Recent studies have developed GPU-based approaches for solving AC power flow and successfully applied them to standalone power flow problems. However, integrating these approaches into modern differentiable learning frameworks while preserving sparsity remains challenging. To this end, we present SABLE, a GPU-based sparse batched power flow accelerator for differentiable learning via an implicit power flow layer. SABLE leverages a block-diagonal embedding that reformulates batched three-dimensional Jacobians as a fixed-pattern two-dimensional sparse template that is shared across PyTorch, CuPy, and cuDSS. This common template enables zero-copy interoperability and memory-efficient sparse reuse across the software stack. On top of this representation, SABLE accelerates repeated power flow computations through reusable sparse templates, custom GPU kernels, a cuDSS-based sparse-direct LU solver, and mixed-precision techniques. Extensive experiments show that SABLE improves standalone power flow solving throughput by up to 253.4$\times$ over pandapower and 5.7$\times$ over ExaPF. In end-to-end training, evaluated on AC optimal power flow learning models based on DC3 and DeepLDE, SABLE expands the feasible training batch range by up to 64$\times$ and improves training throughput by up to 206.7$\times$ over the corresponding baseline.

As an emerging operating system, HarmonyOS has a significant demand for software migration from platforms such as Android and iOS, where the User Interface (UI) translation accounts for a critical link. However, the latest UI development has shifted to declarative paradigms, e.g., Kotlin Jetpack Compose (KJC) for Android, SwiftUI for iOS, and ArkUI for HarmonyOS, rendering prior translation approaches inapplicable, as they target either backend logic or legacy imperative UIs. As such, this paper targets ArkUI and proposes an automatic translation approach, namely ArkTrans, to port UI files from Android and iOS to HarmonyOS. ArkTrans overcomes two salient challenges during the translation: (1) Programming Language (PL) unfamiliarity, and (2) severe syntactic chaos. Towards the first challenge, ArkTrans heuristically constructs ArkUI skeletons by extracting metadata from source PL, thereby guiding LLMs' initial translation. As for the second challenge, ArkTrans executes empirically revealed post-fixing rules via pattern matching to repair most of the remaining syntactic errors. To examine the effectiveness of ArkTrans, we construct a 100-sample parallel UI page translation benchmark from KJC/SwiftUI to ArkUI at the file level. Extensive experiments demonstrate that LLMs with direct/one-shot prompting cannot translate a single compilable UI page. In contrast, at most 90.67\% ArkTrans-translated files can be successfully compiled with high visual fidelity.

Surface reconstruction from point clouds remains challenging when both geometric fidelity and topology control are required. Rotation System Reconstruction (RsR) reconstructs triangle meshes from point clouds while explicitly controlling topology through the Euler characteristic, but its sequential edge insertion limits scalability. We present Hierarchical Rotation System Reconstruction (HRsR), which accelerates RsR through a hierarchical pipeline of edge collapses and vertex splits. HRsR first simplifies the input using a $k$-nearest neighbor graph, performs reconstruction on the reduced structure, and then restores geometric detail while preserving topology. To maintain geometric consistency, we incorporate intersection handling and quality-driven vertex split selection. Experiments demonstrate up to a $6\times$ speedup and more than $8\times$ reduction in memory usage over RsR, while achieving comparable reconstruction results.

Deciding when to stop reviewing the results of a search is a common problem with multiple applications. Existing stopping rules developed within Technology-Assisted Review (TAR) aim to achieve a pre-specified recall target and do not take into account the reason for examining the results, potentially leading to sub-optimal recommendations. This paper applies decision theory to the problem and uses it to derive three practical stopping policies based on the Expected Value of Perfect Information. The approach is applied to two professional search tasks: patent examining and systematic reviewing. Experiments on CLEF-IP and medical systematic review datasets show that the proposed approach generally produces more appropriate stopping decisions than existing methods, as demonstrated by higher net utility under the evaluated cost and payoff settings.

