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cc509bd4d785c041d3700252d4591182c638f1e2a93492f38eaff8c35cf15a0c
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2026-01-01T00:00:00-05:00
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ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments
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arXiv:2512.24680v1 Announce Type: new Abstract: Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view. We first develop a novel sigma point-based approximation approach to fast and accurately estimate mutual information reward under non-Gaussian belief distributions, utilizing informative sampling in state and observation spaces to mitigate the computational intractability of integral calculation. To tackle significant uncertainty associated with inadequate prior target information, we propose the hierarchical particle structure in ReSPIRe, which not only extracts critical particles for global route guidance, but also adjusts the particle number adaptively for planning efficiency. Building upon the hierarchical structure, we develop the reusable belief tree search approach to build a policy tree for online trajectory planning under uncertainty, which reuses rollout evaluation to improve planning efficiency. Extensive simulations and real-world experiments demonstrate that ReSPIRe outperforms representative benchmark methods with smaller MI approximation error, higher search efficiency, and more stable tracking performance, while maintaining outstanding computational efficiency.
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https://arxiv.org/abs/2512.24680
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Academic Papers
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d4459e97e3d19a25efaff108ae2bf18e1941ceba9dbda43883e365992d5fb406
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2026-01-01T00:00:00-05:00
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CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications
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arXiv:2512.24682v1 Announce Type: new Abstract: The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-sentence/clause relationships, and evolving specification behaviors. In this work, we present CellSecInspector, an automated framework for security analysis of 3GPP specifications. CellSecInspector extracts structured state-condition-action (SCA) representations, models mobile network procedures with comprehensive function chains, systematically validates them against 9 foundational security properties under 4 adversarial scenarios, and automatically generates test cases. This end-to-end pipeline enables the automated discovery of vulnerabilities without relying on manually predefined security requirements or rules. Applying CellSecInspector to the well-studied 5G and 4G NAS and RRC specifications, it discovers 43 vulnerabilities, 8 of which are previously unreported. Our findings show that CellSecInspector is a scalable, adaptive, and effective solution to assess 3GPP specifications for safeguarding operational and next-generation cellular networks.
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https://arxiv.org/abs/2512.24682
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Academic Papers
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7c266ebb8025af83eace4390b8ce83df7d8a8819e8fa791572c5e327a08e562c
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2026-01-01T00:00:00-05:00
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Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience
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arXiv:2512.24683v1 Announce Type: new Abstract: AI data-center expansion is increasingly constrained by the coupled availability of deliverable electricity and heat-rejection (cooling) capacity. We propose and evaluate an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service rather than an unavoidable electricity burden. The coupled system is modeled as an input-output 'black box' with transparent boundaries and a standalone benchmark in which mechanical chilling is powered by grid electricity. The central mechanism is energy-grade matching: low-grade WtE thermal output drives absorption cooling to deliver chilled service, thereby displacing baseline cooling electricity. We show that thermoeconomic superiority is governed by three first-order determinants, (i) cooling coverage of IT heat load, (ii) parasitic electricity for transport and auxiliaries, and (iii) distance-driven delivery decay, yielding a break-even corridor beyond which net benefits vanish. Comparative statics characterize sensitivity to IT utilization, feedstock quality (waste LHV and throughput), climate parameterization, and corridor distance. We translate these accounting gains into decision language through a computable prototype for Levelized Cost of Computing (LCOC) and an ESG valuation channel grounded in measurable mechanisms, without re-deriving full lifecycle inventories. The framework provides siting-ready feasibility conditions for WtE-AIDC coupling in urban AI corridors under grid stress.
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https://arxiv.org/abs/2512.24683
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Academic Papers
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39eb09c40aab42010496b94aa24ff3d43dc06b8109bf8a1ef2952e5fb1959c49
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2026-01-01T00:00:00-05:00
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R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory
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arXiv:2512.24684v1 Announce Type: new Abstract: We present R-Debater, an agentic framework for generating multi-turn debates built on argumentative memory. Grounded in rhetoric and memory studies, the system views debate as a process of recalling and adapting prior arguments to maintain stance consistency, respond to opponents, and support claims with evidence. Specifically, R-Debater integrates a debate knowledge base for retrieving case-like evidence and prior debate moves with a role-based agent that composes coherent utterances across turns. We evaluate on standardized ORCHID debates, constructing a 1,000-item retrieval corpus and a held-out set of 32 debates across seven domains. Two tasks are evaluated: next-utterance generation, assessed by InspireScore (subjective, logical, and factual), and adversarial multi-turn simulations, judged by Debatrix (argument, source, language, and overall). Compared with strong LLM baselines, R-Debater achieves higher single-turn and multi-turn scores. Human evaluation with 20 experienced debaters further confirms its consistency and evidence use, showing that combining retrieval grounding with structured planning yields more faithful, stance-aligned, and coherent debates across turns.
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https://arxiv.org/abs/2512.24684
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Academic Papers
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ce32a0d015f0d3a3cf5f5b70302f79bd428af6d2b8a08bac64ce48548fdb8a9f
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2026-01-01T00:00:00-05:00
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BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis
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arXiv:2512.24686v1 Announce Type: new Abstract: Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitations, this paper proposes BatteryAgent, a hierarchical framework that integrates physical knowledge features with the reasoning capabilities of Large Language Models (LLMs). The framework comprises three core modules: (1) A Physical Perception Layer that utilizes 10 mechanism-based features derived from electrochemical principles, balancing dimensionality reduction with physical fidelity; (2) A Detection and Attribution Layer that employs Gradient Boosting Decision Trees and SHAP to quantify feature contributions; and (3) A Reasoning and Diagnosis Layer that leverages an LLM as the agent core. This layer constructs a "numerical-semantic" bridge, combining SHAP attributions with a mechanism knowledge base to generate comprehensive reports containing fault types, root cause analysis, and maintenance suggestions. Experimental results demonstrate that BatteryAgent effectively corrects misclassifications on hard boundary samples, achieving an AUROC of 0.986, which significantly outperforms current state-of-the-art methods. Moreover, the framework extends traditional binary detection to multi-type interpretable diagnosis, offering a new paradigm shift from "passive detection" to "intelligent diagnosis" for battery safety management.
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https://arxiv.org/abs/2512.24686
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Academic Papers
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295a6f6a7f61af590b35d0d95e1bcd929e4f985457bcb2d579ee6dbc83daaa82
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2026-01-01T00:00:00-05:00
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CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System
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arXiv:2512.24688v1 Announce Type: new Abstract: Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot measurements such as bearings, distances, and inertials for tens of robots remains challenging. We present CREPES-X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. CREPES-X starts with a compact hardware design: InfraRed (IR) LEDs, an IR camera, an ultra-wideband module, and an IMU housed in a cube no larger than 6cm on each side. Then CREPES-X implements a two-stage hierarchical estimator to meet different requirements, considering speed, accuracy, and robustness. First, we propose a single-frame relative estimator that provides instant relative poses for multi-robot setups through a closed-form solution and robust bearing outlier rejection. Then a multi-frame relative estimator is designed to offer accurate and robust relative states by exploring IMU pre-integration via robocentric relative kinematics with loosely- and tightly-coupled optimization. Extensive simulations and real-world experiments validate the effectiveness of CREPES-X, showing robustness to up to 90% bearing outliers, proving resilience in challenging conditions, and achieving RMSE of 0.073m and 1.817{\deg} in real-world datasets.
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https://arxiv.org/abs/2512.24688
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Academic Papers
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45c195979f06dbc6d53ad6d953410228a1e9b086682b2f94ebd3eceab74c9fed
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2026-01-01T00:00:00-05:00
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MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models
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arXiv:2512.24693v1 Announce Type: new Abstract: Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human evaluation. Multi-turn reward models (RMs) offer a scalable alternative and can provide valuable signals for guiding LLM training. While recent work has advanced multi-turn \textit{training} techniques, effective automated \textit{evaluation} specifically for multi-turn interactions lags behind. We observe that standard preference datasets, typically contrasting responses based only on the final conversational turn, provide insufficient signal to capture the nuances of multi-turn interactions. Instead, we find that incorporating contrasts spanning \textit{multiple} turns is critical for building robust multi-turn RMs. Motivated by this finding, we propose \textbf{MU}lti-\textbf{S}tep \textbf{I}nstruction \textbf{C}ontrast (MUSIC), an unsupervised data augmentation strategy that synthesizes contrastive conversation pairs exhibiting differences across multiple turns. Leveraging MUSIC on the Skywork preference dataset, we train a multi-turn RM based on the Gemma-2-9B-Instruct model. Empirical results demonstrate that our MUSIC-augmented RM outperforms baseline methods, achieving higher alignment with judgments from advanced proprietary LLM judges on multi-turn conversations, crucially, without compromising performance on standard single-turn RM benchmarks.
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https://arxiv.org/abs/2512.24693
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Academic Papers
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9ddc0af596ea1204860552db24620ac55b04f80335d1fd5cd1f1cd58e863d0f3
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2026-01-01T00:00:00-05:00
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Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach
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arXiv:2512.24694v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility is increasingly embedded in many real-world applications. The user mobility, either natural or induced, enables clients to act as relays or bridges, thus enhancing information flow in sparse networks; however, its impact on DFL has been largely overlooked despite its potential. In this work, we systematically investigate the role of mobility in improving DFL performance. We first establish the convergence of DFL in sparse networks under user mobility and theoretically demonstrate that even random movement of a fraction of users can significantly boost performance. Building upon this insight, we propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to determine their trajectories to enhance information propagation through the network. Through extensive experiments, we empirically confirm our theoretical findings, validate the superiority of our approach over baselines, and provide a comprehensive analysis of how various network parameters influence DFL performance in mobile networks.
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https://arxiv.org/abs/2512.24694
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Academic Papers
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838a4ca8795fb4794f4cf696c69bcf9083e8ba0d93cb13eb418ee0997097b4f6
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2026-01-01T00:00:00-05:00
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Nested Learning: The Illusion of Deep Learning Architectures
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arXiv:2512.24695v1 Announce Type: new Abstract: Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning model with a set of nested, multi-level, and/or parallel optimization problems, each of which with its own context flow. Through the lenses of NL, existing deep learning methods learns from data through compressing their own context flow, and in-context learning naturally emerges in large models. NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities. We advocate for NL by presenting three core contributions: (1) Expressive Optimizers: We show that known gradient-based optimizers, such as Adam, SGD with Momentum, etc., are in fact associative memory modules that aim to compress the gradients' information (by gradient descent). Building on this insight, we present other more expressive optimizers with deep memory and/or more powerful learning rules; (2) Self-Modifying Learning Module: Taking advantage of NL's insights on learning algorithms, we present a sequence model that learns how to modify itself by learning its own update algorithm; and (3) Continuum Memory System: We present a new formulation for memory system that generalizes the traditional viewpoint of long/short-term memory. Combining our self-modifying sequence model with the continuum memory system, we present a continual learning module, called Hope, showing promising results in language modeling, knowledge incorporation, and few-shot generalization tasks, continual learning, and long-context reasoning tasks.
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https://arxiv.org/abs/2512.24695
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Academic Papers
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e3ddb96ecf90581ae006e0bb77f54443eb382e0b22dd0ca71deda8f1463bc566
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2026-01-01T00:00:00-05:00
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Causal Discovery with Mixed Latent Confounding via Precision Decomposition
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arXiv:2512.24696v1 Announce Type: new Abstract: We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many variables while others influence only small subsets. This setting is common in practice and poses a challenge for existing methods: differentiable and score-based DAG learners can misinterpret global latent effects as causal edges, while latent-variable graphical models recover only undirected structure. We propose \textsc{DCL-DECOR}, a modular, precision-led pipeline that separates these roles. The method first isolates pervasive latent effects by decomposing the observed precision matrix into a structured component and a low-rank component. The structured component corresponds to the conditional distribution after accounting for pervasive confounders and retains only local dependence induced by the causal graph and localized confounding. A correlated-noise DAG learner is then applied to this deconfounded representation to recover directed edges while modeling remaining structured error correlations, followed by a simple reconciliation step to enforce bow-freeness. We provide identifiability results that characterize the recoverable causal target under mixed confounding and show how the overall problem reduces to well-studied subproblems with modular guarantees. Synthetic experiments that vary the strength and dimensionality of pervasive confounding demonstrate consistent improvements in directed edge recovery over applying correlated-noise DAG learning directly to the confounded data.
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https://arxiv.org/abs/2512.24696
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Academic Papers
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cbe768037c2c82d4c4e34cf0531410707cbd0b69792df26b65d32a469ddce834
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2026-01-01T00:00:00-05:00
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Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model Homotopy Transfer
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arXiv:2512.24698v1 Announce Type: new Abstract: Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order models can help mitigate these challenges. However, the model discrepancy poses a significant challenge when transferring policies to full-body dynamics environments. In this work, we introduce a continuation-based learning framework that combines simplified model pretraining and model homotopy transfer to efficiently generate and refine complex dynamic behaviors. First, we pretrain the policy using a single rigid body model to capture core motion patterns in a simplified environment. Next, we employ a continuation strategy to progressively transfer the policy to the full-body environment, minimizing performance loss. To define the continuation path, we introduce a model homotopy from the single rigid body model to the full-body model by gradually redistributing mass and inertia between the trunk and legs. The proposed method not only achieves faster convergence but also demonstrates superior stability during the transfer process compared to baseline methods. Our framework is validated on a range of dynamic tasks, including flips and wall-assisted maneuvers, and is successfully deployed on a real quadrupedal robot.
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https://arxiv.org/abs/2512.24698
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Academic Papers
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770da85a59021c0910c56d162dc4c982512182cb7a16d92f5fa09bc1ac88771c
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2026-01-01T00:00:00-05:00
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Average Consensus with Dynamic Quantization Framing and Finite-Time Termination over Limited-Bandwidth Directed Networks
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arXiv:2512.24700v1 Announce Type: new Abstract: This paper proposes a deterministic distributed algorithm, referred to as PP-ACDC, that achieves exact average consensus over possibly unbalanced directed graphs using only a fixed and a priori specified number of quantization bits. The method integrates Push-Pull (surplus) consensus dynamics with a dynamic quantization framing scheme combining zooming and midpoint shifting, enabling agents to preserve the true global average while progressively refining their quantization precision. We establish a rigorous convergence theory showing that PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters. Moreover, we develop a fully distributed and synchronized finite-time termination mechanism, and we provide a formal proof on the detection of $\epsilon$-convergence to the average within a finite number of iterations. Numerical simulations corroborate the theoretical results and demonstrate that PP-ACDC achieves reliable, communication-efficient, and precise average consensus even under very tight bit budgets, underscoring its suitability for large-scale and resource-constrained multi-agent systems operating over directed networks.
