Papers

This 30 years old book is posted for free download by the generosity of Oxford University Press that granted permission to allow access to future generations of young scientists. A printed copy can be purchased from the publisher. The authors received for this book "The 1999 INFORMS Computing Society Prize for Research Excellence in the Interface Between Operations Research and Computer Science" from the Institute for Operations Research and the Management Sciences (INFORMS). For a book review by Janos D. Pinter go to: Journal of Global Optimization, 16: 107-108, 2000.

Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. While considerable progress has been made in horizontal federated learning (HFL), where data with common features is distributed across sites, vertical federated learning (VFL), where sites share observations across distinct feature sets, remains less explored. Advancing Bayesian high-dimensional multivariate reduced-rank regression methods for VFL poses unique challenges: (a) stringent privacy regulations preventing local site data sharing, and (b) fitting local regressions overlooks essential modeling aspects like inter-variable correlations. In contrast HFL allows each site to fit a comparable model independently. We present a novel Bayesian VFL framework for multivariate high-dimensional reduced-rank regression, termed BayesVFLReg, which enables precise coefficient estimation while safeguarding both feature and response privacy. Participating sites use a shared random sketching matrix to compress local variables into privacy-preserving sketches. A central server collects these sketches where Bayesian multivariate reduced-rank regression uses Gaussian scale mixture priors. For feature selection, we introduce a single-step post-processing strategy based on mixture-model clustering of the absolute posterior coefficient means to distinguish signal from noise per response variable. BayesVFLReg is computationally scalable for large, high-dimensional datasets and facilitates efficient variable selection. Theoretically, we establish sharp non-asymptotic bounds on the posterior probability that the fitted density falls within a Hellinger ball centered at the true data-generating density. Comparative simulation studies and real-world data analyses show that BayesVFLReg reliably identifies sparse feature effects, even under feature correlation.

We study online allocation with convex costs (\OACC), a generalization of limited-supply models in which additional resources can be produced dynamically at convex cost. Prior work largely focuses on constructing a single algorithm that achieves strong competitive guarantees. In contrast, our main contribution is a structural characterization of the optimal design space of reserve functions (i.e., normalized pricing rules) for \OACC, yielding a family of optimal online algorithms. Using a principled dynamical-systems approach, we show that optimal online algorithms arise as solutions of a nonlinear eigenvalue problem: the optimal competitive ratio corresponds to the dominant eigenvalue, and the associated eigenfunction determines the optimal reserve function. This perspective establishes the existence of infinitely many optimal designs attaining the best possible competitive ratio, thereby unifying and generalizing a broad class of prior algorithms. Beyond unification, our characterization uncovers new structural phenomena, including the ability to flexibly mix dynamic and static pricing without loss of optimality and the construction of universally competitive algorithms under unknown or stochastic costs. We further extend the analysis to more structured settings such as supply-oblivious arrivals and hard supply constraints, obtaining sharper guarantees that improve upon previous bounds.

Reference signal received power (RSRP) is commonly used to assess 5G cellular coverage, but it may substantially overestimate the connectivity available to cellular-connected unmanned aerial vehicles (UAVs) under strong inter-cell interference (ICI). As a UAV ascends, reduced blockage can strengthen the serving signal, while increased line-of-sight visibility to neighboring sectors can intensify ICI. This paper develops a trajectory-aware, 3GPP-based system-level framework for evaluating UAV radio serviceability during a three-dimensional flight in an urban 5G New Radio network. The framework jointly evaluates RSRP, reference signal received quality (RSRQ), and signal-to-interference-plus-noise ratio (SINR) in a 19-site, 57-sector deployment. We introduce the concept of coverage-serviceability gap to quantify the difference between RSRP availability and joint service availability. Under RSRP, RSRQ, and SINR thresholds of -100 dBm, -18 dB, and 0 dB, respectively, an inter-site distance (ISD) of 500 m provides 99.9% RSRP availability but only 13.6% joint service availability, yielding a coverage-serviceability gap of 86.3 percentage points. With RSRQ and SINR thresholds of -22 dB and -6 dB, respectively, joint availability increases to 55.0% and 63.1% for ISDs of 500 m and 1000 m, respectively. These results demonstrate that RSRP-based coverage substantially overestimates joint UAV serviceability under both evaluation configurations, with SINR remaining the limiting metric.

