Papers

Multi-robot Control Barrier Function (CBF) safety filters can become infeasible, but a failed quadratic program (QP) does not indicate why the conflict occurred or how to resolve it. To address this, we develop an exact feasibility certificate for multi-agent CBF filters with heterogeneous control-affine dynamics and convex input sets. The certificate quantifies a feasibility reserve by separating the demand imposed by safety constraints from the available actuator supply. This decomposition shows when CBF gain tuning or increased actuation can and cannot resolve infeasibility, and identifies the agents and interactions responsible for the conflict. We further propose an algorithm to optimally allocate shared safety constraints by maximizing the worst local feasibility margin, yielding a linear program for polyhedral input sets. In $320$ paired closed-loop simulations, the proposed allocation reduces infeasible control steps from roughly $50\%$ to $6.2\%$, and reduces safety-violating runs from $118/160$ to $24/160$. In addition, across $52$ infeasibility events, the certificate identifies an interaction whose relaxation restores feasibility in $94\%$ of cases.

Statistical data fusion combines two panels that share a block of covariates but observe disjoint outcome blocks, and in its traditional form no row observes both outcomes at once. That rules out the discriminative criterion one would rather train a Deep Boltzmann Machine with, since multi-prediction training needs ground truth for whatever it holds out. We propose observed-block multi-prediction, which restricts the multi-prediction objective to targets drawn from what each row actually observes. It is well defined for any missingness pattern and reduces to the original criterion when rows are complete. Having a discriminative criterion that survives the setting lets us ask whether the joint model is needed at all, by separating what it contributes into a representation part and an inference part. On two datasets of different kinds, a consumer purchase panel and public-domain census microdata, over grids in sample size and covariate width spanning 40 cells and 200 runs per method, almost none of the fine-tuned DBM's advantage comes from generative pre-training, which is confined to the smallest sample size on one dataset and absent on the other. It comes from conditioning on one outcome block when predicting the other. This term amounts to +0.19 and +0.36 percentage points, is positive in all 40 cells, never decays as the panels grow (it is flat on one dataset and grows on the other), and requires neither a second hidden layer nor more inference. Against baselines tuned on validation and given the same conditioning, the fine-tuned DBM is the best method in 37 of the 40 cells. The imputers that can also condition on the other outcome block mostly lose accuracy when they do, whereas the DBM gains in every cell; since fusion data cannot validate that choice, this is the property that matters.

We prove that deciding whether the weak partition connectivity (WPC) of an unweighted hedgegraph is at most a given integer is NP-complete, resolving an open question about its computational complexity. Hardness holds even for connected full-support split systems, where each hedge consists of two nonempty, vertex-disjoint hyperedges covering all vertices. We also obtain hardness on connected edge-colored simple graphs in which each color class is a spanning forest with exactly two nontrivial trees. On both classes, exact computation of fractional WPC is NP-hard, whereas hedge connectivity is computable exactly in polynomial time. A binary matrix formulation yields the reduction and a deterministic polynomial-time exact algorithm under a linear intersection condition on row supports after normalization and column compression. In particular, both integer and fractional WPC are computable in polynomial time when these supports have size at most two, whereas NP-hardness holds already when their size is bounded by three. For every fixed $b$, we give deterministic and randomized efficient polynomial-time approximation schemes on hedgegraphs in which each hedge has at most $b$ connected components, counting isolated vertices. Both schemes return one partition that simultaneously approximates integer and fractional WPC. Unless $\mathrm P=\mathrm{NP}$, neither objective admits a deterministic FPTAS, even on the two hard instance classes.

We propose a linearized Physics-Informed Neural Network (lPINN), a reduced-order neural basis method for forward and inverse differential equations. In an offline stage, lPINN learns operator-compatible continuous neural basis functions from an ensemble of numerical solutions. The basis functions are differentiable through automatic differentiation and are pretrained using solution data together with either derivative information or physics residuals. For each new problem instance, the basis functions are frozen and the solution is obtained by minimizing the governing-equation residual together with applicable initial, boundary, regularization, and observational terms. Unlike surrogate and operator-learning methods, the training data define the trial space offline, while the instance-specific solution is computed online by enforcing the governing physics. Relative to vanilla PINNs, lPINN pretrains the nonlinear hidden-layer representation offline and performs online inference only in the final linear layer. We evaluate lPINN on forward and inverse problems for the advection-diffusion equation, Burgers' equation, and the nonlinear pendulum equation. Compared with vanilla PINNs, lPINN achieves lower solution and parameter errors while reducing online inference times by approximately one to more than three orders of magnitude, with the largest gains generally observed for limited residual or measurement data. Cross-resolution experiments show that the learned continuous representation can be evaluated on finer meshes without retraining and with nearly unchanged accuracy.