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https://arxiv.org/abs/2512.24700
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Academic Papers
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274b1620dd2d5b20aa1cfe6b1aa2ce6a4438bad7107344bf879a1868dd5d16ce
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2026-01-01T00:00:00-05:00
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Evolving, Not Training: Zero-Shot Reasoning Segmentation via Evolutionary Prompting
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arXiv:2512.24702v1 Announce Type: new Abstract: Reasoning Segmentation requires models to interpret complex, context-dependent linguistic queries to achieve pixel-level localization. Current dominant approaches rely heavily on Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). However, SFT suffers from catastrophic forgetting and domain dependency, while RL is often hindered by training instability and rigid reliance on predefined reward functions. Although recent training-free methods circumvent these training burdens, they are fundamentally limited by a static inference paradigm. These methods typically rely on a single-pass "generate-then-segment" chain, which suffers from insufficient reasoning depth and lacks the capability to self-correct linguistic hallucinations or spatial misinterpretations. In this paper, we challenge these limitations and propose EVOL-SAM3, a novel zero-shot framework that reformulates reasoning segmentation as an inference-time evolutionary search process. Instead of relying on a fixed prompt, EVOL-SAM3 maintains a population of prompt hypotheses and iteratively refines them through a "Generate-Evaluate-Evolve" loop. We introduce a Visual Arena to assess prompt fitness via reference-free pairwise tournaments, and a Semantic Mutation operator to inject diversity and correct semantic errors. Furthermore, a Heterogeneous Arena module integrates geometric priors with semantic reasoning to ensure robust final selection. Extensive experiments demonstrate that EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting. The code is available at https://github.com/AHideoKuzeA/Evol-SAM3.
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https://arxiv.org/abs/2512.24702
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Academic Papers
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5b614086237782247d9bcdc1cb55c2319cd7a70cc5919fc30dbec0be4b4837bf
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2026-01-01T00:00:00-05:00
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BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework
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arXiv:2512.24708v1 Announce Type: new Abstract: The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nature of transfer, its transitive-intransitive nature, is still an open problem, and negative transfer remains a significant obstacle. Selection of beneficial auxiliary task sets in multi-task learning is frequently hindered by the high computational cost of their evaluation, the high number of plausible candidate auxiliary sets, and the varying complexity of selection across target tasks. To address these constraints, we introduce BandiK, a novel three-stage multi-task auxiliary task subset selection method using multi-bandits, where each arm pull evaluates candidate auxiliary sets by training and testing a multiple output neural network on a single random train-test dataset split. Firstly, BandiK estimates the pairwise transfers between tasks, which helps in identifying which tasks are likely to benefit from joint learning. In the second stage, it constructs a linear number of candidate sets of auxiliary tasks (in the number of all tasks) for each target task based on the initial estimations, significantly reducing the exponential number of potential auxiliary task sets. Thirdly, it employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits. To enhance efficiency, BandiK integrates these individual task-specific MABs into a multi-bandit structure. The proposed multi-bandit solution exploits that the same neural network realizes multiple arms of different individual bandits corresponding to a given candidate set. This semi-overlapping arm property defines a novel multi-bandit cost/reward structure utilized in BandiK.
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https://arxiv.org/abs/2512.24708
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Academic Papers
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4523ff20eb181b73fa4e67a176e054bb6cb12693b7a3afdce30e7ed7f0900946
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2026-01-01T00:00:00-05:00
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MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints
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arXiv:2512.24711v1 Announce Type: new Abstract: In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose \textbf{MEIC-DT}, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer's input scale within a predefined memory budget. This mechanism incorporates a Statistics-Aware Eviction Strategy (\textbf{SAES}), which utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. Furthermore, we introduce an Internal Regularization Policy (\textbf{IRP}) that strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.
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https://arxiv.org/abs/2512.24711
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Academic Papers
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f4ef1df6b88c0b36254db7c4be37c09ebc5b8b0026b587cdb8054e8706f7d80d
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2026-01-01T00:00:00-05:00
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LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving
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arXiv:2512.24712v1 Announce Type: new Abstract: Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for humans yet difficult to encode explicitly. Although large vision-language models (VLMs) can interpret such semantics, their inference cost makes them impractical for real-time deployment.This work proposes LSRE, a Latent Semantic Rule Encoding framework that converts sparsely sampled VLM judgments into decision boundaries within the latent space of a recurrent world model. By encoding language-defined safety semantics into a lightweight latent classifier, LSRE enables real-time semantic risk assessment at 10 Hz without per-frame VLM queries. Experiments on six semantic-failure scenarios in CARLA demonstrate that LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency. LSRE further generalizes to rarely seen semantic-similar test cases, indicating that language-guided latent classification offers an effective and deployable mechanism for semantic safety monitoring in autonomous driving.
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https://arxiv.org/abs/2512.24712
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Academic Papers
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fcf9c770656c30ac48893107473bc52527862419082c0749170ea69dace2ad4d
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2026-01-01T00:00:00-05:00
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FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference
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arXiv:2512.24713v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To address this challenge, this work introduces an automation framework that leverages weight pruning and low-bit quantization, and presents a hardware-software co-design method that generates accelerators on the Field-Programmable Gate Array (FPGA) platform. In particular, we implement a unified pipeline that applies N:M structured pruning and 4-bit integer quantization to reduce the memory footprint, followed by optimized dequantization and matrix multiplication to enhance LLM inference on several hardware platforms, including CPUs, NVIDIA GPUs with Dense and 2:4 Sparse Tensor Cores, and a custom systolic-array-based FPGA accelerator. Utilizing 2:4 sparsity combined with quantization on $4096 \times 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines. Scaling analysis on the LLaMA-7B model further shows that structured sparsity enhances the throughput per token by $1.36\times$. These results demonstrate the synergy of fine-grained N:M sparsity and quantization for enabling efficient and deployable LLM inference, while the proposed FPGA accelerator offers a flexible architectural path for supporting a broader class of sparsity patterns beyond the fixed 2:4 hardware constraints.
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https://arxiv.org/abs/2512.24713
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Academic Papers
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3174d34f93056b94997214d87061cb03603b4d74252b037f5932465e754e527c
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2026-01-01T00:00:00-05:00
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Boundary error control for numerical solution of BSDEs by the convolution-FFT method
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arXiv:2512.24714v1 Announce Type: new Abstract: We first review the convolution fast-Fourier-transform (CFFT) approach for the numerical solution of backward stochastic differential equations (BSDEs) introduced in (Hyndman and Oyono Ngou, 2017). We then propose a method for improving the boundary errors obtained when valuing options using this approach. We modify the damping and shifting schemes used in the original formulation, which transforms the target function into a bounded periodic function so that Fourier transforms can be applied successfully. Time-dependent shifting reduces boundary error significantly. We present numerical results for our implementation and provide a detailed error analysis showing the improved accuracy and convergence of the modified convolution method.
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https://arxiv.org/abs/2512.24714
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Academic Papers
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6d54a92c0c56d13266b66bd94f956b59235367f73ec39b614caca18d30410176
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2026-01-01T00:00:00-05:00
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MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation
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arXiv:2512.24715v1 Announce Type: new Abstract: Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in recent years. However, due to the strict privacy constraints inherent in FRs, access to user-item interaction data and user profiles across clients is highly restricted, making it difficult to learn globally effective representations for new (cold-start) items. Consequently, the item cold-start problem becomes even more challenging in FRs. Existing solutions typically predict embeddings for new items through the attribute-to-embedding mapping paradigm, which establishes a fixed one-to-one correspondence between item attributes and their embeddings. However, this one-to-one mapping paradigm often fails to model varying data distributions and tends to cause embedding misalignment, as verified by our empirical studies. To this end, we propose MDiffFR, a novel generation-based modality-guided diffusion method for cold-start items in FRs. In this framework, we employ a tailored diffusion model on the server to generate embeddings for new items, which are then distributed to clients for cold-start inference. To align item semantics, we deploy a pre-trained modality encoder to extract modality features as conditional signals to guide the reverse denoising process. Furthermore, our theoretical analysis verifies that the proposed method achieves stronger privacy guarantees compared to existing mapping-based approaches. Extensive experiments on four real datasets demonstrate that our method consistently outperforms all baselines in FRs.
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https://arxiv.org/abs/2512.24715
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Academic Papers
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d76fa73442defec754befe31729b2cafcfa4f3701cc2c1ceabb65af8bf6b1774
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2026-01-01T00:00:00-05:00
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Equivalence of Personalized PageRank and Successor Representations
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arXiv:2512.24722v1 Announce Type: new Abstract: The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each independently been proposed to underlie the hippocampal operation for each function -- personalized page-rank for memory retrieval, and successor representations for planning and navigation, are in fact isomorphic and utilize the same underlying representation -- the stationary distribution of a random walk on a graph. We hypothesize that the core computational function of the hippocampus is to compute this representation on arbitrary input graphs.
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https://arxiv.org/abs/2512.24722
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Academic Papers
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5761f08ab78ade00798a4db9d507a0440cd7edbef5d6aedc95e914f573c9782e
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2026-01-01T00:00:00-05:00
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FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation
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arXiv:2512.24724v1 Announce Type: new Abstract: In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages, respectively. We further introduce simple criteria to choose stage boundaries and provide a velocity-divergence analysis as an effective proxy for identifying capacity-sensitive regions. Across LTX-Video (2B/13B) and WAN 2.1 (1.3B/14B), FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models. FlowBlending is also compatible with existing sampling-acceleration techniques, enabling up to 2x additional speedup. Project page is available at: https://jibin86.github.io/flowblending_project_page.
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https://arxiv.org/abs/2512.24724
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Academic Papers
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f1ac5a869526d761c548c2f69bf7a039fef5dd947a0a17fb7b5463c84507e945
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2026-01-01T00:00:00-05:00
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EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation
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arXiv:2512.24731v1 Announce Type: new Abstract: Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-to-audio (VT2A), the current formulation faces three key limitations: First, an imbalance between visual and textual conditioning that leads to visual dominance; Second, the absence of a concrete definition for fine-grained controllable generation; Third, weak instruction understanding and following, as existing datasets rely on brief categorical tags. To address these limitations, we introduce EchoFoley, a new task designed for video-grounded sound generation with both event level local control and hierarchical semantic control. Our symbolic representation for sounding events specifies when, what, and how each sound is produced within a video or instruction, enabling fine-grained controls like sound generation, insertion, and editing. To support this task, we construct EchoFoley-6k, a large-scale, expert-curated benchmark containing over 6,000 video-instruction-annotation triplets. Building upon this foundation, we propose EchoVidia a sounding-event-centric agentic generation framework with slow-fast thinking strategy. Experiments show that EchoVidia surpasses recent VT2A models by 40.7% in controllability and 12.5% in perceptual quality.
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https://arxiv.org/abs/2512.24731
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Academic Papers
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85521d5a94f90010091ddf379912eb87960d480250b8e98681a9c7a790a9ed26
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2026-01-01T00:00:00-05:00
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BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature
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arXiv:2512.24733v1 Announce Type: new Abstract: Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language models (LLMs) to improve PE-based interpretation, the lack of a standardized benchmark for end-to-end multi-omics pathway mechanism elucidation has largely confined evaluation to small, manually curated datasets or ad hoc case studies, hindering reproducible progress. To address this issue, we introduce BIOME-Bench, constructed via a rigorous four-stage workflow, to evaluate two core capabilities of LLMs in multi-omics analysis: Biomolecular Interaction Inference and end-to-end Multi-Omics Pathway Mechanism Elucidation. We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models. Experimental results demonstrate that existing models still exhibit substantial deficiencies in multi-omics analysis, struggling to reliably distinguish fine-grained biomolecular relation types and to generate faithful, robust pathway-level mechanistic explanations.
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https://arxiv.org/abs/2512.24733
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Academic Papers
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2616e8d5b3d3f0a679c7c4a4f2834ffeffcb8329193d0d757739590387c124a8
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2026-01-01T00:00:00-05:00
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Exact compensation of communication delays for discrete-time heterogeneous multi-agent linear systems with applications to SIR epidemic model
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arXiv:2512.24735v1 Announce Type: new Abstract: This paper investigates the output synchronization problem for discrete-time heterogeneous multi-agent systems (MASs) subject to distinct communication delays. The presence of such delays prevents the instantaneous delivery of information from neighboring nodes, thereby severely degrading the performance of standard distributed control schemes. To overcome this, we propose a prediction-based framework for exact delay compensation. Specifically, we introduce predictors combined with a mechanism of distributed predictors, which enables the recursive reconstruction of future state information across the communication network. Building upon these predictors, we construct prediction-based distributed observers and formulate both prediction-based distributed state-feedback and dynamic output-feedback controllers. Theoretical analysis confirms that the proposed strategy eliminates the impact of delays after a finite number of steps, ensuring output synchronization. The effectiveness of the methods is validated through a numerical example and a Koopman operator-based linear Susceptible-Infected-Recovered (SIR) epidemic model. Notably, for a population of 4 million, the proposed delay compensation strategy achieves a reduction of over 200,000 infected individuals at the peak, underscoring its potential significance in epidemic mitigation.
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https://arxiv.org/abs/2512.24735
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Academic Papers
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f725ba468a351f0b36080669106fac2ef5241672a4fbd7bffa4a4139fedadb9b
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2026-01-01T00:00:00-05:00
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SLM-TTA: A Framework for Test-Time Adaptation of Generative Spoken Language Models
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arXiv:2512.24739v1 Announce Type: new Abstract: Spoken Language Models (SLMs) are increasingly central to modern speech-driven applications, but performance degrades under acoustic shift - real-world noise, reverberation, and microphone variation. Prior solutions rely on offline domain adaptation, which is post-hoc, data-intensive, and slow. We introduce the first test-time adaptation (TTA) framework for generative SLMs that process interleaved audio-text prompts. Our method updates a small, targeted subset of parameters during inference using only the incoming utterance, requiring no source data or labels. This stabilizes token distributions and improves robustness to acoustic variability without degrading core task accuracy. Evaluated on automatic speech recognition, speech translation, and 19 audio understanding tasks from AIR-Bench, our approach yields consistent gains under diverse corruptions. Because adaptation touches only a small fraction of weights, it is both compute- and memory-efficient, supporting deployment on resource-constrained platforms. This work enhances the robustness and adaptability of generative SLMs for real-world speech-driven applications.
