OpenRaiser/Intern-Atlas huggingface dataset:
Reevaluating Self-Consistency Scaling in Multi-Agent Systems
Venue: arXiv cs.AI 2025
Year: 2025

This study examines the trade-offs of increasing sampled reasoning paths in self-consistency for modern large language models (LLMs). Earlier research with older models showed that combining multiple reasoning chains improves results before reaching a plateau. Using Gemini 2.5 models on HotpotQA and Math-500, we revisit those claims under current model conditions. Each configuration pooled outputs from varying sampled reasoning paths and compared them to a single chain-of-thought (CoT) baseline. Larger models exhibited a more stable and consistent improvement curve. The results confirm that performance gains taper off after moderate sampling, aligning with past findings. This plateau suggests diminishing returns driven by overlap among reasoning paths. Self-consistency remains useful, but high-sample configurations offer little benefit relative to their computational cost.
Automatic Policy Search using Population-Based Hyper-heuristics for the Integrated Procurement and Perishable Inventory Problem
Venue: arXiv cs.NE 2025
Year: 2025

This paper addresses the problem of managing perishable inventory under multiple sources of uncertainty, including stochastic demand, unreliable supplier fulfillment, and probabilistic product shelf life. We develop a discrete-event simulation environment to compare two optimization strategies for this multi-item, multi-supplier problem. The first strategy optimizes uniform classic policies (e.g., Constant Order and Base Stock) by tuning their parameters globally, complemented by a direct search to select the best-fitting suppliers for the integrated problem. The second approach is a hyper-heuristic approach, driven by metaheuristics such as a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This framework constructs a composite policy by automating the selection of the heuristic type, its parameters, and the sourcing suppliers on an item-by-item basis. Computational results from twelve distinct instances demonstrate that the hyper-heuristic framework consistently identifies superior policies, with GA and EGA exhibiting the best overall performance. Our primary contribution is verifying that this item-level policy construction yields significant performance gains over simpler global policies, thereby justifying the associated computational cost.
Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) have shown growing potential in software engineering, yet few benchmarks evaluate their ability to repair software during migration across instruction set architectures (ISAs). Cross-ISA migration, such as between x86_64 and aarch64, requires handling complex dependencies, heterogeneous toolchains, and long build logs while ensuring executable verification. To address this challenge, we present Build-bench, an end-to-end benchmark that systematically evaluates the capability of LLMs to repair build failures in cross-ISA settings. Build-bench collects 268 real-world failed packages and integrates auxiliary tools including Structure Extraction, File Content Extraction, Content Modification, and Build Verification to support autonomous, tool-augmented reasoning. The repair process operates in an iterative loop where, upon failure, the model receives updated build logs and previous repair outcomes to refine subsequent attempts. Through a comparative evaluation of six representative LLMs, Build-bench reveals that current models achieve a maximum build success rate of 63% and tool usage patterns differ significantly across models. By coupling real build environments with verifiable outcomes, Build-bench establishes the first architecture-aware benchmark for studying LLM-based software build and repair.
GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
Venue: arXiv cs.SE 2025
Year: 2025

