Computer Science & Mathematics

We propose a causal machine learning framework to integrate dynamic tolling for mobility, equity, and climate objectives, resolving a long-standing policy trilemma. Using Double Machine Learning, we estimate zone-type-specific treatment effects, showing that commercial zones yield nearly twice the delay reduction of residential zones. Our adaptive quota mechanism expands residential coverage 34-fold while preserving 93% of total delay savings and sustaining high return on investment. The framework delivers climate co-benefits, reducing emissions by 743.5 metric tons of CO2 (valued at 141 USD) via congestion-sensitive pricing under moderate traffic conditions with sensitivity bounds from 557.6 to 929.3 tons. Crucially, we uncover a policy-invariant toll range where performance remains robust across demand scenarios, simplifying implementation. This study reveals critical, previously unidentified insights, such as a policy-invariant toll range, that can significantly simplify real-world implementation. Validated on synthetic data mirroring real-world networks, our methodological advances lay the foundation for future public deployment.

This paper introduces the Scatter-Gather parallel-programming and parallel-execution model in the form of a simple imperative Scatter-Gather Language (SGL). Its design is based on past experience with bulk-synchronous parallel (BSP) programming and BSP language design. SGL's novel features are motivated by the last decade's move towards multi-level and heterogeneous parallel architectures involving multi-core processors, graphics accelerators and hierarchical routing networks in the largest multiprocessing systems. The design of SGL is coherent with L. Valiant's Multi-BSP while offering a programming interface that is even simpler than the primitives of Bulk-Synchronous Parallel ML (BSML). SGL appears to cover a large subset of all BSP algorithms while avoiding complex message-passing programming. Like all BSP-inspired systems, it supports predictable, portable and scalable performance. Moreover, SGL's explicit data distribution will allow automatic or programmable load-balancing.

Communicative visualizations can support multiple chart-grounded takeaways, including divergent or conflicting ones; we refer to this variation as interpretive ambiguity. Such ambiguity can undermine a visualization's communicative goal when the supported takeaways diverge from its intended message. Evaluating visualizations with target audiences can surface this variation, but such evaluations can be costly and difficult to scale. In this work, we explore persona-conditioned Vision Language Models (VLMs) as a complementary evaluation tool for interpretive ambiguity. For a given visualization, our pipeline uses topic-relevant personas to elicit candidate contextual takeaways supported by interpretations of perceived visual patterns, such as trends and clusters. We derive a Target--Pattern--Scope framework for comparing these interpretations, use LLM judges to assign Compatible, Disjoint, or Opposing ambiguity labels to statement pairs, and summarize their distribution in an ambiguity profile. To improve chart grounding, we benchmark candidate VLMs on chart comprehension and select the highest-performing model for each chart type. We evaluate statement-pair labeling against human-authored annotations and apply the approach to real-world, public-facing visualizations curated from established datasets. Through case studies, we examine how perceived visual patterns relate to contextual takeaways and how a visualization redesign changes the sampled interpretations. Our findings show how persona-conditioned VLMs can complement human feedback by generating candidate contextual takeaways and surfacing potential ambiguity for further inspection.

The Invisible Internet Project (I2P) is a peer-to-peer anonymity network whose routing and peer-selection mechanics underlie a growing family of state-sponsored covert relay architectures. Studying these mechanics demands a controlled, reproducible environment. We present Garlic-in-a-Box, an installable emulator that deploys a fully isolated network of unmodified I2P Java routers on a single host or cluster of hosts. Each router runs in its own Linux network namespace. A filesystem bootstrap removes the dependency on signed reseed servers, and synthetic geographic diversity activates the same location-aware peer-selection code paths present on the live network. A fifty-router deployment across five geographic locations stabilises in about twenty-five minutes and uses roughly 21 GB of memory. To demonstrate the platform's value, we model I2P peer selection as a two-stage process and conduct a reputation attack. Across five independent trials, the attack promotes the adversary into the victim's eligible selection set with perfect consistency, and the adversary is chosen for 1.38x more tunnels than matched controls. The platform and its measurement pipeline are released to support reproducible I2P security research and hands-on cyber-defence training.

On-policy self-distillation (OPSD) trains a student on its own reasoning trajectories using token-level supervision from a reference-conditioned teacher initialized from the same model. However, access to the reference can induce teacher behaviors that undermine independent reasoning in thinking models, leading to reference hallucinations, where the student cites an unavailable reference to justify reasoning steps, and reconsideration suppression, where the teacher discourages verification and revision at uncertain steps. We introduce Steered-Teacher OPSD (ST-OPSD) to jointly address these failure modes through activation steering. ST-OPSD extracts two directions from model-generated reasoning trajectories and applies them to the teacher's hidden states during distillation target computation, suppressing reference-invoking behavior and encouraging reconsideration with marker-family calibration. The student learns from the resulting teacher distributions without being steered. Experiments on AIME and HMMT show that ST-OPSD improves accuracy over vanilla OPSD and other interventions while restoring reconsideration and reference-hallucination rates to base-model levels.

