Cryptography and Security
Defending the Peg: Real-Time Dynamic Protection and Anomaly Detection in DeFi Stablecoins
Hengxing Zeng, Shipeng Ye and Xiaoqi Li
With the rapid
evolution of the Decentralized Finance (DeFi) ecosystem, stablecoins have emerged as a critical infrastructure bridging the cryptocurrency
market with traditional financial paradigms. However, stablecoin systems rely heavily on smart contracts to execute automated operations. The immutable nature of these systems post-deployment means that the exploitation of security vulnerabilities can lead to irreversible, massive economic losses and potentially trigger systemic financial risks. Current research on stablecoin smart contract security faces challenges such as a lack of domain-specific targeting and the obsolescence of static defense models. To address this, this paper systematically analyzes common attack vectors in stablecoin environments and proposes a practical, real-time dynamic defense architecture. By analyzing 12 real-world security incidents, we elucidate the underlying mechanisms of high-risk patterns such as reentrancy attacks, oracle manipulation, and composite flash loan attacks. Concurrently, we construct a real-time anomaly detection model utilizing multi-dimensional on-chain temporal features and the Bi-LSTM
algorithm. Experimental results demonstrate that this model achieves a classification accuracy of 96.61%, with an average recall rate of 97.70% for malicious attack samples, and a single inference latency ranging from 1.5 to 2.8 milliseconds.
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Machine Learning
M-Fibration Theory with Applications to Weighted Graphs
Paolo Boldi, Osvaldo M. Velarde and Hernan A. Makse
The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a commutative monoid. This is a genuine extension of the theory of graph fibrations (as introduced in "Fibrations of Graphs" [Discrete Math., vol. 243, pp. 21-66, 2002]), that makes it possible to deal with weighted graphs, and also graphs labelled with other algebraic structures. The derived theory also lends itself naturally to consider approximate fibrations. As an example, we show how the derived theory can be applied to the reduction of weighted networks, providing a strong theoretical underpinning to recent empirical results.
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Computation and Language
JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution
Guibin Zhang, Leo Lu, Fangzhou Xie et al. (+15 more)
Agent capability is not determined by the model alone. The
agent harness, encompassing
memory management, planning strategy, action protocol, and tool/skill orchestration, can dominate the contribution of the underlying foundation model. Yet harness design remains manual, task-specific, and fundamentally unscalable. We present JIT-
Agent, a harness intelligence model trained to synthesize task-adaptive
agent harnesses on the fly for arbitrary off-the-shelf agentic LLMs. We formalize the
agent harness as a composable, machine-generatable artifact governed by a fixed four-module protocol, and train JIT-
Agent to customize harnesses for a given task at hand, repair harnesses for stable and reliable execution, and self-evolve by distilling performance signals from an expanding archive of prior harness configurations. Equipped with JIT-
Agent as a harness helper, DeepSeek-V4-Flash surpasses GPT-5.6 on DeepSearchQA (+9.1) and OdysseyBench (+4.3), while the already strong GLM-5.2 gains up to +20.2 points. Across controlled evaluations, JIT-
Agent-generated harnesses are performance-competitive with mature
agent runtimes such as OpenCode and Claude Code and consistently improve multi-scale model families of DeepSeek V4,
Mimo-V2.5, and Qwen3.6. To our knowledge, JIT-
Agent is the first model purpose-built for just-in-time harness generation, establishing harness intelligence as a trainable, transferable, and compounding dimension of
agent capability orthogonal to model scaling.
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All authors Guibin Zhang, Leo Lu, Fangzhou Xie, Kang Zhu, Junhao Wang, Zhifei Xie, Zhaochen Yu, Zihang Liu, Zhongxiang Sun, Qiankun Li, Yue Liao, Heng Chang, Xiaobin Hu, Qibing Ren, Wangchunshu Zhou, Chuanrui Hu, Yafeng Deng, Shuicheng Yan
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery
Hongwei Du, Dingyang Lv, Baole Wei et al. (+5 more)
Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulation, and favorable mechanical compliance. Single models struggle to support reliable multi-objective screening across vast chemical spaces because of training-data distribution mismatch, cross-property
dataset heterogeneity, and scarce kinetic transport data. To overcome these limitations, we develop a hierarchical synergistic deep-learning framework that sequentially coordinates efficiency, accuracy, and reliability through four complementary modules. The in-house-developed L-G-DCNN and a multi-fidelity implementation built on DenseGNN serve as compositional and structural experts for thermodynamic coarse screening and multi-property evaluation, respectively; MatterSim and system-specific DeePMD models provide transport pre-assessment and kinetic validation. Systematic
benchmarks show that each module outperforms mainstream counterparts in its task, while retrospective validation establishes dual closed-loop verification of module-level accuracy and end-to-end workflow reliability. Applied to 30,364,908 Alex/ICSD-derived candidates, the framework identifies 97 high-performance candidates with room-temperature ionic conductivities of 0.109–59.0 mS/cm, including 94 halides, one borohydride, and two
oxides. Consistency with independent experimental data confirms that 76 of the 94 halides fall within reported high-conductivity structural regions. Analysis reveals that Li$^{+}$ jump-network connectivity, rather than the number of
geometric Li sites, is the core determinant of room-temperature ionic conductivity. Li-defect engineering effectively enhances
oxide transport, whereas the inherent
rigidity of the O$^{2-}$ framework suggests a potential upper limit on
oxide electrolyte performance.