For constant-stepsize stochastic approximation (SA), the iterates converge in distribution to a stationary law that depends on the stepsize $\alpha.$ Steady-state convergence (SSC) concerns the limit of the scaled stationary distribution as $\alpha \downarrow 0.$ Existing SSC theory requires i.i.d. or additive noise and global differentiability of the mean operator, and yields suboptimal rates. We develop a unified SSC theory for constant-stepsize contractive SA driven by Markovian, multiplicative noise, covering both locally differentiable and locally nondifferentiable mean operators. A key methodological contribution is a multi-step universality framework that progressively reduces the original stochastic recursion to tractable auxiliary dynamics while preserving its steady-state limit. Under local quadratic linearization at the fixed point, we obtain a Gaussian approximation of the scaled steady state at the optimal rate $O(\sqrt{\alpha})$ in Wasserstein-2 distance, which further gives finite-time Gaussian approximations for the raw iterates. In the locally nondifferentiable regime, we establish a general SSC result and show that the leading-order asymptotic bias can be of order $\sqrt{\alpha}$, in contrast to the $\alpha$-order bias in the smooth regime. We apply the theory to Markovian linear SA and asynchronous Q-learning, neither of which is covered by prior results. We further propose a bias-reduction scheme for Q-learning that requires no knowledge of the local smoothness regime, validated by numerical experiments.

Local PageRank algorithms seek sparse approximations with work independent of graph size. We give a deterministic algorithm for regularized personalized PageRank with additive objective accuracy $\epsilon$ in $\widetilde{\mathcal{O}}(1/(\rho\sqrt\alpha))$ local work, where $\alpha$ is the lazy teleportation parameter and $\rho$ is the regularizer. Accuracy enters only polylogarithmically. The bound charges discovery, repeated neighborhood scans, numerical updates, certification, and output, without graph-wide preprocessing or a supplied solution support. The algorithm combines regularization continuation with accelerated corrections constrained by a degree-scaled box and a mass cap. Two energies for the same recurrence control objective convergence and the response that activates coordinates. A selected-flow argument bounds cumulative scanned volume, and a sparse threshold reporter realizes the bound. We also specify a bounded-arithmetic implementation for rational inputs. A second, randomized algorithm uses support-safe threshold batches. A block-Cholesky and Chebyshev argument bounds their depth, and certified SDD solves give expected work $\widetilde{\mathcal{O}}(V_*\min\{k_*,\alpha^{-1/2}\})$, where $k_*$ and $V_*$ are the optimal support's cardinality and degree volume. Both methods imply the corresponding accelerated degree-normalized PPR approximation. The concurrent September 2026 preprint of Cui, Wei, and Yang also attains the randomized work scale. Our principal distinction is deterministic local acceleration with only polylogarithmic overhead and no SDD oracle.

We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework for measuring how individual concepts influence the organization of scientific knowledge, providing a quantitative framework for detecting scientific revolutions. The observable is defined by the geometric perturbation induced when removing documents associated with a candidate concept from the embedding space before and after its historical emergence. Statistical validation is performed using five historical case studies spanning physics, mathematics, and machine learning: special relativity, Gödel's incompleteness theorems, the Higgs mechanism, deep learning, and the attention mechanism underlying transformer architectures. Across the historical case studies, the framework identifies measurable geometric signatures associated with conceptual reorganization, while the validation studies expose important limitations arising from document assignment and sparse historical data. These results establish embedding geometry as a medium for quantifying conceptual reorganization, providing a new approach for studying how scientific fields restructure over time.

