OpenRaiser/Intern-Atlas huggingface dataset:
An $O(n$)-Algorithm for the Higher-Order Kinematics and Inverse Dynamics of Serial Manipulators using Spatial Representation of Twists
Venue: arXiv cs.RO 2025
Year: 2025

Optimal control in general, and flatness-based control in particular, of robotic arms necessitate to compute the first and second time derivatives of the joint torques/forces required to achieve a desired motion. In view of the required computational efficiency, recursive $O(n)$-algorithms were proposed to this end. Aiming at compact yet efficient formulations, a Lie group formulation was recently proposed, making use of body-fixed and hybrid representation of twists and wrenches. In this paper a formulation is introduced using the spatial representation. The second-order inverse dynamics algorithm is accompanied by a fourth-order forward and inverse kinematics algorithm. An advantage of all Lie group formulations is that they can be parameterized in terms of vectorial quantities that are readily available. The method is demonstrated for the 7 DOF Franka Emika Panda robot.
Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System
Venue: arXiv cs.DC 2025
Year: 2025

Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-based benchmarking for RabbitMQ, one of the main open source messaging frameworks. The involved system is described, as well as required components, and setup scenarios, involving different workloads and configurations among the tests as well as messaging system use cases. Alternative architectures are investigated and compared from an energy consumption point of view, for different message rates and consumer numbers. Differences in architectural selection have been quantified and can lead to up to 31\% reduction in power consumption. The resulting dataset is made publicly available and can thus prove helpful for architectures' comparison, energy-based cost modeling, and beyond.
PREMISE: Scalable and Strategic Prompt Optimization for Efficient Mathematical Reasoning in Large Models
Venue: arXiv cs.CL 2025
Year: 2025

Large reasoning models (LRMs) such as Claude 3.7 Sonnet and OpenAI o1 achieve strong performance on mathematical benchmarks using lengthy chain-of-thought (CoT) reasoning, but the resulting traces are often unnecessarily verbose. This inflates token usage and cost, limiting deployment in latency-sensitive or API-constrained settings. We introduce PREMISE (PRompt-based Efficient Mathematical Inference with Strategic Evaluation), a prompt-only framework that reduces reasoning overhead without modifying model weights. PREMISE combines trace-level diagnostics with gradient-inspired prompt optimization to minimize redundant computation while preserving answer accuracy. The approach jointly optimizes brevity and correctness through a multi-objective textual search that balances token length and answer validity. Unlike prior work, PREMISE runs in a single-pass black-box interface, so it can be applied directly to commercial LLMs. On GSM8K, SVAMP, and Math500 we match or exceed baseline accuracy ($96\%\rightarrow96\%$ with Claude, $91\%\rightarrow92\%$ with Gemini) while reducing reasoning tokens by up to $87.5\%$ and cutting dollar cost by $69$--$82\%$. These results show that prompt-level optimization is a practical and scalable path to efficient LRM inference without compromising reasoning quality.
Think before You Simulate: Symbolic Reasoning to Orchestrate Neural Computation for Counterfactual Question Answering
Venue: arXiv cs.AI 2025
Year: 2025

Causal and temporal reasoning about video dynamics is a challenging problem. While neuro-symbolic models that combine symbolic reasoning with neural-based perception and prediction have shown promise, they exhibit limitations, especially in answering counterfactual questions. This paper introduces a method to enhance a neuro-symbolic model for counterfactual reasoning, leveraging symbolic reasoning about causal relations among events. We define the notion of a causal graph to represent such relations and use Answer Set Programming (ASP), a declarative logic programming method, to find how to coordinate perception and simulation modules. We validate the effectiveness of our approach on two benchmarks, CLEVRER and CRAFT. Our enhancement achieves state-of-the-art performance on the CLEVRER challenge, significantly outperforming existing models. In the case of the CRAFT benchmark, we leverage a large pre-trained language model, such as GPT-3.5 and GPT-4, as a proxy for a dynamics simulator. Our findings show that this method can further improve its performance on counterfactual questions by providing alternative prompts instructed by symbolic causal reasoning.
Integrating Large Language Models into Text Animation: An Intelligent Editing System with Inline and Chat Interaction
Venue: arXiv cs.HC 2025
Year: 2025