Tables in spreadsheets, computational notebooks, and databases often contain rich inter-column relationships. Yet these relationships are typically implicit and are often lost when tables are exported to standard formats. Recovering them can benefit downstream tasks, including table understanding, data quality improvement, and provenance analysis. However, simply mining relationships that hold on an observed table is insufficient, as many are spurious due to coincidence, redundancy, or limited data diversity. In this paper, we introduce functional relationships (FRs) as a unified notion for inter-column relationships in tables, subsuming arithmetic relationships, string transformations, and functional dependencies. We characterize FR reliability through four complementary criteria: accuracy, atomicity, stability, and integrity. Guided by these criteria, we propose Auto-Relate, a mine-then-verify framework that first generates accurate candidate FRs and then verifies the remaining reliability criteria through a Minimality Test, a Perturbation Test, and an Independence Test, respectively. To further improve efficiency, we develop three optimization strategies, including a group-by lower bound for early rejection, a closed-form speedup for arithmetic FRs, and a binomial bound for statistically guided early termination. We construct a large-scale benchmark suite from 58,679 real-world spreadsheets and relational tables, containing 6,414 ground-truth FRs spanning all three FR types. Extensive experiments against 18 baselines show that Auto-Relate consistently achieves the best performance, with an average PR-AUC of 0.87, 59% higher than the best competing baseline across all settings.

Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions (CRDs), Monitor-Analyse-Plan-Execute (MAPE) based control loops, and federated learning (FL) have enabled more proactive and autonomous autoscaling strategies. This paper presents a structured review of these developments. It first introduces a taxonomy of autoscaling techniques based on triggers, targets, prediction models, and evaluation metrics. It then examines predictive autoscaling approaches and CRD-based mechanisms, including Kubernetes operators and reconciliation workflows. Further, it analyses autoscaling in federated learning environments, highlighting reactive and proactive strategies alongside privacy-preserving techniques and container-level isolation. The paper also discusses drift-aware and uncertainty-aware autoscaling, incorporating concepts such as the Autoscaling Drift Index (ADI), feedback-driven correction, and stability control for heterogeneous workloads. Finally, it outlines open challenges and future research directions, providing a foundation for next-generation intelligent predictive autoscaling in cloud-edge environments.

Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many compute devices, which requires frequent collective communications to synchronize activations and parameter updates. Such collective communications have become a major bottleneck. While the performance of the collective algorithm depends on the physical network topology, the baseline collective algorithms in collective communication libraries are largely topology-agnostic. Collective algorithm synthesizers address this inefficiency by automatically generating topology-aware collective algorithms. However, prior works have largely overlooked that collective communication typically occurs only among a subset of devices, known as process groups. Additionally, most existing synthesizers are limited in the range of target collective patterns they can generate. We propose PCCL, a scalable and generic framework for synthesizing topology-aware collective algorithms. PCCL is process group-aware and capable of generating near-optimal collective algorithms even when only a subset of devices participates in collective operations. PCCL synthesizes arbitrary collective patterns, including 512-NPU All-to-All synthesis in 11.68 minutes.

We continue the study of {\em fast} functions, computable by linear-size circuits, that share useful properties of random functions. Motivated by cryptographic applications, we generalize and improve on previous results in this area, obtaining the following results: - For any constant $t$, we construct a fast $t$-wise independent hash function with algebraic degree $\log_2 t$ (over $\mathbb F_2$), simultaneously optimizing both asymptotic circuit size and degree. - We simplify and improve a recent construction (ITCS 2026) of a family of fast codes with fast duals, both meeting the Gilbert-Varshamov bound. Unlike the previous construction, our construction has negligible failure probability, can accommodate general fields and rates, supports a systematic encoding, and admits fast universal encoders. - We strengthen the above to support stronger random-like properties, such as optimal combinatorial list-decoding. This is achieved by constructing, for any constant $t$, a family of fast linear functions that map any $t$ linearly independent inputs to uniform and statistically independent outputs. Prior to our work, this was only known for $t=1$. We demonstrate the usefulness of the above results to cryptography. This includes the first nontrivial protocols for perfectly secure multiparty computation whose circuit complexity scales linearly with the number of parties, as well as protocols for computing encrypted matrix-vector products with optimal asymptotic circuit complexity.

