arXiv:2604.08879v2 Announce Type: replace
Abstract: Moving beyond the traditional binary classification paradigm of Multimodal Sarcasm Detection, Multimodal Sarcasm Target Identification (MSTI) presents a more formidable challenge, requiring precise localization of fine-grained targets such as textual phrases and visual regions. Existing approaches predominantly rely on implicit cross-modal alignment, offering limited interpretability and suboptimal fine-grained localization. To address these limitations, we propose GRASP, Grounded Chain-of-Thought ReAsoning with Dual-Stage Optimization for Multimodal Sarcasm Prediction and Target Identification, a framework that integrates visual grounding with explicit Chain-of-Thought (CoT) reasoning to move beyond black-box MSTI. Specifically, we curate MSTI-MAX, a refined dataset that mitigates class imbalance and enriches multimodal sarcasm cues. We introduce Grounded CoT reasoning, which explicitly anchors sarcasm-related visual regions within the reasoning trajectory and prompts the model to articulate rationales before predicting the final classification labels and sarcasm targets. Furthermore, we employ a dual-stage outcome-supervised joint optimization strategy: Supervised Fine-Tuning with a coordinate-aware weighted loss, followed by Fine-Grained Target Policy Optimization. Extensive experiments demonstrate that GRASP outperforms existing baselines in fine-grained sarcasm target identification across modalities, and an LLM-as-a-Judge evaluation quantitatively measures the quality of internal reasoning chains. Our dataset and source code will be released on GitHub.
Science Journals
arXiv:2604.09806v3 Announce Type: replace
Abstract: We study the standard-form ILP problem $\max\{ c^\top x \colon A x = b,\; x \in Z_{\geq 0}^n \}$, where $A\in Z^{k\times n}$ has full row rank. We obtain refined FPT algorithms parameterized by $k$ and $\Delta$, the maximum absolute value of a $k\times k$ minor of $A$. Our approach combines discrepancy-based dynamic programming with matrix discrepancy bounds in Koml\'os' setting. Let $\kappa_k$ denote the maximum discrepancy over all matrices with $k$ columns whose columns have Euclidean norm at most $1$. Up to polynomial factors in the input size, the optimization problem can be solved in time $O(\kappa_k)^{2k}\Delta^2$, and the corresponding feasibility problem in time $O(\kappa_k)^k\Delta$. Using the best currently known bound $\kappa_k=\widetilde O(\log^{1/4}k)$, this yields running times $O(\log k)^{\frac{k}{2}(1+o(1))}\Delta^2$ and $O(\log k)^{\frac{k}{4}(1+o(1))}\Delta$, respectively. Under the Koml\'os conjecture, the dependence on $k$ in both running times reduces to $2^{O(k)}$.
arXiv:2604.10497v3 Announce Type: replace
Abstract: Although optimization is one of the most promising applications of quantum computers, the development of effective optimization strategies requires real-world test cases. When planning our recent wedding reception, we realized that the problem of optimally seating our guests, given constraints related to guests' relatedness, shared interests, and physical needs, could be mapped to a cost function network (CFN) form solvable with classical or quantum optimization algorithms. We compared the seating optimization performance of classical Monte Carlo CFN solvers in the Masala software suite to that of quantum annealing-based CFN optimization algorithms using one-hot, domain-wall, and approximate binary mappings, which we had developed for protein design problems. Surprisingly, the D-Wave Advantage 2 system, which performs well on similarly-structured CFN problems for protein design, struggled to return optimal seating arrangements that were easily found by classical Monte Carlo methods. We provide our seating optimization benchmark set, and code to convert seating optimization problems to CFN problems, as a plugin library for Masala, permitting this class of real-world problems to be used to benchmark performance of current and future classical CFN solvers, quantum optimization algorithms, and quantum computing hardware.
