OpenRaiser/Intern-Atlas huggingface dataset:
Push or Light: Nudging Standing to Break Prolonged Sitting
Venue: arXiv cs.HC 2025
Year: 2025

Prolonged sitting is a health risk leading to metabolic and cardiovascular diseases. To combat this, various "nudging" strategies encourage stand-ups. Behavior change triggers use explicit prompts such as smartphone push notifications or light controls. However, comparisons of the effects of such interactions, discomfort, and user context have not yet been performed. The present study evaluated these methods in a mixed design experiment with 15 college students. Three intervention methods (none, push notifications, and light dimming) and three user task contexts (computer work, video calls, and reading) were tested. The frequency of standing up and comfort were assessed after each ten-minute session. Results showed that dimming resulted in slightly more breaks (1.4 \pm 1.55) than push notification (1.2 \pm 1.08), but caused discomfort for 66.7% of participants, compared to 20% for notification. The results were influenced by task context. Dimming was most effective during video calls and reading, while push notifications were more effective during computer work. These findings suggest adaptive nudging systems should tailor interventions based on context and individual preferences.
LLMCup: Ranking-Enhanced Comment Updating with LLMs
Venue: arXiv cs.SE 2025
Year: 2025

While comments are essential for enhancing code readability and maintainability in modern software projects, developers are often motivated to update code but not comments, leading to outdated or inconsistent documentation that hinders future understanding and maintenance. Recent approaches such as CUP and HebCup have attempted automatic comment updating using neural sequence-to-sequence models and heuristic rules, respectively. However, these methods can miss or misinterpret crucial information during comment updating, resulting in inaccurate comments, and they often struggle with complex update scenarios. Given these challenges, a promising direction lies in leveraging large language models (LLMs), which have shown impressive performance in software engineering tasks such as comment generation, code synthesis, and program repair. This suggests their strong potential to capture the logic behind code modifications - an ability that is crucial for the task of comment updating. Nevertheless, selecting an appropriate prompt strategy for an LLM on each update case remains challenging. To address this, we propose a novel comment updating framework, LLMCup, which first uses multiple prompt strategies to provide diverse candidate updated comments via an LLM, and then employs a ranking model, CupRank, to select the best candidate as final updated comment. Experimental results demonstrate the effectiveness of LLMCup, with improvements over state-of-the-art baselines (CUP and HebCup) by 49.0%-116.9% in Accuracy, 10.8%-20% in BLEU-4, 4.6% in METEOR, 0.9%-1.9% in F1, and 2.1%-3.4% in SentenceBert similarity. Furthermore, a user study shows that comments updated by LLMCup sometimes surpass human-written updates, highlighting the importance of incorporating human evaluation in comment quality assessment.
On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
Venue: arXiv cs.DS 2025
Year: 2025

We study minimum cost constraint satisfaction problems (MinCostCSP) through the algebraic lens. We show that for any constraint language $Γ$ which has the dual discriminator operation as a polymorphism, there exists a $|D|$-approximation algorithm for MinCostCSP$(Γ)$ where $D$ is the domain. Complementing our algorithmic result, we show that any constraint language $Γ$ where MinCostCSP$(Γ)$ admits a constant-factor approximation must have a \emph{near-unanimity} (NU) polymorphism unless P = NP, extending a similar result by Dalmau et al. on MinCSPs. These results imply a dichotomy of constant-factor approximability for constraint languages that contain all permutation relations (a natural generalization for Boolean CSPs that allow variable negation): either MinCostCSP$(Γ)$ has an NU polymorphism and is $|D|$-approximable, or it does not have any NU polymorphism and is NP-hard to approximate within any constant factor. Finally, we present a constraint language which has a majority polymorphism, but is nonetheless NP-hard to approximate within any constant factor assuming the Unique Games Conjecture, showing that the condition of having an NU polymorphism is in general not sufficient unless UGC fails.
Fine-tuning ORBGRAND with Very Few Channel Soft Values
Venue: arXiv cs.IT 2025
Year: 2025

Guessing random additive noise decoding (GRAND) is a universal decoding paradigm that decodes by repeatedly testing error patterns until identifying a codeword, where the ordering of tests is generated by the received channel values. On one hand, while testing error patterns in a descending order of posterior probabilities leads to maximum likelihood decoding, its implementation complexity is prohibitive. On the other hand, testing error patterns with a prescribed set of error patterns permuted by the ranking among magnitudes of log-likelihood ratios (i.e., ordered reliability bits, ORB) enables efficient implementation, but results in performance loss for finite-length codes. Aiming at harnessing the strengths of these two approaches, this work proposes a fine-tuning method to improve ORBGRAND, adjusting the ordering of tests with the aid of very few exact channel soft values. This method is based on a metric for assessing the ``well-orderedness'' of error patterns. The metric is studied via the lens of the asymptotic theory of integer partitioning, which provides highly accurate estimation in numerical experiments. The metric then leads to an effective identification of fine-tuning to conduct, at the cost of a negligible increment of complexity. Numerical experiments demonstrate that the proposed fine-tuning method achieves a substantial performance enhancement compared with ORBGRAND.
ML-Based Automata Simplification for Symbolic Accelerators
Venue: arXiv cs.LG 2025
Year: 2025