Android malware analysis is currently facing increasing challenges in achieving robust classification and detecting stealth attacks. Modern threats employ advanced evasion strategies such as code obfuscation, dynamic loading, packing, and even steganographic manipulation of traditional static and dynamic features. These techniques reduce the effectiveness of signature-based systems and degrade the reliability of Machine Learning models that depend on explicit semantic indicators such as permissions, API calls, or control-flow structures. In this work, we propose \approachname, a memory forensics malware detection framework that shifts the analysis perspective from semantic program modeling to signal-based structural representation. Both static bytecode and early-execution memory snapshots are transformed into audio waveforms through direct binary-to-waveform mapping, preserving low-level structural patterns without requiring disassembly or feature engineering. The resulting signals are processed using handcrafted spectral descriptors, Convolutional Neural Networks, and transformer-based embeddings. Experiments on CICMalDroid2020 dataset and VirusTotal malware demonstrate that \approachname achieves up to 98.0\% accuracy, outperforming static sonification and competitive state-of-the-art approaches.

Power grids are facing major challenges from growing renewable integration and worsening climate impacts. While flexibility on both the demand and generation sides has been widely explored to address these challenges, network-side flexibility, especially in network topology, remains highly underutilized. Advances in communication, power electronics, and circuit breakers have made network topology increasingly controllable. However, leveraging this topological flexibility poses substantial challenges, primarily due to the inherent non-convexity and hybrid dynamics in associated optimization and control problems. This monograph surveys the development of power grid topology control in both early and recent years. It begins by discussing the fundamental topological constraints involved in topology control problems. Subsequently, it introduces steady-state topology control for transmission and distribution networks separately, covering fundamentals, a state-of-the-art review, and representative recent advances. Additionally, the network topology transition problem, which addresses the implementation of optimal topology solutions and has garnered increasing attention in recent years, is further modeled and analyzed. Beyond utilizing the flexibility of steady-state network topology, controlling network topology during transients can also contribute to system stabilization. Traditional approaches, such as intentional controlled islanding for transmission networks, as well as recently developed topology control methods for microgrid stabilization, exemplify this concept. Finally, a summary of this monograph is provided.

We introduce a novel collective decision making problem that captures the ubiquitous issue of ordering food to cater for varied dietary preferences and requirements. Our settings involve agents with diverse dietary requirements over menu options with varied serving sizes. The goal is to select a menu where everyone has enough food they can consume and wastage of food is minimized. We introduce two different consumption models: optimistic and pessimistic. Optimistic consumption assumes a situation when a central planner can optimally allocate the food ordered among the agents to maximize the number of people who get enough to eat. Pessimistic considers the worst case guarantee on consumption when agents fill their own plates in an arbitrary order. Under either consumption model, we seek valid menus (under which all agents are sufficiently fed) of minimum size. Our work provides two sets of characterizations: (1) we characterize valid menus under either consumption model and (2) we characterize the space of instances that admit polynomial-time algorithms to find minimum sized menus. Our results also help us design Integer Linear Programs to find minimum sized menus in general settings. Furthermore, we present polynomial-time algorithms for important special cases. We then consider the worst case discrepancy between the size of minimum sized optimistic and pessimistic menus. We call this the waste of pessimism, captured by the ratio of the minimum sized pessimistic menu to that of the minimum sized optimistic menu. We show tight upper bounds on this ratio. Our results also provide additional insights on the problem of finding a minimum sized maximal matching, which may be of independent interest.

Live streaming has emerged as one of the fastest-growing forms of online media, enabling instant content broadcasting and real-time engagement between users and streamers. Despite the effectiveness of existing recommendation algorithms in this domain, they often suffer from limited utilization of computational resources, with low FLOPs that hinder further performance enhancement. Generative recommendation techniques, which have gained traction in various industrial tasks, offer a promising avenue for improving live streaming recommendations. However, directly applying generative methods to live streaming is non-trivial due to two major challenges: (1) static semantic IDs (SIDs) cannot reflect the rapidly changing nature of live room content; and (2) generative pipelines generally do not incorporate user--streamer interaction signals (e.g., likes, orders), which are critical for modeling user intent toward both the streamer and showcased products. To address these challenges, we introduce SSRLive: Dynamic Semantic ID-guided Streaming Recommendation for Live platforms. The proposed framework integrates a generative module and a discriminative module in a unified architecture. The generative component employs an encoder-decoder design to produce both static and dynamic SIDs, enabling timely representation of live room content while leveraging multimodal information. The discriminative component refines task-specific representations by combining SIDs with user features, augments them with user-streamer interaction data, and performs multi-task predictions. Online A/B tests in real-world deployment demonstrate tangible benefits: watch time (+3.38%), GMV (+0.72%), follower growth (+3.12%), and interaction volume (+2.92%). These improvements highlight the effectiveness and business value of SSRLive, which is now fully deployed, serving hundreds of millions of active users.