Visual representations can help lower-primary learners understand Math Word Problems, but generating classroom-usable visuals remains difficult. Existing symbolic systems are controllable but limited in coverage, while end-to-end text-to-image systems often fail to satisfy exact mathematical constraints. This paper presents a symbolic visual generation framework for lower-primary MWP generation with broader problem coverage and more scalable asset generation. The framework includes an LLM-based routing layer, three worksheet-oriented generation modules, and two fallback mechanisms for open-world SVG asset acquisition. A human evaluation comparing Math2Visual-X with Stable Diffusion XL, Nano Banana, and GPT Image showed that the proposed method achieved the strongest overall performance. The results indicate that the framework offers a scalable and pedagogically grounded approach for automatic MWP visual generation.

Loss of Load Expectation (LOLE), originally calculated as the expected number of days of load loss, has also been used as a frequency metric. This paper shows that LOLE can indeed be calculated as a frequency metric, but only with restricted conditions, and that it differs fundamentally from the standard frequency metric employed in the frequency and duration (F&D) approach. A lack of understanding of these conditions can lead to incorrect applications and interpretations.

Scaling analog and digital Ising machines to larger problems requires overcoming finite device capacity and the cost of communication between devices. We present cluster mean-field theory (CMFT), a framework that partitions a large interaction graph into clusters sized to fit available hardware. Each cluster performs local updates independently, while interactions across cluster boundaries enter through periodically updated effective biases computed from boundary-spin averages. To reduce the error introduced by fixed cluster boundaries, we introduce dynamic partitioning, which cycles through multiple partitions so that interactions approximated by mean fields at one stage can act through instantaneous spins at another. On three-dimensional spin glasses and planted Pegasus instances, dynamic CMFT exhibits power-law decay of residual energy over sweep budgets. This shows that solution quality continues to improve with computational effort despite the mean-field approximation. An automated graph partitioner combined with a weighted recovery ratio provides a practical heuristic for selecting partition combinations on graphs without natural cut directions. We demonstrate CMFT on four GPUs with approximately four million p-bits, reaching comparable energy densities up to 15 times faster than a single-GPU implementation of the full graph. By coupling locally evolving clusters through programmable effective biases, CMFT provides a route to extreme-scale Ising machines on both analog and digital hardware.

Forecasting a long horizon from only the first observations of a sequence is ill-posed: many trajectories are consistent with the same short history. We study this problem in oil and gas production forecasting, where forecasts made after roughly the first fifth of a well's producing life drive development and abandonment decisions, and where a usable forecast must describe a monotone decline. We present Physics-SIMS-TS, a conditional diffusion forecaster that combines negative guidance against synthetic artifacts, decline-curve constraints and an isotonic projection applied during sampling, spatial training augmentation, and an ensembled stochastic sampler yielding a full predictive distribution. Across three jurisdictions and more than 35,000 wells, under a shared-space, validation-frozen protocol, Physics-SIMS-TS is the most accurate diffusion forecaster in the comparison and is competitive with, but not superior to, ensembled transformer forecasters. Its forecasts are monotone by construction at a cost of at most 0.5% in mean squared error, and its trajectory ensemble yields calibrated intervals after one dispersion factor is fitted per jurisdiction. On six standard benchmarks a reversible-instance-normalization variant of the backbone is the leading diffusion baseline. We also quantify four protocol choices on which the measured ranking depends. Code and evaluation artifacts are released.

We develop inference for manifold-valued population minimizers when each observation belongs to a different participant and only locally private messages reach the analyst. The method releases randomized tangent gradients and combines them through Riemannian stochastic approximation and Polyak-Ruppert averaging. Directly inserting a private data surrogate into a nonlinear loss can shift its population target, whereas conditional centring of the released gradient preserves the first-order equation. We introduce symmetric-pair regression (SPR) to estimate the asymptotic variance from the same private messages used for point estimation, without holding out participants or requesting a second release. We prove the central limit theorem and consistency of the fully transcript-based sandwich covariance and intrinsic Wald region under local differential privacy. Simulations across various statistical problems and manifolds support the predicted decrease in estimation error and near-nominal coverage under moderate privacy. An application to NHANES anthropometric data illustrates private estimation of a leading body-size direction and its uncertainty.