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https://arxiv.org/abs/2512.24739
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Academic Papers
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53bf5208647156cd45fd94d44369833e00ab7ebba3a31afca29a440bc829e9a3
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2026-01-01T00:00:00-05:00
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Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints
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arXiv:2512.24740v1 Announce Type: new Abstract: An important function of autonomous microrobots is the ability to perform robust movement over terrain. This paper explores an edge ML approach to microrobot locomotion, allowing for on-device, lower latency control under compute, memory, and power constraints. This paper explores the locomotion of a sub-centimeter quadrupedal microrobot via reinforcement learning (RL) and deploys the resulting controller on an ultra-small system-on-chip (SoC), SC$\mu$M-3C, featuring an ARM Cortex-M0 microcontroller running at 5 MHz. We train a compact FP32 multilayer perceptron (MLP) policy with two hidden layers ($[128, 64]$) in a massively parallel GPU simulation and enhance robustness by utilizing domain randomization over simulation parameters. We then study integer (Int8) quantization (per-tensor and per-feature) to allow for higher inference update rates on our resource-limited hardware, and we connect hardware power budgets to achievable update frequency via a cycles-per-update model for inference on our Cortex-M0. We propose a resource-aware gait scheduling viewpoint: given a device power budget, we can select the gait mode (trot/intermediate/gallop) that maximizes expected RL reward at a corresponding feasible update frequency. Finally, we deploy our MLP policy on a real-world large-scale robot on uneven terrain, qualitatively noting that domain-randomized training can improve out-of-distribution stability. We do not claim real-world large-robot empirical zero-shot transfer in this work.
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https://arxiv.org/abs/2512.24740
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Academic Papers
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c4961bed0b9d8b9ea0610ec12cffec17d97cf1d1d59890ac6e8c8763e3c8760a
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2026-01-01T00:00:00-05:00
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Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression
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arXiv:2512.24742v1 Announce Type: new Abstract: The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard
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https://arxiv.org/abs/2512.24742
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Academic Papers
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e49955b822461c5e4c426cc79143caa0ae12afb53c245ba21db2beb86c134a3f
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2026-01-01T00:00:00-05:00
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Analyzing Communication Predictability in LLM Training
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arXiv:2512.24750v1 Announce Type: new Abstract: Effective communication is essential in distributed training, with predictability being one of its most significant characteristics. However, existing studies primarily focus on exploiting predictability through online profiling for runtime optimization, without a systematic understanding of it. In this work, we aim to systematically formulate communication predictability in distributed training, particularly in Large Language Models (LLMs) that utilize hybrid parallelism. Our analysis focuses on both traffic patterns and communication overhead. Specifically, we investigate predictable traffic patterns in typical LLMs and evaluate how various factors influence GPU utilization and effective bandwidth (two critical variables affecting communication overhead). Furthermore, we develop an analytical formulation to estimate communication overhead in LLM training, which is validated with high accuracy against empirical data. Leveraging this formulation, we propose a configuration tuning tool, ConfigTuner, to optimize training performance. Compared to Megatron-LM, the training configurations optimized by ConfigTuner demonstrate up to a 1.36$\times$ increase in throughput. Compared to Alpa, ConfigTuner generates the same configuration suggestion while significantly reducing the search complexity.
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https://arxiv.org/abs/2512.24750
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Academic Papers
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c92a515711b2e2b8eb49409ba8afbdec1a23e7e641ca8eb848b9ea5ec9640e17
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2026-01-01T00:00:00-05:00
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Trustworthy Equipment Monitoring via Cascaded Anomaly Detection and Thermal Localization
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arXiv:2512.24755v1 Announce Type: new Abstract: Predictive maintenance demands accurate anomaly detection and trustable explanations. Although multimodal fusion of sensor time-series and thermal imagery shows promise, we demonstrate that naive fusion strategies can paradoxically degrade performance. This paper introduces a Cascaded Anomaly Detection framework that decouples detection and localization. Stage 1 employs an LSTM-based sensor encoder with temporal attention for high-accuracy detection, while Stage 2 activates a CNN-based thermal encoder for post-detection fault localization. Our results reveal that sensor-only detection outperforms full fusion by 8.3 percentage points (93.08% vs. 84.79% F1-score), challenging the assumption that additional modalities invariably improve performance. We further contribute an explainability pipeline integrating SHAP, temporal/spatial attention, and gate weight analysis. This analysis uncovers a "modality bias" where fusion models assign 65-87% weight to the weaker thermal modality. Validated on a real-world bearing dataset (78,397 samples), our cascaded approach achieves state-of-the-art accuracy while providing actionable diagnostics for maintenance decision-making.
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https://arxiv.org/abs/2512.24755
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Academic Papers
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d8069ad61141100799e6a654ceea4da386dc576c19efdf907ba9af67726316bc
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2026-01-01T00:00:00-05:00
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OpenOneRec Technical Report
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arXiv:2512.24762v1 Announce Type: new Abstract: While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation systems and general intelligence. Constrained by isolated data, they operate as domain specialists-proficient in pattern matching but lacking world knowledge, reasoning capabilities, and instruction following. This limitation is further compounded by the lack of a holistic benchmark to evaluate such integrated capabilities. To address this, our contributions are: 1) RecIF Bench & Open Data: We propose RecIF-Bench, a holistic benchmark covering 8 diverse tasks that thoroughly evaluate capabilities from fundamental prediction to complex reasoning. Concurrently, we release a massive training dataset comprising 96 million interactions from 160,000 users to facilitate reproducible research. 2) Framework & Scaling: To ensure full reproducibility, we open-source our comprehensive training pipeline, encompassing data processing, co-pretraining, and post-training. Leveraging this framework, we demonstrate that recommendation capabilities can scale predictably while mitigating catastrophic forgetting of general knowledge. 3) OneRec-Foundation: We release OneRec Foundation (1.7B and 8B), a family of models establishing new state-of-the-art (SOTA) results across all tasks in RecIF-Bench. Furthermore, when transferred to the Amazon benchmark, our models surpass the strongest baselines with an average 26.8% improvement in Recall@10 across 10 diverse datasets (Figure 1). This work marks a step towards building truly intelligent recommender systems. Nonetheless, realizing this vision presents significant technical and theoretical challenges, highlighting the need for broader research engagement in this promising direction.
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https://arxiv.org/abs/2512.24762
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Academic Papers
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cc320f1528d8229d273c885d8cba714647b7746b31791d3e0abb41f20973e0e0
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2026-01-01T00:00:00-05:00
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UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning
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arXiv:2512.24763v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions. Existing methods use a two-stage approach in which some rely on contrastive learning with hyperparameter-sensitive clustering, while others preprocess labels for consistency. We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives. This embedding is then efficiently decoded into instance labels through a novel "Embedding-to-Label" process, effectively integrating the optimization. While this unified framework offers substantial benefits, we observed artifacts at the object boundaries. To address the object boundary issues, we propose hard-mining samples along these boundaries. However, directly applying hard mining to the feature embeddings proved unstable. Therefore, we apply a linear layer to the rasterized feature embeddings before calculating the triplet loss, which stabilizes training and significantly improves performance. Our method outperforms baselines qualitatively and quantitatively on the ScanNet, Replica3D, and Messy-Rooms datasets.
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https://arxiv.org/abs/2512.24763
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Academic Papers
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9b2a142cad569b2446f20a65826f66918ce3ad96d294926a7d45b51789ff061c
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2026-01-01T00:00:00-05:00
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Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow
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arXiv:2512.24766v1 Announce Type: new Abstract: Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial image and task instruction, these models excel at synthesizing sensible object motions. Thus, we introduce Dream2Flow, a framework that bridges video generation and robotic control through 3D object flow as an intermediate representation. Our method reconstructs 3D object motions from generated videos and formulates manipulation as object trajectory tracking. By separating the state changes from the actuators that realize those changes, Dream2Flow overcomes the embodiment gap and enables zero-shot guidance from pre-trained video models to manipulate objects of diverse categories-including rigid, articulated, deformable, and granular. Through trajectory optimization or reinforcement learning, Dream2Flow converts reconstructed 3D object flow into executable low-level commands without task-specific demonstrations. Simulation and real-world experiments highlight 3D object flow as a general and scalable interface for adapting video generation models to open-world robotic manipulation. Videos and visualizations are available at https://dream2flow.github.io/.
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https://arxiv.org/abs/2512.24766
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Academic Papers
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dab4e6d5c0d6f4d656c2e1cbfc94a4e8699ae91b8e9bff32c9cc4df2a30f6a5a
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2026-01-01T00:00:00-05:00
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From Trial to Deployment: A SEM Analysis of Traveler Adoptions to Fully Operational Autonomous Taxis
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arXiv:2512.24767v1 Announce Type: new Abstract: Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies have investigated actual user behavior based on operational AV services. This study addresses that gap by leveraging survey data from Wuhan, China, where Baidu's Apollo Robotaxi service operates at scale. We design a realistic survey incorporating actual service attributes and collect 336 valid responses from actual users. Using Structural Equation Modeling, we identify six latent psychological constructs, namely Trust \& Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. Their influences on adoption behavior, measured by the selection frequency of autonomous taxis in ten scenarios, are examined and interpreted. Results show that Cost Sensitivity and Behavioral Intention are the strongest positive predictors of adoption, while other latent constructs play more nuanced roles. The model demonstrates strong goodness-of-fit across multiple indices. Our findings offer empirical evidence to support policymaking, fare design, and public outreach strategies for scaling autonomous taxis deployments in real-world urban settings.
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https://arxiv.org/abs/2512.24767
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Academic Papers
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44b109bdae1d4caa3e3dd5d94ce92b28d9567e359fa5154bab6fa66d1339bcff
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2026-01-01T00:00:00-05:00
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Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection
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arXiv:2512.24772v1 Announce Type: new Abstract: Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Network. It combines teacher-student pseudo-labelling, ensemble learning, and augmentation of data. Our framework uses a group of teacher models. Their predictions come together through soft voting. An uncertainty-based threshold filters out low-confidence pseudo-labels to reduce noise and improve learning stability. We also use a confidence-weighted training method that focuses on reliable pseudo-labelled samples. This greatly boosts robustness across languages. Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines. It significantly reduces the performance gap between settings that have plenty of resources and those that do not. Detailed experiments and studies confirm that our framework is effective and can be used in various situations. This shows that it is suitable for scalable, cross-language mental health monitoring where labelled resources are limited.
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https://arxiv.org/abs/2512.24772
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Academic Papers
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23e235317cda7cb0674ac89dc41815345ad19748c6884fa9a5bc50dd2025817c
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2026-01-01T00:00:00-05:00
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Throughput Optimization in UAV-Mounted RIS under Jittering and Imperfect CSI via DRL
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arXiv:2512.24773v1 Announce Type: new Abstract: Reconfigurable intelligent surfaces (RISs) mounted on unmanned aerial vehicles (UAVs) can reshape wireless propagation on-demand. However, their performance is sensitive to UAV jitter and cascaded channel uncertainty. This paper investigates a downlink multiple-input single-output UAV-mounted RIS system in which a ground multiple-antenna base station (BS) serves multiple single-antenna users under practical impairments. Our goal is to maximize the expected throughput under stochastic three-dimensional UAV jitter and imperfect cascaded channel state information (CSI) based only on the available channel estimates. This leads to a stochastic nonconvex optimization problem subject to a BS transmit power constraint and strict unit-modulus constraints on all RIS elements. To address this problem, we design a model-free deep reinforcement learning (DRL) framework with a contextual bandit formulation. A differentiable feasibility layer is utilized to map continuous actions to feasible solutions, while the reward is a Monte Carlo estimate of the expected throughput. We instantiate this framework with constrained variants of deep deterministic policy gradient (DDPG) and twin delayed deep deterministic policy gradient (TD3) that do not use target networks. Simulations show that the proposed algorithms yield higher throughput than conventional alternating optimization-based weighted minimum mean-square error (AO-WMMSE) baselines under severe jitter and low CSI quality. Across different scenarios, the proposed methods achieve performance that is either comparable to or slightly below the AO-WMMSE benchmark, based on sample average approximation (SAA) with a relative gap ranging from 0-12%. Moreover, the proposed DRL controllers achieve online inference times of 0.6 ms per decision versus roughly 370-550 ms for AO-WMMSE solvers.
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https://arxiv.org/abs/2512.24773
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Academic Papers
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aefcd52e61076fd8f6f91af660f1966938bc16311a7a1d37d65d5fdb71f7a61d
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2026-01-01T00:00:00-05:00
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Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models
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arXiv:2512.24776v1 Announce Type: new Abstract: Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with generating long reasoning sequences. For industrial applications, model selection depends not only on raw accuracy but also on resource constraints and inference costs. In this work, we conduct a test-time-compute aware evaluation of both contemporary and older open-source LLMs, mapping their Pareto frontiers across math- and reasoning-intensive benchmarks. Our findings identify the Mixture of Experts (MoE) architecture as a strong candidate to balance performance and efficiency in our evaluation setting. Furthermore, we trace the trajectory of Pareto efficiency over time to derive an emergent trend regarding accuracy gain per unit of compute. Finally, we demonstrate that there is a saturation point for inference-time compute. Beyond a certain threshold, accuracy gains diminish, indicating that while extended reasoning capabilities are beneficial, they cannot overcome intrinsic model limitations regarding specific complexities.
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https://arxiv.org/abs/2512.24776
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Academic Papers
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c60f9ec5eb04ca39b120a1f514dca1f3c101d1e0a402a0830a72ed006fc87c16
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2026-01-01T00:00:00-05:00
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Gradient Descent as Implicit EM in Distance-Based Neural Models
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arXiv:2512.24780v1 Announce Type: new Abstract: Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking. These phenomena appear across architectures -- in attention mechanisms, classification heads, and energy-based models -- yet existing explanations rely on loose analogies to mixture models or post-hoc architectural interpretation. We provide a direct derivation. For any objective with log-sum-exp structure over distances or energies, the gradient with respect to each distance is exactly the negative posterior responsibility of the corresponding component: $\partial L / \partial d_j = -r_j$. This is an algebraic identity, not an approximation. The immediate consequence is that gradient descent on such objectives performs expectation-maximization implicitly -- responsibilities are not auxiliary variables to be computed but gradients to be applied. No explicit inference algorithm is required because inference is embedded in optimization. This result unifies three regimes of learning under a single mechanism: unsupervised mixture modeling, where responsibilities are fully latent; attention, where responsibilities are conditioned on queries; and cross-entropy classification, where supervision clamps responsibilities to targets. The Bayesian structure recently observed in trained transformers is not an emergent property but a necessary consequence of the objective geometry. Optimization and inference are the same process.