With the software industry shifting toward a data-driven culture, online A/B testing is a key tool for evaluating new technologies. However, deploying such experiments requires substantial resources, may negatively impact users, and involves long data collection periods. To address this, \textit{off-policy evaluation (OPE)}, or offline A/B testing, uses logged data to assess technologies and is fundamental in Reinforcement Learning, making it crucial in domains where online testing is costly or risky, such as healthcare, recommender systems, education, dialog systems, and robotics. Despite advances in coding LLMs and agentic AI, little is known about leveraging them to optimize OPE results. We investigate whether LLMs and LLM-based agents can improve OPE performance via code optimization. We propose \textit{GrowthHacker}, a benchmark with agent and baseline methods on large-scale real-world datasets, which iteratively optimizes code, evaluates results, and begins new optimization cycles. We collected datasets, established protocols, implemented baselines for OPE on the Open Bandit Pipeline (OBP)~\cite{saito2021openbanditdatasetpipeline} and Scope-RL~\cite{kiyohara2023scope}, and developed the \textit{two_agent} framework, which reduces system complexity while preserving optimization effectiveness. Results show the two_agent framework achieves 100% reliability and the highest average improvement of 106.7% among positive outcomes. Both two_agent and CrewAI reach 45% success rates, outperforming AutoGen's 34%. These findings demonstrate the feasibility of LLM-based agents as automated "growth hackers" to enhance OPE systems, with implications for scaling data-driven decision-making in production.
REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query-table relevance and ignore table table compatibility. We introduce REAR (Retrieve, Expand and Refine), a three-stage, LLM-free framework that separates semantic relevance from structural joinability for efficient, high-fidelity multi-table retrieval. REAR (i) retrieves query-aligned tables, (ii) expands these with structurally joinable tables via fast, precomputed column-embedding comparisons, and (iii) refines them by pruning noisy or weakly related candidates. Empirically, REAR is retriever-agnostic and consistently improves dense/sparse retrievers on complex table QA datasets (BIRD, MMQA, and Spider) by improving both multi-table retrieval quality and downstream SQL execution. Despite being LLM-free, it delivers performance competitive with state-of-the-art LLM-augmented retrieval systems (e.g.,ARM) while achieving much lower latency and cost. Ablations confirm complementary gains from expansion and refinement, underscoring REAR as a practical, scalable building block for table-based downstream tasks (e.g., Text-to-SQL).
Beyond Single-Tokenomics: How Farcaster's Pluralistic Incentives Reshape Social Networking
Venue: arXiv cs.SI 2025
Year: 2025

This paper presents the first empirical analysis of how diverse token-based reward mechanisms impact platform dynamics and user behaviors. For this, we gather a unique, large-scale dataset from Farcaster. This blockchain-based, decentralized social network incorporates multiple incentive mechanisms spanning platform-native rewards, third-party token programs, and peer-to-peer tipping. Our dataset captures token transactions and social interactions from 574,829 wallet-linked users, representing 64.25% of the platform's user base. Our socioeconomic analyses reveal how different tokenomics design shape varying participation rates (7.6%--70%) and wealth concentration patterns (Gini 0.72--0.94), whereas inter-community tipping is 1.3--2x more frequent among non-following pairs, thereby mitigating echo chambers. Our causal analyses further uncover several critical trade-offs: (1) while most token rewards boost content creation, they often fail to enhance -- sometimes undermining -- content quality; (2) token rewards increase follower acquisition but show neutral or negative effects on outbound following, suggesting potential asymmetric network growth; (3) repeated algorithmic rewards demonstrate strong cumulative effects that may encourage strategic optimization. Our findings advance understanding of cryptocurrency integration in social platforms and highlight challenges in aligning economic incentives with authentic social value.
Portal UX Agent -- A Plug-and-Play Engine for Rendering UIs from Natural Language Specifications
Venue: arXiv cs.HC 2025
Year: 2025

The rapid appearance of large language models (LLMs) has led to systems that turn natural-language intent into real user interfaces (UIs). Free-form code generation maximizes expressiveness but often hurts reliability, security, and design-system compliance. In contrast, fully static UIs are easy to govern but lack adaptability. We present the Portal UX Agent, a practical middle way that makes bounded generation work: an LLM plans the UI at a high level, and a deterministic renderer assembles the final interface from a vetted set of components and layout templates. The agent maps intents to a typed composition-template and component specifications-constrained by a schema. This enables auditability, reuse, and safety while preserving flexibility. We also introduce a mixed-methods evaluation framework that combines automatic checks (coverage, property fidelity, layout, accessibility, performance) with an LLM-as-a-Judge rubric to assess semantic alignment and visual polish. Experiments on multi-domain portal scenarios show that the Portal UX Agent reliably turns intent into coherent, usable UIs and performs well on compositionality and clarity. This work advances agentic UI design by combining model-driven representations, plug-and-play rendering, and structured evaluation, paving the way for controllable and trustworthy UI generation.
FlowLog: Efficient and Extensible Datalog via Incrementality
Venue: arXiv cs.DB 2025
Year: 2025