Security Digital Twins (SDTs) provide continuously updated virtual replicas of infrastructure for threat simulation, yet they rely on theoretical CVSS scores to assign lateral-movement probabilities -- creating the Contextual Reality Gap: risk is overestimated where unacknowledged mitigations neutralize exploits, and drastically underestimated where logic flaws bypass all memory-safety defenses. We present the Host Active Verification Engine (HAVE), an SDT extension that deploys a safety-constrained host agent to measure the empirical probability of compromise $\hat{p}$ via maximum-likelihood estimation over snapshot-isolated Bernoulli trials. A Wilson interval-width confidence weight $α_w$ propagates $\hat{p}$ into Monte Carlo simulations via a Bayesian blending rule formally related to the Beta-Binomial posterior. Evaluation across four vulnerability classes, three security tiers, and two production binaries shows HAVE reduces $P_{\text{reach}}$ by 38.2% in false-positive scenarios and increases it by 132.4% in false-negative scenarios, with a net +124.1% correction; post-HAVE estimates vary by only $1.12\times$ across calibration exponents $κ$, versus $4.6\times$ for CVSS-only baselines.

5G network slicing implies a step forward in customizing radio access and core networks by allowing the creation of logical networks adapted to service requirements. In addition, softwarisation has fueled the emergence of 5G solutions which do not require specialized hardware platforms. Therefore, a key requirement to drive the adoption of 5G slicing by verticals is to simplify its management through automated orchestration. In this paper, we present i2Slicer, a flexible solution to orchestrate the deployment of 5G standalone end-to-end network slices with multi-tenancy and multi-service capabilities. The implementation and evaluation of i2Slicer using state-of-the-art 5G software and hardware demonstrate that it offers a practical and efficient lifecycle management of network slices.

Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While SIDs offer a strong semantic prior, current SID-based methods assign each item a single static identifier through offline tokenization before sufficient user feedback is observed. For cold-start items, this one-shot commitment produces poorly discriminative codes, generating misaligned paths that remain unrefined because the associated tokens are rarely sampled during training. We identify this early static commitment, not model capacity, as the fundamental cold-start bottleneck in SID-based generative recommendation. To overcome this bottleneck and bridge the disjoint objectives of tokenization and generation, we propose DREAM (Dynamic Refinement of Early Assignment Mappings), a three-stage framework that resolves this flaw through progressive refinement. First, an intent-aware tokenizer rebuilds the SID space through counterfactual contrastive learning, generating a diverse pool of behavior-aligned candidates per cold-start item. Second, the frozen recommendation backbone serves as an evaluator, selecting the most reliable candidate based on multi-context user support without retraining. Third, a dynamic beam mechanism maintains multiple weighted SID hypotheses throughout training and inference, preventing premature collapse to a single assignment. Extensive experiments on three Amazon benchmarks show that DREAM substantially outperforms state-of-the-art generative and sequential baselines on cold-start metrics.

Social interactions are shaped by the interplay of dispositional traits and situational context, yet systematically investigating how personality configurations between individuals jointly influence social behavior across diverse social contexts remains methodologically challenging. We address this gap by introducing a simulation pipeline adapted from the CHARISMA framework, which employs well-known movie characters and public figures as psychologically grounded agents for multi-LLM social simulation using a method we term personality anchoring. We present a large-scale empirical study examining how dyadic Agreeableness composition influences social interaction outcomes across 1,010 simulated conversations. Our results reveal a monotonic relationship between dyadic Agreeableness composition and shared goal achievement, with Homogeneous-Agreeable pairs achieving success 10 times the rate of Homogeneous-Disagreeable pairs (62% vs. 6%). Behavioral mediation analysis reveals that Agreeableness shapes goal achievement partially through cooperative strategy selection, though it continues to predict outcomes within the same dominant strategy, indicating pathways beyond observable conversational behavior. Robustness analyses confirm high consistency of results across repeated simulations (ICC = 0.89) and stable personality expression across diverse scenarios, validating personality anchoring as a viable operationalization strategy.