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https://arxiv.org/abs/2512.24780
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Academic Papers
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4e27503cf820e89ba3920db2ed120f5872ebb05ae7dc27674e8bdcc09c781b60
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2026-01-01T00:00:00-05:00
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HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment
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arXiv:2512.24787v1 Announce Type: new Abstract: Slate recommendation, where users are presented with a ranked list of items simultaneously, is widely adopted in online platforms. Recent advances in generative models have shown promise in slate recommendation by modeling sequences of discrete semantic IDs autoregressively. However, existing autoregressive approaches suffer from semantically entangled item tokenization and inefficient sequential decoding that lacks holistic slate planning. To address these limitations, we propose HiGR, an efficient generative slate recommendation framework that integrates hierarchical planning with listwise preference alignment. First, we propose an auto-encoder utilizing residual quantization and contrastive constraints to tokenize items into semantically structured IDs for controllable generation. Second, HiGR decouples generation into a list-level planning stage for global slate intent, followed by an item-level decoding stage for specific item selection. Third, we introduce a listwise preference alignment objective to directly optimize slate quality using implicit user feedback. Experiments on our large-scale commercial media platform demonstrate that HiGR delivers consistent improvements in both offline evaluations and online deployment. Specifically, it outperforms state-of-the-art methods by over 10% in offline recommendation quality with a 5x inference speedup, while further achieving a 1.22% and 1.73% increase in Average Watch Time and Average Video Views in online A/B tests.
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https://arxiv.org/abs/2512.24787
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Academic Papers
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27735d5aa2d9dc2ae60345c73cb356ca3ee1e8087714a5e54abc68452e9d98de
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2026-01-01T00:00:00-05:00
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Projection-based Adversarial Attack using Physics-in-the-Loop Optimization for Monocular Depth Estimation
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arXiv:2512.24792v1 Announce Type: new Abstract: Deep neural networks (DNNs) remain vulnerable to adversarial attacks that cause misclassification when specific perturbations are added to input images. This vulnerability also threatens the reliability of DNN-based monocular depth estimation (MDE) models, making robustness enhancement a critical need in practical applications. To validate the vulnerability of DNN-based MDE models, this study proposes a projection-based adversarial attack method that projects perturbation light onto a target object. The proposed method employs physics-in-the-loop (PITL) optimization -- evaluating candidate solutions in actual environments to account for device specifications and disturbances -- and utilizes a distributed covariance matrix adaptation evolution strategy. Experiments confirmed that the proposed method successfully created adversarial examples that lead to depth misestimations, resulting in parts of objects disappearing from the target scene.
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https://arxiv.org/abs/2512.24792
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Academic Papers
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35dd3c31bbb85dac193f8c58c1048c3f96ecb4c68de262fc5f691ca63f8feac3
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2026-01-01T00:00:00-05:00
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Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks
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arXiv:2512.24793v1 Announce Type: new Abstract: Neural architecture search (NAS), which automates the architectural design process of deep neural networks (DNN), has attracted increasing attention. Multimodal DNNs that necessitate feature fusion from multiple modalities benefit from NAS due to their structural complexity; however, constructing an architecture for multimodal DNNs through NAS requires a substantial amount of labeled training data. Thus, this paper proposes a self-supervised learning (SSL) method for architecture search of multimodal DNNs. The proposed method applies SSL comprehensively for both the architecture search and model pretraining processes. Experimental results demonstrated that the proposed method successfully designed architectures for DNNs from unlabeled training data.
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https://arxiv.org/abs/2512.24793
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Academic Papers
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2a6f8f774ac709822512b75a1e26d9df116f470f0038fe8c04639a97fbc4d870
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2026-01-01T00:00:00-05:00
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Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training
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arXiv:2512.24794v1 Announce Type: new Abstract: The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major limitation: nonlinear functions applied to the noisy targets will skew the results. This bias occurs because the nonlinearity makes the expected value of the noisy targets different from the clean target image. Since nonlinear functions are common in image processing, avoiding them limits the types of preprocessing that can be performed on the noisy targets. Our main insight is that certain nonlinear functions can be applied to the noisy targets without adding significant bias to the results. We develop a theoretical framework for analyzing the effects of these nonlinearities, and describe a class of nonlinear functions with minimal bias. We demonstrate our method on the denoising of high dynamic range (HDR) images produced by Monte Carlo rendering. Noise2Noise training can have trouble with HDR images, where the training process is overwhelmed by outliers and performs poorly. We consider a commonly used method of addressing these training issues: applying a nonlinear tone mapping function to the model output and target images to reduce their dynamic range. This method was previously thought to be incompatible with Noise2Noise training because of the nonlinearities involved. We show that certain combinations of loss functions and tone mapping functions can reduce the effect of outliers while introducing minimal bias. We apply our method to an existing machine learning-based Monte Carlo denoiser, where the original implementation was trained with high-sample count reference images. Our results approach those of the original implementation, but are produced using only noisy training data.
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https://arxiv.org/abs/2512.24794
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Academic Papers
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391d2efc6e5fca00892be66a3dcb1d6aa8340350557291eeab72542bb380fe5a
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2026-01-01T00:00:00-05:00
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LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories)
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arXiv:2512.24796v1 Announce Type: new Abstract: Large language models (LLMs) have made rapid progress in formal theorem proving, yet current benchmarks under-measure the kind of abstraction and library-mediated reasoning that organizes modern mathematics. In parallel with FATE's emphasis on frontier algebra, we introduce LeanCat, a Lean benchmark for category-theoretic formalization -- a unifying language for mathematical structure and a core layer of modern proof engineering -- serving as a stress test of structural, interface-level reasoning. Part I: 1-Categories contains 100 fully formalized statement-level tasks, curated into topic families and three difficulty tiers via an LLM-assisted + human grading process. The best model solves 8.25% of tasks at pass@1 (32.50%/4.17%/0.00% by Easy/Medium/High) and 12.00% at pass@4 (50.00%/4.76%/0.00%). We also evaluate LeanBridge which use LeanExplore to search Mathlib, and observe consistent gains over single-model baselines. LeanCat is intended as a compact, reusable checkpoint for tracking both AI and human progress toward reliable, research-level formalization in Lean.
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https://arxiv.org/abs/2512.24796
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Academic Papers
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c5e4997c1b285bdec82fb0751ec729f0cd635ee2502b9dc64b8d1b0c58290cca
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2026-01-01T00:00:00-05:00
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Sidelink Positioning: Standardization Advancements, Challenges and Opportunities
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arXiv:2512.24803v1 Announce Type: new Abstract: With the integration of cellular networks in vertical industries that demand precise location information, such as vehicle-to-everything (V2X), public safety, and Industrial Internet of Things (IIoT), positioning has become an imperative component for future wireless networks. By exploiting a wider spectrum, multiple antennas and flexible architectures, cellular positioning achieves ever-increasing positioning accuracy. Still, it faces fundamental performance degradation when the distance between user equipment (UE) and the base station (BS) is large or in non-line-of-sight (NLoS) scenarios. To this end, the 3rd generation partnership project (3GPP) Rel-18 proposes to standardize sidelink (SL) positioning, which provides unique opportunities to extend the positioning coverage via direct positioning signaling between UEs. Despite the standardization advancements, the capability of SL positioning is controversial, especially how much spectrum is required to achieve the positioning accuracy defined in 3GPP. To this end, this article summarizes the latest standardization advancements of 3GPP on SL positioning comprehensively, covering a) network architecture; b) positioning types; and c) performance requirements. The capability of SL positioning using various positioning methods under different imperfect factors is evaluated and discussed in-depth. Finally, according to the evolution of SL in 3GPP Rel-19, we discuss the possible research directions and challenges of SL positioning.
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https://arxiv.org/abs/2512.24803
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Academic Papers
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0812dfe02c58e1803c8773ea57df676acef209b4354db41996cdbf3e7876b33f
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2026-01-01T00:00:00-05:00
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DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes
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arXiv:2512.24810v1 Announce Type: new Abstract: Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel operations, such as Bayesian classification with rejection, top-$K$ selection, and ranking. We propose a deep kernel learning-based GP architecture (DTI-GP), which incorporates a combined neural embedding module for chemical compounds and protein targets, and a GP module. The workflow continues with sampling from the predictive distribution to estimate a Bayesian precedence matrix, which is used in fast and accurate selection and ranking operations. DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility.
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https://arxiv.org/abs/2512.24810
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Academic Papers
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3cb214c5c96e49744ce045cf22056fc1bc6bae9015798b050f37c0fa560709c2
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2026-01-01T00:00:00-05:00
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Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback
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arXiv:2512.24818v1 Announce Type: new Abstract: Aligning large language models (LLMs) with human preferences has proven effective for enhancing model capabilities, yet standard preference modeling using the Bradley-Terry model assumes transitivity, overlooking the inherent complexity of human population preferences. Nash learning from human feedback (NLHF) addresses this by framing non-transitive preferences as a two-player zero-sum game, where alignment reduces to finding the Nash equilibrium (NE). However, existing algorithms typically rely on regularization, incurring unavoidable bias when computing the duality gap in the original game. In this work, we provide the first convergence guarantee for Optimistic Multiplicative Weights Update ($\mathtt{OMWU}$) in NLHF, showing that it achieves last-iterate linear convergence after a burn-in phase whenever an NE with full support exists, with an instance-dependent linear convergence rate to the original NE, measured by duality gaps. Compared to prior results in Wei et al. (2020), we do not require the assumption of NE uniqueness. Our analysis identifies a novel marginal convergence behavior, where the probability of rarely played actions grows exponentially from exponentially small values, enabling exponentially better dependence on instance-dependent constants than prior results. Experiments corroborate the theoretical strengths of $\mathtt{OMWU}$ in both tabular and neural policy classes, demonstrating its potential for LLM applications.
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https://arxiv.org/abs/2512.24818
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c3a3cb3900e72ea3484a07ff72fe0846114400ffda28557f79e3ec65d60b0030
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2026-01-01T00:00:00-05:00
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LMG Index: A Robust Learned Index for Multi-Dimensional Performance Balance
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arXiv:2512.24824v1 Announce Type: new Abstract: Index structures are fundamental for efficient query processing on large-scale datasets. Learned indexes model the indexing process as a prediction problem to overcome the inherent trade-offs of traditional indexes. However, most existing learned indexes optimize only for limited objectives like query latency or space usage, neglecting other practical evaluation dimensions such as update efficiency and stability. Moreover, many learned indexes rely on assumptions about data distributions or workloads, lacking theoretical guarantees when facing unknown or evolving scenarios, which limits their generality in real-world systems. In this paper, we propose LMIndex, a robust framework for learned indexing that leverages a efficient query/update top-layer structure (theoretically $O(1)$ when the key type is fixed) and a efficient optimal error threshold training algorithm (approach $O(1)$ in practice). Building upon this, we develop LMG (LMIndex with gaps), a variant employing a novel gap allocation strategy to enhance update performance and maintain stability under dynamic workloads. Extensive evaluations show that LMG achieves competitive or leading performance, including bulk loading (up to 8.25$\times$ faster), point queries (up to 1.49$\times$ faster), range queries (up to 4.02$\times$ faster than B+Tree), update (up to 1.5$\times$ faster on read-write workloads), stability (up to 82.59$\times$ lower coefficient of variation), and space usage (up to 1.38$\times$ smaller). These results demonstrate that LMG effectively breaks the multi-dimensional performance trade-offs inherent in state-of-the-art approaches, offering a balanced and versatile framework.
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https://arxiv.org/abs/2512.24824
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2ee13ea42e6ebdc1276f146203550e2586d4adf1fbabcc491df9ea563add6787
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2026-01-01T00:00:00-05:00
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Practising responsibility: Ethics in NLP as a hands-on course
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arXiv:2512.24825v1 Announce Type: new Abstract: As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula.
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https://arxiv.org/abs/2512.24825
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305ff843b3917333072cf5b1bbfa47ba408aac8bb7fee90d8668a9662cb385b9
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2026-01-01T00:00:00-05:00
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Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control
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arXiv:2512.24826v1 Announce Type: new Abstract: Cross-modal systems trained on 2D visual inputs are presented with a dimensional shift when processing 3D scenes. An in-scene camera bridges the dimensionality gap but requires learning a control module. We introduce a new method that improves multivariate mutual information estimates by regret minimisation with derivative-free optimisation. Our algorithm enables off-the-shelf cross-modal systems trained on 2D visual inputs to adapt online to object occlusions and differentiate features. The pairing of expressive measures and value-based optimisation assists control of an in-scene camera to learn directly from the noisy outputs of vision-language models. The resulting pipeline improves performance in cross-modal tasks on multi-object 3D scenes without resorting to pretraining or finetuning.
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https://arxiv.org/abs/2512.24826
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a2a9c405fdd50ff6438a2547c4caf69cb8e55b67b16e714cf6d0890d51aeddb5
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2026-01-01T00:00:00-05:00
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Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics
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arXiv:2512.24827v1 Announce Type: new Abstract: Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of multi-agent options particularly challenging. Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviours. Toward addressing these limitations, we describe a novel approach for multi-agent option discovery. Specifically, we propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours. Our approach builds on the inductive bias that synchronisation over agent states provides a natural foundation for coordination in the absence of explicit objectives. We first approximate a fictitious state of maximal alignment with the team, the \textit{Fermat} state, and use it to define a measure of \textit{spreadness}, capturing team-level misalignment on each individual state dimension. Building on this representation, we then employ a neural graph Laplacian estimator to derive options that capture state synchronisation patterns between agents. We evaluate the resulting options across multiple scenarios in two multi-agent domains, showing that they yield stronger downstream coordination capabilities compared to alternative option discovery methods.
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https://arxiv.org/abs/2512.24827
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Academic Papers
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5016c64057d10d36d291dd99327ccb86af677e626cb3c12ab733147e1f6db56c
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2026-01-01T00:00:00-05:00
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Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
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arXiv:2512.24829v1 Announce Type: new Abstract: Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning.
