The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models

Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to enhance LLM reasoning efficiency. By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling. Experiments on reasoning benchmarks show that UPFT matches the performance of supervised methods such as Rejection Sampling Fine-Tuning, while reducing training time by 75% and sampling cost by 99%. Further analysis reveals that errors tend to appear in later stages of the reasoning process and that prefix-based training preserves the model's structural knowledge. This work demonstrates how minimal unsupervised fine-tuning can unlock substantial reasoning gains in LLMs, offering a scalable and resource-efficient alternative to conventional approaches.

VisFactor: Benchmarking Fundamental Visual Cognition in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have demonstrated remarkable advancements in multimodal understanding; however, their fundamental visual cognitive abilities remain largely underexplored. To bridge this gap, we introduce VisFactor, a novel benchmark derived from the Factor-Referenced Cognitive Test (FRCT), a well-established psychometric assessment of human cognition. VisFactor digitalizes vision-related FRCT subtests to systematically evaluate MLLMs across essential visual cognitive tasks including spatial reasoning, perceptual speed, and pattern recognition. We present a comprehensive evaluation of state-of-the-art MLLMs, such as GPT-4o, Gemini-Pro, and Qwen-VL, using VisFactor under diverse prompting strategies like Chain-of-Thought and Multi-Agent Debate. Our findings reveal a concerning deficiency in current MLLMs' fundamental visual cognition, with performance frequently approaching random guessing and showing only marginal improvements even with advanced prompting techniques. These results underscore the critical need for focused research to enhance the core visual reasoning capabilities of MLLMs. To foster further investigation in this area, we release our VisFactor benchmark at https://github.com/CUHK-ARISE/VisFactor.

Can LLMs Design Good Questions Based on Context?

This paper evaluates questions generated by LLMs from context, comparing them to human-generated questions across six dimensions. We introduce an automated LLM-based evaluation method, focusing on aspects like question length, type, context coverage, and answerability. Our findings highlight unique characteristics of LLM-generated questions, contributing insights that can support further research in question quality and downstream applications.

Insight Over Sight? Exploring the Vision-Knowledge Conflicts in Multimodal LLMs

This paper explores the problem of commonsense-level vision-knowledge conflict in Multimodal Large Language Models (MLLMs), where visual information contradicts model's internal commonsense knowledge (see Figure 1). To study this issue, we introduce an automated pipeline, augmented with human-in-the-loop quality control, to establish a benchmark aimed at simulating and assessing the conflicts in MLLMs. Utilizing this pipeline, we have crafted a diagnostic benchmark comprising 374 original images and 1,122 high-quality question-answer (QA) pairs. This benchmark covers two types of conflict target and three question difficulty levels, providing a thorough assessment tool. Through this benchmark, we evaluate the conflict-resolution capabilities of nine representative MLLMs across various model families and find a noticeable over-reliance on textual queries. Drawing on these findings, we propose a novel prompting strategy, "Focus-on-Vision" (FoV), which markedly enhances MLLMs' ability to favor visual data over conflicting textual knowledge. Our detailed analysis and the newly proposed strategy significantly advance the understanding and mitigating of vision-knowledge conflicts in MLLMs. The data and code are made publicly available.

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits

In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits. This ensures that a range of proposed applications can be deployed on real quantum hardware. The efficiency of quantum circuit is desired both in the number of trainable gates and the depth of the overall circuit. The major concern of barren plateaus has made this need for efficiency even more acute. The problem of efficient quantum circuit realization has been extensively studied in the literature to reduce gate complexity and circuit depth. Another important approach is to design a method to reduce the \emph{parameter complexity} in a variational quantum circuit. Existing methods include hyperparameter-based parameter pruning which introduces an additional challenge of finding the best hyperparameters for different applications. In this paper, we present \emph{QAdaPrune} - an adaptive parameter pruning algorithm that automatically determines the threshold and then intelligently prunes the redundant and non-performing parameters. We show that the resulting sparse parameter sets yield quantum circuits that perform comparably to the unpruned quantum circuits and in some cases may enhance trainability of the circuits even if the original quantum circuit gets stuck in a barren plateau.\\ \noindent{\bf Reproducibility}: The source code and data are available at \url{https://github.com/aicaffeinelife/QAdaPrune.git}