arXiv:2604.15699v2 Announce Type: replace
Abstract: Graph self-supervised learning can reduce the need for labeled graph data and has been widely used in recommendation, social networks, and other web applications. However, existing methods often underuse high-frequency signals and may overfit to specific local patterns, which limits representation quality and generalization. We propose Frequency-Corrupt Based Graph Self-Supervised Learning (FC-GSSL), a method that builds corrupted graphs biased toward high-frequency information by corrupting nodes and edges according to their low-frequency contributions. These corrupted graphs are used as inputs to an autoencoder, while low-frequency and general features are reconstructed as supervision targets, forcing the model to fuse information from multiple frequency bands. We further design multiple sampling strategies and generate diverse corrupted graphs from the intersections and unions of the sampling results. By aligning node representations from these views, the model can discover useful frequency combinations, reduce reliance on specific high-frequency components, and improve robustness. Experiments on 14 datasets across node classification, graph prediction, and transfer learning show that FC-GSSL consistently improves performance and generalization.
arXiv:2604.16643v2 Announce Type: replace
Abstract: Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to improve the quality of forecasting services provided by operational centers, particularly given the speed at which machine-learning based models generate predictions. Despite the clear promise of these systems, questions remain about the ability of the current generation of machine-learning models to generate physically consistent predictions of the full suite of required forecast fields under all conditions. Answering these questions will require careful comparisons between the well-understood physically based models, current state-of-the-art machine-learning models, and the hybrid models that combine elements of these two archetypes. The Weather Prediction Model Intercomparison Project (WP-MIP) is a World Meteorological Organization-supported initiative whose initial goal is to create a centralized database of physically based, machine-learning, and hybrid model forecasts to enable a distributed assessment and evaluation effort. The first instance of WP-MIP focuses on global deterministic predictions using both center-specific and common initializations to facilitate sensitivity studies. Forecasts contributed by institutions across six continents will be used to develop AI-ready verification techniques that highlight the strengths and weaknesses of each class of prediction system, with the goal of establishing best-practice guidance to model developers and national weather centers. The broad engagement of the operational and forecast-evaluation communities in WP-MIP will ensure that the project results are highly relevant to the development and deployment of next-generation weather prediction systems.
arXiv:2604.18801v2 Announce Type: replace
Abstract: Scientific particle simulations in cosmology, molecular dynamics, and fluid dynamics produce large-scale datasets whose storage, movement, and analysis increasingly rely on lossy compression. However, existing compressors typically bound only pointwise position errors, providing no guarantee on the fidelity of structures derived from particle coordinates, such as single-linkage clustering (also known as Friends-of-Friends algorithm), where clusters are connected components of a proximity graph formed by linking particle pairs within a distance threshold. Even small coordinate perturbations near this threshold can break true links or create false links, thereby splitting or merging entire clusters. We propose a compressor-independent correction technique for preserving single-linkage cluster membership under lossy compression. Our method operates on reconstructed outputs from off-the-shelf compressors such as SZ3, ZFP, Draco, and LCP, and stores a compact corrective edit stream. Our key observation is that cluster-membership queries depend on connected components rather than the complete set of proximity links. Based on this observation, we introduce three constraint-selection modes, vulnerable-pair, safe-component, and halo-forest, that progressively reduce the constraints enforced during correction. Projected gradient descent then corrects the reconstructed coordinates to eliminate the selected violations while respecting the original pointwise error bound. Experiments on cosmology, molecular dynamics, and fluid dynamics datasets with single-GPU and distributed-memory implementations show that our method preserves cluster membership while improving compression ratio by up to 4$\times$ and maintains competitive end-to-end throughput compared to the same base compressors configured with sufficiently tight error bounds to preserve clustering.
arXiv:2604.19541v2 Announce Type: replace
Abstract: Desktop interaction streams provide a continuous, privacy-sensitive record of interleaved user tasks. Transforming these streams into task-organized personal logs on-device faces two main challenges: exhaustive Vision-Language Model (VLM) processing strains local resources, and global stream processing causes cross-task context pollution. We present FOCAL (Filtered On-device Continuous Activity Logging), a privacy-first multi-agent system utilizing a unified filter-plan-log architecture. It cascades a lightweight Filter Agent for noise suppression, a text-only Brain Agent for task attribution, a Record Agent for selective visual reasoning, and a task-isolated Memory Agent for context-coherent summarization. Experiments on DesktopBench (comprising 2,572 screenshots across 420 complex sessions) show FOCAL reduces total token consumption by 60.4% and VLM call count by 72.3% versus a baseline, while boosting Key Information Recall (KIR) from 0.38 to 0.61. Crucially, under $A{\to}B{\to}A$ task interruptions, FOCAL maintains Task Acc 0.81 and KIR 0.80, whereas the baseline collapses to Task Acc 0.03. FOCAL pioneers the efficient, on-device summarization of instruction-free desktop streams into multi-perspective personal logs.