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All authors Hongwei Du, Dingyang Lv, Baole Wei, Yongheng Li, Feng Yu, Ziheng Lu, Siqi Shi, Hong Wang
Machine Learning
Individual Fairness in Hierarchical Clustering
Binita Maity and Shrutimoy Das
Hierarchical clustering produces ultrametric representations that impose strong global
geometric constraints and may distort local similarities in ways that disproportionately affect individual data points. We study hierarchical clustering under an individual
fairness requirement that bounds relative distortion within local $k$-nearest neighborhoods. We formulate this requirement as a feasibility problem over dominated ultrametrics and characterize the minimal multiplicative slack required for feasibility. We identify a sharp local threshold, prove stability under bounded perturbations, establish monotonicity in $k$, and show an intrinsic $\Theta(\log n)$ separation between local and global realizability. Experiments on synthetic and real world
datasets support our theoretical results.
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Robotics
RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation
Sanghwan Jang, Minjin Jeon, Minsoo Kim et al. (+3 more)
Vision-
Language-Action (VLA) models provide a versatile foundation for general robotic manipulation, yet they exhibit significant brittleness when confronted with novel task distributions. While In-Context Imitation Learning (ICIL) offers a training-free alternative, existing frameworks suffer from an adaptation bottleneck that hinders the effective translation of expert context to executable actions. This failure originates from superficial
retrieval mechanisms and an inherent behavioral inertia that anchors the policy to its pre-trained priors. To address these limitations, we present RA-VLA, a
retrieval-augmented VLA framework that integrates behavior-aligned context
retrieval with a grounded execution pipeline. By enforcing faithful adherence to functional cues within a scalable architecture, RA-VLA facilitates seamless task adaptation while preserving inference efficiency. Our empirical evaluations across the LIBERO
benchmark and a real-world UR5e environment demonstrate that RA-VLA achieves superior success rates and computational efficiency, establishing a robust framework for training-free robotic adaptation.
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All authors Sanghwan Jang, Minjin Jeon, Minsoo Kim, Seongjin Choi, Dongha Kim, Hwanjo Yu
Computation and Language
GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning
Lam So, Canhui Wu and Han Lin
Reasoning-oriented large
language models often achieve strong problem-solving performance by generating long chains of thought, but this behavior substantially increases inference cost and latency. In contrast, instruction-tuned models tend to answer more concisely, yet often lack comparable
reasoning ability. This accuracy-efficiency mismatch motivates a lightweight approach that combines the strengths of both models without full model retraining. In this paper, we propose GRIP (Granular Reward-guided Interpolation of Parameters), a reward-guided parameter interpolation framework for efficient
reasoning. Given a
reasoning model and an instruction model with identical architectures, GRIP assigns learnable interpolation ratios to individual modules and optimizes only these ratios while keeping both source models frozen. The interpolation ratios are trained with a reward signal that favors responses that are both correct and concise. Experiments show that GRIP achieves a better accuracy-efficiency trade-off than fixed or search-based merging baselines and further reveals module-wise fusion patterns associated with efficient
reasoning.
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Human-Computer Interaction
Are Concept Bottleneck Models Effective as Decision-Support Systems?
Alessandro Bogani, Nicola Debole, Emanuele Marconato et al. (+3 more)
Concept Bottleneck Models (CBMs) are interpretable-by-design neural networks that detect human-understandable concepts from the input and use them to generate predictions. By allowing users to inspect the concepts underlying a prediction and explore how predictions change under alternative concept configurations, CBMs have emerged as one of the most prominent approaches to supporting human-AI collaboration. However, user studies investigating their actual effectiveness as decision-support systems remain limited. We present two large-scale user studies (N participants = 705, N observations = 6,959) evaluating how concept-based explanations and user interventions on the model's concepts affect the performance of the human-AI team in two distinct binary classification tasks. Our results show that CBMs, and particularly their interactive component, can improve human-AI team accuracy relative to both unaided human performance and performance with non-interpretable AI support. However, these benefits emerge only under certain conditions: classification tasks perceived as difficult, easily identifiable concepts, and active interaction with the model. We also discuss how inaccurate concept detection may undermine users' trust in the model. Overall, this work provides practical guidance for the deployment of CBMs as effective decision-support tools.