Residual Vector Quantization (RVQ)-based neural audio codecs (NACs) enable high-fidelity audio distribution at unprecedentedly low bitrates through discrete token-based representations. However, this shift disrupts traditional forensics, as non-linear neural transcoding obscures the underlying traces of legacy compression. This study defines the forensic gap and proposes a Transformer-based framework designed to leverage the hierarchical and temporal dependencies inherent in RVQ sequences. By modeling inter-layer causal relationships and dynamic forensic significance, our model effectively disentangles superimposed artifacts from legacy-to-neural transcoding. Experimental results achieve 97%+ accuracy for codec identification and robust joint identification performance across 32-128 kbps. These results demonstrate that traditional codec traces persist even after neural transcoding, supporting the feasibility and necessity of neural-codec-aware audio forensics.

Repository-level repair requires not only correct patches but also inspectable records that explain how issue requirements are translated into code changes and post-edit evidence. We contribute THEMIS, a stage-aware repair workflow that externalizes this requirement-to-repair process through semantic interpretation, a runtime requirement-code graph, graph-derived Developer guidance, retained repair rationale and patches, and post-edit audit records. A retrospective audit of 300 SWE-bench Lite cases demonstrates that these artifacts provide broad support for cross-stage inspection: a complete Developer rationale is available for 288 cases, and 214 cases (71.3%) retain a complete audited field set connecting the selected stages. The retained records further enable systematic measurement of cross-stage correspondence: target symbols recur in 62.6% of Developer rationales and in 62.8% of patches, rising to 75.8% when related symbols are included. In a paired 100-case comparison, the relational workflow resolves 19 cases versus 9 for the direct same-input condition; because the two conditions also differ in Analyzer output, graph-derived distillation, and Judge records, we report this as preliminary, workflow-level evidence rather than a causal effect of the graph component. Together, these results show that THEMIS makes otherwise implicit requirement-to-repair transitions inspectable, enabling systematic examination of how repair decisions persist, align, and evolve across stages.

The mathematics of Origami have been well studied and shown to develop several interesting results. We use Lean 4 tactics and build on Mathlib to redefine the 7 Huzita operations as theorems instead of axioms and prove their existence. We develop proofs for important origami constructions (such as trisecting an angle), implement origami-constructible numbers and prove the associated Cardano's formula, and formalize Haga's theorem. A Crease Pattern Inspector explores physical folding by providing a full pipeline to create and visualize models constrained by the Huzita formalism. The Lean codebase brings 100+ theorems and lemmas.

Generative AI is changing what can happen after a source artifact reaches its audience. A viewer's interpretation can now be externalized into a derivative artifact, allowing private sensemaking to become part of subsequent communication. Once such a derivative artifact circulates, it can enter subsequent viewers' information environments and shape the conditions under which their later sensemaking occurs. In this paper, we examine how this shift changes visual information communication. We first consider the viewer's immediate interaction with a source artifact and generative AI. We then examine what becomes consequential when the viewer's sensemaking takes communicative form, including communicative commitment, the legibility of transformations and source relationships, and the literacy required to interpret already-mediated information. Finally, we broaden the unit of analysis to consider how repeated and distributed AI mediation may accumulate over time, shaping what subsequent viewers notice, consider plausible, trust, and carry into subsequent sensemaking. We argue that understanding these longer-term forms of influence is a research direction for AI-mediated visual communication.

Machine learning (ML) predictions are increasingly being used to guide decision-making, giving rise to the problem of decision-focused learning (DFL) where predictors are optimized for downstream decision quality rather than accuracy alone. However, most existing work assumes a single decision-maker optimizing in isolation. This paper formalizes strategic decision-focused learning, where an ML system predicts an exogenous state that some agents observe before playing a game. For example, a park ranger may predict wildlife locations to allocate anti-poaching patrols against strategic poachers. While the exogenous state is unaffected by agent actions, predictions influence agents' strategies and the resulting equilibrium. We find that strategic considerations fundamentally change the learning problem. In particular, we show the prediction accuracy-equilibrium payoff landscape can be non-monotonic, i.e., better predictions can degrade performance. We propose algorithmic approaches to address these challenges and validate them across benchmarks in wildlife conservation and infrastructure protection. Our theory and experiments highlight the importance of accounting for strategic interactions when designing predictors.