Autoregressive video world models enable temporally coherent generation for a single observer. Extending them to multiple agents requires consistency across independently controlled views and temporal gaps under causal streaming. We present ConsistWorld, a multi-agent world model that generates camera-controlled video streams of a static scene from one shared image. We formulate consistency as routing evidence from committed multi-agent history and concurrently generated peer views to the tokens being generated. Pose Conditioned Memory Retrieval selects relevant historical observations from all agents, recovering evidence beyond the recent context window. Visibility-Gated Peer Sharing regulates current peer information according to estimated historical coverage and current-view overlap. Together, they determine which historical observations enter the context and where concurrent peer information contributes, supporting long-term recall and coordinated exploration. Both mechanisms use camera geometry and maintain a bounded active context for a fixed agent count and retrieval budget. Experiments on evidence sharing cases and video length and agent number generalizations show that ConsistWorld achieves a strong cross-time and cross-agent consistency while preserving competitive generation quality.

We study the complexity of determining the interval numbers of tournaments in the $\overrightarrow{P_3}$ and $\overrightarrow{P_3^*}$ convexities, denoted by $\overrightarrow{\mathrm{in}}_{P_3}(T)$ and $\overrightarrow{\mathrm{in}}_{P_3^*}(T)$ on a tournament $T$. For each $\overrightarrow{\mathcal{X}} \in \{\overrightarrow{P_3}, \overrightarrow{P_3^*}\}$, we show that determining whether $\overrightarrow{\mathrm{in}}_{\mathcal{X}}(T) \leq k$ is W[2]-complete when parameterized by $k$. Moreover, under ETH, we show that there is no parameterized algorithm for that problem with running time $f(k)\, n^{o(k)}$ on an $n$-vertex tournament, where $f$ is any computable function. For the $\overrightarrow{P_3}$-convexity, we also show that $\overrightarrow{\mathrm{in}}_{P_3}(T) = \mathcal{O}(\log n)$, which yields a simple quasi-polynomial $n^{\mathcal{O}(\log n)}$ brute-force algorithm. On the other hand, under ETH, we show that the problem is NP-intermediate, that is, it is neither NP-hard nor in P. For the $\overrightarrow{P_3^*}$-convexity, the same brute force algorithm is not quasi-polynomial, since we present a family of instances with $\overrightarrow{\mathrm{in}}_{P_3^*}(T) = \Theta(n)$. We conjecture that this problem is NP-complete.

Data prefetching is an established technique to mitigate cache miss latency and keep the processor saturated with data. Software exists in a unique position to issue prefetches, having algorithmic knowledge of the workload at hand. However, inserting software prefetches is a time consuming, unpredictable task, with a high degree of sensitivity to microarchitecture or future code changes. While several compiler- and FDO-based approaches insert and tune software prefetches automatically, they use heuristics that do not generalize and do not consider issues that arise in large codebases. In this paper, we show that semantic understanding and reasoning about a program is a vital component in inserting effective software prefetches. We also identify that prefetching at the scale of large codebases poses new challenges, including prefetch codependence and interference. To remedy this, we build Presage, a system that leverages the semantic reasoning of Large Language Models to insert effective software prefetches. Presage processes a workload through a custom agent harness specifically designed to navigate the wide space of potential prefetches on large-scale workloads. First, a proposer agent with access to performance metrics identifies promising software prefetching candidates. Then, several long-horizon optimizer agents optimize prefetching candidates in a loop, working towards discovery of performance wins. Finally, a combiner agent handles composition of individually effective prefetches. Through this method, Presage is able to insert prefetches that improve performance by a geomean of 10% across 81 workloads by exploring tens of different prefetching alternatives per workload, including a 2.7% runtime reduction on SPEC CPU 2026 where prior SOTA fails. When compared against prior SOTA on its evaluation suite, Presage achieves a geomean runtime improvement of 17% compared to SOTA's 10%.