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All authors Alessandro Bogani, Nicola Debole, Emanuele Marconato, Andrea Pugnana, Katya Tentori, Andrea Passerini
Computer Vision
V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning
Shulin Tian, Minglun Li, Yuhao Dong et al. (+6 more)
Vision-
language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or intermediate inference can undermine an otherwise plausible response. We argue that this is a credit-assignment failure in
multimodal post-training. Scalar outcome rewards indicate whether an answer is acceptable, but do not identify which visual facts are grounded, which
reasoning steps are valid, or which instruction constraints are missed. We introduce Visual Rubrics-Based Reinforcement Learning, which decomposes reference responses into atomic propositions and scores generated answers along Visual Faithfulness (VF),
Reasoning Consistency (RC), and Instruction Following (IF). The resulting rubric items provide structured partial credit and localize rubric credit when supporting evidence spans are available. We first obtain an SFT checkpoint by fine-tuning Qwen3-VL-8B-Instruct on the public OpenMMReasoner-SFT-874K corpus, adapting OpenMMReasoner's cold-start data recipe. We construct V-Rubrics 50K, a 50,248-example training set from 17 visually grounded sources, by applying rule-based filters before deriving example difficulty from rejection-sampling scores and then annotating every example with Gemini-3-Pro under the same structured
prompt and protocol. We train our model based on the same SFT checkpoint using component-wise, prefix-localized rubric credit. Experiments show that our rubricbased GRPO improves over both the shared SFT baseline and answer-only GRPO, with the largest gains on knowledge-oriented and visually grounded
reasoning benchmarks. The results show rubrics as a useful reward abstraction for visual post-training.
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All authors Shulin Tian, Minglun Li, Yuhao Dong, Hao Ding, Jiarui Yao, Haiwen Diao, Jingkang Yang, Hongyuan Zhu, Ziwei Liu
Computation and Language
Cross-Dataset Stability of Expert-Informed Skill Prompting and Fine-Tuning for Chinese Metaphor Identification
Yufeng Wu and Meichun Liu
Metaphor-identification performance can change markedly across
datasets that differ in text distribution and annotation policy. We examine whether a fixed expert-informed procedure produces a more even cross-
dataset profile than task-specific parameter adaptation. Four prespecified conditions are compared for Chinese sentence-level metaphor identification: BERT fine-tuning (BERT-FT), QLoRA-based large
language model fine-tuning (LLM-FT), direct zero-shot LLM prompting (LLM-ZS), and zero-shot prompting with a frozen procedural Skill (Skill-ZS). The Skill operationalizes established criteria involving contextual meaning, basic meaning, contrast, and comparison. Evaluation covers CMRE Test and two external
datasets, CCIME and CMC. Fine-tuned scores are means over three seeds, whereas each zero-shot score comes from one deterministic configuration. Fine-tuning remains strongest on the native test set: BERT-FT reaches 91.76 Macro-F1. LLM-FT has the highest external mean (83.52), while Skill-ZS is close at 82.92 and has both the highest external floor (82.64) and the smallest observed range across all three
datasets (4.08 points). In the matched zero-shot comparison, adding the Skill reduces metaphorical predictions on every
dataset. This sharply lowers false positives on CCIME but increases false negatives on CMRE Test and CMC. The results position expert-informed Skill prompting as a complementary route to more even observed cross-
dataset performance, while fine-tuning retains its advantage in native-data accuracy. To our knowledge, this is the first study to compare an expert-informed procedural Skill with task-specific fine-tuning in the same cross-
dataset evaluation of Chinese sentence-level metaphor identification.
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Databases
PolyMemDB: A Polyglot Database System for AI Memory Management
Yu Wang and Jiaheng Lu
With the widespread adoption of personal intelligent
agents, users generate massive, heterogeneous data during long-term interactions. Leveraging this data as long-term
memory helps reduce
token overhead and deliver personalized experiences. However, existing
memory systems face two primary limitations: they rely on single-storage paradigms that fragment multi-dimensional data, and they lack fine-grained data provenance to resolve long-term factual conflicts, thereby worsening LLM
hallucinations. In this demonstration, we introduce PolyMemDB, a novel system tailored for managing
agent memory. PolyMemDB has a polyglot storage architecture designed to track and manage various
memory types, including graph, vector, probability and spatial-temporal data. To ensure factual consistency and reduce
hallucinations, it features a probabilistic inference engine that integrates temporal decay with semiring aggregation, resolving long-term factual conflicts, providing detailed data provenance, and enabling users to trace
reasoning chains transparently.
