Papers

Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued representation of the wireless channel response, and a sequence of CSI frames forms a temporal channel trajectory. Modern deep learning-based CSI predictors, however, often provide only point predictions and lack calibrated uncertainty estimates. This limitation is particularly problematic in multi-step CSI prediction, where the target is a sequence of future CSI matrices, and downstream decisions such as beamforming or scheduling may fail if any part of the predicted trajectory is unreliable. We propose trajectory-adaptive calibration and error profiling with conformal risk control (TRACE-CRC), a method for trajectory-aware uncertainty quantification in multi-step CSI prediction. TRACE-CRC constructs Frobenius-norm uncertainty balls around predicted CSI matrices and controls the risk that at least one future frame is uncovered. Instead of calibrating each future step independently, TRACE-CRC combines future-step-dependent error profiling, trajectory difficulty stratification, and learn-then-test (LTT) risk control. Empirically, TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections, while avoiding the trajectory undercoverage of compact stepwise and adaptive conformal baselines.

Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, directly applying existing video-editing methods to human-centric live streaming remains challenging, as they may introduce facial-expression inconsistencies and typically depend on multiple offline inference steps, making them unsuitable for real-time interaction. We propose EditaLive, a novel framework for real-time streaming character video editing. In detail, we start from a pretrained image animation model (Wan-Animate), which naturally decouples appearance from motion, and repurpose it as the base model for instruction-based human-centric video editing by reference frame editing and video reconstruction via the collected CharEdit-50K dataset. Besides, we adapt the model from offline bidirectional to causal streaming generation, and design an aligned self-rollout distillation strategy that compresses the model into a two-step sampler, where fixed RoPE and align forcing reduce training–inference discrepancies, and first-frame preserved sparse attention filters redundant historical information to mitigate appearance drift. Extensive experiments demonstrate that EditaLive delivers state-of-the-art editing performance with faithful preservation of facial expressions and low-latency real-time streaming inference.

We study the reuse of positive trial profiles under finite perturbations of nonsymmetric matrix pencils B-\lambda G. The lower and upper cone Rayleigh levels need not coincide and are defined without requiring positive eigenvectors. In the positive orthant with positive diagonal $G$, we derive computable perturbation bounds and determine the exact worst-case trial gap over prescribed independent entrywise perturbation classes. This yields the largest uniform radius meeting a given trial-gap tolerance for fixed profiles, certifying trial-value accuracy without recomputation. Experiments on a nonnegative operator and five signed matrices compare sufficient and optimal radii, directional thresholds, and cold- and warm-start recomputation. Several signed cases exhibit severely limited uniform profile reuse despite the optimality of the radius. Exact rational checks verify the reported bounds and worst-case constructions for stored numerical inputs. A finite-budget linear program and differentiable constraints illustrate applications to verified control and learning.

Aesthetics are an important part of the symbolism of artistic works. Although subjective, humans categorize art based on the emotion evoked regardless of modality. What remains under-explored is how AI models form their own aesthetic categorization of human-produced media without explicit labels or cross-modal supervision. We present a self-supervised framework that projects four modalities (text, audio, image and video) into a shared 256-dimensional embedding space and applies iterative clustering to discover aesthetic structure. We discuss the divergence between AI-generated cluster assignments and human affective register labels on a weakly supervised multimodal dataset. This work has applications in understanding how AI structures cross-modal similarity, organizing heterogeneous media collections for Retrieval-Augmented Generation (RAG), and automated data labeling.

Motivated by the recent introduction of chromatic persistent homology, we generalize the Euclidean minimum spanning tree (EMST) for $n$ points in $\mathbb{R}^2$ to the lunar EMST for the case in which the points come in $s+1$ colors. Calling the intersection of $s+1$ disks of radius $r$ centered at points with pairwise different colors a lune, the generalized EMST reflects the history of the union of lunes as $r$ goes from $0$ to $\infty$, and its cost is twice the difference between the radii when the arcs and nodes of the tree are formed. If the points are chosen uniformly at random in $[0,1]^2$ and colored randomly, the expected cost converges to some constant (that depends on $s$) times $\sqrt{n}$, as $n$ goes to infinity. The main contribution of this paper is a proof that this constant exists, however similar to the case of the classic EMST, its precise value remains elusive.