The Hohenberg-Kohn theorem establishes that, in principle, the ground state (GS) charge density contains all GS information of a many-electron system, such that all GS observables can be expressed as functionals of the GS charge density. Conventional Kohn-Sham density functional theory requires iterative solution of the self-consistent-field equations at substantial computational cost, motivating the development of deep learning surrogates for electronic structure calculations and, in turn, accelerating computer-aided materials design. Here, we propose AIDEN, an Atomic-Interaction Density Equivariant Network for solving real-space charge density. AIDEN separates the element-dependent one-center density from environment-induced density redistribution and represents the latter through complementary atom- and edge-centered tensor correlations. A continuous low-rank Gaussian decoder then reconstructs the density at arbitrary spatial coordinates while reusing atomic encodings independently of the evaluation grid. AIDEN achieves state-of-the-art accuracy on periodic crystal benchmarks while remaining competitive for molecular systems, and further demonstrates zero-shot transferability across several structurally distinct out-of-distribution case studies. Furthermore, AIDEN provides substantially faster inference than both baseline models and full SCF calculations, enabling efficient charge density reconstruction for large-scale electronic structure calculations.

Autonomous surface vehicles operating in inland waterways require a persistent representation of both surrounding structures and the water surface. LiDAR-based simultaneous localization and mapping often produces sparse or missing water returns, leaving this operational surface absent from the reconstructed scene. We propose HydroMap, an odometry-decoupled framework that reconstructs water surface elevation from stereo observations and integrates it with the structural map. Per-frame water points form joint cell observations with propagated stereo and pose uncertainty, and successive observations are fused into a persistent probabilistic elevation map. Semantic map conversion then combines the elevation map with structural geometry in a unified 2.5D representation of water, boundaries, structures, and overhead regions. On the Pohang Canal and Leuven Vaart datasets, the elevation RMSE remains below 5 cm relative to LiDAR references expressed in the same map frame. The elevation and semantic maps are published at 2 Hz and 1 Hz, respectively. HydroMap thereby complements LiDAR maps with a persistent representation of the water surface for downstream navigation in inland waterways.

Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley estimators generally assume that observations used to evaluate the coalitional game are fully observed under a common feature space. This assumption is routinely violated in multi-site studies across biomedicine, social science, and environmental monitoring, where institutions record different features under different protocols, producing systematic blockwise missingness across sources. We first show that the standard remedy of imputing missing features before computing Shapley values introduces systematic, coalition-dependent bias into the resulting attributions. We then propose FUSHAP (Fusion Shapley Attribution from Partially-observed data), a method that leverages partially-observed auxiliary sites to reduce the variance of a preliminary single-site Shapley estimate without imputation. A permutation-based screening step detects and excludes sites whose data distributions are incompatible with the target population. In synthetic experiments, FUSHAP achieves $3$–$8\times$ lower MSE than the single-site estimator and $2$–$3\times$ lower MSE than imputation baselines without incurring imputation-induced bias, and the screening procedure identifies misaligned sites with $82\%$ power at moderate misalignment and $100\%$ for strong misalignment. On multi-site air quality and multi-center clinical data, FUSHAP reduces MSE by approximately $3$–$7\times$ relative to the single-site estimator; in the clinical application, standard imputation can increase MSE above the single-site baseline.

AI-generated summaries increasingly mediate how people interpret information across platforms, including product reviews on e-commerce sites. Using Amazon's AI summaries as a case study, we conducted a preregistered, randomized experiment (N = 278) comparing how AI summaries and user reviews shaped product perceptions, and how their influence varied with valence and presentation order. We found that both AI summaries and user reviews influenced participants' opinions, with negative summaries having a larger effect than positive ones. Presentation order was the most important factor: the first source anchored judgment and only user reviews could displace an existing anchor. Although participants reported preferring user reviews, they often underestimated the influence of AI summaries on their judgments. Our findings show how the placement of AI summaries shapes user perception and highlight opportunities to design interfaces that support more deliberate judgments about when to rely on summaries and when to examine the underlying content directly.