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Computer Vision
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao et al. (+3 more)
Pretrained
vision-
language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent studies mitigate this issue by augmenting training with synthetic hard negatives generated by a cascade of large
language models and text-to-image models, which incurs substantial pipeline overhead. We instead propose MLLMCLIP, a heterogeneous
distillation framework that transfers
multimodal knowledge directly from a generative
Multimodal Large
Language Model (MLLM) teacher into a discriminative CLIP student, bypassing synthetic data entirely. To bridge the architectural mismatch between the two paradigms, we introduce an
attention-based per-layer
token selection and a CKA-based
distillation loss. Compared to prior CLIP-enhancement methods, MLLMCLIP achieves state-of-the-art compositional accuracy while delivering consistent gains on standard zero-shot classification and image-text
retrieval, showing that feature-level
distillation strengthens both compositional and general
vision-
language representation capability.
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All authors Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao, Hiromi Wakaki, Junmo Kim, Yuki Mitsufuji
Computation and Language
Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study
Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil et al. (+6 more)
Automatic
Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While
embedding-based metrics correlate better with human judgments, the respective roles of encoder and decoder-based Large
Language Models (LLMs) remain underexplored. This paper presents a comparative study of both families for ASR evaluation. We analyze BERTScore and SemDist across different LLMs, layers, and pooling strategies, showing that both metrics can achieve strong correlation with human judgments when properly configured. For decoder models, we investigate generative LLMs in two settings: pairwise hypothesis selection via prompting and direct qualitative error classification. Our results show that encoder-based metrics remain highly competitive, while generative LLMs perform strongly in hypothesis comparison and improve the interpretability of ASR evaluation.
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All authors Thibault Bañeras-Roux, Shashi Kumar, Driss Khalil, Sergio Burdisso, Petr Motlicek, Shiran Liu, Mickael Rouvier, Jane Wottawa, Richard Dufour
Databases
DBcover: A White-box SQL Test Generation Framework for Coverage Improvement
Yankai Rong, Shuang Liu, Jinhao Dong et al. (+4 more)
Relational Database Management Systems (RDBMSs) are the backbone of modern data-intensive applications, making reliability and robustness critical. However, achieving high coverage in RDBMS testing remains challenging because of large codebases and complex execution logic. Traditional fuzzing relies on random SQL generation and cannot capture the correspondence between SQL inputs and internal execution paths, while symbolic execution suffers from prohibitive cost and scalability limitations. We propose DBcover, an LLM-driven white-box SQL test generation framework based on contextual
reasoning. DBcover uses lightweight dynamic analysis to extract SQL-to-path correspondence and call graphs as global context, and collects source-level information around target functions as local context. These contexts are organized in a unified knowledge graph for efficient
retrieval and reuse. DBcover then performs two-phase test generation: it first selects a semantically relevant seed whose execution path is close to the uncovered target, and then guides the LLM with global and local context to generate SQL test cases that trigger previously uncovered code regions. Experiments show that DBcover achieves 80.1% and 82.3% coverage on PostgreSQL and MySQL, and is also effective on the enterprise RDBMS KingbaseES, demonstrating its practical applicability to closed-source systems.
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All authors Yankai Rong, Shuang Liu, Jinhao Dong, Qiang Yin, Wei Lu, Jianhua Wang, Xiaoyong Du
Robotics
ConfAL-WM: Confidence-Guided Active Learning for Action-Conditioned World Models
Xiang Liu, Kunwei Wu, Miao Liu et al. (+2 more)
Action-conditioned world models have become an important foundation for
embodied prediction, planning, and synthetic data generation, but their errors under new task and scene distributions are often concentrated in localized spatiotemporal regions such as
robot arms, manipulated objects, contact areas, and occluded objects. This paper presents ConfAL-WM, a confidence-guided active learning framework for post-training
embodied world models. Building upon EnerVerse-AC (EVAC), we attach a lightweight confidence probe to UNet decoder features and predict dense confidence maps in the latent space. These maps are aggregated into task-, frame-, and patch-level scores, enabling data-budget allocation and localized training enhancement. Our pipeline trains the probe and warms up EVAC on a small target-domain subset. EVAC-v1 then supplies task-level acquisition and optional frame/patch weighting signals; all selected-data models are initialized from the original pretrained EVAC checkpoint for retraining. Experiments on RoboTwin2.0 at the default 40% data budget show that confidence-guided selection improves post-training quality, while dense frame and patch weighting offers complementary reconstruction and semantic gains compared with scalar reward, progress, and judge-based scoring baselines. A quick visual overview of this work is available at https://ConfAL-WM.github.io.