Text animation, a foundational element in video creation, enables efficient and cost-effective communication, thriving in advertisements, journalism, and social media. However, traditional animation workflows present significant usability barriers for non-professionals, with intricate operational procedures severely hindering creative productivity. To address this, we propose a Large Language Model (LLM)-aided text animation editing system that enables real-time intent tracking and flexible editing. The system introduces an agent-based dual-stream pipeline that integrates context-aware inline suggestions and conversational guidance as well as employs a semantic-animation mapping to facilitate LLM-driven creative intent translation. Besides, the system supports synchronized text-animation previews and parametric adjustments via unified controls to improve editing workflow. A user study evaluates the system, highlighting its ability to help non-professional users complete animation workflows while validating the pipeline. The findings encourage further exploration of integrating LLMs into a comprehensive video creation workflow.
Hazel Deriver: A Live Editor for Constructing Rule-Based Derivations
Venue: arXiv cs.PL 2025
Year: 2025

Students in programming languages and formal logic courses often struggle with constructing rule-based derivation trees due to the complexity of applying inference rules, the lack of immediate feedback, and the manual effort required for handwritten proofs. We present Hazel Deriver, a live, web-based editor designed to scaffold derivation construction through multiple layers of support. Built on the Hazel live programming environment, it provides a structured, interactive experience that encourages iterative exploration and real-time feedback. A preliminary user study with former students suggests that Hazel Deriver reduces the perceived difficulty of derivation tasks while improving conceptual understanding and engagement. We discuss the design of its layered scaffolding features and raise questions about balancing system guidance with learner autonomy.
LLM-Driven Personalized Answer Generation and Evaluation
Venue: arXiv cs.CY 2025
Year: 2025

Online learning has experienced rapid growth due to its flexibility and accessibility. Personalization, adapted to the needs of individual learners, is crucial for enhancing the learning experience, particularly in online settings. A key aspect of personalization is providing learners with answers customized to their specific questions. This paper therefore explores the potential of Large Language Models (LLMs) to generate personalized answers to learners' questions, thereby enhancing engagement and reducing the workload on educators. To evaluate the effectiveness of LLMs in this context, we conducted a comprehensive study using the StackExchange platform in two distinct areas: language learning and programming. We developed a framework and a dataset for validating automatically generated personalized answers. Subsequently, we generated personalized answers using different strategies, including 0-shot, 1-shot, and few-shot scenarios. The generated answers were evaluated using three methods: 1. BERTScore, 2. LLM evaluation, and 3. human evaluation. Our findings indicated that providing LLMs with examples of desired answers (from the learner or similar learners) can significantly enhance the LLMs' ability to tailor responses to individual learners' needs.
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
Venue: arXiv cs.CL 2025
Year: 2025

Medical dialogue systems (MDS) have emerged as crucial online platforms for enabling multi-turn, context-aware conversations with patients. However, existing MDS often struggle to (1) identify relevant medical knowledge and (2) generate personalized, medically accurate responses. To address these challenges, we propose MedRef, a novel MDS that incorporates knowledge refining and dynamic prompt adjustment. First, we employ a knowledge refining mechanism to filter out irrelevant medical data, improving predictions of critical medical entities in responses. Additionally, we design a comprehensive prompt structure that incorporates historical details and evident details. To enable real-time adaptability to diverse patient conditions, we implement two key modules, Triplet Filter and Demo Selector, providing appropriate knowledge and demonstrations equipped in the system prompt. Extensive experiments on MedDG and KaMed benchmarks show that MedRef outperforms state-of-the-art baselines in both generation quality and medical entity accuracy, underscoring its effectiveness and reliability for real-world healthcare applications.
The Diffusion Duality
Venue: arXiv cs.LG 2025
Year: 2025