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https://arxiv.org/abs/2512.24829
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75cb0244abc53ec9105743f7cffc6504889bb7fb57a1cb59bbec6b75535f53c4
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2026-01-01T00:00:00-05:00
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GenZ: Foundational models as latent variable generators within traditional statistical models
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arXiv:2512.24834v1 Announce Type: new Abstract: We present GenZ, a hybrid model that bridges foundational models and statistical modeling through interpretable semantic features. While large language models possess broad domain knowledge, they often fail to capture dataset-specific patterns critical for prediction tasks. Our approach addresses this by discovering semantic feature descriptions through an iterative process that contrasts groups of items identified via statistical modeling errors, rather than relying solely on the foundational model's domain understanding. We formulate this as a generalized EM algorithm that jointly optimizes semantic feature descriptors and statistical model parameters. The method prompts a frozen foundational model to classify items based on discovered features, treating these judgments as noisy observations of latent binary features that predict real-valued targets through learned statistical relationships. We demonstrate the approach on two domains: house price prediction (hedonic regression) and cold-start collaborative filtering for movie recommendations. On house prices, our model achieves 12\% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38\% error) that relies on the LLM's general domain knowledge. For Netflix movie embeddings, our model predicts collaborative filtering representations with 0.59 cosine similarity purely from semantic descriptions -- matching the performance that would require approximately 4000 user ratings through traditional collaborative filtering. The discovered features reveal dataset-specific patterns (e.g., architectural details predicting local housing markets, franchise membership predicting user preferences) that diverge from the model's domain knowledge alone.
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https://arxiv.org/abs/2512.24834
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1784fc7f3158d6005d858ea07b99588ea9e019b9737bbb07c8008ec3a4132cbd
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2026-01-01T00:00:00-05:00
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CropTrack: A Tracking with Re-Identification Framework for Precision Agriculture
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arXiv:2512.24838v1 Announce Type: new Abstract: Multiple-object tracking (MOT) in agricultural environments presents major challenges due to repetitive patterns, similar object appearances, sudden illumination changes, and frequent occlusions. Contemporary trackers in this domain rely on the motion of objects rather than appearance for association. Nevertheless, they struggle to maintain object identities when targets undergo frequent and strong occlusions. The high similarity of object appearances makes integrating appearance-based association nontrivial for agricultural scenarios. To solve this problem we propose CropTrack, a novel MOT framework based on the combination of appearance and motion information. CropTrack integrates a reranking-enhanced appearance association, a one-to-many association with appearance-based conflict resolution strategy, and an exponential moving average prototype feature bank to improve appearance-based association. Evaluated on publicly available agricultural MOT datasets, CropTrack demonstrates consistent identity preservation, outperforming traditional motion-based tracking methods. Compared to the state of the art, CropTrack achieves significant gains in identification F1 and association accuracy scores with a lower number of identity switches.
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https://arxiv.org/abs/2512.24838
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Academic Papers
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8b8a06b81edc10f6936d476860c8684c5e8bf936958cf0db7bdc12b59dafbe63
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2026-01-01T00:00:00-05:00
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When Does the Silhouette Score Work? A Comprehensive Study in Network Clustering
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arXiv:2512.24841v1 Announce Type: new Abstract: Selecting the number of communities is a fundamental challenge in network clustering. The silhouette score offers an intuitive, model-free criterion that balances within-cluster cohesion and between-cluster separation. Albeit its widespread use in clustering analysis, its performance in network-based community detection remains insufficiently characterized. In this study, we comprehensively evaluate the performance of the silhouette score across unweighted, weighted, and fully connected networks, examining how network size, separation strength, and community size imbalance influence its performance. Simulation studies show that the silhouette score accurately identifies the true number of communities when clusters are well separated and balanced, but it tends to underestimate under strong imbalance or weak separation and to overestimate in sparse networks. Extending the evaluation to a real airline reachability network, we demonstrate that the silhouette-based clustering can recover geographically interpretable and market-oriented clusters. These findings provide empirical guidance for applying the silhouette score in network clustering and clarify the conditions under which its use is most reliable.
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https://arxiv.org/abs/2512.24841
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Academic Papers
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bb13c0aa75e98a571b7f34bb3cd4db2dcc5eef1d988bdff2bbe2386730abca14
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2026-01-01T00:00:00-05:00
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Triangulation as an Acceptance Rule for Multilingual Mechanistic Interpretability
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arXiv:2512.24842v1 Announce Type: new Abstract: Multilingual language models achieve strong aggregate performance yet often behave unpredictably across languages, scripts, and cultures. We argue that mechanistic explanations for such models should satisfy a \emph{causal} standard: claims must survive causal interventions and must \emph{cross-reference} across environments that perturb surface form while preserving meaning. We formalize \emph{reference families} as predicate-preserving variants and introduce \emph{triangulation}, an acceptance rule requiring necessity (ablating the circuit degrades the target behavior), sufficiency (patching activations transfers the behavior), and invariance (both effects remain directionally stable and of sufficient magnitude across the reference family). To supply candidate subgraphs, we adopt automatic circuit discovery and \emph{accept or reject} those candidates by triangulation. We ground triangulation in causal abstraction by casting it as an approximate transformation score over a distribution of interchange interventions, connect it to the pragmatic interpretability agenda, and present a comparative experimental protocol across multiple model families, language pairs, and tasks. Triangulation provides a falsifiable standard for mechanistic claims that filters spurious circuits passing single-environment tests but failing cross-lingual invariance.
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https://arxiv.org/abs/2512.24842
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Academic Papers
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274229029c7d122e4fd05bab61dd00211b77e587ac9e63344f853c0476ae8079
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2026-01-01T00:00:00-05:00
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ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation
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arXiv:2512.24845v1 Announce Type: new Abstract: 3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observations are prone to visual ambiguity, while methods that estimate parameters from state changes typically rely on constrained settings such as fixed cameras and unobstructed views. Furthermore, fine-grained functional elements like small handles are frequently missed by general object detectors. To bridge this gap, we present ArtiSG, a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory. Our approach leverages a robust articulation data collection pipeline utilizing a portable setup to accurately estimate 6-DoF articulation trajectories and axes even under camera ego-motion. We integrate these kinematic priors into a hierarchical and open-vocabulary graph while utilizing interaction data to discover inconspicuous functional elements missed by visual perception. Extensive real-world experiments demonstrate that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision. Moreover, we show that the constructed graph serves as a reliable functional memory that effectively guides robots to perform language-directed manipulation tasks in real-world environments containing diverse articulated objects.
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https://arxiv.org/abs/2512.24845
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8347658146fcfe00dc00e0b552bef80a96b1813b38cde1059bbcf40f90c2be26
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2026-01-01T00:00:00-05:00
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AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference
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arXiv:2512.24847v1 Announce Type: new Abstract: High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstruction framework based on diffusion-based Bayesian inference. By leveraging the learned spatiotemporal probability distribution of the AOD field as a generative prior, this framework can be flexibly adapted to various reconstruction tasks without requiring task-specific retraining. We first introduce a corruption-aware training strategy to learns a spatiotemporal AOD prior solely from naturally incomplete data. Subsequently, we employ a decoupled annealing posterior sampling strategy that enables the more effective and integration of heterogeneous observations as constraints to guide the generation process. We validate the proposed framework through extensive experiments on Reanalysis data. Results across downscaling and inpainting tasks confirm the efficacy and robustness of AODDiff, specifically demonstrating its advantage in maintaining high spatial spectral fidelity. Furthermore, as a generative model, AODDiff inherently enables uncertainty quantification via multiple sampling, offering critical confidence metrics for downstream applications.
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https://arxiv.org/abs/2512.24847
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Academic Papers
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33cc0849b97e1748cadf7d218b3180f475205ed9ba478cdaa7bfa4343555e5de
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2026-01-01T00:00:00-05:00
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PrivacyBench: A Conversational Benchmark for Evaluating Privacy in Personalized AI
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arXiv:2512.24848v1 Announce Type: new Abstract: Personalized AI agents rely on access to a user's digital footprint, which often includes sensitive data from private emails, chats and purchase histories. Yet this access creates a fundamental societal and privacy risk: systems lacking social-context awareness can unintentionally expose user secrets, threatening digital well-being. We introduce PrivacyBench, a benchmark with socially grounded datasets containing embedded secrets and a multi-turn conversational evaluation to measure secret preservation. Testing Retrieval-Augmented Generation (RAG) assistants reveals that they leak secrets in up to 26.56% of interactions. A privacy-aware prompt lowers leakage to 5.12%, yet this measure offers only partial mitigation. The retrieval mechanism continues to access sensitive data indiscriminately, which shifts the entire burden of privacy preservation onto the generator. This creates a single point of failure, rendering current architectures unsafe for wide-scale deployment. Our findings underscore the urgent need for structural, privacy-by-design safeguards to ensure an ethical and inclusive web for everyone.
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https://arxiv.org/abs/2512.24848
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Academic Papers
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a70faf7221b1b04bfdcec1ec0daec210a5ee188a92b6fd033447fad666ff5e04
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2026-01-01T00:00:00-05:00
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On an Erd\H{o}s--Lov'asz problem: 3-critical 3-graphs of minimum degree 7
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arXiv:2512.24850v1 Announce Type: new Abstract: Erd\H{o}s and Lov'asz asked whether there exists a "3-critical" 3-uniform hypergraph in which every vertex has degree at least 7. The original formulation does not specify what 3-critical means, and two non-equivalent notions have appeared in the literature and in later discussions of the problem. In this paper we resolve the question under both interpretations. For the transversal interpretation (criticality with respect to the transversal number), we prove that a 3-uniform hypergraph $H$ with $\tau(H)=3$ and $\tau(H-e)=2$ for every edge $e$ has at most 10 edges; in particular, $\delta(H)\le 6$, and this bound is sharp, witnessed by the complete 3-graph $K^{(3)}_5$. For the chromatic interpretation (criticality with respect to weak vertex-colourings), we give an explicit 3-uniform hypergraph on 9 vertices with $\chi(H)=3$ and minimum degree $\delta(H)=7$ such that deleting any single edge or any single vertex makes it 2-colourable. The criticality of the example is certified by explicit witness 2-colourings listed in the appendices, together with a short verification script.
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https://arxiv.org/abs/2512.24850
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2eb262165c0a558062d2f11c9fd476737c2dfecad7208b9c4e81e86bd63d0ce1
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2026-01-01T00:00:00-05:00
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VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents
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arXiv:2512.24851v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, which requires multi-round dialogue spatial reasoning and sequential action prediction, needs further exploration. Our work investigates this potential in the context of Vision-and-Language Navigation (VLN) by introducing a unified and extensible evaluation framework to probe MLLMs as zero-shot agents by bridging traditional navigation datasets into a standardized benchmark, named VLN-MME. We simplify the evaluation with a highly modular and accessible design. This flexibility streamlines experiments, enabling structured comparisons and component-level ablations across diverse MLLM architectures, agent designs, and navigation tasks. Crucially, enabled by our framework, we observe that enhancing our baseline agent with Chain-of-Thought (CoT) reasoning and self-reflection leads to an unexpected performance decrease. This suggests MLLMs exhibit poor context awareness in embodied navigation tasks; although they can follow instructions and structure their output, their 3D spatial reasoning fidelity is low. VLN-MME lays the groundwork for systematic evaluation of general-purpose MLLMs in embodied navigation settings and reveals limitations in their sequential decision-making capabilities. We believe these findings offer crucial guidance for MLLM post-training as embodied agents.
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https://arxiv.org/abs/2512.24851
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Academic Papers
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25224fe29573d6bfb424369c80e164804ddfbc082f089b6d884c5dc7d7195f66
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2026-01-01T00:00:00-05:00
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A study on constraint extraction and exception exclusion in care worker scheduling
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arXiv:2512.24853v1 Announce Type: new Abstract: Technologies for automatically generating work schedules have been extensively studied; however, in long-term care facilities, the conditions vary between facilities, making it essential to interview the managers who create shift schedules to design facility-specific constraint conditions. The proposed method utilizes constraint templates to extract combinations of various components, such as shift patterns for consecutive days or staff combinations. The templates can extract a variety of constraints by changing the number of days and the number of staff members to focus on and changing the extraction focus to patterns or frequency. In addition, unlike existing constraint extraction techniques, this study incorporates mechanisms to exclude exceptional constraints. The extracted constraints can be employed by a constraint programming solver to create care worker schedules. Experiments demonstrated that our proposed method successfully created schedules that satisfied all hard constraints and reduced the number of violations for soft constraints by circumventing the extraction of exceptional constraints.
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https://arxiv.org/abs/2512.24853
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597a112699ff72f65024585978080b4c151e2a5c950f888ce9c6a39de4a15b33
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2026-01-01T00:00:00-05:00
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Feature Slice Matching for Precise Bug Detection
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arXiv:2512.24858v1 Announce Type: new Abstract: Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the noise interference in existing works does not manage the tough job, i.e., eliminating the noise in the targets. In this paper, we propose MATUS to mitigate the target noise for precise bug detection based on similarity measurement. Feature slices are extracted from both the buggy query and the targets to represent the semantic feature of (potential) bug logics. In particular, MATUS guides the target slicing with the prior knowledge from the buggy code, in an end-to-end way to pinpoint the slicing criterion in the targets. All feature slices are embedded and compared based on the vector similarity. Buggy candidates are audited to confirm unknown bugs in the targets. Experiments show that MATUS holds advantages in bug detection for real-world projects with acceptable efficiency. In total, MATUS has spotted 31 unknown bugs in the Linux kernel. All of them have been confirmed by the kernel developers, and 11 have been assigned CVEs.
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https://arxiv.org/abs/2512.24858
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4fc4f50552a1267794ef276d708f5b9bf01ba45592273836b865763ade8776ef
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2026-01-01T00:00:00-05:00
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OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation
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arXiv:2512.24861v1 Announce Type: new Abstract: The Segment Anything Model 2 (SAM2) has demonstrated remarkable promptable visual segmentation capabilities in video data, showing potential for extension to medical image segmentation (MIS) tasks involving 3D volumes and temporally correlated 2D image sequences. However, adapting SAM2 to MIS presents several challenges, including the need for extensive annotated medical data for fine-tuning and high-quality manual prompts, which are both labor-intensive and require intervention from medical experts. To address these challenges, we introduce OFL-SAM2, a prompt-free SAM2 framework for label-efficient MIS. Our core idea is to leverage limited annotated samples to train a lightweight mapping network that captures medical knowledge and transforms generic image features into target features, thereby providing additional discriminative target representations for each frame and eliminating the need for manual prompts. Crucially, the mapping network supports online parameter update during inference, enhancing the model's generalization across test sequences. Technically, we introduce two key components: (1) an online few-shot learner that trains the mapping network to generate target features using limited data, and (2) an adaptive fusion module that dynamically integrates the target features with the memory-attention features generated by frozen SAM2, leading to accurate and robust target representation. Extensive experiments on three diverse MIS datasets demonstrate that OFL-SAM2 achieves state-of-the-art performance with limited training data.