DDFAD: Dataset Distillation Framework for Audio Data

Deep neural networks (DNNs) have achieved significant success in numerous applications. The remarkable performance of DNNs is largely attributed to the availability of massive, high-quality training datasets. However, processing such massive training data requires huge computational and storage resources. Dataset distillation is a promising solution to this problem, offering the capability to compress a large dataset into a smaller distilled dataset. The model trained on the distilled dataset can achieve comparable performance to the model trained on the whole dataset. While dataset distillation has been demonstrated in image data, none have explored dataset distillation for audio data. In this work, for the first time, we propose a Dataset Distillation Framework for Audio Data (DDFAD). Specifically, we first propose the Fused Differential MFCC (FD-MFCC) as extracted features for audio data. After that, the FD-MFCC is distilled through the matching training trajectory distillation method. Finally, we propose an audio signal reconstruction algorithm based on the Griffin-Lim Algorithm to reconstruct the audio signal from the distilled FD-MFCC. Extensive experiments demonstrate the effectiveness of DDFAD on various audio datasets. In addition, we show that DDFAD has promising application prospects in many applications, such as continual learning and neural architecture search.

Improving Scene Graph Generation with Relation Words' Debiasing in Vision-Language Models

Scene Graph Generation (SGG) provides basic language representation of visual scenes, requiring models to grasp complex and diverse semantics between various objects. However, this complexity and diversity in SGG also leads to underrepresentation, where part of test triplets are rare or even unseen during training, resulting in imprecise predictions. To tackle this, we propose using the SGG models with pretrained vision-language models (VLMs) to enhance representation. However, due to the gap between the pretraining and SGG, directly ensembling the pretrained VLMs leads to severe biases across relation words. Thus, we introduce LM Estimation to approximate the words' distribution underlies in the pretraining language sets, and then use the distribution for debiasing. After that, we ensemble VLMs with SGG models to enhance representation. Considering that each model may represent better at different samples, we use a certainty-aware indicator to score each sample and dynamically adjust the ensemble weights. Our method effectively addresses the words biases, enhances SGG's representation, and achieve markable performance enhancements. It is training-free and integrates well with existing SGG models.

Effective and Efficient Federated Tree Learning on Hybrid Data

Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, most existing federated learning studies focus on either horizontal or vertical data settings, where the data of different parties are assumed to be from the same feature or sample space. In practice, a common scenario is the hybrid data setting, where data from different parties may differ both in the features and samples. To address this, we propose HybridTree, a novel federated learning approach that enables federated tree learning on hybrid data. We observe the existence of consistent split rules in trees. With the help of these split rules, we theoretically show that the knowledge of parties can be incorporated into the lower layers of a tree. Based on our theoretical analysis, we propose a layer-level solution that does not need frequent communication traffic to train a tree. Our experiments demonstrate that HybridTree can achieve comparable accuracy to the centralized setting with low computational and communication overhead. HybridTree can achieve up to 8 times speedup compared with the other baselines.

Model Inversion Attacks on Homogeneous and Heterogeneous Graph Neural Networks

Recently, Graph Neural Networks (GNNs), including Homogeneous Graph Neural Networks (HomoGNNs) and Heterogeneous Graph Neural Networks (HeteGNNs), have made remarkable progress in many physical scenarios, especially in communication applications. Despite achieving great success, the privacy issue of such models has also received considerable attention. Previous studies have shown that given a well-fitted target GNN, the attacker can reconstruct the sensitive training graph of this model via model inversion attacks, leading to significant privacy worries for the AI service provider. We advocate that the vulnerability comes from the target GNN itself and the prior knowledge about the shared properties in real-world graphs. Inspired by this, we propose a novel model inversion attack method on HomoGNNs and HeteGNNs, namely HomoGMI and HeteGMI. Specifically, HomoGMI and HeteGMI are gradient-descent-based optimization methods that aim to maximize the cross-entropy loss on the target GNN and the $1^{st}$ and $2^{nd}$-order proximities on the reconstructed graph. Notably, to the best of our knowledge, HeteGMI is the first attempt to perform model inversion attacks on HeteGNNs. Extensive experiments on multiple benchmarks demonstrate that the proposed method can achieve better performance than the competitors.

Learning To Optimize Quantum Neural Network Without Gradients

Quantum Machine Learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. The similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r.t objective function have to be computed on the quantum device. This computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a \emph{meta-optimizer} network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets.