Chain-of-Thought (CoT) reasoning is crucial for the performance of Large Reasoning Models (LRMs) but is often hindered by redundant and distracting segments, which incur excessive inference costs and degrade robustness.Existing approaches try to solve this problem by enforcing brevity through external supervision, such as length-based penalties or heuristic truncation.However, these approaches often degrade performance because they disregard the model's intrinsic reasoning dependency and thus fail to distinguish between essential and redundant CoT segments.To address this problem, we propose SGP-CoT, a novel Self-Guided Pruning framework that leverages the model's intrinsic likelihood landscape to identify segments that are extraneous to its specific reasoning pattern.Specifically, SGP-CoT treats the reasoning trajectory as a sequence of semantic units and assesses the necessity of each one via internal likelihood signals, measuring its contribution to the answer and local coherence.Based on this, it selectively removes non-essential segments and then forms high-quality pruning-based preference pairs, enabling the model to learn focused reasoning via self-optimization.Extensive experiments across diverse benchmarks demonstrate that the proposed SGP-CoT significantly reduces output length while maintaining or improving accuracy.These results validate that LRMs intrinsically possess the capability to discern reasoning utility, positioning SGP-CoT as a robust pathway toward scalable inference.

Driven by the remarkable progress in diffusion models, text-to-image generation has achieved substantial advancements, underscoring the urgent need for robust automatic quality assessment.This task is inherently complex, requiring evaluations that range from object presence and attribute correctness to relational consistency and visual fidelity.Consequently, current state-of-the-art MLLM-based approaches often rely on powerful commercial models such as GPT-4o, which offer superior reasoning and instruction-following capabilities but are not universally accessible.In contrast, while opensource MLLMs demonstrate promising skills in vision and language understanding, they underperform in comprehensive image quality assessment.To address these challenges, we propose a task decomposition evaluation framework based on GPT-4o to automatically construct a specialized training dataset, breaking down the multifaceted evaluation process into simpler sub-tasks and thus reducing learning complexity.Building on this dataset, we design novel training strategies to distill GPT-4o's evaluation capabilities into a 7B open-source MLLM, MiniCPM-V-2.6,enabling it to better follow instructions across diverse assessment criteria.Furthermore, to reliably and comprehensively assess prior works and our proposed model, we manually annotate a meta-evaluation benchmark that includes chain-of-thought explanations alongside quality scores for generated images.Experimental results demonstrate that our distilled open-source MLLM significantly outperforms the current state-of-the-art GPT-4o-base baseline, VIEScore, with over 4.6% improvement in Spearman and Kendall correlations with human judgments.

In real-world scenarios, the data usually appears in a streaming fashion. To achieve remarkable retrieval performance in such scenarios, online multi-modal hashing has drawn great research attention due to its high retrieval speed and low storage cost. However, existing online multi-modal hashing methods still fail to achieve satisfactory retrieval performance in the scenarios where the new streaming datapoints all belong to the new classes. Therefore, to further improve the retrieval performance in these scenarios, we propose a novel Prospective Layout-Guided Multi-modal Online Hashing, termed PLG-MOH. Specifically, PLG-MOH first establishes the layout of the Hamming space by generating a series of hashing centers to split the space. Each hashing center will be gradually assigned to a new appearing class, and these assigned centers correspond one-to-one with the classes. Moreover, we propose a novel prospective layout-guided loss, which leverages all the hashing centers, including those not yet assigned to the classes, to supervise the training of hashing model. As the unassigned hashing centers will be designated to the new classes emerging in the future, it signifies that during each round of training, PLG-MOH has already considered the forthcoming data from new classes in the future rounds. Consequently, PLG-MOH can effectively adapt its hashing functions to address the new arriving samples and learn semantic similarity-preserved hash codes for them, meanwhile it can effectively retain the information learned from the old data. Extensive experiments on two public datasets demonstrate that the proposed PLG-MOH achieves better retrieval performance than state-of-the-art baselines on online scenarios.