arXiv:2604.20288v2 Announce Type: replace
Abstract: Flight diversions are rare but high-impact events in aviation, making their reliable prediction vital for both safety and operational efficiency. However, their scarcity in historical records impedes the training of machine learning models used to predict them. This study addresses this challenge by proposing a generative augmentation framework for imbalanced aviation tabular records. The principal contribution lies in the design of a composite optimisation objective specifically tailored to flight data, which integrates four complementary quality dimensions into a single score used to guide automated hyperparameter search via the Tree-structured Parzen Estimator (TPE) algorithm: realism, statistical similarity, fidelity, and predictive utility. These dimensions were selected and defined to reflect the operational and statistical requirements specific to aviation records, and were complemented by two descriptive evaluation dimensions, diversity and operational validity, forming a six-stage assessment framework. The composite objective was then used to tune three deep generative models, namely Tabular Variational Autoencoder (TVAE), Conditional Tabular Generative Adversarial Network (CTGAN), and CopulaGAN, with Gaussian Copula (GC) serving as a statistical baseline. Results show that optimised models substantially outperform their default counterparts across all six assessment dimensions, and that augmentation with the resulting synthetic data improves diversion prediction compared to training on real data alone. These findings demonstrate that domain-adapted multi-objective optimisation is an effective strategy for generative augmentation of rare events in aviation, with applicability to other imbalanced tabular prediction tasks.
arXiv:2604.23716v3 Announce Type: replace
Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mutual information underpins representation learning and feature selection, and transfer entropy reveals directed influence in dynamical systems. Despite wide adoption, measure selection is often decoupled from estimator assumptions, failure modes, and safe inferential claims. This survey provides a practical decision framework for four foundational measures - Entropy, KL divergence/cross-entropy, Mutual Information, and Transfer Entropy - organized around three prescriptive questions for each: (i) what question does the measure answer and in which AI context; (ii) which estimator is appropriate for the data type and dimensionality; and (iii) what is the most dangerous misuse. The framework is operationalized in two complementary artifacts: a measure-selection flowchart and a master decision table. We cover both AI/ML and decision-making agent application domains per measure, with standardized Bridge notes linking IT quantities to cognitive and neuroscientific constructs. Two worked examples illustrate the framework on concrete practitioner scenarios spanning representation learning and temporal influence analysis, and a reproducible multi-agent case study across three learning architectures validates the transfer-entropy surrogate-testing guardrail against a null control.
arXiv:2604.26897v2 Announce Type: replace
Abstract: Origami-inspired robotic grippers have shown promising potential for object manipulation tasks due to their compact volume and mechanical flexibility. However, robust capture of objects with random shapes in dynamic working environments often comes at the cost of additional actuation channels and control complexity. Here, we introduce a tendon-driven, robust origami tentacle gripper by exploiting a synergy between local, deterministic deformation programming and global, stochastic entanglements. Each tentacle features carefully placed holes (for routing an actuation tendon), origami creases, and a tapered shape. By tailoring these design features, one can prescribe the shrinking, bending, and twisting deformation, eventually creating deterministic coiling with a simple tendon pull. Then, when multiple coiling tentacles are placed in proximity, stochastic entanglement emerges, allowing the tentacles to braid, knot, and grip objects with random shapes. We derived a simulation model by integrating origami mechanics with Cosserat rods to correlate origami design, tentacle deformation, and collective grasping performance. Then, we experimentally tested how these entangling origami tentacles can grasp objects under gravity and in water. A stow-and-release deployment mechanism was also tested to simulate in-orbit grasping. Overall, this entanglement-enabled tentacle gripper presents a route toward robust object grasping with simple design and actuation.
