Recently, the use of large language models (LLMs) for Verilog code generation has attracted great research interest to enable hardware design automation. However, previous works have shown a gap between the ability of LLMs and the practical demands of hardware description language (HDL) engineering. This gap includes differences in how engineers phrase questions and hallucinations in the code generated. To address these chal-lenges, we introduce Haven, a novel LLM framework designed to mitigate hallucinations and align Verilog code generation with the practices of HDL engineers. Haven tackles hallucination issues by proposing a comprehensive taxonomy and employing a chain-of-thought (CoT) mechanism to translate symbolic modalities (e.g. truth tables, state diagrams, etc.) into accurate natural language descriptions. Furthermore, Haven bridges this gap by using a data augmentation strategy. It synthesizes high-quality instruction-code pairs that match real HDL engineering practices. Our experiments demonstrate that Haven significantly improves the correctness of Verilog code generation, outperforming state-of-the-art LLM-based Verilog generation methods on VerilogEval and RTLLM benchmark. Haven is publicly available at https://github.com/Intelli2ent-Computing-Research-Group/HaVen.

Recent advancements in code generation have shown remarkable success across software domains, yet hardware description languages (HDLs) such as Verilog remain underexplored due to their concurrency semantics, syntactic rigidity, and simulation complexity. In this work, we address these challenges by introducing a reinforcement learning (RL) framework tailored for Verilog code generation. We first construct Veribench-53K, a high-quality dataset curated from over 700K Verilog problems, enriched with structured prompts, complexity labels, and diverse testbenches. To tackle the problem of sparse and noisy reward signals, we propose a Trace-back based Rescore mechanism that leverages reasoning paths and iterative refinement to enhance feedback reliability and support reward model training. Furthermore, to mitigate catastrophic forgetting and overfitting during RL fine-tuning, we introduce a sample-balanced weighting strategy that adaptively balances learning dynamics based on reward-probability distributions. These innovations are integrated into an iterative RL pipeline that co-evolves the policy and reward models. In contrast to recent work such as CraftRTL, which relies on large-scale closed-source model distillation, and DeepSeekstyle approaches that struggle with sparse feedback, our method demonstrates superior performance using a smaller but high-quality dataset combined with RL optimization. Experiments on Verilog generation tasks demonstrate state-of-the-art performance, with substantial gains in test pass rate, functional correctness, and compilation robustness. Our findings highlight the potential of RL-driven approaches for structured code generation in hardware-centric domains. VeriRL is publicly available at https://github.com/omniAI-Lab/VeriRL.

Multiple-instance learning (MIL) was initially proposed to identify key instances within a set (bag) of instances when only one bag-level label is provided. Current deep MIL models mostly solve multi-instance problem in feature space. Nevertheless, with the increasing complexity of data, we found this paradigm faces significant risks in representation learning stage, which could lead to algorithm degradation in deep MIL models. We speculate that the degradation issue stems from the persistent drift of instances in feature space during learning. In this paper, we propose a novel Probability-Space MIL network (PSMIL) as a countermeasure. In PSMIL, a self-training alignment strategy is introduced in probability space to cope with the drift problem in feature space, and the alignment target objective is proven mathematically optimal. Furthermore, we reveal that the widely-used attention-based pooling mechanism in current deep MIL models is easily affected by the perturbation in feature space and further introduce an alternative called probability-space attention pooling. It effectively captures the key instance in each bag from feature space to probability space, and further eliminates the impact of selection drift in the pooling stage. To summarize, PSMIL seeks to solve a MIL problem in probability space rather than feature space. Experimental results illustrate that PSMIL could potentially achieve performance close to supervised learning level in complex tasks (gap within 5\%), with the incremental alignment in propability space bring more than 19\% accuracy improvements for current existing mainstream models in simulated CIFAR datasets. For existing publicly available MIL benchmarks/datasets, attention in probability space also achieves competitive performance to the state-of-the-art deep MIL models. Codes are available at \url{https://github.com/LMBDA-design/PSAMIL}.