Datalog-based languages are regaining popularity as a powerful abstraction for expressing recursive computations in domains such as program analysis and graph processing. However, existing systems often face a trade-off between efficiency and extensibility. Engines like Souffle achieve high efficiency through domain-specific designs, but lack general-purpose flexibility. Others, like RecStep, offer modularity by layering Datalog on traditional databases, but struggle to integrate Datalog-specific optimizations. This paper bridges this gap by presenting FlowLog, a new Datalog engine that uses an explicit relational IR per-rule to cleanly separate recursive control (e.g., semi-naive execution) from each rule's logical plan. This boundary lets us retain fine-grained, Datalog-aware optimizations at the logical layer, but also reuse off-the-shelf database primitives at execution. At the logical level (i.e. IR), we apply proven SQL optimizations, such as logic fusion and subplan reuse. To address high volatility in recursive workloads, we adopt a robustness-first approach that pairs a structural optimizer (avoiding worst-case joins) with sideways information passing (early filtering). Built atop Differential Dataflow--a mature framework for streaming analytics--FlowLog supports both batch and incremental Datalog and adds novel recursion-aware optimizations called Boolean (or algebraic) specialization. Our evaluation shows that FlowLog outperforms state-of-the-art Datalog engines and modern databases across a broad range of recursive workloads, achieving superior scalability while preserving a simple and extensible architecture.
FlexiCache: Leveraging Temporal Stability of Attention Heads for Efficient KV Cache Management
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Model (LLM) serving is increasingly constrained by the growing size of the key-value (KV) cache, which scales with both context length and generation length. Prior work shows that attention is dominated by a small subset of critical tokens, yet existing systems struggle to exploit this efficiently without degrading accuracy, especially in long generation. We make a key observation: the temporal stability of these critical tokens varies significantly across KV heads: some heads consistently focus on the same tokens, while others shift frequently. Building on this insight, we introduce FlexiCache, a hierarchical KV-cache management system that leverages the temporal stability of KV heads to reduce GPU memory usage and computation overhead, while preserving model accuracy. FlexiCache classifies KV heads as stable or unstable: it retains all KV-cache pages from unstable heads in GPU memory, whereas for stable heads, it keeps only the top-K pages on the GPU and offloads the rest to host memory. By exploiting temporal stability, FlexiCache performs periodic reranking for stable heads to fetch newly promoted top pages. Implemented atop vLLM, FlexiCache reduces GPU memory footprint for long-context requests by up to 70%, improves offline serving throughput by 1.38-1.55x, and lowers online token latency by 1.6-2.1x, all while maintaining accuracy in long-context, long-generation scenarios.
A Comprehensive Empirical Evaluation of Agent Frameworks on Code-centric Software Engineering Tasks
Venue: arXiv cs.SE 2025
Year: 2025