arXiv:2604.27787v3 Announce Type: replace
Abstract: We study when a sound arithmetic theory $\mathcal S\supseteq S^1_2$ with polynomial-time decidable axioms efficiently proves the bounded consistency statements $Con_{\mathcal S+\phi}(n)$ for a true sentence $\phi$. Equivalently, we ask when $\mathcal S$, viewed as a proof system, simulates $\mathcal S+\phi$. The paper gives two unconditional constraints on possible characterizations. First, for finitely axiomatized sequential $\mathcal S$, if $EA\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then $\mathcal S$ interprets $\mathcal S+\phi$, implying $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S}(p(n))\rightarrow Con_{\mathcal S+\phi}(n)$ for some polynomial $p$, and hence $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S+\phi}(n)$. Second, if $\mathcal S$ fails to simulate $\mathcal S+\phi$ for some true $\phi$, then for all sufficiently large $k$ it also fails to simulate $S^1_2+\phi_{BB}(k)$, where $\phi_{BB}(k)$ asserts the exact value of the $k$-state Busy Beaver function. Thus any hard true extension yields a canonical Busy Beaver witness to nonsimulation. $\mathcal B$-certified simulation of a target $\mathcal U$ yields $\mathcal B{\vdash}Con_{\mathcal S}{\rightarrow}Con_{\mathcal U}$, giving certification barriers rather than external lower bounds.
The paper's central conjectural proposal is: for sound, finitely axiomatized sequential $\mathcal S$, if $EA\not\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then for every constant $c>0$, $\mathcal S\not\vdash^{n^c}Con_{\mathcal S+\phi}(n)$. Under this proposal, hardness follows when $\phi$ is $Con_{\mathcal S}$ or a Kolmogorov-randomness axiom. The latter yields further conjectural consequences and extensions.
arXiv:2604.28061v2 Announce Type: replace
Abstract: Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to understand the 'downstream' effects or impacts of open science. PLOS and DataSeer have developed a new LLM-based indicator to measure an important effect of open science: the reuse of research data. Our results show a data reuse rate of 43%, which is higher than established bibliometric techniques. We show that data reuse can be measured at scale using LLMs and generative artificial intelligence. The positive effects of research data sharing and reuse may currently be underestimated.
arXiv:2605.00261v2 Announce Type: replace
Abstract: Legged robots maintain dynamic feasibility through multicontact interactions with terrain. Learned foothold prediction can provide feasibility-aware costs for motion planning and path selection, but accurately predicting future contacts from perceptual inputs such as height scans remains challenging on highly unstructured terrain, even with a repetitive gait cycle. In this work, we show that modeling epistemic uncertainty in predicted footholds, conditioned on terrain observations and commanded motion, distinguishes in-distribution from out-of-distribution operating regimes in simulation and real-world settings. This allows a single learned model, trained on limited data distributions, to express uncertainty caused by missing training coverage. We use this learned uncertainty to detect OOD regions and incorporate them into a unified costmap-generation framework for uncertainty-aware path planning. Using these uncertainty-aware costmaps, we evaluate feasibility error across in-distribution and OOD terrains in simulation and real-world settings. The results show improved OOD detection, up to a 37% reduction in simulation feasibility error, and more reliable planning behavior than geometry-only baselines.
arXiv:2605.01598v3 Announce Type: replace
Abstract: We present a theoretical model of the ionization-recombination balance in the ultracold Rydberg gas-plasma mixture, which is caused by the collective processes rather than by individual interparticle interactions. We consider the electron-ion system where each electron moves for the most part of its time in the effective centrifugal potential formed by the nearest ion and sometimes jumps to the neighboring potential wells due to the perturbations from remote particles. These perturbations are described by a thermal bath with the effective "virial" temperature. Then, the electron with a sufficiently high energy, which can escape from the local potential well, should be considered as free (conducting) one; while the electron with low energy, confined within the well, as forming the Rydberg atom. As follows from our calculations, there is a sharp crossover from the insulating phase (Rydberg gas) to the conducting one (plasma) with increase in the particle density, which somewhat resembles the Mott transition in condensed-matter physics. This model should be well relevant to the steady-state ultracold plasmas obtained in the most recent experiments and represents a promising direction for further research.