Three-dimensional fetal brain ultrasound offers non-ionizing, cost-effective imaging with rich spatial information for comprehensive anatomical assessment, yet its development remains limited by scarce data and annotations. In contrast, fetal brain magnetic resonance imaging has advanced further, supported by larger datasets and mature analysis methods. To leverage these resources, anatomical label maps provide a promising modality-invariant bridge for transferring knowledge from fetal brain magnetic resonance imaging to ultrasound. However, generating ultrasound images from label maps remains challenging because anatomy is tightly entangled with ultrasound appearance. To address this challenge, we separate anatomical correspondence learning from ultrasound appearance adaptation at both the data and model levels. Specifically, we propose the anatomy wavelet residual network, a two-stage framework combining wavelet diffusion and residual refinement. The first stage learns to generate volumes from label maps using atlas pairs in the wavelet domain, while the second stage learns bounded residual corrections from real clinical ultrasound in the image domain. This separation enables realistic synthesis with consistent preservation of normal and abnormal anatomy. Experiments on real fetal brain ultrasound show that our method outperforms representative synthesis methods, with normalized cross correlation of 0.518 versus 0.482 and Fréchet Inception Distance of 8.905 versus 13.319 for the strongest baseline. Beyond synthesis quality, volumes generated from fetal brain magnetic resonance imaging label maps also improve downstream segmentation, particularly for severe abnormal cases. Overall, these results highlight the potential of the proposed framework to leverage rich fetal brain magnetic resonance imaging resources for advancing three-dimensional ultrasound analysis.

Bilingual language models (LMs) offer a controlled setting for studying how training conditions shape second-language (L2) behaviour, but prior work typically varies exposure structure, scale, and architecture at once, making it difficult to attribute effects to any single factor. We introduce Beetle, a controlled language model pretraining framework in which tokeniser, target language, training budget, and exposure structure are each independently manipulable, enabling systematic and comparable experimentation of training conditions. Using Beetle, we train and release 285 bilingual and 45 monolingual open-source LMs with rich checkpoints across a range of exposure schedules, data scales and first languages (L1s) to study multilingual pretraining and computational modelling of bilingualism and second language learning. Evaluating models on human bilingual and second language reading-time prediction and grammaticality judgement tasks, we find that staged and temporally structured curricula consistently improve alignment with language learner reading time compared to balanced bilingual training, with the largest gains at smaller data scales and for typologically closer language pairs. The Beetle models are well suited tools to help move computational psycholinguistics beyond its prevailing monolingual, English-centric focus toward models of human bilingual processing, to study cross-lingual learning dynamics, while supporting community-based development of controlled model families.

We study binary classification with abstention under separate class-conditional error constraints, with the objective of minimizing abstention while keeping both errors below prescribed thresholds. We characterize the distribution-free minimax rate of excess abstention risk, up to logarithmic factors, in terms of the complexity of the hypothesis class and the sample size. To make the framework amenable to computation with models such as neural networks, we introduce surrogate-loss formulations and derive finite-sample guarantees for excess surrogate ambiguity risk. We formulate the resulting learning task as a constrained optimization problem and characterize its computational complexity in the convex setting. Finally, we evaluate our approach on various datasets and compare its performance with a competing method for this problem.

Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction and rhythm abnormalities. However, many high-performing deep learning models remain difficult to deploy in clinical settings due to limited transparency and lack of reliability validation. In this work, we present X- Beat, an explainable and reliability-aware benchmark framework for ECG image classification designed to support trustworthy AI systems in healthcare. The proposed framework combines transfer learning with post-hoc explainability and systematic reliability evaluation across four cardiac classes: Abnormal Heartbeat, History of Myocardial Infarction, Myocardial In- farction, and Normal Heartbeat. Multiple ImageNet-pretrained CNN backbones, including EfficientNet-B0, ResNet-50, DenseNet- 121, and MobileNetV3-Large, are evaluated under a unified training protocol. Beyond standard performance metrics, we incorporate Grad-CAM-based visual explanations together with additional analyses, including explanation stability, regional sen- sitivity, and confidence-based reliability assessment, to examine whether model predictions are supported by clinically meaningful evidence. Experimental results show that ResNet-50 achieves the best performance, reaching 91.94% accuracy and a macro F1- score of 0.9098, with strong class separability (AUC up to 0.995). Explanation analyses indicate that the model primarily focuses on waveform-relevant regions, while reliability evaluation suggests that most incorrect predictions occur with lower confidence. Overall, this work provides a structured and reproducible bench- mark for evaluating both predictive performance and explanation reliability in ECG image classification, contributing toward the development of trustworthy and interpretable AI components for clinical decision support systems.

Subjective user experience is important to human-robot interaction, but the outcomes users value, and how those preferences vary across individuals and contexts, are often unknown. While inverse learning approaches using human data can help identify user rewards, in many assistive settings the experimental costs of executing a control policy, measuring biomechanical or physiological outcomes, and collecting user feedback often limit the number of queries and optimization iterations. Here, we present Simultaneous Forward and Inverse Human-In-the-Loop Optimization (SFIHILO), which efficiently infers individual-specific reward functions from preferences over human outcomes and identifies a final control policy that maximizes the learned reward. SFIHILO bootstraps a forward model to predict user outcomes from control policies, uses this model for active querying to accelerate inverse reward learning, and then optimizes the final control policy without additional user trials. We score candidate policies by their expected reduction in uncertainty across both forward and inverse beliefs, targeting a regional preference boundary to robustly inform this simultaneous learning process. In simulation, we show that SFIHILO was effective across user heterogeneity, outcome dimensionalities, precision requirements, noise levels, and nonstationarity; compared with mutual information approaches, the proposed active querying strategy significantly improved sample efficiency in inverse learning while preserving forward model accuracy. This approach demonstrates the potential to infer latent human goals, enabling more transferable and effective human-robot interaction.

Every document QA system begins with a choice that is rarely studied on its own: whether to feed the model page images, extracted text, or both. We isolate this choice, holding the prompt, judge, and scoring pipeline fixed, across four commercial model endpoints, two corpora, and two context regimes (gold evidence pages and the full document). On documents that fit the image budget, page images lead on accuracy at every document length on both corpora, but this advantage carries a growing latency and cost premium: text latency stays roughly flat as documents lengthen while image latency rises steadily. Text and images also fail on different questions, with exactly one representation correct on 19–25% of items across the reported cells, so neither subsumes the other. Exploiting this complementarity, a lightweight TF-IDF router that reads only the question text gains 2.6 points over always-text while cutting median latency 30% relative to always-vision, on a document-disjoint held-out split.

Pull requests (PRs) underpin pull-based software development by enabling distributed code review and collaborative contribution in open-source projects. Yet many become inactive before integration and are eventually abandoned or closed, wasting contributor and maintainer effort. Although prior work has examined PR abandonment and review delays, less is known about the contribution types, discussion-level barriers, and post-stalling collaboration patterns associated with inactivity. We investigate which PR types most often stall, why inactivity occurs from authors' and reviewers' perspectives, and how stalling relates to later contributor and reviewer engagement. We analyzed 14,234 stalled PRs and 164,562 review comments from 19 popular GitHub repositories using stale-bot workflows. An LLM-based voting classifier categorized PRs by contribution type, while quantitative analysis was combined with qualitative coding of general and inline review discussions. Feature-enhancement and issue-fixing PRs formed the largest share, together exceeding 77% of classified stalled PRs. General comments linked stalling mainly to communication and coordination breakdowns, including missing interaction, delayed feedback, and unclear follow-up. Inline comments showed that inactivity does not always reflect disengagement: many PRs were blocked by technical or dependency issues, including failing checks, configuration problems, compatibility concerns, and environment mismatches. Only 39.56% of contributors later submitted another PR, and reviewer re-engagement with the same contributors was approximately 21%. PR inactivity is a socio-technical coordination problem involving communication, technical readiness, review ownership, and automation practices. We recommend type-aware triage, clearer review feedback, explicit ownership of next actions, CI blocker management, and cause-aware stale-bot interventions.