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All authors Xiang Liu, Kunwei Wu, Miao Liu, Sen Cui, Changshui Zhang
Machine Learning
Beyond Scaling: Self-Evolving LLM Agents for Hardware Kernel Optimization via an Experience-Driven Workflow and Experience Graph Memory
Siyuan Chen, Runlin Hou, Shenxiu Wu et al. (+7 more)
Hardware kernel optimization requires repeated compilation, correctness testing, profiling, and revision. LLM
agents can automate parts of this process, and stronger foundation models, longer context windows, and longer execution horizons have improved optimization within individual tasks. These advances alone do not enable an
agent to learn from completed optimization runs. Existing kernel-optimization
agents seldom preserve a decision, its observed execution feedback, and the later decisions that use that evidence. Retaining every prior trajectory is also impractical because an expanding history competes with the current task for context. We present KOPE, an experience-driven framework for hardware kernel optimization. KOPE records optimization trajectories with correctness and performance feedback in Experience Graph
Memory, then uses Active Context Management and Injection to retrieve relevant experience under a fixed
token budget. The graph retains decision order, observed outcomes, and alternative branches, allowing evidence collected on the target hardware to inform later optimization steps and tasks. Under the same GLM-5.2 setting, the
geometric mean of KOPE's per-operator speedups is $1.54\times$ that of CANNBot, the strongest competing baseline. In a complete 53-operator ablation, Active Context Management and Injection raises pass rate from 60.0% to 84.6%, increases the evaluator-reported positive-field
geometric mean from 0.0382 to 0.0661, and reduces optimization
token consumption from 15.9B to 1.113B
tokens relative to passive
agent-led context construction. Enabling Experience Graph
Memory raises full-suite pass rate from 55.2% to 84.6% and yields a $1.43\times$
geometric-mean speedup on valid timing comparisons. These results support continual optimization through external experience while the foundation model remains fixed.
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All authors Siyuan Chen, Runlin Hou, Shenxiu Wu, Yansong Sun, Junming Cao, Yiyu Zhang, Shudi Shao, Junhao Qiu, Zhichao Lu, Qingfu Zhang
Computation and Language
Controllable Affective Generation via Latent Vector Steering
Xixian Yong, Siyuan Chang, Yingying Zhang et al. (+2 more)
Large
Language Models (LLMs) often produce emotionally flattened responses after
alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose EmoVec, a lightweight framework for controllable affective generation via latent vector steering. EmoVec extracts
emotion-specific directions from paired neutral and
emotion-conditioned responses using contrastive activation addition, and further refines them through task-specific debiasing and principal subspace removal. During inference, these vectors are injected into the final residual stream with static or scenario-adaptive scaling, enabling continuous control over emotional intensity without updating model weights. Experiments across three LLMs and eight
emotions show that EmoVec consistently improves emotional salience while largely preserving semantic content, fluency, and coherence. Ablation studies and human evaluation further confirm the effectiveness of vector purification and adaptive scaling, establishing EmoVec as a practical inference-time method for affective control in deployed LLMs.
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All authors Xixian Yong, Siyuan Chang, Yingying Zhang, Xian Wu, Xiao Zhou
Computer Vision
CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression
Haobo Xiong, Shaobo Liu, Kai Liu et al. (+1 more)
To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine
vision tasks has attracted growing
attention. However, existing methods typically insert fine-tuning modules independently into frozen backbones, lacking explicit mechanisms for cross-layer coordination. To address this limitation, we propose a novel framework named CrossMambaTuning, which integrates State Space Models with cross-layer interaction mechanisms for parameter-efficient fine-tuning. Specifically, we design an efficient Mamba adapter equipped with task-specific
prompts and multi-scale branching to precisely capture both local features and global dependencies. Furthermore, we introduce a Scale-Invariant Cross-Layer Adapter (SICA) utilizing a parameter-sharing strategy to fuse task information across different scales and reduce redundancy. Extensive experiments demonstrate that CrossMambaTuning achieves state-of-the-art (SOTA) performance on multiple machine
vision tasks, reducing parameter overhead by 72% compared to SOTA methods. Code is available at https://github.com/rsr1123/CrossMambaTuning.
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All authors Haobo Xiong, Shaobo Liu, Kai Liu, Chongyang Ding
Numerical Analysis
Combined High-Dimensional and Reduced-Order Modeling Based on an Iterative Domain Decomposition Method
Taiji Saito, Tomoshi Miyamura and Yasunori Yusa
A method is developed for structural analysis in which parts of an analysis domain are modeled by reduced-order models (ROMs) and the remaining part is modeled by a high-dimensional model (HDM). The multiple ROMs and HDM are combined based on an iterative domain decomposition method (DDM) with an arbitrary number of non-overlapping subdomains. A diagonal preconditioner is derived for the iterative DDM. The method is implemented based on a framework of the existing DDM-based analysis code. The ROM is implemented on GPU. Snapshot generation methods for the ROM are also proposed. Static structural analyses of
nuclear power plant models are conducted using the combined multiple ROMs and HDM with multiple-subdomains model. The analyses are performed in parallel by the hybrid MPI-CUDA implementation. The convergence property of the iterative DDM is improved by the preconditioner.