Uniform-state discrete diffusion models hold the promise of fast text generation due to their inherent ability to self-correct. However, they are typically outperformed by autoregressive models and masked diffusion models. In this work, we narrow this performance gap by leveraging a key insight: Uniform-state diffusion processes naturally emerge from an underlying Gaussian diffusion. Our method, Duo, transfers powerful techniques from Gaussian diffusion to improve both training and sampling. First, we introduce a curriculum learning strategy guided by the Gaussian process, doubling training speed by reducing variance. Models trained with curriculum learning surpass autoregressive models in zero-shot perplexity on 3 of 7 benchmarks. Second, we present Discrete Consistency Distillation, which adapts consistency distillation from the continuous to the discrete setting. This algorithm unlocks few-step generation in diffusion language models by accelerating sampling by two orders of magnitude. We provide the code, model checkpoints, and video tutorials on the project page: http://s-sahoo.github.io/duo
M4V: Multi-Modal Mamba for Text-to-Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators. However, modeling the vast spatiotemporal space remains computationally demanding, particularly when employing Transformers, which incur quadratic complexity in sequence processing and thus limit practical applications. Recent advancements in linear-time sequence modeling, particularly the Mamba architecture, offer a more efficient alternative. Nevertheless, its plain design limits its direct applicability to multi-modal and spatiotemporal video generation tasks. To address these challenges, we introduce M4V, a Multi-Modal Mamba framework for text-to-video generation. Specifically, we propose a multi-modal diffusion Mamba (MM-DiM) block that enables seamless integration of multi-modal information and spatiotemporal modeling through a multi-modal token re-composition design. As a result, the Mamba blocks in M4V reduce FLOPs by 45% compared to the attention-based alternative when generating videos at 768$\times$1280 resolution. Additionally, to mitigate the visual quality degradation in long-context autoregressive generation processes, we introduce a reward learning strategy that further enhances per-frame visual realism. Extensive experiments on text-to-video benchmarks demonstrate M4V's ability to produce high-quality videos while significantly lowering computational costs. Code and models will be publicly available at https://huangjch526.github.io/M4V_project.
Video-Mediated Emotion Disclosure: Expressions of Fear, Sadness, and Joy by People with Schizophrenia on YouTube
Venue: arXiv cs.HC 2025
Year: 2025

Individuals with schizophrenia frequently experience intense emotions and often turn to vlogging as a medium for emotional expression. While previous research has predominantly focused on text based disclosure, little is known about how individuals construct narratives around emotions and emotional experiences in video blogs. Our study addresses this gap by analyzing 200 YouTube videos created by individuals with schizophrenia. Drawing on media research and self presentation theories, we developed a visual analysis framework to disentangle these videos. Our analysis revealed diverse practices of emotion disclosure through both verbal and visual channels, highlighting the dynamic interplay between these modes of expression. We found that the deliberate construction of visual elements, including environmental settings and specific aesthetic choices, appears to foster more supportive and engaged viewer responses. These findings underscore the need for future large scale quantitative research examining how visual features shape video mediated communication on social media platforms. Such investigations would inform the development of care centered video sharing platforms that better support individuals managing illness experiences.
Monitoring Decomposition Attacks in LLMs with Lightweight Sequential Monitors
Venue: arXiv cs.CR 2025
Year: 2025

Current LLM safety defenses fail under decomposition attacks, where a malicious goal is decomposed into benign subtasks that circumvent refusals. The challenge lies in the existing shallow safety alignment techniques: they only detect harm in the immediate prompt and do not reason about long-range intent, leaving them blind to malicious intent that emerges over a sequence of seemingly benign instructions. We therefore propose adding an external monitor that observes the conversation at a higher granularity. To facilitate our study of monitoring decomposition attacks, we curate the largest and most diverse dataset to date, including question-answering, text-to-image, and agentic tasks. We verify our datasets by testing them on frontier LLMs and show an 87% attack success rate on average on GPT-4o. This confirms that decomposition attack is broadly effective. Additionally, we find that random tasks can be injected into the decomposed subtasks to further obfuscate malicious intents. To defend in real time, we propose a lightweight sequential monitoring framework that cumulatively evaluates each subtask. We show that a carefully prompt engineered lightweight monitor achieves a 93% defense success rate, beating reasoning models like o3 mini as a monitor. Moreover, it remains robust against random task injection and cuts cost by 90% and latency by 50%. Our findings suggest that lightweight sequential monitors are highly effective in mitigating decomposition attacks and are viable in deployment.
ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the overall efficiency and accuracy of content review. However, existing resources for harmful content detection are predominantly focused on English, with Chinese datasets remaining scarce and often limited in scope. We present a comprehensive, professionally annotated benchmark for Chinese content harm detection, which covers six representative categories and is constructed entirely from real-world data. Our annotation process further yields a knowledge rule base that provides explicit expert knowledge to assist LLMs in Chinese harmful content detection. In addition, we propose a knowledge-augmented baseline that integrates both human-annotated knowledge rules and implicit knowledge from large language models, enabling smaller models to achieve performance comparable to state-of-the-art LLMs. Code and data are available at https://github.com/zjunlp/ChineseHarm-bench.
Collaboration Tools and their Role in Agile Software Projects
Venue: arXiv cs.SE 2025
Year: 2025