This paper studies the real-time reconstruction and remote actuation of two binary Markov sources over a shared wireless channel with multi-packet reception (MPR). Unlike many existing collision-based formulations that discard simulta- neous transmissions, we exploit MPR and evaluate communica- tion through reconstruction and actuation errors rather than raw delivery rates. We consider two sensors observing the sources and aim to find sampling policies that minimize the weighted real- time reconstruction error (RTE), or equivalently the weighted cost of actuation error (CAE) for the considered binary sources, under per-sensor sampling constraints. We first obtain closed- form expressions for the RTE and CAE in terms of the effective update probabilities. When the sensors randomize independently, the MPR-induced update-rate map becomes bilinear, and the constrained optimization is nonconvex. We exploit the geometry of the achievable update-rate region to show that the search over Pareto-efficient independently randomized policies reduces to a finite set of one-dimensional boundary-branch searches with closed-form candidates. As a benchmark, we allow time sharing among joint sensor actions, and we prove that at most two Pareto- extreme modes are sufficient, and use this benchmark to quantify the loss caused by independent randomization. Numerical results reveal that MPR capability alone is not sufficient; simultaneous decoding improves the task-oriented objective only when con- current reception is reliable for both sources; otherwise, policies that avoid simultaneous transmissions can be equally effective.

Cross-lingual alignment in multilingual language models is typically attributed to joint training: shared parameters, mixed-language batches, or explicit alignment objectives. We ask whether monolingual models trained on non-parallel data learn alignable representations without joint training. By testing on strictly monolingual language models, such as the Goldfish model families and independently developed models from different research labs, we find three results. Correlation: these models develop alignable representational geometry across layers, with alignment strengthening as data scale, model scale, or linguistic proximity increases. Construction: a single Procrustes rotation fit on parallel sentences maps hidden states between models. Causation: the same rotation transfers functional content; patching a rotated English residual into a German model on a factual cloze flips the prediction to the donor's capital in most cases. We confirm that cross-lingual alignment can emerge from the structure of language and the information it carries rather than from joint training, and this points to practical future directions including model stitching, merging, and modular multilingual systems built from monolingual components.

We study nearest and farthest Voronoi diagrams of lines in three-dimensional Euclidean space, which partition space according to the closest and farthest input lines, respectively. For arbitrary lines, a gap remains between the known quadratic lower bound and near-cubic upper bounds for the complexity of the nearest diagram. We consider input lines chosen from one of two continuous families of straight lines on a smooth surface defined by a quadratic equation, with one line from each family passing through every point of the surface. Under the geometric assumptions stated in the paper, we prove quadratic upper bounds on the total number of vertices, edges, faces, and connected three-dimensional regions in both diagrams. We also construct quadratically many nearest vertices using lines from a single family on one fixed surface, establishing a tight quadratic worst-case bound. The farthest diagram has quadratic total complexity for every input satisfying our assumptions. Our proof uses a parametrization that arranges the input lines in a cyclic order and expresses tangency to a sphere by a polynomial equation of degree four. At a Voronoi vertex, the four tangent input lines give four distinct roots. The sign changes between these roots, together with the nearest or farthest condition, force two intervening parameter intervals to contain no other input parameters. Thus, only quadratically many choices of four lines can define a vertex, and each choice yields at most eight isolated candidate centers.

Motivated by theoretical problems in deep learning, we conjecture that post-composing a fixed number of pairwise distinct nonconstant polynomials with a generic polynomial of sufficiently large degree yields linearly independent polynomials. This generalizes Newman–Slater's theorem on powers of polynomials. We establish several cases of this conjecture and its origin-passing variant: We prove the result for two polynomials, and for an arbitrary number of polynomials when their degrees are bounded. Furthermore, we show how the conjecture implies a complete understanding of the identifiability (i.e., parameter symmetries) of deep fully connected neural network architectures with generic polynomial activation functions. In particular, for network architectures with layer-specific activations of increasing degree, our established versions of the conjecture fully characterize the set of parameters yielding the same end-to-end network function. As a special case, we fully resolve the identifiability of shallow polynomial networks.