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Human-Computer Interaction
Maru: Information Architecture as a Shared Language for Generating Aligned and Persistent User Interfaces
Eunhye Kim, DaEun Choi, Bryan Min et al. (+3 more)
Generative user interfaces (GenUIs) promise on-demand components tailored to users' needs. As users iterate on information tasks, they construct personal structures over information they encounter—how items are grouped, what gets prioritized, and what terms mean in their context. Yet, current systems leave these structural decisions to the model at each generation, ignoring the structural logic users have established. Without a persistent representational structure shared between user and system, GenUIs have no basis to remain aligned with what users have established. We draw on Information Architecture (IA), a design practice for organizing and structuring information, as a shared
language to bridge user-constructed structure and system generation. We present a framework identifying four IA elements—partition, hierarchy, order, and vocabulary—and characterize how each maps to concrete UI generation decisions. We instantiate this framework in Maru, a conversational system that captures user
prompts and interactions as IA preferences, persisting as rules both user and system draw on across generations. A user study revealed that IA persistence kept generated UIs aligned as sessions progressed, while
alignment without it degraded, with diverse patterns emerging across users and contexts, pointing to the value of IA persistence in aligning GenUI to individual needs.
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All authors Eunhye Kim, DaEun Choi, Bryan Min, Hyunjung Yi, Yue Jiang, Juho Kim
Machine Learning
Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs
Ahmad Ishaque Karimi, Uvini Balasuriya Mudiyanselage and Kookjin Lee
Physics-informed neural networks (PINNs) often rely on over-parameterized models to optimize coupled solution and differential-residual objectives, leaving unclear how much capacity is necessary and what
pruning should preserve. We study foresight
pruning at initialization for sparse PirateNet PDE solvers. Standard neural tangent kernel spectrum-aware
pruning (NTK-SAP) aims to preserve output-side training dynamics but may overlook parameters whose main influence arises through derivatives in the governing equations. We introduce physics-informed spectrum-aware
pruning (PI-SAP), which assigns saliency using sensitivity of the PDE residual. Experiments on the Gray-Scott equations, complex Ginzburg-Landau equation, Burgers' equation, and linear convection equation show that PI-SAP more consistently preserves Gray-Scott residual fidelity and is competitive under aggressive sparsity. However, no criterion is uniformly optimal across equations or sparsity levels. Small-batch PINN-NTK diagnostics further show that residual fidelity, solution accuracy, and kernel conditioning are distinct objectives, motivating
pruning methods that explicitly balance solution-side and residual-side training dynamics during optimization.
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Computation and Language
EgoArgus: Benchmarking VLMs as Situational Assistants for Modality-Grounded User Supports
Yu-Chien Tang, Yu-Hsiang Liu and An-Zi Yen
VLMs are increasingly positioned as daily assistants that perceive first-person environments, follow user dialogue, and decide how to help. Existing
egocentric benchmarks mainly evaluate visual understanding in isolation, leaving open whether models can arbitrate between visual evidence and user-provided
language when the two are helpful, irrelevant, or conflicting. We introduce EgoArgus, a human-annotated
dataset for evaluating
egocentric assistants on understanding and decision tasks in five dialogue-
video daily scenarios. Our results demonstrate that it is still challenging for current VLMs as reliable
egocentric assistants, which requires identifying which modality is trustworthy and deciding when intervention is warranted. Deeper analysis also shows that existing modality bias mitigation methods are quite restricted to enhance performance, providing insights to aid practioners into the deployment of current VLMs as daily assistants.
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Computer Vision
AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research
Xintong Zhang, Xiaomeng Fan, Shilin Yan et al. (+7 more)
Video deep research aims to answer questions by jointly understanding
video content and retrieving external knowledge from the open Web. However, these tasks often require diverse and multi-step tool-use trajectories, which may lead to unnecessary
video tool calls, incurring additional costs and failure risk. In this paper, we propose AdaVDR, an adaptive
video deep research
agent with adaptive tool use and reflection, which selects tools based on task and model capabilities while backtracking when needed. To this end, we develop a data construction pipeline that builds adaptive tool-use trajectories according to task types and model capabilities. Specifically, we construct QA pairs that require both
video and external information, and organize the information acquisition processes into task-specific tool-use trajectories. We then apply model-conditioned tool necessity filtering to remove tools that can be bypassed using
video understanding and internal knowledge of the model. Based on this pipeline, we construct a training
dataset with adaptive trajectories and perform SFT on them to initialize these capabilities of AdaVDR. To further improve adaptive capabilities, we propose NASA-GRPO, a necessity-aware segment-guided credit assignment method, which evaluates segment-guided evidence contribution and tool necessity to construct normalized tool-level rewards. We also construct VDR-EE, a
benchmark built using the proposed pipeline. Experiments show that AdaVDR outperforms the base model by 15.20 and 29.00 percentage points on VDR-EE and the existing VideoDR, respectively, demonstrating its superiority.