Scanned mail, uploaded PDFs, and consolidated attachments often arrive as page streams that must be split into individual documents before downstream classification, extraction, or routing. Zero-shot large language models can detect document boundaries without task-specific training, but standard Page Classification (PC) and Boundary Decision (BD) formulations resolve only one boundary per model call. We introduce Multi-Split Boundary Decision (MSBD), which predicts multiple boundaries within a page window in a single call, reducing the number of inference requests. We evaluate MSBD across multiple language models, document collections, input modalities, and window sizes. The results reveal a model- and corpus-dependent operating range in which MSBD preserves strong segmentation accuracy while substantially improving inference efficiency, followed by a sharp decline at larger windows. MSBD provided the strongest overall accuracy–efficiency trade-off, while large windows expose distinct over- and under-segmentation behavior across models. These findings show that multi-boundary prediction can make zero-shot page stream segmentation more efficient when the window size is selected for the target corpus.

Tracking recovery of walking function requires detecting meaningful gait change across rehabilitation sessions, yet objective 3D measurement remains confined to specialized motion-capture laboratories. Small camera sets and body-worn inertial sensors broaden access, but reliability varies across joints and time, allowing sensing failures to masquerade as patient change. We present GaitVista, a reliability-aware measurement layer whose lightweight gate assigns joint- and frame-specific visual contributions using camera coverage, local visual quality, cross-modal disagreement, and root-motion continuity, and exposes them for inspection. Across seven clean and degraded sensing conditions on TotalCapture, GaitVista reduces average full-body and lower-body error by 27.7% and 27.8%, attains the lowest worst-condition error among fusion methods, and reduces the gap to a joint-frame oracle from $2.76$–$5.33$~cm for condition-blind baselines to $1.11$~cm. On MoVi with image-derived keypoints, it is the only deployable fusion method to improve over both unimodal streams, reducing marker-supported error by 6.4% relative to the strongest learned fusion baseline. On TotalCapture, it improves bilateral knee-flexion waveform accuracy by 18.9%. Raw inertial measurements from five TotalCapture participants show location- and time-varying magnetic disturbance, supporting the design's reliability premise. Both benchmarks contain neurologically healthy participants in controlled settings and retain participant-specific IMU calibration; we therefore report progress toward accessible gait assessment, not validated clinical deployment.

Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how behavior is expected to evolve. Existing PMF methods, however, forecast directly-follows graphs, which cannot explicitly represent concurrency. We extend PMF to causal nets by forecasting time series of relation and binding counts and using these forecasts to reconstruct future process models with AND/XOR semantics. To evaluate the resulting models, we introduce a protocol that accounts for partial traces and constructs the workflow nets required for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance levels close to those of models re-mined from observations in the corresponding future windows. They also outperform static discovery baselines, which retain high precision on the structurally stable log but exhibit substantial precision losses on the other three logs. Filtering infrequent bindings improves most conformance metrics, although it also removes much of the concurrent behavior captured by the models.

Humanoid robots can acquire complex skills by imitating kinematic humanoid motion references, yet reliable references for contact-rich interactions remain difficult to obtain: motion capture deteriorates under occlusion and close physical contact, while retargeting introduces additional contact and geometric inconsistencies. We present HIGenNTO, a framework that synthesizes humanoid-scene interaction motion references by optimizing the initial noise of a pretrained text-conditioned motion model under sparse spatiotemporal and scene constraints. The same formulation satisfies desired contacts, avoids collisions, and maintains stable support while retaining the prior's realism and temporal coherence, generating interaction motions from scratch and composing long-horizon behaviors stage-wise. Across robot-environment and robot-object tasks, HIGenNTO produces motions that can be executed by tracking policies in simulation and used to train depth-conditioned visuomotor policies operating solely from onboard sensing. We deploy these policies on a Unitree G1 across four contact-rich tasks. Finally, the task specifications themselves can be written by a coding agent, which proposes interaction tasks and compiles them into prompt, constraint, and scene programs, authoring three of our eight evaluated tasks and four further behaviors. Together, these results establish a scalable path from high-level task descriptions to physically executable humanoid interactions.

