state-of-the-art LLMs can infer the author of an anonymous research paper solely from the text, without any additional information. To investigate this novel challenge, which we define as Open-World Authorship Attribution, we introduce a benchmark comprising thousands of research papers across various fields to quantitatively assess model capabilities. Then, at the core of this paper, we tailor a two-stage framework to tackle this problem: candidate selection and authorship decision. Specifically, in the first stage, LLMs are prompted to generate multi-level key information, which are then used to identify potential candidates through Internet searches. In the second stage, we introduce key perspectives to guide LLMs in determining the most likely author from these candidates. Extensive experiments on our benchmark demonstrate the effectiveness of the proposed approach, achieving 60.7% and 44.3% accuracy in the two stages, respectively. We will release our benchmark and source codes to facilitate future research in this field.

The emotional state of a speaker can be influenced by many different factors in dialogues, such as dialogue scene, dialogue topic, and interlocutor stimulus. The currently available data resources to support such multimodal affective analysis in dialogues are however limited in scale and diversity. In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M³ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. M³ED is annotated with 7 emotion categories (happy, surprise, sad, disgust, anger, fear, and neutral) at utterance level, and encompasses acoustic, visual, and textual modalities. To the best of our knowledge, M³ED is the first multimodal emotional dialogue dataset in Chinese.It is valuable for cross-culture emotion analysis and recognition. We apply several state-of-the-art methods on the M³ED dataset to verify the validity and quality of the dataset. We also propose a general Multimodal Dialogue-aware Interaction framework, MDI, to model the dialogue context for emotion recognition, which achieves comparable performance to the state-of-the-art methods on the M³ED. The full dataset and codes are available.