Kubernetes Horizontal Pod Autoscaler (HPA) is widely used for inference services, but reactive scaling is often too slow when pods take tens of seconds to become ready. The problem gets worse in shared clusters, where independent HPAs compete for the same resource budget and can hurt different tenants from run to run. We present InferScale, a Kubernetes-native autoscaler that combines inference-aware signals, short-horizon demand prediction, SLO-state control, fast-path spike handling, and tenant-priority headroom without replacing the default scheduler. We evaluate InferScale on Azure Kubernetes Service in two settings. On a real ResNet-50 ONNX workload with 21-second cold start, repeated ramp-burst runs improve SLO compliance from 47.8% +/- 1.5% under a stronger external-metric HPA to 65.3% +/- 9.0% under InferScale. In repeated controlled multi-tenant contention experiments, InferScale cuts Gold-tier SLO standard deviation from 16.1 to 1.2 percentage points and makes contention outcomes follow the priority policy more closely. Repeated single-service ablations show 6-8 percentage point degradation, with an exploratory priority ablation showing a 27-point drop under contention. The main takeaway is that, in startup-sensitive inference serving, the practical value of prediction lies less in precise forecasting than in moving the controller early enough to offset cold-start delay.

With the rapid development of modern scientific research, tensor data have become increasingly prevalent across a wide range of fields. How to effectively characterize the relationship between tensor predictors and a response while preserving the intrinsic structure of tensor data has therefore become an important problem in high-dimensional statistical analysis. Existing tensor regression methods mainly focus on conditional means or conditional quantiles. When the response distribution is asymmetric, heavy-tailed, or contaminated by outlying observations, the conditional modal provides a robust and interpretable alternative characterization. In this paper, we propose a partial modal tensor regression (PMTR) method. The proposed method constructs the partial modal tensor covariance, which incorporates information on the conditional modal to guide tensor dimension reduction, and subsequently establishes a modal tensor regression model in the reduced tensor space. This strategy alleviates the dimensionality burden caused by direct vectorization while preserving the intrinsic structural information of tensor predictors. Under suitable regularity conditions, we establish the consistency and asymptotic properties of the proposed estimators and employ a data-driven dimension-selection procedure to determine the effective dimension along each tensor mode.

Conventional federated learners implicitly optimize for global means, creating a "one-size-fits-all" paradigm that inevitably suppresses the minority under cross-site heterogeneity. To bridge this critical gap, we present Fed-Universe, a generalizable Semantic-Geometric-Topological-Human (S-G-T-H) architecture that transforms passive averaging into an active negotiation for decentralized alignment. The S-layer deploys an edge-capable LLM as a semantic surrogate to translate heterogeneous inputs into standardized patient profile prompts. The G-layer enforces geometric quality assurance (GQA) via a mapping f: Theta -> Q from the statistical parameter space to a physically interpretable quality domain, employing a cosine similarity gate to retain minority updates based on physical geometry rather than statistical proximity. The T-layer executes topological Pareto control, identifying a sensitivity-based knee point (lambda_k) to balance multi-objective aggregation without majority degradation. Finally, the H-layer operationalizes a cognitive twin dynamic to mirror the user's real-time psychological state, dynamically adapting human-computer interaction modes via active intent alignment. To ground this theoretical architecture empirically, we validated the S-G-T core on a binational synthetic clinical network simulation. This proof-of-concept demonstrated a "zero-sum escape": a minority node (

Determining the cross-correlation distributions between $m$-sequences and their decimated sequences is an important problem in sequence design. For two positive integers $n$ and $k$ with $n=2k$, this paper investigates the cross-correlation function between a binary $m$-sequence $\{u(t)\}$ of period $2^{n}-1$ and its $2^{\gcd(s,k)}+1$ short-period decimated sequences $\{u(dt+l)\}$, where the decimation $d=\frac{2^s}{2^s+1}(2^k-1)+1$ satisfies $1

Prosthetic and assistive interfaces increasingly depend on decoding hand gestures from surface electromyography (sEMG). Because the signal is shaped by person-specific factors, the recurrent neural networks (RNNs) commonly used to model these temporal streams must adapt to new wearers and new gesture classes after deployment. Continual learning (CL) enables this adaptation without retraining from scratch, yet applying it to recurrent sEMG models is not straightforward. Standard recurrent training practices can destabilize learning, and which CL strategy holds up under realistic cross-user drift is unestablished. We evaluate three representative CL strategies under domain- and class-incremental drift on NinaPro DB1, and find experience replay to outperform elastic weight consolidation (EWC) and naive fine-tuning. It reaches 83.3% final accuracy for class-incremental drift while limiting forgetting to 1.8-5.8% accuracy drop, as against 16.2-28.5% for EWC and naive training. We further show these models must be trained on shuffled, independently-windowed segments. Streaming windows in temporal order collapses the network into predicting a single dominant gesture, while a shuffled, stateless regime trains stably. Finally, hand-crafted time-domain features raise per-task accuracy by up to about 40% over raw signals, but only replay preserves that gain under class-incremental drift.