Modern code review is central to software quality, but its effectiveness depends on reviewer expertise, change characteristics, and how tools present changes. Most platforms display modified files alphabetically by default, although our prior work shows that developers find this ordering cognitively misaligned with how they understand multi-file pull requests. Whether these ordering-related attention patterns affect outcomes at scale remains unclear. We present a large-scale study of file ordering and review effectiveness, mining 330,343 multi-file pull requests and 756,814 file instances from 182 GitHub projects in five programming languages. We examine whether file position is associated with later bug-fixing changes, whether pull request size moderates this relationship, and whether reviewer attention, proxied by comments, aligns with latent bug outcomes. Results show statistically significant but modest associations among file position, pull request size, review activity, and latent bug likelihood. Latent bug rates rise from 56.7% at position 1 to 61.5% at position 30. Pull request size has a non-linear relationship with latent bug likelihood: pull requests of about ten files have the lowest risk, while very small and very large ones have elevated rates. Hurdle models show that attention is diluted as pull request size grows: each additional modified file reduces the odds of receiving any review comment by about 8.7%. These findings reveal an attention-effectiveness gap: visible review activity does not necessarily prevent defects. Alphabetical ordering is therefore not a neutral interface default, but a structural feature shaping attention allocation, review coverage, and confidence in review outcomes. We propose context-aware file ordering, dependency-aware grouping, risk-aware prioritization, and per-file coverage indicators to make review attention more visible and actionable.

Insertion is a fundamental operation in robotic construction assembly, where variations in material properties and assembly conditions make it difficult to select contact forces that complete the task without exceeding the assembly's capacity. Although construction documents encode engineering knowledge about materials and their conditions, translating this knowledge into load limits for a specific assembly remains difficult. This paper presents SAGE (Source-grounded Assembly Gating and Execution), a system that converts documented material evidence into capacity estimates for robotic insertion. SAGE restricts a large language model (LLM) to extracting tensile and compressive strengths from retrieved passages and tables and records their sources. A response model then interpolates offline finite element (FE) solutions to convert these strengths and the assembly conditions into axial load capacity. For fits with positive clearance, the estimated capacity sets the policy's axial force limit; for interference fits, it is compared with measured support demand to determine admission. On the primary benchmark, SAGE reduces mean capacity error from 80.65% for direct LLM estimates based on the same evidence to 10.74%. Without refitting, the mean error remains 8.00% on 16 additional geometries. Under the assigned support release model, SAGE correctly classifies 59 of 62 scored simulation runs, with only conservative errors. In recorded xArm6 demonstrations, SAGE takes material documents as input and completes physical insertion in 9 of 13 trials. These results show that assigning document interpretation to the LLM and force calculation to an explicit mechanical model produces accurate capacity estimates and traceable insertion decisions.

Projectile interception is a challenging dynamic manipulation problem. Intercepting a thrown object with a robot arm requires reaching a point on the object's path as the object passes through it. Slicing also fixes the blade's velocity and orientation at contact. The goal is therefore a subset of the states of the robot and arrival times that moves as the object falls, and the arm must reach it within its actuator limits in milliseconds. We present FRUITNINJA, an anytime sampling-based planner that grows a tree on the GPU in batches toward the interception manifold. Each edge is an exact cubic whose travel time is found by a parallel search against the arm's dynamics, so every edge satisfies the actuator limits. Plans are ranked by a risk-aware objective over the uncertainty in the object's position and the arm's arrival time. We evaluate on a Franka Research 3 against six baselines in a calibrated real-time simulator, where FRUITNINJA cuts 96.7% of tosses in the open and 68.3% among five obstacles, versus the best baseline's 68.3% and 35.0% respectively.

