Among the programming languages for Programmable Logic Controllers (PLCs), Structured Text (ST) is widely adopted for industrial automation due to its expressiveness and flexibility. However, major vendors implement ST with proprietary extensions and hardware-specific libraries - Siemens’ SCL and CODESYS’ ST each differ in syntax and functionality. This fragmentation forces engineers to relearn implementation details across platforms, creating substantial productivity barriers. To address this challenge, we developed AutoPLC, a framework capable of automatically generating vendor-aware ST code directly from natural language requirements. Our solution begins by building two essential knowledge sources tailored to each vendor’s specifications: a structured API library containing platform-exclusive functions, and an annotated case database that captures real-world implementation experience. Building on these foundations, we created a four-stage generation process that combines step-wise planning (enhanced with a lightweight natural language state machine support for control logic), contextual case retrieval using LLM-based reranking, API recommendation guided by industrial data, and dynamic validation through direct interaction with vendor IDEs. Implemented for Siemens TIA Portal and the CODESYS platform, AutoPLC achieves 90%+ compilation success on our 914-task benchmark (covering general-purpose and process control functions), outperforming all selected baselines, at an average cost of only $0.13 per task. Experienced PLC engineers positively assessed the practical utility of the generated code, including cases that failed compilation.

Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving. While recent research explores multi-agent collaboration among LLMs, most approaches rely on static organizational structures that struggle to adapt as task complexity and agent numbers grow, resulting in coordination overhead and inefficiencies. To this end, we propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a centralized orchestrator ("puppeteer") dynamically directs agents ("puppets") in response to evolving task states. This orchestrator is trained via reinforcement learning to adaptively sequence and prioritize agents, enabling flexible and evolvable collective reasoning. Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs. Analyses further reveal that the key improvements consistently stem from the emergence of more compact, cyclic reasoning structures under the orchestrator’s evolution. Our code is available at https://github.com/OpenBMB/ChatDev/tree/puppeteer.

Recent work suggests that large language models (LLMs) can perform multi-hop reasoning implicitly---producing correct answers without explicitly verbalizing intermediate steps---but the underlying mechanisms remain poorly understood. In this paper, we study how such implicit reasoning emerges by training transformers from scratch in a controlled symbolic environment. Our analysis reveals a three-stage developmental trajectory: early memorization, followed by in-distribution generalization, and eventually cross-distribution generalization. We find that training with atomic triples is not necessary but accelerates learning, and that second-hop generalization relies on query-level exposure to specific compositional structures. To interpret these behaviors, we introduce two diagnostic tools: cross-query semantic patching, which identifies semantically reusable intermediate representations, and a cosine-based representational lens, which reveals that successful reasoning correlates with the cosine-base clustering in hidden space. This clustering phenomenon in turn provides a coherent explanation for the behavioral dynamics observed across training, linking representational structure to reasoning capability. These findings provide new insights into the interpretability of implicit multi-hop reasoning in LLMs, helping to clarify how complex reasoning processes unfold internally and offering pathways to enhance the transparency of such models.

Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, YiFei Wang, Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, Maosong Sun. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) for years. Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed. While offering exciting new prospects, GANs in NLG by far are nevertheless reportedly suffering from training instability and mode collapse, and therefore outperformed by conventional MLE models. In this work, we propose techniques for improving GANs in NLG, namely Best Student Forcing (BSF), a novel yet simple adversarial training mechanism in which generated sequences of high quality are selected as temporary ground-truth to further train the generator. We also use an ensemble of discriminators to increase training stability and sample diversity. Evaluation shows that the combination of BSF and multiple discriminators consistently performs better than previous GAN approaches over various metrics, and outperforms a baseline MLE in terms of Fr ́ech ́et Distance, a recently proposed metric capturing both sample quality and diversity.

In this paper we propose an integrated framework of automatic bilingual subtitle generation for lecture videos, especially for MOOCs. The framework consists of Automatic Speech Recognition (ASR), Sentence Boundary Detection (SBD), and Machine Translation (MT). Then we quantitatively evaluate the auto-generated subtitles, the manually produced subtitles from scratch, and the auto-generated subtitles with manual modification in term of accuracy and time expenditure, in both original and target languages. The result shows that the auto-generated subtitles in the original language (English) are fairly accurate already. By using them as the draft, human subtitle producers can save 54% of the working time and simultaneously reduce the error rate by 54.3%, which is a significant improvement. However, the effectiveness of machine translated subtitles (English to Chinese) is limited. In the end, if the proposed framework is applied, the total working time in preparing bilingual subtitles can be shortened by approximately 1/3, with no decline in quality.

In this paper we propose a method to evaluate the importance of lecture video segments in online courses. The video will be first segmented based on the slide transition. Then we evaluate the importance of each segment based on our analysis of the teacher's focus. This focus is mainly identified by exploring features in the slide and the speech. Since the whole analysis process is based on multimedia materials, it could be done before the official start of the course. By setting survey questions and collecting forum statistics in the MOOC "Web Technologies", the proposed method is evaluated. Both the general trend and the high accuracy of selected key segments (over 70%) prove the effectiveness of the proposed method.

In this paper we showcase a system for real-time text detection and recognition. We apply deep features created by Convolutional Neural Networks (CNNs) for both text detection and word recognition task. For text detection we follow the common localization-verification scheme which already shown its excellent ability in numerous previous work. In text localization stage, textual regions are roughly detected by using a MSERs (Maximally Stable Extremal Regions) detector with high recall rate. False alarms are then eliminated by using a CNNs classifier, and remaining text regions are further grouped into words. In the word recognition stage, we developed an skeleton-based text binarization method for segmenting text from its background. A CNNs based recognizer is then applied for recognizing character. The initial experiments show the powerful ability of deep features for text classification comparing with commonly used visual features. Our current implementation demonstrates real-time performance for recognizing scene text by using a standard PC with webcam.

In this paper we propose a solution which segments lecture video by analyzing its supplementary synchronized slides. The slides content derives automatically from OCR (Optical Character Recognition) process with an approximate accuracy of 90%. Then we partition the slides into different subtopics by examining their logical relevance. Since the slides are synchronized with the video stream, the subtopics of the slides indicate exactly the segments of the video. Our evaluation reveals that the average length of segments for each lecture is ranged from 5 to 15 minutes, and 45% segments achieved from test datasets are logically reasonable.