arXiv:2605.02566v3 Announce Type: replace
Abstract: Artificial intelligence now produces convincing-looking scientific judgment (reviews, rankings, attributions, verifications) at almost no marginal cost. An influential reading of AI economics holds that prediction becomes cheap while human judgment stays scarce. For science, that reading understates the problem: what has become cheap is a counterfeit of judgment itself. This matters most for institutions whose product is trusted judgment, which is what journals, universities, funders, and learned societies exist to manufacture. They do not merely adapt to the technology; they compete with it for the same functional role. Four things become scarce instead: verified signal, legitimacy, authentic provenance, and integration capacity. Integration capacity means how much AI-delegated judgment a scientific community will accept before it stops trusting the journals, panels, and conferences that admitted it. It is the least developed of the four and the most binding: better tooling cannot buy it. The way forward for AI-augmented science is not acceleration but the redesign of its certifying infrastructure around these new scarcities.
arXiv:2605.03646v5 Announce Type: replace
Abstract: Turbophoresis in inhomogeneous turbulent flows leads to the formation of large-scale nonuniform particle number density distributions of inertial particles. This effect is associated with an effective drift velocity directed toward regions of lower turbulence intensity. It depends on the Stokes and Reynolds numbers, as well as on the gradient of the turbulence intensity. In the present study, turbophoretic transport is experimentally investigated in air turbulent flows generated by one-grid and two-grid oscillating systems. The flow velocity field is measured using Particle Image Velocimetry, and the particle spatial distribution is obtained by applying image processing techniques. To isolate the effect of particle accumulation due to turbophoresis from that associated with mean fluid flow, the measured particle number density of inertial particles is normalized by the corresponding distribution obtained for noninertial tracer particles under identical flow conditions. The measurements show preferential accumulation of inertial particles in regions of minimum mean-square turbulent velocity, consistent with the expected behavior of turbophoretic transport.
arXiv:2605.04445v2 Announce Type: replace
Abstract: The rapid advancement of generative technologies has made synthetic images nearly indistinguishable from real ones, thereby creating an urgent need for robust detectors to counter misinformation. However, existing methods mainly rely on universal artifact features that are shared across multiple generators. We observe that as the diversity of generators increases, the overlap of these common features gradually decreases. This severely undermines model generalization. In contrast, focusing only on unique artifacts tends to cause overfitting to specific forgery patterns. To address this challenge, we propose LEGO (LoRA-Enabled Generator-Oriented Framework). The core mechanism of LEGO employs an MLP to modulate multiple LoRA (Low-Rank Adaptation) blocks, each pretrained to capture the unique artifacts of a specific generator, followed by attention-based feature fusion. Unlike conventional methods that seek a single universal solution, LEGO delegates unique artifact extraction to specialized LoRA modules by dividing its training procedure into two stages. Each LoRA module is individually trained on a single-generator dataset to learn generator-specific representations, then MLP and attention layers are trained on mixed datasets to dynamically regulate the contribution of each module. Benefiting from its modular yet robust design, LEGO can be naturally extended by incorporating new LoRA modules for adaptation to newly emerging next-generation datasets, while still achieving substantially better performance than prior SOTA methods with fewer than 30,000 training images, less than 10% of their training data, and only 5 epochs in each training stage.