Neural 3D scene editing is often evaluated by semantic alignment alone, although a convincing result may alter unrelated content or become inconsistent across views. We introduce EditBench3D, a representation-agnostic benchmark that treats editing as controlled information replacement. It evaluates four complementary properties: instruction fidelity, spatial locality, cross-view consistency, and preservation of non-target content. The protocol combines visibility-aware 3D target supports, paired descriptions, held-out cameras, and five edit families covering appearance, material, geometry, and object-level changes. We evaluate eight representative NeRF, 3D Gaussian Splatting, hybrid, and proxy-based editors on 240 scene-edit pairs. The study shows that semantic fidelity is only weakly associated with the other editing properties, and that no single method is optimal across all dimensions. Explicit Gaussian editors offer a strong overall balance, whereas direct proxy manipulation provides the most conservative edits at the cost of open-ended fidelity. These findings support reporting editability as a multi-objective profile rather than a single semantic score.

The growing adoption of large language model (LLM)-based systems for large-scale text processing has created a critical need for dynamic autoscaling to manage high-latency, bursty, and computationally intensive workloads. This paper proposes an agentic autoscaling framework through worker-pool orchestration for LLM-driven text classification. The framework integrates a priority task queue, a dynamic pool of agent workers, a real-time metrics collector, and an application-layer autoscaler. Its classifier-agnostic design supports both zero-shot and fine-tuned language models without modifying the autoscaling logic. The framework is evaluated using Autoscaling+BART and Autoscaling+DeBERTa against static allocation and standalone RoBERTa and DistilBERT baselines. On the AG News dataset, Autoscaling+BART achieves 84.5% accuracy, while Autoscaling+DeBERTa improves it to 90.5%. On the SMS Spam Collection dataset, Autoscaling+DeBERTa achieves 99.5% accuracy, whereas Autoscaling+BART attains 84.5% accuracy with lower execution time. Overall, the proposed framework consistently outperforms the baseline approaches in resource efficiency while maintaining high classification performance, demonstrating that elastic worker-pool orchestration provides an effective and cost-efficient solution for scalable LLM-driven text classification in cloud environments.

A notable byproduct of LLM alignment training is mode collapse: the progressive loss of output diversity that narrows a model's expressivity at inference time. This degradation is especially limiting for applications requiring open-ended exploration and pluralistic perspectives, such as scientific ideation and creative writing. We present MoDA (Mode-conditioned Diversity Alignment), an online post-training RL algorithm that jointly optimizes generation quality and diversity, inspired by the coordination perspective in multi-agent reinforcement learning (MARL). MoDA trains a single shared LLM policy conditioned on abstract numbered roles, where each role acts as an agent competing to produce outputs distinct from the others. This formulation encourages mode-conditioned agents to explore complementary regions of the high-quality output space without requiring hand-crafted personas or architectural modifications. MoDA employs a prompt-adaptive quality gating mechanism that calibrates a reference quality threshold and grants diversity rewards only to responses that meet the threshold, preventing reward-hacking behaviors that compromise response quality. To study quality-diversity tradeoffs, we evaluate MoDA on a comprehensive suite of benchmarks spanning seven general capability tasks and four domain-specific diversity tasks in scientific ideation and creative writing. MoDA improves SBERT diversity by 265% on the Infinite-Chat held-out prompts, while increasing average general capability pass@1 by 10.3% over the Qwen3-8B baseline. Compared with the strongest DivPO baseline, MoDA improves SBERT diversity from 0.274 to 0.482 (+75.9%) and E-Vendi from 2.86 to 4.4 (+53.8%), while improving average general capability pass@1 by 7.0%. Overall, MoDA provides a drop-in alternative to standard post-training methods that preserves and expands the model's expressive output space while improving quality.

Dynamic workloads and latency-sensitive applications require efficient autoscaling in cloud computing environments. However, most existing approaches rely on reactive mechanisms based on static thresholds, resulting in delayed responses and scaling oscillations under workload uncertainty. To address these limitations, we propose a double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS). The proposed (DDQN+RRS) enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling. Experimental results demonstrate that the proposed method outperforms both reactive and existing proactive approaches. Specifically, DDQN+RRS achieves a lower Service Level Agreement (SLA) violation rate (11.81%), higher CPU utilization (52.23%), improved scaling stability, fewer scaling events (2,488), and reduced pod restarts (1,246). Furthermore, the approach ensures smoother autoscaling behavior by significantly reducing oscillations over time (0-60 s). While reactive methods exhibit substantial fluctuations in pod allocation, Reactive reduces these variations, and DDQN+RRS achieves the most stable and smooth scaling, particularly during the 15-30 s, 40-45 s, and 55-60 s intervals.