Symbolic accelerators are increasingly used for symbolic data processing in domains such as genomics, NLP, and cybersecurity. However, these accelerators face scalability issues due to excessive memory use and routing complexity, especially when targeting a large set. We present AutoSlim, a machine learning-based graph simplification framework designed to reduce the complexity of symbolic accelerators built on Non-deterministic Finite Automata (NFA) deployed on FPGA-based overlays such as NAPOLY+. AutoSlim uses Random Forest classification to prune low-impact transitions based on edge scores and structural features, significantly reducing automata graph density while preserving semantic correctness. Unlike prior tools, AutoSlim targets automated score-aware simplification with weighted transitions, enabling efficient ranking-based sequence analysis. We evaluated data sets (1K to 64K nodes) in NAPOLY+ and conducted performance measurements including latency, throughput, and resource usage. AutoSlim achieves up to 40 percent reduction in FPGA LUTs and over 30 percent pruning in transitions, while scaling to graphs an order of magnitude larger than existing benchmarks. Our results also demonstrate how hardware interconnection (fanout) heavily influences hardware cost and that AutoSlim's pruning mitigates resource blowup.
Column Twisted Reed-Solomon Codes as MDS Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we study column twisted Reed-Solomon(TRS) codes. We establish some conditions for column TRS codes to be MDS codes and show that the dimension of their Schur square codes is $2k$. Consequently, these TRS codes are not equivalent to Reed-Solomon(RS) codes. Moreover, this construction method provides more flexible parameters compared to previous twisted generalized Reed-Solomon(TGRS) code constructions. For large odd prime power $q$, different from the systematically constructed TGRS codes whose length was previously limited to $\frac{q+1}{2}$, our construction achieves code lengths up to $\frac{q+3}{2}$. Finally, we present the dual codes of column TRS codes. This paper provides a new approach to construct MDS codes by adding column vectors to generator matrix of RS codes.
BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity
Venue: arXiv cs.LG 2025
Year: 2025

To alleviate the computational burden of large language models (LLMs), architectures with activation sparsity, represented by mixture-of-experts (MoE), have attracted increasing attention. However, the non-differentiable and inflexible routing of vanilla MoE hurts model performance. Moreover, while each token activates only a few parameters, these sparsely-activated architectures exhibit low chunk-level sparsity, indicating that the union of multiple consecutive tokens activates a large ratio of parameters. Such a sparsity pattern is unfriendly for acceleration under low-resource conditions (e.g., end-side devices) and incompatible with mainstream acceleration techniques (e.g., speculative decoding). To address these challenges, we introduce a novel MoE architecture, BlockFFN, as well as its efficient training and deployment techniques. Specifically, we use a router integrating ReLU activation and RMSNorm for differentiable and flexible routing. Next, to promote both token-level sparsity (TLS) and chunk-level sparsity (CLS), CLS-aware training objectives are designed, making BlockFFN more acceleration-friendly. Finally, we implement efficient acceleration kernels, combining activation sparsity and speculative decoding for the first time. The experimental results demonstrate the superior performance of BlockFFN over other MoE baselines, achieving over 80% TLS and 70% 8-token CLS. Our kernels achieve up to 3.67$\times$ speedup on real end-side devices than dense models. All codes and checkpoints are available publicly (https://github.com/thunlp/BlockFFN).
A New Approach for Multicriteria Assessment in the Ranking of Alternatives Using Cardinal and Ordinal Data
Venue: arXiv cs.AI 2025
Year: 2025

Modern methods for multi-criteria assessment (MCA), such as Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), and Multiple Criteria Decision-Making (MCDM), are utilized to appraise a collection of Decision-Making Units (DMUs), also known as alternatives, based on several criteria. These methodologies inherently rely on assumptions and can be influenced by subjective judgment to effectively tackle the complex evaluation challenges in various fields. In real-world scenarios, it is essential to incorporate both quantitative and qualitative criteria as they consist of cardinal and ordinal data. Despite the inherent variability in the criterion values of different alternatives, the homogeneity assumption is often employed, significantly affecting evaluations. To tackle these challenges and determine the most appropriate alternative, we propose a novel MCA approach that combines two Virtual Gap Analysis (VGA) models. The VGA framework, rooted in linear programming, is pivotal in the MCA methodology. This approach improves efficiency and fairness, ensuring that evaluations are both comprehensive and dependable, thus offering a strong and adaptive solution. Two comprehensive numerical examples demonstrate the accuracy and transparency of our proposed method. The goal is to encourage continued advancement and stimulate progress in automated decision systems and decision support systems.
ODIA: Oriented Distillation for Inline Acceleration of LLM-based Function Calling
Venue: arXiv cs.LG 2025
Year: 2025