arXiv:2605.05061v2 Announce Type: replace
Abstract: Snow slab avalanches are among the most dangerous hazards in mountain regions. Recent numerical, field, and laboratory studies have renewed interest in shear-failure interpretations of avalanche release, particularly in relation to dynamic crack propagation and supershear fracture. Yet most analytical models either idealize the weak layer as perfectly brittle or neglect its pre-peak elasticity, although post-peak dissipation and compliance both control stress redistribution and critical length. Here, we derive an analytical solution for shear-failure propagation beneath an elastic snow slab with finite linear softening. Building on the weak-spot model of Gaume et al. (2013), failure is described by a fully softened residual core, a fracture process zone, and an intact elastic region. The solution recovers the classical brittle length as softening vanishes, distinguishes the residual crack length from the total affected length, and links weak-spot and fracture-energy descriptions through the softening law. Depth-averaged Material Point Method simulations confirm the analytical stress and displacement profiles and the predicted characteristic lengths. We then extend the same compliant-softening framework to collapse-driven anticrack propagation. A simplified Timoshenko anticrack analogue shows that slab bending and transverse shear deformation amplify normal stress at the weak-layer front and introduce a bending-controlled softening length with an approximately fourth-root dependence on softening displacement, supported by three-dimensional MPM simulations. Finally, a mixed-mode Timoshenko formulation couples weak-layer compression, slope-parallel shear, and slab rotation. A compact sharp-front model and a fully coupled finite-softening model reproduce the observed slope-angle dependence of critical cut length using realistic elastic and failure-envelope parameters.
arXiv:2605.07032v3 Announce Type: replace
Abstract: The evolution of generative models from next-token predictors to autonomous engines of complex systems necessitates rigorous safety hardening. Adversarial jailbreaking, the strategic manipulation of models to elicit harmful output, remains a primary threat to safe deployment. While Reinforcement Learning (RL) frames jailbreaking as a multi-step attack through sequential optimization, a mechanistic understanding of why the framework succeeds remains incomplete. To fill this gap, we present the first systematic decomposition of RL jailbreaking. We deconstruct the framework into problem formalization (reward function, action space, episode length), and algorithmic measures (RL algorithm, training data, reward-shaping) to identify the structural determinants of adversarial success. Our results reveal that the RL-jailbreaker successfully compromised all targeted models and safeguards. Through this first-of-its-kind analysis, we demonstrate that environment formalization, specifically dense rewards and extended episode lengths, is the primary driver of jailbreaking success. This work provides a tool for improving RL-jailbreaker efficiency and, ultimately, harden generative models resistant to RL-based attacks.
arXiv:2605.08601v5 Announce Type: replace
Abstract: Many applications process a stream of tuples over a window duration, and require the results within a specified deadline after the end of the window. For such scenarios, processing tuples intermittently (in batches) instead of eagerly processing tuples as they arrive significantly reduces the overall cost. Earlier work on intermittent query processing has addressed only fixed environments. In this paper, we propose scheduling schemes for batched processing of tuples, in an elastic parallel environment, scaling nodes up or down. Our scheduling schemes ensure to meet the deadlines, while incurring minimum cost. Our schemes also handle multiple concurrent queries, the arrival of new queries, and input rate variations. We have implemented our schemes on top of Apache Spark, in the AWS EMR environment, and evaluated performance with both TPC-H and Yahoo Streaming datasets. Our experimental results show that our scheduling algorithms significantly outperform alternatives, such as using a fixed set of nodes without elasticity, or using Spark streaming.
arXiv:2607.17201v1 Announce Type: cross
Abstract: In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning. This is an active sequential hypothesis testing problem in which the learner's objective is to identify an optimal policy in a Markov Decision Process (MDP) with high confidence, while minimizing the expected sample complexity to do so. We consider an online setting with deterministic rewards, where the agent must strategically navigate through the MDP in order to effectively explore. Previous works in the literature have provided asymptotically optimal methods for BPI, such as the Navigate and Stop (NaS) algorithm and its variants, however existing analysis remains asymptotic. In this work, we fill that gap by providing the first non-asymptotic sample complexity guarantees for NaS, showing that its sample complexity depends not only on the characteristic time, but also on the connectivity of the underlying MDP, the curvature of the optimal characteristic time, and other instance-dependent quantities. We identify these additional attributes and make explicit their contributions to the overall sample complexity.
arXiv:2607.17086v1 Announce Type: cross
Abstract: Any viable interpretation of quantum theory needs to account for the Born rule, from which the theory gets its probabilistic empirical predictions. In this paper, we give an overview of possible approaches to this problem in the context of the Many Worlds interpretation. We argue that, for structural reasons, none of them can possibly succeed. More precisely, we argue that the Many Worlds interpretation must obtain the Born rule by proceeding either axiomatically, deductively, or inductively, and that all three of these approaches run into general, fundamental obstructions.