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https://arxiv.org/abs/2512.24861
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d3766801932ac91d51cfd9d914afb1ace8c99c94f2d4f2fef116a046d8e2d0c1
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2026-01-01T00:00:00-05:00
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Big AI is accelerating the metacrisis: What can we do?
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arXiv:2512.24863v1 Announce Type: new Abstract: The world is in the grip of ecological, meaning, and language crises which are converging into a metacrisis. Big AI is accelerating them all. Language engineers are playing a central role, persisting with a scalability story that is failing humanity, supplying critical talent to plutocrats and kleptocrats, and creating new technologies as if the whole endeavour was value-free. We urgently need to explore alternatives, applying our collective intelligence to design a life-affirming future for NLP that is centered on human flourishing on a living planet.
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https://arxiv.org/abs/2512.24863
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1cbbb38f5ce29adb062bee892d048f12bd1de0dee168e76420c9f67c1e09a89b
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2026-01-01T00:00:00-05:00
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Characterization of Transfer Using Multi-task Learning Curves
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arXiv:2512.24866v1 Announce Type: new Abstract: Transfer effects manifest themselves both during training using a fixed data set and in inductive inference using accumulating data. We hypothesize that perturbing the data set by including more samples, instead of perturbing the model by gradient updates, provides a complementary and more fundamental characterization of transfer effects. To capture this phenomenon, we quantitatively model transfer effects using multi-task learning curves approximating the inductive performance over varying sample sizes. We describe an efficient method to approximate multi-task learning curves analogous to the Task Affinity Grouping method applied during training. We compare the statistical and computational approaches to transfer, which indicates considerably higher compute costs for the previous but better power and broader applicability. Evaluations are performed using a benchmark drug-target interaction data set. Our results show that learning curves can better capture the effects of multi-task learning and their multi-task extensions can delineate pairwise and contextual transfer effects in foundation models.
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https://arxiv.org/abs/2512.24866
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Academic Papers
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901366c52f370c3f3139ab840ec5bc00231cccc6468404e920ab41f75b21c5ae
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2026-01-01T00:00:00-05:00
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Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements
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arXiv:2512.24867v1 Announce Type: new Abstract: Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predominantly curate questions at the question level, suffering from three fundamental limitations: vulnerability to data contamination, restriction to single-knowledge-point assessment, and reliance on costly domain expert annotation. We propose Encyclo-K, a statement-based benchmark that rethinks benchmark construction from the ground up. Our key insight is that knowledge statements, not questions, can serve as the unit of curation, and questions can then be constructed from them. We extract standalone knowledge statements from authoritative textbooks and dynamically compose them into evaluation questions through random sampling at test time. This design directly addresses all three limitations: the combinatorial space is too vast to memorize, and model rankings remain stable across dynamically generated question sets, enabling reliable periodic dataset refresh; each question aggregates 8-10 statements for comprehensive multi-knowledge assessment; annotators only verify formatting compliance without requiring domain expertise, substantially reducing annotation costs. Experiments on over 50 LLMs demonstrate that Encyclo-K poses substantial challenges with strong discriminative power. Even the top-performing OpenAI-GPT-5.1 achieves only 62.07% accuracy, and model performance displays a clear gradient distribution--reasoning models span from 16.04% to 62.07%, while chat models range from 9.71% to 50.40%. These results validate the challenges introduced by dynamic evaluation and multi-statement comprehensive understanding. These findings establish Encyclo-K as a scalable framework for dynamic evaluation of LLMs' comprehensive understanding over multiple fine-grained disciplinary knowledge statements.
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https://arxiv.org/abs/2512.24867
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Academic Papers
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0ba2993d849de2650dd33c4b6a9051e6a419b7f0f72dc6be2c8e376e8627a0ac
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2026-01-01T00:00:00-05:00
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Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem
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arXiv:2512.24873v1 Announce Type: new Abstract: Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agent LLMs. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME (ROME is Obviously an Agentic Model), an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-based Policy Alignment (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.
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https://arxiv.org/abs/2512.24873
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Academic Papers
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6ced47211c37c02a44efc83743609bd624570f08f84bbc873c4e7bfcac168675
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2026-01-01T00:00:00-05:00
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A structure-preserving parametric approximation for anisotropic geometric flows via an $\alpha$-surface energy matrix
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arXiv:2512.24875v1 Announce Type: new Abstract: We propose a structure-preserving parametric approximation for geometric flows with general anisotropic effects. By introducing a hyperparameter $\alpha$, we construct a unified surface energy matrix $\hat{\boldsymbol{G}}_k^\alpha(\theta)$ that encompasses all existing formulations of surface energy matrices, and apply it to anisotropic curvature flow. We prove that $\alpha=-1$ is the unique choice achieving optimal energy stability under the necessary and sufficient condition $3\hat{\gamma}(\theta)\geq\hat{\gamma}(\theta-\pi)$, while all other $\alpha\neq-1$ require strictly stronger conditions. The framework extends naturally to general anisotropic geometric flows through a unified velocity discretization that ensures energy stability. Numerical experiments validate the theoretical optimality of $\alpha=-1$ and demonstrate the effectiveness and robustness.
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https://arxiv.org/abs/2512.24875
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Academic Papers
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dc32503d2fd0b5e2c298f081621ff62c49103a8f9ebc7c13bd41b75510b75888
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2026-01-01T00:00:00-05:00
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Random compressible Euler flows
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arXiv:2512.24879v1 Announce Type: new Abstract: We propose a finite volume stochastic collocation method for the random Euler system. We rigorously prove the convergence of random finite volume solutions under the assumption that the discrete differential quotients remain bounded in probability. Convergence analysis combines results on the convergence of a deterministic FV method with stochastic compactness arguments due to Skorokhod and Gy\"ongy-Krylov.
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https://arxiv.org/abs/2512.24879
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Academic Papers
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9067e9760b6a98182a564f6730d5d91a62d8e5f416db79d375e4970c2a395e88
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2026-01-01T00:00:00-05:00
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mHC: Manifold-Constrained Hyper-Connections
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arXiv:2512.24880v1 Announce Type: new Abstract: Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.
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https://arxiv.org/abs/2512.24880
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Academic Papers
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bbd32c38c8398b3cd7fb5ef7fc92851be6a28efb7747ed01b0a418d13b252e4e
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2026-01-01T00:00:00-05:00
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BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
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arXiv:2512.24885v1 Announce Type: new Abstract: Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversarial and Alignment, and by operationalizing them via probabilistic constraints on what an agent may generate. We instantiate this idea in BEDA, a framework that consists of the world set, the belief estimator for belief estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three settings, Conditional Keeper Burglar (CKBG, adversarial), Mutual Friends (MF, cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines. These results indicate that casting belief estimation as constraints provides a simple, general mechanism for reliable strategic dialogue.
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https://arxiv.org/abs/2512.24885
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Academic Papers
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1f74ad0393dd9263f2ed1bc34c4445d5a8fc6724035ec32c07035885c940ddd6
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2026-01-01T00:00:00-05:00
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Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves
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arXiv:2512.24886v1 Announce Type: new Abstract: Multi-agent target tracking in the presence of nonlinear dynamics and agent heterogeneity, where state-space dimensions may differ, is a challenging problem that traditional graph Laplacian methods cannot easily address. This work leverages the framework of cellular sheaves, a mathematical generalization of graph theory, to natively model such heterogeneous systems. While existing coordination sheaf frameworks focus on cooperative problems like consensus, this work extends them to the non-cooperative target-tracking problem. The tracking of multiple, unknown targets is formulated as a harmonic extension problem on a cellular sheaf, accommodating nonlinear dynamics and external disturbances for all agents. A decentralized control law is developed using the sheaf Laplacian, and a corresponding Lyapunov-based stability analysis is provided to guarantee tracking error convergence, with results validated by simulation.
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https://arxiv.org/abs/2512.24886
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Academic Papers
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d73b9cc26dcf060df80cafb251c839db4cdcc8a80b779d2a0e6f29fe6c466298
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2026-01-01T00:00:00-05:00
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SoK: Web3 RegTech for Cryptocurrency VASP AML/CFT Compliance
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arXiv:2512.24888v1 Announce Type: new Abstract: The decentralized architecture of Web3 technologies creates fundamental challenges for Anti-Money Laundering and Counter-Financing of Terrorism compliance. Traditional regulatory technology solutions designed for centralized financial systems prove inadequate for blockchain's transparent yet pseudonymous networks. This systematization examines how blockchain-native RegTech solutions leverage distributed ledger properties to enable novel compliance capabilities. We develop three taxonomies organizing the Web3 RegTech domain: a regulatory paradigm evolution framework across ten dimensions, a compliance protocol taxonomy encompassing five verification layers, and a RegTech lifecycle framework spanning preventive, real-time, and investigative phases. Through analysis of 41 operational commercial platforms and 28 academic prototypes selected from systematic literature review (2015-2025), we demonstrate that Web3 RegTech enables transaction graph analysis, real-time risk assessment, cross-chain analytics, and privacy-preserving verification approaches that are difficult to achieve or less commonly deployed in traditional centralized systems. Our analysis reveals critical gaps between academic innovation and industry deployment, alongside persistent challenges in cross-chain tracking, DeFi interaction analysis, privacy protocol monitoring, and scalability. We synthesize architectural best practices and identify research directions addressing these gaps while respecting Web3's core principles of decentralization, transparency, and user sovereignty.
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https://arxiv.org/abs/2512.24888
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Academic Papers
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f24754432d4202db806610df94d19219218e32d6f61ec6dac480d21184b9f3df
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2026-01-01T00:00:00-05:00
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Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing
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arXiv:2512.24896v1 Announce Type: new Abstract: This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimodal dataset of driving scenarios recorded in Polish conditions. Manual annotation of such heterogeneous data is both costly and time-consuming. To address this challenge, the proposed solution adopts a human-in-the-loop approach that combines artificial intelligence with human expertise to reduce annotation cost and duration. The system automatically generates initial annotations, enables iterative model retraining, and incorporates data anonymization and domain adaptation techniques. At its core, the tool relies on 3D object detection algorithms to produce preliminary annotations. Overall, the developed tools and methodology result in substantial time savings while ensuring consistent, high-quality annotations across different sensor modalities. The solution directly supports the DARTS project by accelerating the preparation of large annotated dataset in the project's standardized format, strengthening the technological base for autonomous vehicle research in Poland.
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https://arxiv.org/abs/2512.24896
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Academic Papers
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26acd566c1f0a4095a536b3c9d6ecd9fe2683e140a046919538a452b1b3fd1bf
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2026-01-01T00:00:00-05:00
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PRISM: A hierarchical multiscale approach for time series forecasting
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arXiv:2512.24898v1 Announce Type: new Abstract: Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we present a new forecasting method, PRISM (Partitioned Representation for Iterative Sequence Modeling), that addresses this challenge through a learnable tree-based partitioning of the signal. At the root of the tree, a global representation captures coarse trends in the signal, while recursive splits reveal increasingly localized views of the signal. At each level of the tree, data are projected onto a time-frequency basis (e.g., wavelets or exponential moving averages) to extract scale-specific features, which are then aggregated across the hierarchy. This design allows the model to jointly capture global structure and local dynamics of the signal, enabling accurate forecasting. Experiments across benchmark datasets show that our method outperforms state-of-the-art methods for forecasting. Overall, these results demonstrate that our hierarchical approach provides a lightweight and flexible framework for forecasting multivariate time series. The code is available at https://github.com/nerdslab/prism.
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https://arxiv.org/abs/2512.24898
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Academic Papers
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95acf6b04cf8eb5eb141dc3adb678b12a9c16cf375d55a78660c52d88171b7e0
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2026-01-01T00:00:00-05:00
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MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy
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arXiv:2512.24899v1 Announce Type: new Abstract: The proliferation of streaming data analytics in data-driven applications raises critical privacy concerns, as directly collecting user data may compromise personal privacy. Although existing $w$-event local differential privacy (LDP) mechanisms provide formal guarantees without relying on trusted third parties, their practical deployment is hindered by two key limitations. First, these methods are designed primarily for publishing simple statistics at each timestamp, making them inherently unsuitable for complex queries. Second, they handle data at each timestamp independently, failing to capture temporal correlations and consequently degrading the overall utility. To address these issues, we propose MTSP-LDP, a novel framework for \textbf{M}ulti-\textbf{T}ask \textbf{S}treaming data \textbf{P}ublication under $w$-event LDP. MTSP-LDP adopts an \emph{Optimal Privacy Budget Allocation} algorithm to dynamically allocate privacy budgets by analyzing temporal correlations within each window. It then constructs a \emph{data-adaptive private binary tree structure} to support complex queries, which is further refined by cross-timestamp grouping and smoothing operations to enhance estimation accuracy. Furthermore, a unified \emph{Budget-Free Multi-Task Processing} mechanism is introduced to support a variety of streaming queries without consuming additional privacy budget. Extensive experiments on real-world datasets demonstrate that MTSP-LDP consistently achieves high utility across various streaming tasks, significantly outperforming existing methods.
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https://arxiv.org/abs/2512.24899
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Academic Papers
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28c0641c2f04fe3a451c22c72d26a93f089fa69c8a6af55232491bdf884d9283
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2026-01-01T00:00:00-05:00
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Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes
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arXiv:2512.24901v1 Announce Type: new Abstract: Cognitive task classification using machine learning plays a central role in decoding brain states from neuroimaging data. By integrating machine learning with brain network analysis, complex connectivity patterns can be extracted from functional magnetic resonance imaging connectomes. This process transforms raw blood-oxygen-level-dependent (BOLD) signals into interpretable representations of cognitive processes. Graph neural networks (GNNs) further advance this paradigm by modeling brain regions as nodes and functional connections as edges, capturing topological dependencies and multi-scale interactions that are often missed by conventional approaches. Our proposed SpectralBrainGNN model, a spectral convolution framework based on graph Fourier transforms (GFT) computed via normalized Laplacian eigendecomposition. Experiments on the Human Connectome Project-Task (HCPTask) dataset demonstrate the effectiveness of the proposed approach, achieving a classification accuracy of 96.25\%. The implementation is publicly available at https://github.com/gnnplayground/SpectralBrainGNN to support reproducibility and future research.