Unlike traditional automation tools or static LLM-based systems, agents combine decision-making and tool utilization to accomplish complex tasks, showing great potential in software engineering. However, existing studies largely focus on specific tasks or isolated aspects, providing an incomplete picture of agents' practical capabilities. To address this, we conduct a comprehensive empirical study evaluating seven general-purpose agent frameworks across three representative code-centric tasks: software development, vulnerability detection, and program repair. Each task is assessed using standard, widely adopted benchmarks to ensure objective and comparable evaluation. Agent performance is systematically analyzed from three complementary perspectives: effectiveness (task success), efficiency (execution process), and overhead (token consumption). Our findings reveal distinct capability patterns and trade-offs among the evaluated frameworks. In terms of effectiveness, agents achieve moderate overall performance. Regarding efficiency, AgentOrchestra tends to exhibit the longest trajectories and the most correction attempts due to coordination overhead, whereas OpenHands demonstrate stronger reflective reasoning abilities. For overhead, software development incurs the highest monetary cost, while GPTswarm remains the most cost-efficient. Furthermore, we conduct an in-depth cross-analysis of the relationship between effectiveness and efficiency, exploring the underlying reasons behind their interplay. These findings guide both practical adoption and future research toward more efficient software engineering agents.
Controlling Gender Bias in Retrieval via a Backpack Architecture
Venue: arXiv cs.IR 2025
Year: 2025

The presence of social biases in large language models (LLMs) has become a significant concern in AI research. These biases, often embedded in training data, can perpetuate harmful stereotypes and distort decision-making processes. When LLMs are integrated into ranking systems, they can propagate these biases, leading to unfair outcomes in critical applications such as search engines and recommendation systems. Backpack Language Models, unlike traditional transformer-based models that treat text sequences as monolithic structures, generate outputs as weighted combinations of non-contextual, learned word aspects, also known as senses. Leveraging this architecture, we propose a framework for debiasing ranking tasks. Our experimental results show that this framework effectively mitigates gender bias in text retrieval and ranking with minimal degradation in performance.
HyRES: A Hybrid Replication and Erasure Coding Approach to Data Storage
Venue: arXiv cs.IT 2025
Year: 2025

Reliability in distributed storage systems has typically focused on the design and deployment of data replication or erasure coding techniques. Although some scenarios have considered the use of replication for hot data and erasure coding for cold data in the same system, each is designed in isolation. We propose HyRES, a hybrid scheme incorporates the best characteristics of each scheme, thus, resulting in additional design flexibility and better potential performance for the system. We show that HyRES generalizes previously proposed hybrid schemes. We characterize the theoretical performance of HyRES as well as that of replication and erasure coding considering the effects of the size of the storage networks. We validate our theoretical results using simulations. These results show that HyRES can yield simultaneously lower storage costs than replication, lower probabilities of file loss than replication and erasure coding with similar worst case performance, and even lower effective repair traffic than replication when considering the network size.
Learning with Category-Equivariant Representations for Human Activity Recognition
Venue: arXiv cs.LG 2025
Year: 2025

Human activity recognition is challenging because sensor signals shift with context, motion, and environment; effective models must therefore remain stable as the world around them changes. We introduce a categorical symmetry-aware learning framework that captures how signals vary over time, scale, and sensor hierarchy. We build these factors into the structure of feature representations, yielding models that automatically preserve the relationships between sensors and remain stable under realistic distortions such as time shifts, amplitude drift, and device orientation changes. On the UCI Human Activity Recognition benchmark, this categorical symmetry-driven design improves out-of-distribution accuracy by approx. 46 percentage points (approx. 3.6x over the baseline), demonstrating that abstract symmetry principles can translate into concrete performance gains in everyday sensing tasks via category-equivariant representation theory.
Towards Data-Enabled Physical Activity Planning: An Exploratory Study of HCP Perspectives On The Integration Of Patient-Generated Health Data
Venue: arXiv cs.HC 2025
Year: 2025

Physical activity planning is an essential part of cardiovascular rehabilitation. Through a two-part formative design exploration, we investigated integrating patient-generated health data (PGHD) into clinical workflows supporting shared decision-making (SDM) in physical activity planning. In part one, during a two-week situated study, to reduce risk of working with cardiovascular disease patients, we recruited healthy participants who self-tracked health and physical activity data and attended a physical activity planning session with a healthcare professional (HCP). Subsequently both HCPs and participants were interviewed. In part two, findings from part one were presented to HCPs in a card-sorting workshop to corroborate findings and identify information needs of HCPs alongside patient journeys and clinical workflows. Our outcomes highlight HCP information needs around patient risk factors, vital signs, and adherence to physical activity. Enablers for PGHD integration include adaptive data sense-making, standardization and organizational support for integration. Barriers include lack of time, data quality, trust and liability concerns. Our research highlights implications for designing digital health technologies that support PGHD in physical activity planning during cardiac rehabilitation.
Leakage-abuse Attack Against Substring-SSE with Partially Known Dataset
Venue: arXiv cs.CR 2025
Year: 2025