In this paper, we derive an explicit solution of the stationary prediction with expert advice PDE for five experts. The formula is given in three regions. In the first two regions, it is the four-expert solution plus a single integral with an elementary positive density. In the third region, it is a finite sum of hyperbolic products whose coefficients are determined by one scalar quadrature. Our formula establishes that the rank ordered adversary $(1,0,1,0,0)$ achieves the Hamiltonian maximum in the PDE everywhere in the ordered sector, while the COMB strategy $(1,0,1,0,1)$ is optimal only on a lower dimensional subset of the sector (where $x_1=x_2$ and $x_3=x_4$). The verification of the Hamiltonian inequalities is a tedious task, part of which is completed with a computer assisted proof. The verification reduces to 21 scalar inequalities, which we prove using 147 exact rational Bernstein polynomial certificates. The exact certificates and their independent arithmetic checks are included in a supplement to this paper, and a Lean 4 formalization machine-checks the verification and both main theorems, apart from the viscosity characterization.

Data-centric applications increasingly use speculative execution to explore multiple candidate paths where each path modifies state distributed across heterogeneous data stores. This trend is intensified by the rise of tool-calling agents. Hence, applications need data systems that can create branches quickly, isolate state-modifying paths, and merge changes consistently across stores without imposing substantial query overhead. Existing systems provide only partial support, forcing applications to coordinate branches and merges manually, which increases overhead and risks inconsistent cross-store state. To solve this problem, we introduce Chronos, a bolt-on system that provides branching capability across heterogeneous data stores. We make two contributions. First, Chronos introduces a compact interval-based versioning technique that enables efficient branching and data sharing through simple query rewrite. Second, Chronos introduces a bolt-on architecture that separates branch management from data path within each store. Combined with interval-based versioning, this separation provides atomic cross-store visibility for merges and enables Chronos to support diverse data stores without modifying their engines. We implement Chronos for PostgreSQL, SQLite, DuckDB, Qdrant, and a DBMS-backed filesystem. We evaluate it using cross-store agent workflows, MCTS-style exploration, and per-store benchmarks. Chronos runs MCTS-style exploration up to 16.7x faster than existing approaches while maintaining practical query performance across the underlying stores. Under concurrent cross-store workflows, Chronos prevents partially visible merges while substantially outperforming serialized execution.

In this paper, we present a cross-layer empirical study of organizational cybersecurity risk in Small and medium-sized enterprises (SMEs), analyzing 281 validated security findings from 22 real-world SME cybersecurity assessments conducted over two years through a pro bono university cybersecurity clinic. We first identify recurring organizational security functions through iterative thematic coding, then estimate an empirical Risk Manifestation Model linking these functions to exposure conditions, attack mechanisms, and cybersecurity outcomes, and use probability propagation to identify dominant risk pathways. The model characterizes empirical associations observed in this sample rather than causal or predictive relationships. Our analysis identifies eight organizational security functions associated with two exposure conditions, five attack mechanisms, and six outcome categories. Across most functions, the dominant pathway follows asset exposure to credential compromise to unauthorized access, whereas infrastructure and network security primarily propagates through network exposure; these pathways remain stable under leave-one-organization-out analysis. Finally, we evaluate whether SME cybersecurity assessments can be simplified while preserving meaningful security coverage. Retaining six functions reduces assessment burden by 24% while preserving 97% of critical findings and 92% of risk-pathway coverage, a security-oriented reduction, while retaining five functions reduces burden by 45% while preserving 89% of critical findings and 85% of risk-pathway coverage, a more efficiency-oriented alternative.

Online mental health communities thrive on peer support, yet those who volunteer to help often lack formal training and may struggle to articulate supportive responses. AI co-writing could lower this barrier; however, peer support derives much of its value from being perceived as personal, raising questions around authorship, ownership, and trust. We built PeerPen, a writing assistance tool embedded within a Reddit-like interface, supporting two main features: draft generation and revision of user-written responses. Through semi-structured interviews with 15 participants, we find that PeerPen reduced the burden of composing responses and increased confidence in offering support. Participants wanted AI to assist their writing without taking over authorship and anticipated tensions around authenticity and trust. Such assistance could make authorship uncertain even for responses written without it, weakening trust across the community. We contribute design implications for AI writing assistance that scaffolds supportive communication, preserves authorship, and accounts for community-level trust.