Synthetic populations of eleven Italian municipalities (1,814,317 individuals in 887,937 households) generated from published aggregates alone: ISTAT census and register tables, census-section counts, the national civic-address register, public-use survey microdata, and six municipal open-data portals, every source certified in a registry with licence, fingerprint and declared affordances. Four rings give every attribute a declared place: a maximum-entropy joint model of up to nine demographic attributes; whole-vector donation of twenty-three attitudinal and health variables from survey respondents; placement to census section, single year of age and address; and households constrained by the census size distribution per section. Every downstream layer (detailed titles, work, names, biographies) is a declared derivation adding no information. The pipeline is deterministic to the byte: regenerating all eleven municipalities from the tagged commit reproduces every file of every ring bit for bit, in 33 minutes on one workstation. Populations are released in a public regime enforced in the data (no names, no addresses, coordinates randomised within census section), browsable in Animarium, a dependency-free web viewer where every number carries its comparison and every view is a citable URL, and downloadable as an open dataset. The report documents the architecture, the sources and their certification, the reproducibility and quality measurements at the release tag, the viewer, and the narrative layer that renders records into personas for LLM-driven simulation, with the platform's controllability demonstrated in companion experiments, and validation explicitly out of scope.

Reliable medical conversational AI requires authentic expert–patient interaction data, yet such datasets remain scarce, especially for low-resource languages such as Bengali. We present DocTalkBN, a large-scale multimodal dataset of real-world expert telemedicine conversations in Bengali, collected from nationally broadcast telemedicine programs featuring board-certified physicians. DocTalkBN contains 557.63 hours of paired audio and text, 1,515 multi-turn patient calls, 10,274 host–doctor question–answer exchanges, totaling 1.7M tokens, spanning 26 medical specialties. Unlike prior resources derived from medical forums, written health content, or synthetic data, our dataset preserves the spontaneity, contextual richness, and spoken characteristics of authentic medical interactions in a low-resource setting. To support benchmark-driven research, we further construct three downstream tasks from the corpus, medical triage classification, advice safety evaluation, and medical named entity recognition, and benchmark a diverse set of large language models and encoder-based baselines. Our results show that DocTalkBN is a practically useful resource, particularly for clinically grounded reasoning tasks. We release this resource to facilitate future research on reliable medical NLP and safer, more culturally grounded healthcare systems for low-resource languages. Our source codes and dataset are publicly available at https://anonymous.4open.science/r/doctalk.

Federated Learning (FL) enables privacy-aware distributed training, yet gradient updates remain exploitable: Man-in-the-Middle (MitM) interception exposes updates in transit, while model poisoning corrupts global convergence. We first introduce GASHE (Gradient-Aware Selective Homomorphic Encryption), a novel selective encryption strategy that dynamically identifies and encrypts only the gradient components exceeding a DP-calibrated sensitivity threshold, rather than encrypting all parameters uniformly as in static layer-based or full-parameter CKKS schemes. Building on GASHE, we introduce SecureDrive-FL, a federated driver monitoring framework that couples DP-SGD with GASHE to create the first closed-loop DP+HE privacy pipeline: DP-SGD calibration parameters directly derive the GASHE encryption mask, unifying training-time privacy and communication-time confidentiality. Evaluated on a ten-class distracted driver classification task under non-IID federated splits, SecureDrive-FL matches DP-SGD alone's poisoning resistance (73.6% vs. 74.0% accuracy, 3.9% Attack Success Rate for both) while additionally withstanding MitM interception, where DP-SGD alone collapses to near-random accuracy (78.2% vs. 10.4%), all under only approx. 8–10% additional runtime overhead relative to DP-SGD alone—under DP-SGD noise injection with per-round privacy parameter epsilon_0=4.

This work develops a regularization analysis of non-accelerated and accelerated primal-dual methods for solving linear inverse problems in the presence of noisy data. We investigate a Condat-Vũ algorithm and an accelerated primal-dual hybrid gradient method in Hilbert spaces, with focus on quantifying the effect of data perturbations on the reconstruction error. For the non-accelerated scheme, we derive error estimates in terms of Bregman distances, whereas for the accelerated scheme we establish error estimates in norm. The study accommodates a general class of convex data fidelities satisfying suitable perturbation conditions, which are verified explicitly for equality constrained and Morozov regularization. For the non-accelerated method, the analysis is further extended to Banach spaces, taking into account non-Euclidean geometries and including a particular non-reflexive setting tailored to nonnegative solution reconstruction. The results recover known behavior in classical settings while extending the regularization analysis to these more general frameworks. Numerical experiments with representative regularizers, including sparsity and entropic models, support the theoretical findings and illustrate practical performance under noise.

Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authoritative even when ungrounded or incorrect. When such an output contributes to harm, who is answerable, and through what mechanisms can responsibility be traced, explained, and acted upon? Following PRISMA guidance, five databases were searched from January 2022 to March 2026 against four review questions; of 4,512 records identified, 122 primary studies were included, together with 12 regulatory and standards documents analysed as primary sources. The review consolidates a sociotechnical account of accountability as an actor-forum relation resolved into five dimensions, and synthesises mechanisms across four families: technical controls, human oversight, organisational governance, and documentation and traceability, each with a maturity assessment. The corpus is read through a four-layer classification device spanning provenance, application logic, human oversight, and governance and redress, cross-cut by traceability, role clarity, and continuous monitoring. Both are mapped onto the EU AI Act, whose high-risk obligations have applied since 2 August 2026, the NIST AI RMF with its Generative AI Profile, ISO/IEC 42001, and sectoral guidance in healthcare, consumer finance, education, and the public sector. Four persistent gaps emerge: under-specification of human oversight, absence of shared accountability metrics, disciplinary disconnection, and limited empirical evaluation, alongside five structural tensions that no surveyed instrument resolves. The review closes by consolidating the classification device into an integrated accountability architecture, LAAF, with cybersecurity aligned to the OWASP LLM Top 10 (2025); it is a synthesis of the surveyed evidence rather than a validated artefact.

Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs), which can hallucinate domain terms, produce inconsistent formats, and favor hierarchical over associative relations. In the LLMs4OL 2026 Challenge, we address both the End-to-End Flagship Task (Task A) and Ontology Extension Reuse Task (Task B) using an offline retrieval-augmented few-shot prompting pipeline. Our system employs Qwen2.5-14B-Instruct with all-MiniLM-L6-v2 for demonstration retrieval, selecting the top-5 examples for Task A and top-2 for Task B. A left-truncated context-windowing strategy preserves task instructions within long prompts. For Task B, generated triples undergo deterministic vocabulary-constrained filtering, retaining triples when at least one endpoint belongs to the sample's closed term/type vocabulary and removing duplicates of the initial ontology. The approach achieves Semantic Graph Similarity of 0.8692, Term-Typing F1 of 0.9200, and Taxonomy Discovery F1 of 0.8540 on Task B, while Task A achieves 0.7416 Semantic Graph Similarity. However, no non-taxonomic relations are extracted, highlighting limitations of closed, taxonomy-oriented relation vocabularies.

Reproducibility in machine-learning research depends on experimental choices such as random seeds, dependency versions, data partitioning, and evaluation configuration. Existing repository validation workflows may execute successfully without detecting changes to such choices. We study this problem using MLReproMutate, research software that applies controlled, reproducibility-relevant mutations to ML research repositories and evaluates them against validation workflows already present in those repositories. We conducted an outcome-blind empirical study of 39 frozen repository-operator cases using four mutation classes: random seed, dependency pin, data split, and cross-validation fold count. Repository revisions, mutation candidates, and validation workflows were fixed before mutation outcomes were observed. Primary execution yielded outcomes for 13 of 39 cases; a bounded restoration procedure increased the combined evaluable set to 24. After excluding one confirmed-equivalent mutation, 23 confirmed non-equivalent mutations remained. The selected validation workflows detected 2 of these 23 mutations, corresponding to an observed detection proportion of 8.7%. These results do not imply that the corresponding repositories are irreproducible. Rather, they show that, in this sample, existing validation workflows often did not detect the particular controlled reproducibility-relevant changes introduced by the study. The findings motivate reproducibility-oriented mutation testing as a complementary way to assess whether research-software safeguards constrain experimentally important choices.

Distributed Quantum Computing (DQC) addresses the physical scaling limitations of monolithic quantum processors by networking modular Quantum Processing Units (QPUs). Efficient execution of quantum algorithms on DQC architectures requires compiling them across QPUs while minimizing inter-QPU communication bottlenecks, primarily through circuit partitioning. However, current evaluations of state-of-the-art partitioning heuristics focus primarily on the total entanglement cost of the partitions, failing to capture the broader structural and temporal overheads introduced by distributed network constraints. This paper addresses this evaluation gap by applying established monolithic benchmarking metrics to partitioned distributed circuits to quantify the performance impact of network constraints. Using an open-source, automated evaluation pipeline, we systematically assess diverse partitioning algorithms across standardized workloads and quantum network topologies. Our empirical results reveal that partitioning algorithms with comparable entanglement costs can still introduce drastically different physical execution penalties. By exposing these hidden trade-offs, such as severe increases in circuit depth and substantial reductions in gate density, this study demonstrates that comprehensive circuit-level metrics are essential for guiding the future design of DQC compilers.