Recently, there has been significant progress in automatic pain assessment based on facial expression analysis. However, the performance of pain assessment remains unsatisfactory, due to a lack of analysis on local pain-related action units and emotional ambiguity. In particular, ambiguous pain expressions complicate the estimation of pain. It is argued that certain facial local regions related to pain should receive more attention while estimating pain intensities. Based on this, we propose a multi-task hybrid Conv-Transformer method for facial pain assessment, which utilizes the self-attention mechanism to explore facial local features related to pain intensities and constructs a multi-task joint optimizing module to mitigate facial emotional ambiguity. In particular, the proposed method modifies the network structure of the vision transformer model to better estimate continuous pain intensities. Meanwhile, a multi-task module is constructed to jointly optimize the classification and the regression tasks of pain assessment, which effectively regularizes the extracted features and facilitates a better fit of the regressed prediction to the given label. Finally, experimental results on the UNBC Pain dataset illustrate that the proposed method performs better with pain assessment compared with state-of-the-art methods.

The long-tailed data distribution frequently occurs in the real-world scenarios, whereas deep learning is not effective enough for such distribution. In order to improve the effectiveness for the long-tailed data, data augmentation is widely used to balance the distribution of classes by generating new samples. However, most existing studies are designed from the perspective of the class-independence assumption by default, ignoring the effect of interrelation among classes for data augmentation, which causes that some generated samples may be unrepresentative and useless for balancing the class-distribution. Inspired by this, we propose a new data augmentation method based the sparse class-correlation exploitation in this paper, which can generate more representative samples by utilizing the class-correlation, to effectively balance the class-distribution for the long-tailed data. In the proposed method, a sparse class-correlation exploration module is first proposed to explore the potential correlations among multiple classes for boosting the classification performance. Based on the class-correlations, the pivotal seed-samples are generated by maximizing the sparse representation of challenging samples. Meanwhile, an ambiguity-filtered translation module is designed to generate more representative new samples for the target classes based the obtained seed-samples by enhancing the class-consistency and suppressing the deviation from the target classes. In addition, we introduce the self-supervised feature and fuse it with the discriminative feature to explore more accurate class-correlations. Experimental results illustrate that the proposed method obtains better performance only with a small number of generated samples than the state-of-the-art methods.

Software version migration and program translation are an important and costly part of the lifecycle of large codebases.Traditional machine translation relies on parallel corpora for supervised translation, which is not feasible for program translation due to a dearth of aligned data.Recent unsupervised neural machine translation techniques have overcome data limitations by included techniques such as back translation and low level compiler intermediate representations (IR).These methods face significant challenges due to the noise in code snippet alignment and the diversity of IRs respectively.In this paper we propose a novel model called Code Distillation (CoDist) whereby we capture the semantic and structural equivalence of code in a language agnostic intermediate representation.Distilled code serves as a translation pivot for any programming language, leading by construction to parallel corpora which scale to all available source code by simply applying the distillation compiler.We demonstrate that our approach achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks, with an average absolute increase of 12.7% on the TransCoder GeeksforGeeks translation benchmark compare to TransCoder-ST.

Program translation, i.e. transcompilation has been attracting increasing attention from researchers due to its enormous application value.However, we observe that current program translating models still make elementary syntax errors, particularly when the source language uses syntax elements not present in the target language, which is exactly what developers are concerned about while may not be well exposed by frequently used metrics such as BLEU, CodeBLEU and Computation Accuracy.In this paper, we focus on evaluating the model's ability to address these basic syntax errors and developed an novel active defects probing suite, the Syntactic Unit Tests (SUT) and highly interpretable evaluation harness including Syntax Unit Test Accuracy (SUT Acc) metric and Syntax Element Test Score (SETS), to help diagnose and promote progress in this area.Our Syntactic Unit Test fills the gap in the community for a fine-grained evaluation dataset for program translation.Experimental analysis shows that our evaluation harness is more accurate, reliable, and in line with human judgments compared to previous metrics.

For GAN-based Person Re-identification (Re-ID), the key is to generate pedestrian images with higher identity consistency and meanwhile larger intra-class diversity. Generally, the main discriminative parts focus on some local regions from the foreground of each pedestrian image for Re-ID, and they should be irrelevant to the background. Whereas, most existing methods generate pedestrian images only based on global features, which difficultly achieves emphasizing crucial local regions and weakening the background. Based on this, we propose a CAM-enhancing generative Re-ID method in which the global and local features are jointly used. In the proposed method, an adaptive CAM-enhancing local encoder is designed to explore the significance of local appearances and enhance the effect of crucial local features in generations, where the foreground is divided into multiple local parts and separated from the background by pedestrian segmentation. Moreover, a new generation loss is proposed to supervise the identity consistency by reducing the inconsistency of crucial regions in foregrounds and meanwhile enrich the intra-class diversity by generating variant backgrounds. Experimental results indicate that the proposed method obtains better generation images and Re-ID performance than other methods.