LLM-based query reformulation can improve retrieval, but no single reformulation strategy is consistently optimal across queries, domains, retrievers, or model backbones. This creates an inference-time decision problem: “Given an original query and a pool of candidate reformulations, which one should be issued to the retriever?”. Existing studies are hard to compare because they use different reformulator pools, retrievers, relevance signals, training labels, and evaluation metrics. We introduce QueryRoute, a benchmark that freezes the expensive artifacts needed to study this decision reproducibly: original queries, generated variants, ranked lists under multiple retrievers, retrieval scores, and per-query oracle labels. The benchmark contains 3,757 queries, 11 candidate systems, five reformulator backbones, and three retrievers across TREC DL, BEIR, and BRIGHT, yielding 619,905 retrieval outcomes. We benchmark supervised classification, routing, QPP, and LLM-as-judge selectors. Results show substantial oracle headroom over fixed reformulators, but current selectors recover only part of it; selector rankings change across retrievers, and similar mean effectiveness can hide different query-level behavior. The released artifacts and evaluation harness allow future selectors to be compared without regenerating variants, rerunning retrieval, or rebuilding judge pipelines. Code and data are available at https://github.com/haisonle001/QueryRoute

Cloud-local LLM inference must keep sensitive user data on-device while exploiting cloud-grade reasoning, yet existing sanitization approaches (placeholder substitution, differential-privacy perturbation, and skill distillation) lack a release decision that is simultaneously safe and utility-preserving. We propose DR-SL (Dehydrate-Rehydrate with Self-Learning loop), which formalizes de-identification completeness as two measurable conditions: de-identification sufficiency under Pufferfish semantics, and task-information preservation via QA probes. A fully local two-branch verifier iterates dehydration under a lexicographic gate with guaranteed termination, backed by a deterministic hard line, an external strong-attacker re-test, and human fallback. We prove Fano-type lower bounds, a Pufferfish witness, and a rate-privacy feasibility criterion, and state their scope plainly: the bounds certify leakage, never safety, and are near-vacuous at our operating point, so release safety rests on empirical calibration, the hard line, and human review. On a worst-case fully task-coupled benchmark the loop reduces leakage from 0.457 to 0.304 (p approx. 0) and the release chain delivers 0.000 literal leakage at egress (160 instances, two strong attackers), the system degrading to certification-and-routing exactly as the feasibility criterion predicts. On a mixed-coupling benchmark the same safe point releases 67.5% of instances automatically at zero measured leakage, Pareto-dominating placeholder and selective-LDP corners under an identical release rule. Two human studies anchor the semantic utility metric (Spearman rho = 0.839) and the annotation gold (type-level recall at least 0.987). The exploratory self-learning hypothesis was not supported and is reported as such. All theoretical bounds pass numerical verification; code, synthetic datasets, protocol, and human-study packages are public.

Nucleotide sequence analysis is central to problems spanning regulatory genomics, evolutionary biology, and phenotype prediction. Classical bioinformatics methods extract interpretable sequence properties such as motifs and k-mer composition, but their flexibility is limited. In contrast, modern deep learning models can learn powerful predictive representations directly from raw sequences, yet their internal representations and decision mechanisms are difficult to inspect. Interpretable machine learning methods (e.g., sparse linear models and decision trees) provide human-understandable representations of predictive relationships but are not designed to operate directly on nucleotide sequences. Here, we introduce SeqMaestro, a machine learning framework that proposes biological hypotheses from nucleotide sequences using interpretable models. Our solution is centered around a two-layer interface that connects nucleotide sequences with the broader ecosystem of interpretable machine learning. SeqMaestro uses this interface to fit diverse combinations of interpretable models, feature representations, and extraction strategies, leveraging variability across transparent models to identify robust biological signals and richer predictive relationships than feature importance alone can provide. The system also supports data transformation and cleaning, model fitting, hyperparameter tuning, reliability analysis, and synthesis of results into a contextualized written report. By providing these capabilities through a no-code workflow, SeqMaestro is designed to make interpretable sequence analysis accessible to researchers without requiring extensive programming or machine learning expertise. SeqMaestro thereby provides an accessible route from nucleotide sequences to biological hypotheses.