We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, is inaccurate and produces stereotyped predictions (lacking natural diversity). It has previously been shown that LLMs can be bound to personas using naturalistic, freetext dialog avoiding stereotyping. Here we show that binding can also be achieved using short, individual samples of dialog from specific people. Demographics can be added later without negative effects by simply querying the model. We use the term Persona Mixture Models (PMMs) for well-calibrated human models, currently realized as pretrained base models. We show that PMMs produce more accurate predictions than instruction-tuned models and retain more of the lexical, semantic, and pragmatic diversity found in human dialog. We measure realism and diversity of LLMs simulating human interlocutors across a diverse set of corpora spanning open-domain text, human-AI chat, and task-oriented dialogue between human speakers. However, base pretrained models can produce out-of-domain dialog and may lose some of the human's internal state over long contexts. We propose and explore tandem models which combine a pre-trained model with an instruction-tuned supervisor. Tandem models achieve the best overall accuracy and diversity in our experiments.

Vision-Tactile-Language-Action (VTLA) models have demonstrated clear advantages over Vision-Language-Action (VLA) models in contact-rich manipulation. However, developing VTLA models is severely constrained by the massive amounts of vision-tactile data and computational resources required. To address this bottleneck, we propose VT-Bridge, a lightweight residual adaptation strategy that bridges pretrained foundation VLAs to VTLAs. Rather than training a VTLA model from scratch or modifying the original architecture of a pretrained VLA, VT-Bridge employs an identical lightweight residual-adapter architecture across VLA backbones and uses backbone-specific weights to refine actions at the robot execution frequency. This design substantially lowers the data and training barriers. Specifically, it requires up to 50 vision-tactile demonstrations per task to fine-tune a VLA backbone and train a 0.98M-parameter residual adapter. Experiments with three representative VLA backbones ($\pi_0$, $\pi_{0.5}$, and SmolVLA) across four contact-rich manipulation tasks further demonstrate its consistent effectiveness across VLA architectures. On average, VT-Bridge raises the task completion rate from 11.7% with task-level VLA fine-tuning alone to 62.9%. Together, these findings demonstrate the broad applicability, effectiveness, and accessibility of VT-Bridge for contact-rich manipulation. The project page is available at https://hoxnocha.github.io/vt-bridge-web/.

In the Directed Steiner Network (DSN) problem we are given a directed graph and a set of demands $(s_i,t_i)$, and asked to find a cheap subgraph connecting each terminal pair. In its online version, the demands arrive online and must be served by buying edges irrevocably. DSN is a fundamental hard problem in network design, heavily studied in both the offline and the online setting. Offline, it has a superpolylogarithmic hardness of approximation. However, offline hardness says nothing about online algorithms, which are computationally unrestricted. It has been an open question whether uncertainty itself (needing to commit to a solution without knowing future demands) rules out polylogarithmic-competitive online algorithms. In this work, we show the first such unconditional, information-theoretic hardness. Namely, we give an $\exp\!\bigl(\Omega(\sqrt{\log n})\bigr)$ bound on the competitive ratio, which holds even for randomized algorithms against an oblivious adversary, and on unit-cost DAGs. Our proof uses a novel connection between online network design and algebraic coding theory. We encode requests using a hidden low-degree polynomial, whose past evaluations reveal nothing about future ones. We then use list-recovery bounds to show that an algorithm cannot make cheaply reusable decisions without knowing those future evaluations.

Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metrics struggle to support this: ROUGE captures only surface overlap, while LLM-as-judge scores saturate to near-identical values that fail to rank models effectively. We observe this saturation across three public datasets, two proprietary datasets, and multilingual settings. Motivated by this, we introduce Semantic Scaffold, an evaluation framework that extracts a hierarchical representation of facts, questions, and entity attributes from a source text, labeling each as a main point or supporting detail, and reusing this structure as a fixed reference for scoring summaries. From this representation, we derive three diagnostic metrics: Fact Preservation Score (FPS), Question Preservation Score (QPS), and Entity Preservation Score (EPS), designed to reward the preservation of essential information while penalizing detail overload, and position them as interpretable diagnostics that remain informative where holistic axes collapse. Finally, we analyze four recurring failure modes of ROUGE and LLM-as-judge scores, demonstrating that scaffold-based evaluation remains informative where conventional metrics collapse.