arXiv:2502.08829v2 Announce Type: replace
Abstract: Federated learning (FL) with non-IID data often degrades client performance below local training baselines. Partial FL addresses this by federating only early layers that learn transferable features, but existing methods rely on ad-hoc, architecture-specific heuristics. We first conduct a systematic analysis of layer-wise generalization dynamics in FL, revealing an early-emerging transition between generalizable (safe-to-federate) and task-specific (should-remain-local) layers. Building on this, we introduce Principled Layer-wise Federated Learning (PLayer-FL), which aims to deliver the benefits of federation more robustly. PLayer-FL computes a novel federation-sensitivity metric efficiently after a single training epoch to choose the optimal split point for a given task. Inspired by model pruning, the metric quantifies each layer's robustness to aggregation and highlights where federation shifts from beneficial to detrimental. We show that this metric correlates strongly with established generalization measures across diverse architectures. Crucially, experiments demonstrate that PLayer-FL achieves consistently competitive performance across a wide range of tasks while distributing gains more equitably and reducing client-side regressions relative to baselines.
arXiv:2603.17068v2 Announce Type: replace
Abstract: Structured 3D representations such as keypoints and meshes offer compact, expressive descriptions of deformable objects, jointly capturing geometric and topological information useful for downstream tasks such as dynamics modeling and motion planning. However, robustly extracting such representations remains challenging, as current perception methods struggle to handle complex deformations. Moreover, large-scale 3D data collection remains a bottleneck: existing approaches either require prohibitive data collection efforts, such as labor-intensive annotation or expensive motion capture setups, or rely on simplifying assumptions that break down in unstructured environments. As a result, large-scale 3D datasets and benchmarks for deformable objects remain scarce. To address these challenges, this paper presents an affordable and autonomous framework for collecting 3D datasets of deformable objects using only RGB-D cameras. The proposed method identifies 3D keypoints and robustly tracks their trajectories, incorporating motion consistency constraints to produce temporally smooth and geometrically coherent data. TrackDeform3D is evaluated against several state-of-the-art tracking methods across diverse object categories and demonstrates consistent improvements in both geometric and tracking accuracy. Using this framework, this paper presents a high-quality, large-scale dataset consisting of 6 deformable objects, totaling 110 minutes of trajectory data. Project page: https://roahmlab.github.io/trackDeform3D-core-tracking/
arXiv:2603.17154v2 Announce Type: replace
Abstract: We study the problem of coded information retrieval for block-structured data, motivated by DNA-based storage systems where a database is partitioned into multiple files that must each be recoverable as an atomic unit. We initiate and formalize the block-structured retrieval problem, wherein $k$ information symbols are partitioned into two files $F_1$ and $F_2$ of sizes $s_1$ and $s_2 = k - s_1$. The objective is to characterize the set of achievable expected retrieval time pairs $\bigl(E_1(G), E_2(G)\bigr)$ over all $[n,k]$ linear codes with generator matrix $G$. We derive a family of linear lower bounds via mutual exclusivity of recovery sets and develop a nonlinear geometric bound via column projection that holds for every linear code. For codes with no mixed columns, this yields the hyperbolic constraint $s_1/E_1 + s_2/E_2 \le 1$, which we conjecture to hold universally whenever $\max\{s_1,s_2\} \ge 2$. We analyze explicit codes, such as the identity code, file-dedicated MDS codes, and the systematic global MDS code, and compute their exact expected retrieval times. For file-dedicated codes, we prove MDS optimality within the family and verify the hyperbolic constraint. For global MDS codes, we establish dominance by the proportional local MDS allocation via a convex-ordering argument for hypergeometric distributions, simplifying and extending prior work to the asymmetric case. Finally, we characterize the limiting achievability region as $n \to \infty$: the hyperbolic boundary is asymptotically achieved by file-dedicated MDS codes, and is conjectured to be the exact boundary of the limiting achievability region.