Substring-searchable symmetric encryption (substring-SSE) has become increasingly critical for privacy-preserving applications in cloud systems. However, existing schemes remain vulnerable to information leakage during search operations, particularly when adversaries possess partial knowledge of the target dataset. Although leakage-abuse attacks have been widely studied for traditional SSE, their applicability to substring-SSE under partially known data assumptions remains unexplored. In this paper, we present the first leakage-abuse attack on substring-SSE under partially-known dataset conditions. We develop a novel matrix-based correlation technique that extends and optimizes the LEAP framework for substring-SSE, enabling efficient recovery of plaintext data from encrypted suffix tree structures. Unlike existing approaches that rely on independent auxiliary datasets, our method directly exploits known data fragments to establish high-confidence mappings between ciphertext tokens and plaintext substrings through iterative matrix transformations. Comprehensive experiments on real-world datasets demonstrate the effectiveness of the attack, with recovery rates reaching 98.32% for substrings given 50% auxiliary knowledge. Even with only 10% prior knowledge, the attack achieves 74.42% substring recovery while maintaining strong scalability across datasets of varying sizes. The result reveals significant privacy risks in current substring-SSE designs and highlights the urgent need for leakage-resilient constructions.
Motion-Robust Multimodal Fusion of PPG and Accelerometer Signals for Three-Class Heart Rhythm Classification
Venue: arXiv cs.LG 2025
Year: 2025

Atrial fibrillation (AF) is a leading cause of stroke and mortality, particularly in elderly patients. Wrist-worn photoplethysmography (PPG) enables non-invasive, continuous rhythm monitoring, yet suffers from significant vulnerability to motion artifacts and physiological noise. Many existing approaches rely solely on single-channel PPG and are limited to binary AF detection, often failing to capture the broader range of arrhythmias encountered in clinical settings. We introduce RhythmiNet, a residual neural network enhanced with temporal and channel attention modules that jointly leverage PPG and accelerometer (ACC) signals. The model performs three-class rhythm classification: AF, sinus rhythm (SR), and Other. To assess robustness across varying movement conditions, test data are stratified by accelerometer-based motion intensity percentiles without excluding any segments. RhythmiNet achieved a 4.3% improvement in macro-AUC over the PPG-only baseline. In addition, performance surpassed a logistic regression model based on handcrafted HRV features by 12%, highlighting the benefit of multimodal fusion and attention-based learning in noisy, real-world clinical data.
RefVTON: person-to-person Try on with Additional Unpaired Visual Reference
Venue: arXiv cs.CV 2025
Year: 2025

We introduce RefTON, a flux-based person-to-person virtual try-on framework that enhances garment realism through unpaired visual references. Unlike conventional approaches that rely on complex auxiliary inputs such as body parsing and warped mask or require finely designed extract branches to process various input conditions, RefTON streamlines the process by directly generating try-on results from a source image and a target garment, without the need for structural guidance or auxiliary components to handle diverse inputs. Moreover, inspired by human clothing selection behavior, RefTON leverages additional reference images (the target garment worn on different individuals) to provide powerful guidance for refining texture alignment and maintaining the garment details. To enable this capability, we built a dataset containing unpaired reference images for training. Extensive experiments on public benchmarks demonstrate that RefTON achieves competitive or superior performance compared to state-of-the-art methods, while maintaining a simple and efficient person-to-person design.