Function Calling is a crucial technique that enables Large Language Models (LLMs) to interact with external systems through APIs. However, the high latency associated with LLM-based Function Calling significantly impacts user experience. This paper presents a novel approach called Oriented Distillation for Inline Acceleration (ODIA) that leverages online user interaction data to accelerate Function Calling. By automatically identifying "simple queries" from production traffic and distilling knowledge from larger models to smaller ones, our method reduces response latency by 45% (expected) and 78% (median) while maintaining accuracy. We demonstrate the effectiveness of our approach through real-world deployment in a music application, where the smaller model successfully handles 60% of traffic with negligible accuracy loss. Our method requires minimal human intervention and continuously improves through automated data collection and model updating, making it a practical solution for production environments.
Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) have showcased remarkable generalizability in language comprehension and hold significant potential to revolutionize human-computer interaction in smart homes. Existing LLM-based smart home assistants typically transmit user commands, along with user profiles and home configurations, to remote servers to obtain personalized services. However, users are increasingly concerned about the potential privacy leaks to the remote servers. To address this issue, we develop HomeLLaMA, an on-device assistant for privacy-preserving and personalized smart home serving with a tailored small language model (SLM). HomeLLaMA learns from cloud LLMs to deliver satisfactory responses and enable user-friendly interactions. Once deployed, HomeLLaMA facilitates proactive interactions by continuously updating local SLMs and user profiles. To further enhance user experience while protecting their privacy, we develop PrivShield to offer an optional privacy-preserving LLM-based smart home serving for those users, who are unsatisfied with local responses and willing to send less-sensitive queries to remote servers. For evaluation, we build a comprehensive benchmark DevFinder to assess the service quality. Extensive experiments and user studies (M=100) demonstrate that HomeLLaMA can provide personalized services while significantly enhancing user privacy.
Agent-based visualization of streaming text
Venue: arXiv cs.MA 2025
Year: 2025

We present a visualization infrastructure that maps data elements to agents, which have behaviors parameterized by those elements. Dynamic visualizations emerge as the agents change position, alter appearance and respond to one other. Agents move to minimize the difference between displayed agent-to-agent distances, and an input matrix of ideal distances. Our current application is visualization of streaming text. Each agent represents a significant word, visualizing it by displaying the word itself, centered in a circle sized by the frequency of word occurrence. We derive the ideal distance matrix from word cooccurrence, mapping higher co-occurrence to lower distance. To depict co-occurrence in its textual context, the ratio of intersection to circle area approximates the ratio of word co-occurrence to frequency. A networked backend process gathers articles from news feeds, blogs, Digg or Twitter, exploiting online search APIs to focus on user-chosen topics. Resulting visuals reveal the primary topics in text streams as clusters, with agent-based layout moving without instability as data streams change dynamically.
'Teens Need to Be Educated on the Danger': Digital Access, Online Risks, and Safety Practices Among Nigerian Adolescents
Venue: arXiv cs.HC 2025
Year: 2025

Adolescents increasingly rely on online technologies to explore their identities, form social connections, and access information and entertainment. However, their growing digital engagement exposes them to significant online risks, particularly in underrepresented contexts like West Africa. This study investigates the online experiences of 409 secondary school adolescents in Nigeria's Federal Capital Territory (FCT), focusing on their access to technology, exposure to risks, coping strategies, key stakeholders influencing their online interactions, and recommendations for improving online safety. Using self-administered surveys, we found that while most adolescents reported moderate access to online technology and connectivity, those who encountered risks frequently reported exposure to inappropriate content and online scams. Blocking and reporting tools were the most commonly used strategies, though some adolescents responded with inaction due to limited resources or awareness. Parents emerged as the primary support network, though monitoring practices and communication varied widely. Guided by Protection Motivation Theory (PMT), our analysis interprets adolescents' online safety behaviors as shaped by both their threat perceptions and their confidence in available coping strategies. A thematic analysis of their recommendations highlights the need for greater awareness and education, parental mediation, enhanced safety tools, stricter age restrictions, improved content moderation, government accountability, and resilience-building initiatives. Our findings underscore the importance of culturally and contextually relevant interventions to empower adolescents in navigating the digital world, with implications for parents, educators, designers, and policymakers.
How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs
Venue: arXiv cs.MA 2025
Year: 2025