A track layout of a graph is a partition of its vertices into linearly ordered independent sets, called tracks, such that no two edges between the same pair of tracks cross. Given a graph, the goal in this context is to determine its track number, that is, the minimum number of tracks required for the graph to admit a track layout. In this work, we present upper bounds on the track number of graphs admitting a product structure. Our main contribution is an algorithm that computes a track layout with at most $(2h+1) \cdot r \cdot tn(H)$ tracks for every subgraph of the strong product $P^h \boxtimes K_r \boxtimes H$, where $P^h$ is the $h$-th power of a path $P$, $K_r$ is the complete graph on $r$ vertices, and $H$ is a graph with track number $tn(H)$. Combined with existing product-structure results from the literature, this algorithm yields upper bounds on the track number of several graph classes. For planar graphs, the obtained bound matches the current best-known upper bound of $225$. For $1$-planar and optimal $2$-planar graphs, our algorithm yields track layouts with at most $375$ tracks, while for genus-$k$, $k$-planar, $k$-framed, $k$-map, and $k$-string graphs it provides track layouts with a number of tracks that depends solely on $k$, thus establishing new upper bounds on the track number for these graph classes. The algorithm runs in linear time for planar graphs and, more generally, in $O(n + h \cdot r \cdot t + f_t(H))$ time whenever a corresponding product-structure decomposition of the input $n$-vertex graph is provided as part of the input, where $t=tn(H)$ and $f_t(H)$ is the time needed to compute a $t$-track layout of $H$. Furthermore, our algorithm only uses elementary linked-list data structures.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are leading causes of vision loss, and optical coherence tomography (OCT) is the standard modality for detecting and monitoring the subtle lesions that drive treatment decisions. Most deep-learning segmentation work for OCT is validated only in-domain, leaving generalization to clinical data collected under different acquisition protocols largely untested. This work develops and systematically ablates four lesion-segmentation pipelines – 2D and 3D variants for AMD and DME – reaching Dice scores of 0.76 to 0.82 with strong volumetric and surface calibration (r vol, r surf greater than or equal to 0.97 across all four pipelines) on an in-domain validation set. The ablation process establishes a full-volume, calibration-aware adoption standard that catches mechanisms an ordinary slice-level evaluation would keep, and identifies ensemble composition as the most consistent driver of improvement. To test generalization, the models are evaluated on OLIVES, an external clinical cohort with no lesion-level ground truth, using a proxy-metric framework built around biomarker AUROC, central subfield thickness (CST) correlation, and longitudinal concordance. Predictions track clinical biomarkers outside the training distribution, though less strongly than in-domain – evidence for, not validation of, automated lesion-burden tracking as a clinical tool.

Large language models have shown strong potential for software engineering tasks, particularly software testing. Model-based testing (MBT) is a software testing technique. To address the broad scalability challenge for industrial adoption of MBTs, our paper presents an empirical evaluation of Large Language Models (LLMs) for automated model-based test generation, compared with a state-of-the-art model-based testing tool (GraphWalker) and its built-in algorithms (random and quick random for edge and vertex coverage settings). Our evaluation indicates strong potential to optimize and shorten test paths and step sizes using the recent five state-of-the-art LLMs (GPT-5.1, GPT-5.2, Claude Opus 4.5, Claude Sonnet 4.5, and Gemini 2.5 Pro) against four GraphWalker models (two web applications (Parabank and Testinium) and two hardware applications (TLC and RISC-V) ) of escalating complexity.