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All authors Xintong Zhang, Xiaomeng Fan, Shilin Yan, Ekko He, Zicheng Liu, Zijian Zou, Guannan Zhang, Yuwei Wu, Zhi Gao, Hongwei Xue
Computation and Language
Virgil: Navigating Explainability for Transformer-based Language Models
Martino Ciaperoni, Sezer Kutluk, Benedetta Muscato et al. (+2 more)
Explainability for
transformer-based
language models is becoming crucial as these systems are deployed in high-stakes applications. As a result, the ecosystem of explainability tools is rapidly evolving, becoming richer, but also more fragmented and harder to navigate. To address this challenge, we present Virgil, an interactive system that lets practitioners and researchers, including non-experts, navigate explainability tools for
transformer language models. Supported by a curated knowledge base, the system enables users to discover and compare explainability tools within a unified interface.
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All authors Martino Ciaperoni, Sezer Kutluk, Benedetta Muscato, Marta Marchiori Manerba, Fosca Giannotti
Information Retrieval
When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory
Kazuki Nakayashiki
Provenance links keep the evidence behind an inherited belief reachable; an
agent with a verification budget must still choose which links to inspect. We study a consolidated
memory that states a decision constraint and whose source record has since been superseded by a record that withdraws it: provenance is immutable, the current record has changed, and the
memory is stale. In a controlled six-
memory scenario with a budget of two records, sixteen
language models rarely re-verified a constraint that read as settled: they inspected its provenance path in about one episode in five and, once the constraint had been superseded, produced stale-consistent decisions in 77.3%, 74.7% and 74.7% of episodes across a primary run, a replication and a held-out domain. Re-assigning one of the same two slots to the critical path removed most of them: +74.0, +72.7 and +61.3 points (positive in every model), +80.7 in a prospectively frozen interleaved replication with a repaired non-critical control, and +62.0 on a panel of 10 models from 9 organisations; a corrected re-run of the held-out scenario gave +73.3. The forced-critical policy uses experimenter knowledge of the critical path: it quantifies how much stale-decision risk the same budget can recover and is not a scheduler. Two further deposited experiments locate the failure and a remedy: in this store the constraint's path is selected in 17.0% of episodes at two slots and 88.7% at four of six (above uniform allocation), and at two slots a one-sentence, target-blind rule (prefer
memories that state a limit on a candidate direction) moved the
agent's own allocation onto the constraint's path and recovered the oracle contrast on decisions (+89.3 points) where that constraint limits the tempting action, while a content-free freshness cue did not materially redirect allocation and a content-matched control rule changed neither selection nor decisions.
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Machine Learning
Beyond Optimal Rates in Stochastic Optimization: Trajectory-Adaptive Stopping Rules
Liviu Aolaritei, Lucas Lévy, Francis Bach et al. (+1 more)
Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the
algorithm is run, even though practical stopping decisions are made adaptively by inspecting the evolving trajectory. This mismatch creates a fundamental certification problem: fixed-time guarantees do not generally remain valid at data-dependent stopping times, while deterministic horizons derived from worst-case bounds can be highly conservative. We address this problem for strongly convex
stochastic optimization by constructing fully observable, trajectory-adaptive upper confidence sequences for the squared distance of the last iterate to the optimizer and the suboptimality of a weighted average. These bounds hold simultaneously over time, attain the optimal $1/t$ decay rate up to iterated-logarithmic factors in the worst case, and adapt to the realized
stochastic gradients, allowing SGD to stop as soon as a prescribed accuracy is certified without sacrificing statistical validity. Our approach treats the evolving SGD trajectory as a sequential experiment whose observations provide evidence about the unknown optimization error. To formalize this perspective, we develop new recursive confidence-sequence techniques and a general time-uniform empirical Bernstein inequality for adapted processes with time-varying conditional means and predictable ranges that may grow without bound. We further extend these confidence-sequence constructions to minibatch SGD, with the empirical Bernstein bounds exploiting the realized second-moment structure within each minibatch. Numerical experiments show that the resulting stopping rules can require several orders of magnitude fewer iterations than natural deterministic horizons.