Large Language Models (LLMs) have achieved strong performance on a wide range of complex reasoning tasks, yet further gains are often possible by leveraging the complementary strengths of multiple models. While multi-agent frameworks can improve solution quality by leveraging multiple LLMs, existing methods are often computationally expensive, both at training and inference time. In this work, we introduce a hierarchical multi-agent framework that addresses these challenges by training only a single leader LLM to coordinate a team of untrained peer agents. To this end, we propose Multi-agent guided Leader Policy \textbf{O}ptimization (MLPO), a novel approach which trains the leader to evaluate and synthesize agent responses without auxiliary value networks or explicit agent feedback. Leaders trained with MLPO exhibit improved performance not only when interacting with the agent team at inference time, but also enjoy improved performance when deployed in single-agent settings without the team. Empirical results on Big-Bench Hard (BBH), MATH, and MMLU demonstrate that our framework achieves substantial performance improvements over both single-agent and multi-agent baselines. Our results highlight the effectiveness and efficiency of training a single, flexible leader for collaborative reasoning in multi-agent LLM systems.
Exploiting Leaderboards for Large-Scale Distribution of Malicious Models
Venue: arXiv cs.LG 2025
Year: 2025

While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplored. In this paper, we shed light on how model leaderboards -- ranked platforms for model discovery and evaluation -- can serve as a powerful channel for adversaries for stealthy large-scale distribution of poisoned models. We present TrojanClimb, a general framework that enables injection of malicious behaviors while maintaining competitive leaderboard performance. We demonstrate its effectiveness across four diverse modalities: text-embedding, text-generation, text-to-speech and text-to-image, showing that adversaries can successfully achieve high leaderboard rankings while embedding arbitrary harmful functionalities, from backdoors to bias injection. Our findings reveal a significant vulnerability in the machine learning ecosystem, highlighting the urgent need to redesign leaderboard evaluation mechanisms to detect and filter malicious (e.g., poisoned) models, while exposing broader security implications for the machine learning community regarding the risks of adopting models from unverified sources.
Orchestration for Domain-specific Edge-Cloud Language Models
Venue: arXiv cs.DB 2025
Year: 2025

The remarkable performance of Large Language Models (LLMs) has inspired many applications, which often necessitate edge-cloud collaboration due to connectivity, privacy, and cost considerations. Traditional methods primarily focus on selecting the best LLM model for optimizing performance, while neglecting the critical interplay between the components of the LLM serving pipeline (context retrieval, query preprocessing, etc.) or the changing latency and cost constraints. We introduce ECO-LLM (Edge-Cloud Orchestrator for LLMs), a novel system that reframes this problem as a joint optimization challenge and solves it by systematically exploring component configurations and dynamically selecting optimal strategies at the query level. ECO-LLM consists of two components: (1) the ECO-LLM Emulator, which efficiently explores the vast configuration space utilizing query clustering and pareto-optimal path selection, gathering domain-specific performance metrics without exhaustive evaluation; and (2) the ECO-LLM Runtime, which leverages these metrics to dynamically select optimal resolution strategies for user queries while meeting user-defined Service Level Objectives (SLOs). We evaluate ECO-LLM on a smart home and a smart car assistant scenarios. With an exhaustive exploration of all possible configurations for seen queries, ECO-LLM outperforms cloud-based models like GPT-4o in terms of accuracy (90% vs. 74% on average) while reducing costs by 90% and latency by 55%, demonstrating the value of its joint optimization at the query level. In practical deployment for previously unseen queries, ECO-LLM selects configurations that reduce costs by 62% or improve response times by 62% on average compared to state-of-the-art model routing approaches, while maintaining higher accuracy and consistently adhering to specified latency and cost constraints.
Towards Extracting Software Requirements from App Reviews using Seq2seq Framework
Venue: arXiv cs.SE 2025
Year: 2025

Mobile app reviews are a large-scale data source for software improvements. A key task in this context is effectively extracting requirements from app reviews to analyze the users' needs and support the software's evolution. Recent studies show that existing methods fail at this task since app reviews usually contain informal language, grammatical and spelling errors, and a large amount of irrelevant information that might not have direct practical value for developers. To address this, we propose a novel reformulation of requirements extraction as a Named Entity Recognition (NER) task based on the sequence-to-sequence (Seq2seq) generation approach. With this aim, we propose a Seq2seq framework, incorporating a BiLSTM encoder and an LSTM decoder, enhanced with a self-attention mechanism, GloVe embeddings, and a CRF model. We evaluated our framework on two datasets: a manually annotated set of 1,000 reviews (Dataset 1) and a crowdsourced set of 23,816 reviews (Dataset 2). The quantitative evaluation of our framework showed that it outperformed existing state-of-the-art methods with an F1 score of 0.96 on Dataset 2, and achieved comparable performance on Dataset 1 with an F1 score of 0.47.