A tool-using LLM agent that reads attacker-controlled web content while holding a secret faces indirect prompt injection: the content may make it exfiltrate the secret. In a safe synthetic lab (canary secret, mock tools, matched clean-vs-poisoned metric) we report the framing gap: across six models, ten overt injection classes are refused (gpt-4o 0%), but reframing the identical leak as a mandatory integrity signature, config field, or look-alike "trusted" host drives gpt-4o 0% to 100%. The attack is cheap, and its cost is three-level: paraphrasing a known mechanism is trivial (96% at 3 wordings), swapping the field inside a known-effective template is also cheap (up to 60%), while authoring a fresh page around a new mechanism is hard (0/130) – the reusable asset is the template, not the mechanism. An ablation shows the mechanism is instruction/data confusion, not defeated alignment: removing the confidentiality policy leaves base attacks at 0% and moves reframing only 31.9% to 38.1%. What closes the gap is payload-blind checks: a destination allow-list (0%, when destinations are closed) and a capability-isolating planner/reader split (0%). A broad "in any form" policy clause also closes it at the acting model (to 0%) but is brittle (dropping the catch-all reopens it to 48.8%). A published fine-tuning defense (SecAlign, CCS 2025) does not close it on a tool agent (32.5%, positive-control-validated), nor does channel separation (38.8%); an output-normalizing guard loses to a held-out encoding (ROT13, 100%). Robustness comes from constraining the destination or isolating the capability, not from the acting model recognizing the attack.

Byzantine fault-tolerant (BFT) systems are, in principle, an appealing foundation for transactional applications involving mutually distrustful participants. Yet their adoption has been hampered by two persistent stumbling blocks-performance and developer convenience-which are often in tension with one another. Recent systems show promising progress on both fronts by shifting to a client-centric architecture; clients execute transactions locally and concurrently, while the system resolves any data conflicts to maintain database serializability. We argue that, in its current form, this approach introduces a critical vulnerability: it leaves the integrity of the database exposed to Byzantine clients, which may issue malicious or incorrect transactions. We address this threat with Sintr, a framework that prevents Byzantine clients from compromising database integrity by executing rogue transactions. Sintr combines redundant execution to validate transaction outcomes with a flexible, heterogeneous policy framework for expressing an application's data-integrity requirements. We apply Sintr to harden several existing BFT database systems and find that it imposes only modest overheads-3%-16% in throughput and 3%-21% in latency.

Bug localization is a labor-intensive task, particularly in large software systems. When abnormal behavior occurs, developers must perform repetitive and time-consuming steps to identify faulty files. Previous studies have mainly focused on single-objective localization methods, many of which are limited to specific programming languages. In addition, relying solely on lexical similarity between source code and bug reports is often insufficient due to the natural language nature of bug descriptions. In this study, we propose a class-level automated multi-objective search-based system to identify and rank potentially buggy classes from bug reports. The main objective is to maximize similarity while minimizing the number of suggested faulty files. The evolutionary optimization algorithm SPEA-2 was applied to six open-source Java projects comprising more than 22,000 bug reports. The proposed approach was evaluated against two widely used algorithms, NSGA-II and MOEA/D. Results indicate that SPEA-2 achieved higher precision and recall than both multi-objective and single-objective baseline methods. The proposed recommender system successfully identified buggy classes or files for 88.5% of bug reports within the top 10 recommendations and 94% within the top 20. The effectiveness of the model was further validated on an industrial Android project written in Kotlin, demonstrating its adaptability across programming languages.

While most phenotyping platforms rely primarily on image-based measurements, advanced plant characterization requires the integration of active physiological sensing modali- ties such as chlorophyll fluorescence. We present an autonomous robotic platform designed to perform targeted fluorescence measurements on plant leaves. The system combines 3D plant reconstruction, geometric analysis, and motion planning to localize suitable measurement points and generate collision-free trajectories for a robotic manipulator. A dense 3D model of the plant is reconstructed from multi-view data and used to extract candidate leaf surfaces based on orientation, accessibility, and sensing constraints. These targets are then integrated into a task-level planning framework that guides the end-effector to precise contact or near-contact configurations required for point-based fluorescence acquisition. The platform enables automated, repeatable, and spatially resolved physiological measurements that go beyond passive imaging. By tightly coupling perception, geometric reasoning, and manipulation, the proposed system provides a robotics-driven approach to high-resolution plant phenotyping and opens new directions for autonomous agricultural inspection and plant-aware manipulation.

We introduce a new family of benchmarks for the problem of diagrammatic equivalence between circuits. Three variants of this problem are considered, ranging from basic to challenging, and benchmarks are generated for each variant. We provide first-order encodings in both TPTP and SMT-LIB formats, together with scripts that automatically generate benchmark instances, and evaluate these benchmarks on state-of-the-art automated theorem provers and SMT solvers.