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All authors Liviu Aolaritei, Lucas Lévy, Francis Bach, Michael I. Jordan
Software Engineering
A Hybrid Usability Approach for Rating Evaluation of M-Commerce Applications
Ahmad Ibtisam, Bilal Khan and Arshad Ali
The success of any mobile application relies on its usefulness and rating is considered as an important measure in this regard. This research work focuses on identifying usability factors, which contribute significantly towards the rating of M-commerce apps. This work intends to explore existing usability models consisting of different factors along with a set of criteria and evaluate in terms of rating estimation by considering 5 well-known mobile applications, namely (i) daraz, (ii) shophive, (iii) home shopping, (iv) Symbios. (v) yayvo. Then, this work provides a hybrid usability model for rating prediction of M-commerce applications. The initial hybrid usability model comprises of (i) learnability, (ii) consistency, (iii) human factors,(iv)communicativeness,(v)effectiveness, (vi) Operability, (vii) efficiency, (viii) satisfaction. Each factor consists of some criteria. Keeping in view the factors of hybrid usability model, the data was collected from 40 users for each application. Furthermore, Forward Stepwise Multiple Linear Regression based rating prediction model is suggested by analyzing each criterion of all factors of hybrid usability model. Finally, the model is assessed and validated by using PRED(x) and K-fold techniques.
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Systems and Control
Throughput Maximization for MapReduce-Based Collaborative Computing over Energy-Harvesting Wireless Devices
Yuhang Li, Siqi Sun, Hongen Zheng et al. (+3 more)
This paper studies resource allocation for MapReduce-based collaborative computing over heterogeneous wireless devices powered by renewable energy harvesting. We formulate a long-run average throughput maximization problem that jointly optimizes computing load, phase time allocations, transmit power, and per-device energy consumption, subject to battery
evolution, CPU frequency, and latency constraints. To solve this problem online without prior knowledge of channel states or energy arrivals, we propose a DDPG-CVX
algorithm that couples Deep Deterministic Policy Gradient (DDPG) with convex programming. DDPG determines the per-slot energy budget for each device from observed battery and channel states; the remaining resource allocation variables are then resolved to global optimality by an embedded convex solver. This two-phase decomposition reduces the action-space dimensionality of DDPG while preserving per-slot solution quality. Simulations show that DDPG-CVX achieves 1.25$\times$$\sim$32.36$\times$ the throughput of representative
benchmarks.
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All authors Yuhang Li, Siqi Sun, Hongen Zheng, Xiaojing Chen, Shunqing Zhang, Yanzan Sun
Machine Learning
Interpreting Protein Language Model Embeddings via Orthogonal Projection for Protein Fitness Prediction
Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer et al. (+6 more)
Recently, there has been a growing adoption of
protein language models (PLMs) in biomedical science. Their
embeddings provide a rich numerical representation of
protein sequences which achieve state-of-the-art performance on several downstream tasks including
protein fitness prediction. However, PLM
embeddings are not directly interpretable and, thereby, it remains unclear what features they encode. To gain insight into which biochemical properties of the
protein are driving the prediction, we leverage an orthogonal projection technique that removes linear effects of known tabular features from
embeddings and extend it to high-order and interaction effects. In this way, we remove the effects of interpretable biochemical features from PLM
embeddings. In an ablation study, we show that this leads to a decrease in performance for a downstream classifier trained only on the
embeddings to predict
protein fitness. In an additional evaluation, we find that these biochemical features explain a substantial part of the variance in the predictions of this classifier. Hence, we can show that PLM
embeddings encode patterns correlated with biochemical properties and quantify their contribution to predicting
protein fitness. This computationally efficient approach is not limited to the features or
embeddings considered here and is readily transferable to problem settings beyond
protein fitness prediction.
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All authors Paulo Yanez Sarmiento, Pia Francesca Rissom, Manuel Pfeuffer, Marco Simnacher, Jordan F. Safer, Sumaiya Iqbal, Henrike O. Heyne, Nadja Klein, Bernhard Y. Renard
Robotics
A Tendon-Driven Five-Fingered Hand with Distributed Tactile Perception for Dexterous Manipulation
Huayang Chen and Longhui Qin
To apply the techniques of
embodied artificial intelligence to human-oid
robots for complex manipulations, dexterous robotic hands are indispensable, which are restricted by the dexterity and tactile perception capability. In this work, we proposed a novel design of tendon-driven five-fingered hand with dis-tributed tactile perception. With a soft-rigid-hybrid structure employed, both compliance and operational force are endowed to the hand. Dual-modality tactile sensing elements are distributed on the distal and middle phalanges of all five fingers, enabling the simultaneous detection of static contact and dynamic force variations. Manipulation experiments, including counting gestures, finger-to-thumb pinching, object grasping, and bottle-grasp tactile recording, demonstrate the feasibility of the integrated actuation-perception system.
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