The purpose of this review is to understand the importance of collaboration tools which are Slack, Microsoft Teams, Confluence in Agile and software projects. Agile methodologies rely on flexibility, using cycles and integration throughout various levels of developing cycles. However, it is still a great problem for many teams to collaborate and communicate even if staff members and teams are working remotely. In terms of collaboration, the applications and technologies mean better organization of work, increased mutually understandable openness and fast and efficient inter team and interpersonal interactions to enhance results of projects into productivity. This paper examines how these tools fit the Agile principles, how they facilitate iterative development, and encouraging effective initiation and tracking of tasks in small and large projects. The insights focus on how Slack, Microsoft Teams, and Confluence are essential for gaining better task coordination, supporting knowledge sharing, and adopting agile values across cross-functional contexts.
Contract-based Verification of Digital Twins
Venue: arXiv cs.SE 2025
Year: 2025

Digital twins are becoming powerful tools in industrial applications, offering virtual representations of cyber-physical systems. However, verification of these models remains a significant challenge due to the potentially large datasets used by the digital twin. This paper introduces an innovative methodology for verifying neural network-based digital twin models, in a black-box fashion, by integrating model checking into the process. The latter relies on defining and applying system-level contracts that capture the system's requirements, to verify the behavior of digital twin models, implemented in Simulink. We develop an automated solution that simulates the digital twin model for certain inputs, and feeds the predicted outputs together with the inputs to the contract model described as a network of timed automata in the UPPAAL model checker. The latter verifies whether the predicted outputs fulfill the specified contracts. This approach allows us to identify scenarios where the digital twin's behavior fails to meet the contracts, without requiring the digital twin's design technicalities. We apply our method to a boiler system case study for which we identify prediction errors via contract verification. Our work demonstrates the effectiveness of integrating model checking with digital twin models for continuous improvement.
A Theory-driven Interpretation and Elaboration of Verification and Validation
Venue: arXiv cs.SE 2025
Year: 2025

This paper presents a formal theory of verification and validation (V&V) within systems engineering, grounded in the axiom that V&V are fundamentally knowledge-building activities. Using dynamic epistemic modal logic, we develop precise definitions of verification and validation, articulating their roles in confirming and contextualizing knowledge about systems. The theory formalizes the interplay between epistemic states, evidence, and reasoning processes, allowing for the derivation of theorems that clarify the conceptual underpinnings of V&V. By providing a formal foundation, this work addresses ambiguities in traditional V&V practices, offering a structured framework to enhance precision and consistency in systems engineering methodologies. The insights gained have implications for both academic research and practical applications, fostering a deeper understanding of V&V as critical components of engineering knowledge generation.
Automated Extraction and Analysis of Developer's Rationale in Open Source Software
Venue: arXiv cs.SE 2025
Year: 2025

Contributors to open source software must deeply understand a project's history to make coherent decisions which do not conflict with past reasoning. However, inspecting all related changes to a proposed contribution requires intensive manual effort, and previous research has not yet produced an automated mechanism to expose and analyze these conflicts. In this article, we propose such an automated approach for rationale analyses, based on an instantiation of Kantara, an existing high-level rationale extraction and management architecture. Our implementation leverages pre-trained models and Large Language Models, and includes structure-based mechanisms to detect reasoning conflicts and problems which could cause design erosion in a project over time. We show the feasibility of our extraction and analysis approach using the OOM-Killer module of the Linux Kernel project, and investigate the approach's generalization to five other highly active open source projects. The results confirm that our automated approach can support rationale analyses with reasonable performance, by finding interesting relationships and to detect potential conflicts and reasoning problems. We also show the effectiveness of the automated extraction of decision and rationale sentences and the prospects for generalizing this to other open source projects. This automated approach could therefore be used by open source software developers to proactively address hidden issues and to ensure that new changes do not conflict with past decisions.
Impact of Comments on LLM Comprehension of Legacy Code
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) have been increasingly integrated into software engineering and maintenance tasks due to their high performance with software engineering tasks and robust understanding of modern programming languages. However, the ability of LLMs to comprehend code written with legacy languages remains a research gap challenged by real-world legacy systems lacking or containing inaccurate documentation that may impact LLM comprehension. To assess LLM comprehension of legacy languages, there is a need for objective LLM evaluation. In order to objectively measure LLM comprehension of legacy languages, we need an efficient, quantitative evaluation method. We leverage multiple-choice question answering (MCQA), an emerging LLM evaluation methodology, to evaluate LLM comprehension of legacy code and the impact of comment prevalence and inaccurate comments. In this work, we present preliminary findings on the impact of documentation on LLM comprehension of legacy code and outline strategic objectives for future work.