Enterprises deploying AI for supply chain decisions commonly default to the largest available language model, a procurement heuristic that neglects both empirical performance and environmental cost. We benchmark six large language models across 520 supply chain tasks, simultaneously measuring decision quality and estimated generation-related operational carbon. Drawing on the Technology-Organization-Environment (TOE) framework, we develop a Carbon-Aware AI Procurement Framework (CAAPF), a Green IS design artifact that operationalizes sustainable AI governance for enterprise procurement. Within this bounded sample, quality spans 0.497-0.723, and the models with the largest disclosed parameter totals do not achieve the highest scores. The design does not isolate size, provider, architecture, or benchmark-construction effects. A category-by-tier calibrated GreenRoute proof of concept reaches 0.733 mean out-of-sample quality at an estimated 0.402 gCO2/task. Static Haiku reaches 0.699 at 0.022 gCO2/task, while Sonnet reaches 0.723 at 0.401 gCO2/task, demonstrating that the preferred strategy depends on the organization's quality requirement. Our "benchmark first, select green" principle suggests that environmental responsibility and decision quality can be mutually reinforcing, contributing to sustainable digital infrastructure governance aligned with SDG 12 and SDG 13.

A stencil computation repeatedly updates every cell of a grid from its neighbours' values at the previous timestep. Simulating T steps on N cells directly costs Theta(NT), and a line of work beginning with Ahmad et al. reduces this by composing many timesteps into one linear operator and applying it with a Fast Fourier Transform. That technique needs to know which cells will still obey the same operator when the composed step ends, and in a free-boundary problem they do not: the region governed by a given rule is determined by the solution and moves as it evolves. We study one spatial dimension, a three-point stencil with time-varying coefficients, and a computed region that is a single interval whose two endpoints move by arbitrary amounts at every step, revealed online. Let B be the horizon plus the total variation of the boundary trajectory. We give a schedule whose work is O((B+N) log T log(N+B)) and whose span is O(T log T log(N+B)), and we prove that the values it computes are exact. The best existing bound for a region that moves requires its boundary to travel at most one cell per timestep. We drop that requirement and lose nothing by it: a boundary obeying it has B <= 3T, so our bound stays near-linear on every trajectory the earlier result covers. Elsewhere, B grows only by the distance the boundary actually travels – one jump of width N costs T + 2N. The reason total variation suffices is that everything the two endpoints touch over a time window of any length lies in two intervals, one per endpoint. This cannot be relaxed: with p regions the bound degrades by a factor p, and at p = sqrt(T) there is an instance on which the work is Theta(T^{3/2}) while B + N = Theta(T). All results are machine-checked in Lean 4, apart from the classical convolution bound, which is imported as an interface.

Real-world reinforcement learning (RL) offers a promising route to dexterous manipulation policies that can adapt directly from physical interaction, but learning is hindered by inefficient early exploration and costly failures. We propose a framework that uses sampling-based model predictive control (MPC) as scaffolding for real-world dexterous RL, providing structured prior experience and task-directed guidance during learning without human demonstrations or corrective actions. A small set of MPC trajectories is first used to populate an offline replay buffer and to pretrain the actor and critic. During online learning, MPC intermittently guides data collection while an off-policy Soft Actor-Critic learner trains from both prior MPC experience and newly collected physical interaction, with control gradually transitioning to the learned policy. On continuous in-hand rotation with a 16-DoF Allegro hand, initialized from 20 MPC trajectories collected in 12 minutes on hardware, the policy reaches 100% success after 7 minutes of online RL, with about three object drops on average during training. After 20 minutes of online learning, the policy achieves more than five times the rotation speed of the MPC controller and completes 1000 consecutive rotations without a drop. Ablations show complementary benefits from MPC-based pretraining, retained MPC experience, and online MPC guidance, while additional experiments demonstrate rapid adaptation to new object geometries and successful goal-conditioned reorientation.