With the rapid demand for energy, even the incorporation of bulk renewable energy sources is not entirely sufficient to meet demand besides adding supply uncertainty. Distributed Energy Resource Aggregators (DERAs) have the potential to address this uncertainty via aggregation and control of decentralized distributed energy sources, thereby acting like virtual power plants. We present a new approach that combines forecasting and model-predictive control to assign DERAs to follow net-demand patterns, while accounting for the dynamics of the aggregate energy sources and their capacity limits. Each DERA is represented as a flexible ``virtual battery" with constraints on state-of-charge and power limits. The dispatch problem is set up as a long-term model predictive control task that aims to minimize differences from desired charge levels, output ramping, and net-load tracking errors. To keep operations efficient in real time, we implement a rolling-horizon MPC, which updates decisions regularly using the latest marginal-demand forecasts. For forecasting, we present two models: linear regression and long-short term memory (LSTM) neural network. Using high-resolution CAISO net-demand data and five typical DERA types, our simulations demonstrate how well our approach tracks marginal-demand; in particular, we highlight the tradeoffs between forecasting horizon times and MPC update rate as well as the dependence on the choice of the load forecasting model. Our results also indicate a slight edge for LSTM models over linear regression for desired time shifts and horizon choices.

An edge node monitoring a wildfire observes more than a duty-limited or windowed down-link can carry. The receiver must predict the H-step-ahead hazard map from whatever the link delivers. We argue the operative design problem is not which neural architecture to use but how to derive a structured belief sufficient for the receiver's prediction task and maintain it through a scheduler that anticipates future transmission opportunities. We formalize this as a partially observed sequential allocation problem with three coupled per-region action axes (sensing, representation, transmission), and derive each component of the structured belief from the H-step forward operator's input requirements. Identifying these mechanisms requires independent control over the window period P, per-window capacity C, predictive horizon H, and fuel composition, which is not separable in real-landscape data; we therefore evaluate on a physics-calibrated synthetic environment. Three empirical observations support the principle: the gap between a non-myopic activity-paced reference and uniform pacing is unimodal in window-period sparsity, peaking at intermediate spacing; ablating the structured belief, the dominant operative component flips between a default landscape (temporal staleness) and a structured landscape (static-risk prior), while the per-cell intensity belief is redundant in both; and a 40 k-parameter lightweight cross-region attention encoder exceeds the FAIR activity-paced reference by ~28% on the default landscape and ~11% on the structured landscape. A deeper Transformer encoder does not improve over the lightweight encoder in mean predictive loss and exhibits higher training-seed variance. Within this task class and regime, a modest architectural inductive bias suffices when the belief and the scheduling problem are correctly posed.

Privacy deceptive patterns in web interfaces systematically manipulate users into disclosing personal data, yet existing defenses are fragmented, static, and increasingly vulnerable to manipulation by large language models. Moreover, data voids, areas of information scarcity within the web ecosystem, create fertile ground for adversaries to inject misleading content that can be scraped and learned by AI systems, thereby amplifying both deceptive design and model misbehavior. In this paper, we formalize a new threat model, AI grooming, where attackers exploit data voids to seed benign-looking but malicious samples that corrupt model reasoning and normalize deceptive practices. To address this threat in privacy deceptive patterns, we present DPAgent, an agentic and reasoning-aware framework that orchestrates four specialized agents to mitigate the AI Grooming threat via a proactive defense that combines latent space purification with defensive prompting and operates directly in live web environments to proactively explore, detect, and repair privacy deceptive user interfaces before they reach end users. Extensive evaluations show that DPAgent detects 90.98% of groomed samples, achieves state-of-the-art privacy deceptive pattern detection with a micro F1 of 0.816, explores over 80% of pattern types while visiting only about 10% of the pages required by baselines, and successfully repairs 77% of detected deceptive interfaces. A large-scale study of 485 websites in the wild reveals that up to 98% contain at least one privacy deceptive pattern, over 90% of which can be mitigated by DPAgent. User studies further confirm that DPAgent effectively reduces privacy risks while preserving browsing experience. Our results demonstrate the promise of agent-in-the-middle defenses for securing the web UI supply chain against deceptive design and emerging AI threats rooted in data void exploitation.