Enterprise agentic systems must coordinate changing capabilities, execution capacity, and independently governed data. We define Skill, Harness, Scaffold, and an external data substrate as responsibility contracts. The central hypothesis, cost-aware capability-capacity separability, asks whether compatible capacity changes preserve semantic outcomes while capability changes preserve the capacity-response relationship within declared margins and enforcement budgets. We operationalize the data boundary through a source-oriented Data Wiki, an output-oriented Theme Wiki, and a versioned Intermediate Relation. Executable 5W1H+Which predicates bind source identity, validity, authorization, semantics, operations, relations, and evidence requirements. Request-bound tickets add execution-time revalidation and typed rejection. A conditional soundness argument states the required trust and atomicity assumptions; dependency invalidation makes change propagation explicit. A single-process reference model agrees with a declared specification oracle on all 1,024 combinations in a finite synthetic fault domain and passes five lifecycle checks. These are conformance results, not measurements of retrieval quality, production safety, or scaling. We specify a held-out data study and a cluster-period crossover with distinct supported, falsified, conditional-engineering, and inconclusive verdicts. The core separability hypothesis remains empirically untested.

We study the problem of decentralized, self-organized task sharing for a swarm of heterogeneous robots that collaborate in transportation or other objectives that require coordinated motion planning. To this end, we present theoretical and practical results for the simple but effective mechanism of bucket brigades for load balancing, in which a team of heterogenous robots share a spatial task in a confined, one-dimensional space, while only being able to sense collisions with neighbors or walls. The goal is to optimize throughput of the overall system, without central control or information, aiming at an interval partition proportional to robot velocities. We address possible chaotic system behavior by developing a stabilization mechanism based on simple local aid, a “token”, that temporarily decelerates robots after an encounter. This purely local change eliminates persistent oscillations, resulting in convergence towards a stable system state. We accelerate system convergence by comparing a single boundary token to ubiquitous two-directional tokens and optimizing the deceleration factor. Event-driven simulations report convergence times and robustness: For a large variety of perturbations (such as robot deletion, position or velocity jittering), the system reliably re-converges. The results suggest a local, practical mechanism for robust load balancing for heterogeneous teams of robots that promises an effective tool as basis for more complex scenarios.

This paper considers the local convergence for solving general nonlinear equations. We establish randomized quasi-Gauss–Newton methods based on the approximation of the Gram matrix, addressing both the underdetermined and the overdetermined settings. For the underdetermined case, we show that our methods achieve local superlinear convergence to the optimal solution. For the overdetermined case, we show that our methods achieve local condition-number-free convergence to the stationary point of the nonlinear least-square formulation of the problem. In contrast, existing quasi-Newton methods mostly focus on square systems and additionally require the initial Jacobian estimate to be sufficiently close to the exact one.

Many scientific and engineering applications require estimating unknown parameters from experimentally observable data – an inverse problem that is inherently challenging due to nonlinearity, noise, and ill-posedness. In this paper, we propose an active diffusion-based inverse problem solver. A DM is trained to learn the mapping between the parameter space and the observable space. By iteratively detecting and correcting model misspecification through posterior uncertainty, the method discovers and learns the correct region of parameter space, even when initial training bounds exclude the true parameters. This provides a principled, Bayesian justification for adaptive domain augmentation and ensures robust inference for inverse problems under incomplete prior knowledge. We demonstrate the effectiveness of our inverse solver for a toy inverse problem with infinite solutions, and for the parameterization of the quantum correlation functions to event observables in a Quantum Chromodynamics analysis of nucleon structure.

Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks remains challenging. Task success often hinges on subtle, view-dependent visual cues, while task-level rewards provide little guidance about which regions matter, making it difficult to learn task-relevant visual grounding from limited real-robot interaction. Online adaptation is further constrained by the computational cost of VLA inference and replay-based updates. We introduce GRAFT (Grounded Reinforcement Adaptation for Fast Task Learning), a framework for efficient online VLA adaptation through grounded perception. GRAFT uses region-level supervision to learn view-specific visual anchors that focus perception on task-relevant local cues without requiring region proposals at deployment. It further combines single-step action generation with cached visual-language prefix reuse to accelerate online learning. Across four biomedical manipulation tasks, GRAFT improves success rates by 32.5 percentage points under matched adaptation budgets, while reducing the computational overhead of online policy updates.