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https://arxiv.org/abs/2512.24901
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Academic Papers
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b72f175d68e96b40737aca4a857eec056e573e33e5f01c60b76d37e28727b7df
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2026-01-01T00:00:00-05:00
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FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation
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arXiv:2512.24903v1 Announce Type: new Abstract: We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated problems incorporate 12 types of implicit financial scenarios (e.g., Portfolio Management), challenging models to perform expert-level reasoning based on assumptions; (2) Document Understanding: 837 Chinese/English documents spanning 9 types (e.g., Company Research) average 50.8 pages with rich visual elements, significantly surpassing existing benchmarks in both breadth and depth of financial documents; (3) Multi-Step Computation: Problems demand 11-step reasoning on average (5.3 extraction + 5.7 calculation steps), with 65.0% requiring cross-page evidence (2.4 pages average). The best-performing MLLM achieves only 58.0% accuracy, and different retrieval-augmented generation (RAG) methods show significant performance variations on this task. We expect FinMMDocR to drive improvements in MLLMs and reasoning-enhanced methods on complex multimodal reasoning tasks in real-world scenarios.
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https://arxiv.org/abs/2512.24903
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Academic Papers
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b80bc2bffe6259c6353d116baa7010b4554887474f9a5739323ca14576230338
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2026-01-01T00:00:00-05:00
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One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication
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arXiv:2512.24905v1 Announce Type: new Abstract: Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between printhead motion and material extrusion systems, notably corner over-extrusion. Existing methods require specialized hardware, extensive calibration, or firmware modifications that are inaccessible to most users. This work presents a practical, end-to-end optimization framework that enhances high-speed printing using only standard 3D printers and a phone camera, without requiring additional complex setup. The method employs a one-shot calibration approach in which two simple printed patterns, captured by a phone camera, enable identification of extrusion dynamics and cornering behavior. The identified systems enable a model-based constrained optimal control strategy that generates optimized G-code, synchronizing motion and extrusion. Experiments show reduced width tracking error, mitigated corner defects, and lower surface roughness, achieving surface quality at 3600 mm/min comparable to conventional printing at 1600 mm/min, effectively doubling production speed while maintaining print quality. This accessible, hardware-minimal approach enables a wide range of fused filament fabrication users to achieve high-quality, high-speed additive manufacturing.
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https://arxiv.org/abs/2512.24905
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Academic Papers
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e6abda0e1b249b25910e457def91f7219f7e4f6fb0141e84417edb836af9ed34
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2026-01-01T00:00:00-05:00
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AI-Driven Cloud Resource Optimization for Multi-Cluster Environments
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arXiv:2512.24914v1 Announce Type: new Abstract: Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms.
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https://arxiv.org/abs/2512.24914
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Academic Papers
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8414880e661e186c188a8b12e4f26fabd005e5a7059fb76eb69cb55d189fff86
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2026-01-01T00:00:00-05:00
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Frequent subgraph-based persistent homology for graph classification
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arXiv:2512.24917v1 Announce Type: new Abstract: Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. However, most PH methods on graphs rely on a limited set of filtrations, such as degree-based or weight-based filtrations, which overlook richer features like recurring information across the dataset and thus restrict expressive power. In this work, we propose a novel graph filtration called Frequent Subgraph Filtration (FSF), which is derived from frequent subgraphs and produces stable and information-rich frequency-based persistent homology (FPH) features. We study the theoretical properties of FSF and provide both proofs and experimental validation. Beyond persistent homology itself, we introduce two approaches for graph classification: an FPH-based machine learning model (FPH-ML) and a hybrid framework that integrates FPH with graph neural networks (FPH-GNNs) to enhance topology-aware graph representation learning. Our frameworks bridge frequent subgraph mining and topological data analysis, offering a new perspective on topology-aware feature extraction. Experimental results show that FPH-ML achieves competitive or superior accuracy compared with kernel-based and degree-based filtration methods. When integrated into graph neural networks, FPH yields relative performance gains ranging from 0.4 to 21 percent, with improvements of up to 8.2 percentage points over GCN and GIN backbones across benchmarks.
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https://arxiv.org/abs/2512.24917
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Academic Papers
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e27be2b666b43d6ffe9f38bd5191e214cbf3137e85062ab12e36af6fc580c290
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2026-01-01T00:00:00-05:00
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Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection
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arXiv:2512.24922v1 Announce Type: new Abstract: 3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving benchmarks, they often struggle to generalize across different domains - for instance, a model trained in the U.S. may perform poorly in regions like Asia or Europe. This paper presents a novel lidar domain adaptation method based on neuron activation patterns, demonstrating that state-of-the-art performance can be achieved by annotating only a small, representative, and diverse subset of samples from the target domain if they are correctly selected. The proposed approach requires very small annotation budget and, when combined with post-training techniques inspired by continual learning prevent weight drift from the original model. Empirical evaluation shows that the proposed domain adaptation approach outperforms both linear probing and state-of-the-art domain adaptation techniques.
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https://arxiv.org/abs/2512.24922
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Academic Papers
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0e0923cf595784eabdb3dce01400d4874feafb352f8a07ad2df447189d61322b
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2026-01-01T00:00:00-05:00
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Towards Provably Secure Generative AI: Reliable Consensus Sampling
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arXiv:2512.24925v1 Announce Type: new Abstract: Existing research on generative AI security is primarily driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. This dynamic frequently gives rise to previously unknown attacks that can circumvent current detection and prevention. This necessitates the continual updating of security mechanisms. Constructing generative AI with provable security and theoretically controllable risk is therefore necessary. Consensus Sampling (CS) is a promising algorithm toward provably secure AI. It controls risk by leveraging overlap in model output probabilities. However, we find that CS relies on frequent abstention to avoid unsafe outputs, which reduces utility. Moreover, CS becomes highly vulnerable when unsafe models are maliciously manipulated. To address these issues, we propose a new primitive called Reliable Consensus Sampling (RCS), that traces acceptance probability to tolerate extreme adversarial behaviors, improving robustness. RCS also eliminates the need for abstention entirely. We further develop a feedback algorithm to continuously and dynamically enhance the safety of RCS. We provide theoretical guarantees that RCS maintains a controllable risk threshold. Extensive experiments show that RCS significantly improves robustness and utility while maintaining latency comparable to CS. We hope this work contributes to the development of provably secure generative AI.
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https://arxiv.org/abs/2512.24925
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Academic Papers
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fea19b205444af5951f0d474b48edc7466ed001f1bdce94d01a4bc1eed72021c
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2026-01-01T00:00:00-05:00
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A finite element approach for minimizing line and surface energies arising in the study of singularities in liquid crystals
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arXiv:2512.24928v1 Announce Type: new Abstract: Motivated by a problem originating in the study of defect structures in nematic liquid crystals, we describe and study a numerical algorithm for the resolution of a Plateau-like problem. The energy contains the area of a two-dimensional surface $T$ and the length of its boundary $\partial T$ reduced by a prescribed curve to make our problem non-trivial. We additionally include an obstacle $E$ for $T$ and pose a surface energy on $E$. We present an algorithm based on the Alternating Direction Method of Multipliers that minimizes a discretized version of the energy using finite elements, generalizing existing TV-minimization methods. We study different inclusion shapes demonstrating the rich structure of minimizing configurations and provide physical interpretation of our findings for colloidal particles in nematic liquid crystal.
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https://arxiv.org/abs/2512.24928
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Academic Papers
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95999ee478ab26886652115e5669ea7826e76293098523e0ff7a9f27da2a0653
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2026-01-01T00:00:00-05:00
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Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline
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arXiv:2512.24933v1 Announce Type: new Abstract: Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter-step dependencies. Existing end-to-end prompt optimization methods struggle under these conditions and often yield suboptimal or unstable updates. We propose ADOPT, an Adaptive Dependency-aware Prompt Optimization framework for multi-step LLM pipelines. ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives. It decouples textual gradient estimation from gradient updates, reducing multi-prompt optimization to flexible single-prompt optimization steps, and employs a Shapley-based mechanism to adaptively allocate optimization resources. Experiments on real-world datasets and diverse pipeline structures show that ADOPT is effective and robust, consistently outperforming state-of-the-art prompt optimization baselines.
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https://arxiv.org/abs/2512.24933
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Academic Papers
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825f59513692c90ba5b3aa4065b00b4061e170452c1669d2d4fb28f4dc393ddc
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2026-01-01T00:00:00-05:00
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Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information
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arXiv:2512.24934v1 Announce Type: new Abstract: We study the problem of fair $k$-committee selection under an egalitarian objective. Given $n$ agents partitioned into $m$ groups (\eg, demographic quotas), the goal is to aggregate their preferences to form a committee of size $k$ that guarantees minimum representation from each group while minimizing the maximum \emph{cost} incurred by any agent. We model this setting as the ordinal fair $k$-center problem, where agents are embedded in an unknown metric space, and each agent reports a complete preference ranking (i.e., ordinal information) over all agents, consistent with the underlying distance metric (i.e., cardinal information). The cost incurred by an agent with respect to a committee is defined as its distance to the closest committee member. The quality of an algorithm is evaluated using the notion of distortion, which measures the worst-case ratio between the cost of the committee produced by the algorithm and the cost of an optimal committee, when given complete access to the underlying metric space. When cardinal information is not available, no constant distortion is possible for the ordinal $k$-center problem, even without fairness constraints, when $k\geq 3$ [Burkhardt et.al., AAAI'24]. To overcome this hardness, we allow limited access to cardinal information by querying the metric space. In this setting, our main contribution is a factor-$5$ distortion algorithm that requires only $O(k \log^2 k)$ queries. Along the way, we present an improved factor-$3$ distortion algorithm using $O(k^2)$ queries.
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https://arxiv.org/abs/2512.24934
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Academic Papers
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598283c75482571cc2d7d9cb0c8f9321ff499d174094cd6d883b88509831caa9
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2026-01-01T00:00:00-05:00
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Vibe Coding, Interface Flattening
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arXiv:2512.24939v1 Announce Type: new Abstract: Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct modalities (GUI, CLI, and API) appear to converge into a single conversational surface, even as the underlying chain of translation from intention to machinic effect lengthens and thickens. Drawing on Friedrich Kittler's materialist media theory and Alexander Galloway's account of interfaces as sites of protocol control, the paper situates programming as a historically localised interface arrangement rather than an essential relation to computation. Through a materialist reconstruction of the contemporary vibe-coding stack, it shows how remote compute infrastructures, latency and connectivity, structured outputs, function/tool calling, and interoperability standards such as the Model Context Protocol relocate control and meaning-making power to model and protocol providers. The apparent democratisation of technical capability therefore depends on new dependencies and new literacies. By foregrounding the tension between experiential flattening and infrastructural thickening, I demonstrate how LLM-mediated development redistributes symbolic labour/power, obscures responsibility, and privatises competencies previously dispersed across programming communities, contributing a critical lens on the political economy of AI-mediated human-computer interaction.
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https://arxiv.org/abs/2512.24939
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Academic Papers
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0e223049330029239743fe675e2b4686435e14be0eea0ee156054ed2ce043765
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2026-01-01T00:00:00-05:00
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Iterative Deployment Improves Planning Skills in LLMs
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arXiv:2512.24940v1 Announce Type: new Abstract: We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change the properties of the resultant models. By testing this mechanism on various planning domains, we observe substantial improvements in planning skills, with later models displaying emergent generalization by discovering much longer plans than the initial models. We then provide theoretical analysis showing that iterative deployment effectively implements reinforcement learning (RL) training in the outer-loop (i.e. not as part of intentional model training), with an implicit reward function. The connection to RL has two important implications: first, for the field of AI safety, as the reward function entailed by repeated deployment is not defined explicitly, and could have unexpected implications to the properties of future model deployments. Second, the mechanism highlighted here can be viewed as an alternative training regime to explicit RL, relying on data curation rather than explicit rewards.
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https://arxiv.org/abs/2512.24940
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Academic Papers
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c4bcccdc53c3d990b2b76da914312763bc2d3d0386c270e5a9e395b7f98c696f
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2026-01-01T00:00:00-05:00
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Securing High-Concurrency Ticket Sales: A Framework Based on Microservice
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arXiv:2512.24941v1 Announce Type: new Abstract: The railway ticketing system is one of the most important public service infrastructure. In peak periods such as holidays, it is often faced with the challenge of high concurrency scenarios because of a large number of users accessing at the same time. The traditional aggregation architecture can not meet the peak user requirements because of its insufficient fault tolerance and low ability. Therefore, the system needs to use microservice architecture for development, and add multiple security methods to ensure that the system can have good stability and data consistency under high concurrency scenarios, and can respond quickly to user requests. This paper introduces the use of B/S architecture and Spring Cloud to design and develop a railway ticket purchase system that can maintain stability and reliability under high concurrency scenarios, and formulate multiple security design methods for the system. This system integrates a range of functions, such as real-time train inquiries, dynamic seat updates, online seat selection, and ticket purchasing, effectively addressing common problems associated with offline ticket purchasing, such as long queues and delayed information. It enables a complete online process from inquiry and booking to payment and refunds. Furthermore, the "add passenger" function allows users to purchase tickets for others, extending the convenience of online ticketing to people with limited internet access. The system design prioritizes security and stability, while also focusing on high performance, and achieves these goals through a carefully designed architecture and the integration of multiple middleware components. After the completion of the system development, the core interface of the system is tested, and then the results are analyzed. The test data proves that the system has good ability and stability under high concurrency.
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https://arxiv.org/abs/2512.24941
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Academic Papers
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35d771e2333e87409658bf8f116b56c3996a76a874bc4de05cb6249a3431c9af
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2026-01-01T00:00:00-05:00
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RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment
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arXiv:2512.24943v1 Announce Type: new Abstract: Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient complexity for comprehensive model assessment, resulting in an absence of standardized relevance evaluation metrics across the industry. To address this limitation, we propose Rule-Aware benchmark with Image for Relevance assessment(RAIR), a Chinese dataset derived from real-world scenarios. RAIR established a standardized framework for relevance assessment and provides a set of universal rules, which forms the foundation for standardized evaluation. Additionally, RAIR analyzes essential capabilities required for current relevance models and introduces a comprehensive dataset consists of three subset: (1) a general subset with industry-balanced sampling to evaluate fundamental model competencies; (2) a long-tail hard subset focus on challenging cases to assess performance limits; (3) a visual salience subset for evaluating multimodal understanding capabilities. We conducted experiments on RAIR using 14 open and closed-source models. The results demonstrate that RAIR presents sufficient challenges even for GPT-5, which achieved the best performance. RAIR data are now available, serving as an industry benchmark for relevance assessment while providing new insights into general LLM and Visual Language Model(VLM) evaluation.