Software refactoring is essential for maintaining code quality. However, API replacement refactoring, which replaces custom logic with API calls, remains underexplored. Existing refactoring tools provide limited support for detecting such opportunities because they rely on predefined templates and have difficulty capturing complex, multi-statement semantic equivalents. To address this limitation, we conduct the first empirical study of API replacement refactorings by mining 166,299 commits across six open-source Java projects and manually analyzing a curated subset of 1,800 commits, from which we identify 366 validated instances to characterize their scope, categories, and recurring patterns. Based on these insights, we propose AKIRA (Adaptive Knowledge Discovery and Retrieval), a hybrid framework that integrates pattern-deterministic heuristics with a refactoring-aware knowledge base to assess the practical feasibility of recommending API replacement refactorings. Our evaluation shows that AKIRA achieves 90% recall and 88% precision on a manually curated dataset. Furthermore, on the external RETIWA dataset, AKIRA significantly improves the state of the art by increasing recall from 21% to 81% and precision from 40% to 78%. These results demonstrate the effectiveness of combining static pattern matching with semantic reasoning to support the automation of recommending complex API replacement refactorings.

In this paper we describe a communication-strategy study for multi-GPU three-dimensional finite-difference time-domain computation with convolutional perfectly matched layer boundary conditions using CUDA. The metrics used to determine the most effective implementation include runtime, throughput in millions of output points per second, strong-scaling efficiency, CPML overhead, host-staged versus direct GPU-to-GPU exchange speedup, and enlarged-ghost speedup. On a single NVIDIA Quadro RTX 6000 GPU, the CPML implementation sustains 2,889--3,290 million output points per second with less than 1\% boundary-layer overhead, providing the single-GPU baseline for the multi-GPU study. The results show that direct GPU-to-GPU peer exchange is the dominant optimization with a 2.46--2.76$\times$ speedup over host-staged exchange, while enlarged ghost regions give only modest benefits because the reduced communication frequency is partly offset by redundant computation and additional memory traffic. On NVIDIA Quadro RTX 8000 GPUs, the implementation gives up to a 1.51$\times$ speedup on two GPUs for the tested strong-scaling cases, while four GPUs enable larger grids that approach or exceed single-GPU memory capacity.

Selecting appropriate machine learning (ML) configurations for malware detection is a complex, multi-criteria problem. Model choice, feature engineering, and update mechanisms must jointly satisfy operational constraints that vary across deployment contexts. This paper proposes the Framework for Decision-making (FDM) to build ML-based malware detection systems. The FDM formalises this selection process using the Weighted Configuration Compatibility Score (WCCS), a multi-criteria scoring function mapping five operational parameters (platform constraint, resource budget, response latency, update frequency, and detection sensitivity) to ranked recommendations across nine configuration dimensions. To validate the framework, four experiments were conducted on three datasets (a private Windows API dataset, the public Malimg image benchmark, and an Android static API dataset). Key results include: (i) XGBoost achieved the best accuracy-to-resource ratio in binary classification (97.46 % test accuracy, <70 MB RAM), outperforming LSTM/BiLSTM which consumed up to 2.8 GB; (ii) in multi-class classification, classical models (XGBoost 79.03 %) outperformed recurrent deep models (BiLSTM 72.27 %), reversing the binary ranking; (iii) class-incremental learning with EfficientNetB0 maintained 99.13 % accuracy with only 0.65 pp degradation across 11 incremental steps; (iv) transfer learning reduced training time by 2.14 times on average for image-based malware data without significant accuracy cost; and (v) autoencoder pre-processing yielded a 14 times training speedup at a cost of only 0.86 pp accuracy. These findings confirm that the optimal ML configuration is context-dependent, validating the FDM's core premise and demonstrating its practical utility for cybersecurity practitioners.

We consider multiple agents competing to acquire some costly divisible resource (e.g. shares of a financial asset, compute resources, etc.) over time. Leveraging a standard model for price dynamics, we propose a novel game-theoretic model for this problem, generalizing settings studied in diverse literatures. Our analysis considers different assumptions on the information available to agents. Under partial-information with a common prior (which subsumes complete information as a special case), we establish the existence, uniqueness, and efficient computability of the Bayesian Nash equilibrium (BNE), and bound the price of anarchy. Next and more generally, we consider agents with no common prior learning to act optimally given realistic market feedback from repeated interactions. We provide sufficient conditions on agents doing simultaneous learning dynamics for last-iterate convergence to the BNE. For all settings, we provide simulations based on real financial data to illustrate our theoretical results and offer new insights on strategic behavior in the context of trading and resource acquisition.

Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent direct corpus interaction (DCI) work shows that agents can instead interact with the raw corpus through shell tools such as grep and file reads. But unbounded interaction does not scale: every broad shell command is a scan over the whole corpus, and latency degrades sharply as the corpus grows. We argue that the role of retrieval for agentic search is not just to select documents that fit in the LLM context window, but to construct an interaction space: a bounded subset of the corpus the agent can explore with associated tools. Two design consequences follow. The space needs a boundary supplied by retrieval, and the objects within it should be processed for interaction. As a proof of concept, we propose RISE (Retrieving Interaction SpacE): we use BM25 to construct the interaction space; meanwhile, its documents are processed during indexing for shell-style navigation. On BrowseComp-Plus, RISE matches the pure-shell DCI baseline at 78% accuracy with gpt-5.4-mini at roughly one quarter of the per-query cost. At 1M documents, RISE-BM25 reaches 81% on gpt-5.4-mini, whereas DCI on gpt-5.4-nano degrades to 60% with 33 of 100 wall-clock failures.

Bitcoin is permissionless and does not rely on any central administrator, which gives it strong censorship resistance. At the same time, it is important to incentivize miners to behave in ways that align with the interests of the system as a whole. This paper asks whether miners are individually incentivized to propagate blocks, one of the most fundamental processes in Bitcoin. Miners collectively maintain the blockchain by generating blocks and disseminating them across the network. If miners have an incentive not to propagate some blocks, this would indicate a fundamental flaw in Bitcoin's incentive design. Although prior work has studied how propagation delays affect forks and mining rewards, it has not fully characterized miners' incentives to improve block propagation under different tie-breaking rules. To address this gap, we derive analytical reward expressions for each tie-breaking rule based on a blockchain network model that captures the effect of forks on mining fairness. These expressions explicitly characterize how block propagation delays, hashrate distribution, and tie-breaking rules jointly determine mining rewards. We then use them to analyze miners' incentives to improve block propagation. Our results show, for example, that miners have no mining-reward incentive to relay blocks generated by other miners. By contrast, under the first-seen rule, every non-majority miner is incentivized to receive other miners' blocks more quickly and to propagate its own blocks more quickly. Finally, we compare tie-breaking rules and identify a trade-off between propagation incentives and mining fairness. In particular, the first-seen rule provides the strongest incentives to reduce propagation delays, but it also worsens mining fairness the most.

The Learning Analytics (LA) community has undergone rapid development over the 15 years since the first LAK conference was held. However, while epistemological and ethical debates regarding the philosophical foundations of LA have been vigorous, metaphysical discussions have been sparse, signifying a lack of effort to derive the identity of LA from its internal principles. In this paper, we attempt to establish a metaphysics of LA by addressing the ontological question of ``What is LA?'' We do so by tracing back to LA's own definitions and principles to derive an answer from within LA itself. Specifically, we address what kind of existence the data LA operates on constitutes, identify eight agents including learners as ontological prerequisites, and clarify, via the is/ought problem, that LA does not derive norms from data. In particular, this system reveals that a class of LA practices, here termed \textit{norm-embedded LA}, conflates LA's purpose with its operations, creating an ontological tension with the first principle. We also discuss connections with related fields and the limitations of this system. The metaphysics outlined here is not imposed from outside LA, but surfaces what LA itself has always implicitly presupposed.

Developers increasingly use AI tools such as ChatGPT, Copilot, and Claude in everyday software workflows, but prior studies often evaluate LLM outputs in isolation rather than examining how developers adapt them in real projects. We analyze 35,361 GitHub code comments that explicitly reference AI use and their associated code blocks. We first open-code 500 unique comments and code blocks to derive a taxonomy of AI-assisted development activities, then annotate the full dataset using two LLM-based classifiers and aggregate predictions with Dawid-Skene expectation-maximization. We also analyze 12,996 subsequent commit messages to study how AI-assisted code evolves after introduction, and examine temporal trends from December 2022 to March 2026. Our results show that developers primarily use LLMs for code implementation, followed by code enhancement, debugging, documentation, and testing. Subsequent commits frequently involve refactoring and cleanup, feature integration and extension, and bug fixing, indicating sustained human oversight in adapting AI-assisted code. Over time, AI-referencing comments shift from direct code generation toward knowledge and conceptual support and code enhancement. These findings suggest that AI tools are becoming embedded not only as code-generation aids, but also as collaborative support mechanisms whose outputs are refined, extended, and corrected by developers over time.