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https://arxiv.org/abs/2512.24943
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Academic Papers
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svg
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64c0eca0ea4e864a87369686e86c5b40e0bf1df3e4b272725ca716789c40e6ea
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2026-01-01T00:00:00-05:00
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HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films
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arXiv:2512.24946v1 Announce Type: new Abstract: Existing open-source film restoration methods show limited performance compared to commercial methods due to training with low-quality synthetic data and employing noisy optical flows. In addition, high-resolution films have not been explored by the open-source methods.We propose HaineiFRDM(Film Restoration Diffusion Model), a film restoration framework, to explore diffusion model's powerful content-understanding ability to help human expert better restore indistinguishable film defects.Specifically, we employ a patch-wise training and testing strategy to make restoring high-resolution films on one 24GB-VRAMR GPU possible and design a position-aware Global Prompt and Frame Fusion Modules.Also, we introduce a global-local frequency module to reconstruct consistent textures among different patches. Besides, we firstly restore a low-resolution result and use it as global residual to mitigate blocky artifacts caused by patching process.Furthermore, we construct a film restoration dataset that contains restored real-degraded films and realistic synthetic data.Comprehensive experimental results conclusively demonstrate the superiority of our model in defect restoration ability over existing open-source methods. Code and the dataset will be released.
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https://arxiv.org/abs/2512.24946
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Academic Papers
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svg
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4f403f05e66b3eb17787c16a392d1ad03c69310e5fa9990a7d65491125e6882c
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2026-01-01T00:00:00-05:00
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CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinement
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arXiv:2512.24947v1 Announce Type: new Abstract: Accurate and interpretable crop disease diagnosis is essential for agricultural decision-making, yet existing methods often rely on costly supervised fine-tuning and perform poorly under domain shifts. We propose Caption--Prompt--Judge (CPJ), a training-free few-shot framework that enhances Agri-Pest VQA through structured, interpretable image captions. CPJ employs large vision-language models to generate multi-angle captions, refined iteratively via an LLM-as-Judge module, which then inform a dual-answer VQA process for both recognition and management responses. Evaluated on CDDMBench, CPJ significantly improves performance: using GPT-5-mini captions, GPT-5-Nano achieves \textbf{+22.7} pp in disease classification and \textbf{+19.5} points in QA score over no-caption baselines. The framework provides transparent, evidence-based reasoning, advancing robust and explainable agricultural diagnosis without fine-tuning. Our code and data are publicly available at: https://github.com/CPJ-Agricultural/CPJ-Agricultural-Diagnosis.
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https://arxiv.org/abs/2512.24947
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Academic Papers
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svg
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d5f81c219af3678e3c72423ccb9b78b1b5597d52e7ab97411d6c9c3db697c032
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2026-01-01T00:00:00-05:00
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ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT
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arXiv:2512.24948v1 Announce Type: new Abstract: Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respiratory motion. ECG-gated cardiac CTs substantially reduce motion artifacts, but their use in population screening and routine imaging remains limited due to gating requirements and lack of insurance coverage. Although identification of incidental CAC from non-gated chest CT is increasingly considered for it offers an accessible and widely available alternative, this modality is limited by more severe motion artifacts. We present ProDM (Property-aware Progressive Correction Diffusion Model), a generative diffusion framework that restores motion-free calcified lesions from non-gated CTs. ProDM introduces three key components: (1) a CAC motion simulation data engine that synthesizes realistic non-gated acquisitions with diverse motion trajectories directly from cardiac-gated CTs, enabling supervised training without paired data; (2) a property-aware learning strategy incorporating calcium-specific priors through a differentiable calcium consistency loss to preserve lesion integrity; and (3) a progressive correction scheme that reduces artifacts gradually across diffusion steps to enhance stability and calcium fidelity. Experiments on real patient datasets show that ProDM significantly improves CAC scoring accuracy, spatial lesion fidelity, and risk stratification performance compared with several baselines. A reader study on real non-gated scans further confirms that ProDM suppresses motion artifacts and improves clinical usability. These findings highlight the potential of progressive, property-aware frameworks for reliable CAC quantification from routine chest CT imaging.
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https://arxiv.org/abs/2512.24948
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Academic Papers
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svg
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1ec2670fcb346076a92d962ffac79d3611ff0619c4416deca2d4567c8dab55d0
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2026-01-01T00:00:00-05:00
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VIPER: Process-aware Evaluation for Generative Video Reasoning
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arXiv:2512.24952v1 Announce Type: new Abstract: Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation of continuous frames. While these models show promise for Generative Video Reasoning (GVR), existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking, where a model reaches a correct conclusion through an erroneous process. To address this, we propose a process-aware evaluation paradigm. We introduce VIPER, a comprehensive benchmark spanning 16 tasks across temporal, structural, symbolic, spatial, physics, and planning reasoning. Furthermore, we propose Process-outcome Consistency (POC@r), a new metric that utilizes VLM-as-Judge with a hierarchical rubric to evaluate both the validity of the intermediate steps and the final result. Our experiments reveal that state-of-the-art video models achieve only about 20% POC@1.0 and exhibit a significant outcome-hacking. We further explore the impact of test-time scaling and sampling robustness, highlighting a substantial gap between current video generation and true generalized visual reasoning. Our benchmark will be publicly released.
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https://arxiv.org/abs/2512.24952
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Academic Papers
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svg
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84708dae6fdc1ac3c9af4e7150dc258a5e141b24beeea01377225918eaf5fb87
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2026-01-01T00:00:00-05:00
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MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control
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arXiv:2512.24955v1 Announce Type: new Abstract: Achieving provable stability in model-free reinforcement learning (RL) remains a challenge, particularly in balancing exploration with rigorous safety. This article introduces MSACL, a framework that integrates exponential stability theory with maximum entropy RL through multi-step Lyapunov certificate learning. Unlike methods relying on complex reward engineering, MSACL utilizes off-policy multi-step data to learn Lyapunov certificates satisfying theoretical stability conditions. By introducing Exponential Stability Labels (ESL) and a $\lambda$-weighted aggregation mechanism, the framework effectively balances the bias-variance trade-off in multi-step learning. Policy optimization is guided by a stability-aware advantage function, ensuring the learned policy promotes rapid Lyapunov descent. We evaluate MSACL across six benchmarks, including stabilization and nonlinear tracking tasks, demonstrating its superiority over state-of-the-art Lyapunov-based RL algorithms. MSACL achieves exponential stability and rapid convergence under simple rewards, while exhibiting significant robustness to uncertainties and generalization to unseen trajectories. Sensitivity analysis establishes the multi-step horizon $n=20$ as a robust default across diverse systems. By linking Lyapunov theory with off-policy actor-critic frameworks, MSACL provides a foundation for verifiably safe learning-based control. Source code and benchmark environments will be made publicly available.
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https://arxiv.org/abs/2512.24955
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Academic Papers
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svg
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0ec3d4faa1becffe128993afff16f96d3afab7ca160588f8f1ee9b254e12e484
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2026-01-01T00:00:00-05:00
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AMAP Agentic Planning Technical Report
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arXiv:2512.24957v1 Announce Type: new Abstract: We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and itinerary planning. STAgent is a specialized model capable of interacting with ten distinct tools within spatio-temporal scenarios, enabling it to explore, verify, and refine intermediate steps during complex reasoning. Notably, STAgent effectively preserves its general capabilities. We empower STAgent with these capabilities through three key contributions: (1) a stable tool environment that supports over ten domain-specific tools, enabling asynchronous rollout and training; (2) a hierarchical data curation framework that identifies high-quality data like a needle in a haystack, curating high-quality queries with a filter ratio of 1:10,000, emphasizing both diversity and difficulty; and (3) a cascaded training recipe that starts with a seed SFT stage acting as a guardian to measure query difficulty, followed by a second SFT stage fine-tuned on queries with high certainty, and an ultimate RL stage that leverages data of low certainty. Initialized with Qwen3-30B-A3B to establish a strong SFT foundation and leverage insights into sample difficulty, STAgent yields promising performance on TravelBench while maintaining its general capabilities across a wide range of general benchmarks, thereby demonstrating the effectiveness of our proposed agentic model.
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https://arxiv.org/abs/2512.24957
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Academic Papers
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svg
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7dac6f19001f70b9b4d0789ae8de63aa17ef7e539557b77b90377273837bbdb1
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2026-01-01T00:00:00-05:00
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Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning
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arXiv:2512.24959v1 Announce Type: new Abstract: Many modern AI and ML problems require evaluating partners' contributions through shared yet asymmetric, computationally intensive processes and the simultaneous selection of the most beneficial candidates. Sequential approaches to these problems can be unified under a new framework, Sequential Support Network Learning (SSNL), in which the goal is to select the most beneficial candidate set of partners for all participants using trials; that is, to learn a directed graph that represents the highest-performing contributions. We demonstrate that a new pure-exploration model, the semi-overlapping multi-(multi-armed) bandit (SOMMAB), in which a single evaluation provides distinct feedback to multiple bandits due to structural overlap among their arms, can be used to learn a support network from sparse candidate lists efficiently. We develop a generalized GapE algorithm for SOMMABs and derive new exponential error bounds that improve the best known constant in the exponent for multi-bandit best-arm identification. The bounds scale linearly with the degree of overlap, revealing significant sample-complexity gains arising from shared evaluations. From an application point of view, this work provides a theoretical foundation and improved performance guarantees for sequential learning tools for identifying support networks from sparse candidates in multiple learning problems, such as in multi-task learning (MTL), auxiliary task learning (ATL), federated learning (FL), and in multi-agent systems (MAS).
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https://arxiv.org/abs/2512.24959
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Academic Papers
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svg
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b8a8f7ab569889837f23917f6cdc04258d1c135fa1207ddc0e0777d432419475
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2026-01-01T00:00:00-05:00
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Approximating evolution operators of linear delay equations: a general framework for the convergence analysis
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arXiv:2512.24964v1 Announce Type: new Abstract: We consider the problem of discretizing evolution operators of linear delay equations with the aim of approximating their spectra, which is useful in investigating the stability properties of (nonlinear) equations via the principle of linearized stability. We develop a general convergence analysis based on a reformulation of the operators by means of a fixed-point equation, providing a list of hypotheses related to the regularization properties of the equation and the convergence of the chosen approximation techniques on suitable subspaces. This framework unifies the proofs for some methods based on pseudospectral discretization, which we present here in this new form. To exemplify the generality of the framework, we also apply it to a method of weighted residuals found in the literature, which was previously lacking a formal convergence analysis.
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https://arxiv.org/abs/2512.24964
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Academic Papers
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svg
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0ad45719eb6292f873af628cd84dc0db7e2f5912ee4f5e716398abcb5b7721cd
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2026-01-01T00:00:00-05:00
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ShowUI-$\pi$: Flow-based Generative Models as GUI Dexterous Hands
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arXiv:2512.24965v1 Announce Type: new Abstract: Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progress bar) that require continuous, on-the-fly perception and adjustment. In this work, we develop ShowUI-$\pi$, the first flow-based generative model as GUI dexterous hand, featuring the following designs: (i) Unified Discrete-Continuous Actions, integrating discrete clicks and continuous drags within a shared model, enabling flexible adaptation across diverse interaction modes; (ii) Flow-based Action Generation for drag modeling, which predicts incremental cursor adjustments from continuous visual observations via a lightweight action expert, ensuring smooth and stable trajectories; (iii) Drag Training data and Benchmark, where we manually collect and synthesize 20K drag trajectories across five domains (e.g. PowerPoint, Adobe Premiere Pro), and introduce ScreenDrag, a benchmark with comprehensive online and offline evaluation protocols for assessing GUI agents' drag capabilities. Our experiments show that proprietary GUI agents still struggle on ScreenDrag (e.g. Operator scores 13.27, and the best Gemini-2.5-CUA reaches 22.18). In contrast, ShowUI-$\pi$ achieves 26.98 with only 450M parameters, underscoring both the difficulty of the task and the effectiveness of our approach. We hope this work advances GUI agents toward human-like dexterous control in digital world. The code is available at https://github.com/showlab/showui-pi.
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https://arxiv.org/abs/2512.24965
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Academic Papers
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svg
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0dcd6c4b84b408dea67a1366ef0344fc8b71e4c1087e1df3c774000d16a0c5a4
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2026-01-01T00:00:00-05:00
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Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions
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arXiv:2512.24971v1 Announce Type: new Abstract: Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision systems. This paper presents a comprehensive evaluation of compression techniques - quantization, pruning, and weight clustering applied individually and in combination to convolutional neural networks (ResNet-50, VGG-19, and MobileNetV2). Using the CIFAR-10-C and CIFAR 100-C datasets, we analyze the trade-offs between robustness, accuracy, and compression ratio. Our results show that certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures. Utilizing multiobjective assessment, we determine the best configurations, showing that customized technique combinations produce beneficial multi-objective results. This study provides insights into selecting compression methods for robust and efficient deployment of models in corrupted real-world environments.
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https://arxiv.org/abs/2512.24971
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Academic Papers
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svg
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61fdcc405e21e864b226fb65df5b82461bcf3f34740d94526f902bdad0beed69
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2026-01-01T00:00:00-05:00
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Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments
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arXiv:2512.24974v1 Announce Type: new Abstract: Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning and robust deformation tracking. In this work, we propose a novel framework for DLO manipulation in constrained environments. This framework combines hierarchical deformation planning with neural tracking, ensuring reliable performance in both global deformation synthesis and local deformation tracking. Specifically, the deformation planner begins by generating a spatial path set that inherently satisfies the homotopic constraints associated with DLO keypoint paths. Next, a path-set-guided optimization method is applied to synthesize an optimal temporal deformation sequence for the DLO. In manipulation execution, a neural model predictive control approach, leveraging a data-driven deformation model, is designed to accurately track the planned DLO deformation sequence. The effectiveness of the proposed framework is validated in extensive constrained DLO manipulation tasks.
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https://arxiv.org/abs/2512.24974
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Academic Papers
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