With the remarkable progress of Code Large Language Models (Code LLMs) in achieving semantic correctness, execution efficiency has become an increasingly important dimension for evaluating their practical utility. However, existing approaches typically treat full programs as a single optimization target during training, without explicitly modeling the structural factors that influence efficiency. As a result, although these models can generate semantically correct code, they fail to learn, at a fine-grained level, the underlying skeleton features that lead to efficient implementations. To address this limitation, we propose SkelDPO (Skeleton-Guided Direct Preference Optimization), a skeleton-guided preference optimization framework that systematically enhances the efficiency of code generation. SkelDPO first identifies efficient and inefficient implementations from the code dataset and, through comparative analysis, locates their efficiency-prone and inefficiency-prone points, forming alignment signals between efficiency and inefficiency skeletons. During training, a joint code and skeleton preference loss is introduced, enabling the model to learn semantic correctness while reinforcing its understanding of efficiency-critical components in code. Results show that SkelDPO consistently surpasses existing methods: compared with SOTA method that relies solely on efficient and inefficient code preference optimization, it improves Pass@1, Beyond@1, and Effi@1 by 3-6%, 3-7%, and 2-5%, with greater improvements observed on complex tasks. Overall, SkelDPO provides a new perspective on skeleton-level efficiency alignment, breaking the limitation of conventional preference optimization that relies solely on correctness or efficiency pairs. All datasets and source code are publicly available at: https://github.com/icpcSkelDPO/SkelDPO.

Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies reveal that these programs typically execute substantially slower than human-optimized counterparts. Existing approaches to bridging this efficiency gap typically involve either iteratively optimizing code after generation or fine-tuning models on corpora of efficient code. Yet, these methods expose the model to efficiency signals only by mimicking complete, optimized solutions, without explicitly encoding the structural code patterns essential for achieving high runtime performance. Addressing this gap presents two core challenges: (1) extracting and representing latent, efficiency-oriented structural patterns embedded within complex syntax and control flows, and (2) effectively learning these patterns without destabilizing the semantic training of LLMs. To tackle these challenges, we propose EffiSkel, an efficiency skeleton-guided framework that explicitly extracts and learns efficiency skeletons-abstract, reusable structural patterns underpinning efficient code-by leveraging three complementary strategies. These skeletons are integrated into a multi-task learning regime that jointly optimizes code generation and skeleton prediction. Experiments across multiple programming languages and benchmarks demonstrate that EffiSkel significantly enhances both functional correctness and efficiency, resulting on Mercury with DeepSeek-Coder (7B) a +11.11% (vs. EffiCoder) and +3.71% (vs. CodeDPO) higher Efficiency Ratio (ER), and a +0.36 (vs. EffiCoder) and +0.22 (vs. CodeDPO) increase in Average Speedup (AS). These results highlight the effectiveness of explicitly modeling efficiency skeletons in improving the runtime performance of code generated by LLMs.

Comparing genomes is critical for discovering mutations, tracking evolutionary lineages, and advancing cross-species genomics. Fundamentally, this reduces to an O(n^2) string-matching dynamic programming (DP) problem, a challenge that has driven decades of performance research. However, executing a strict O(n^2) DP algorithm is computationally intractable for genomes spanning millions to billions of base pairs. Consequently, modern aligners rely on global heuristics to identify thousands of candidate similarity regions between species. Unfortunately, these methods are burdened by complex serial dependencies. Once candidate regions are identified, the pipeline executes localized DP alignments, which introduce their own non-trivial heuristics and irregular data dependencies. While parallelizing dense, two-dimensional DP is a well-studied problem, accelerating this end-to-end pipeline is significantly more challenging. Parallelizing across candidate regions and offloading irregular, heuristic-laden local alignments to modern hardware (such as GPUs) remains a major hurdle. In this work, we address the challenge of overcoming these serial bottlenecks by optimizing the global pipeline across regions. We take inspiration from four papers: LASTZ, SegAlign, Darwin-WGA, and SNAP, synthesizing findings across each to inform optimizations, which we either prototype or implement directly in LASTZ.