Professionals send working documents to external large language models. A common safeguard is reversible pseudonymisation: personal data are replaced with placeholders before the request and restored locally in the answer. We present an open benchmark of five existing open tools and a regular-expression baseline on Russian and UK English, and an open engine built on the two strongest tools. The benchmark excludes each tool's own authors' dataset from its verdict, compares the engine with each tool on identical datasets for detection, reports masking precision, and tests on a held-out set of synthetic documents whose files, hashes, decision rules, run plan and stopping rule were published before any run. Two local LLMs test whether placeholders survive. All evaluation data are synthetic, and the benchmark was run by the author of the engine; code, data, predictions and raw answers are released. On external datasets no existing tool meets all predefined thresholds: in UK English the identifier and leak thresholds are not met, in Russian the identifier threshold is borderline. On the last held-out set of 1,000 documents per language, the best existing tools leave a whole value visible in 61% (Russian) and 58% (UK) of documents. On these documents, from the generator it was developed on, the engine does so in 0.3% [0.1; 0.9] and 0.2% [0.1; 0.7], leaves any part of a value visible in 0.8% and 0.2%, and meets all detection thresholds in both languages. In UK English it also restores at least 99% of its placeholders after 760 answers. In Russian it restores only 94.6% [93.6; 95.6] of name placeholders, against a 99% threshold, mainly because the inflection it borrows from pii-guard produces wrong surname forms. The engine was developed over six earlier held-out sets from the same generator; real documents were not tested.

More people are seeking out general-purpose AI models for medical advice, including diagnosis. Yet, AI models are susceptible to sycophancy, like validating users' beliefs even when they are untrue, which could lead to false positive diagnoses. Here, we evaluate how nine AI models change their diagnostic decision when a patient presents a belief that they have a specific (but incorrect) medical condition. We vary how close the user belief is to the clinical evidence presented. Models sycophantically adopt the patient's belief on average 23.2\% of the time---sometimes even when the patient proposes a fictional, made-up condition. We also varied different personas, including a Rare Disease Claimer that got models to accept their belief 43.0\% of the time. Additional analyses find that medical fine-tuning and model scale makes models less susceptible to changing their diagnoses. Sycophantic bias in medical diagnosis raises implications for safety, especially as more people are using AI for health support.

Most analyses of neural representations focus on trained endpoints, leaving the process by which internal structure forms comparatively unmeasured. We instead treat representation formation itself as an object of study, asking whether the path a representation takes through training reveals information that its endpoint does not. We formalize this idea by viewing representation learning as a geometric trajectory, using Bures geometry to measure movement through representation space and quantum Fisher information to characterize local sensitivity to input factors. We find that learning concentrates local sensitivity on task-relevant factors, and that changing only the prediction target reorganizes this geometry around the corresponding visual factor. More strikingly, representation trajectories become systematically less direct as the learned computation requires higher-order interactions among input factors. This effect persists under matched-performance and training-duration controls and transfers from synthetic vector inputs to residual CNNs on rendered and natural images. Importantly, formation history contains predictive information about the stability of the learned representation: controlling for interaction order, path tortuosity predicts its subsequent drift under continued training and sensitivity to parameter perturbations, whereas endpoint displacement does not. Finally, representation-aware optimization lets us actively shape these trajectories, showing that formation geometry is not only measurable but actionable. Together, these results establish representation formation as a new source of information about learning: its geometry reveals properties of the computation being constructed, predicts subsequent representational stability, and provides a target for intervention during optimization.

Let $2\le k\le n$ and let $U\le\mathbb{F}_q^n$ be a fixed $(k-1)$-dimensional linear code. We study the $[n,k]$ maximum distance separable (MDS) codes over finite extensions of $\mathbb{F}_q$ that contain the scalar extension of $U$. Our approach constructs an auxiliary linear code from \(U\). Its full-weight words characterize the desired MDS completions. We prove that \(U\) admits an \([n,k]\) MDS completion over some finite extension of \(\mathbb{F}_q\) if and only if \(d(U)\ge n-k+1\). When this condition holds, the number of these MDS codes over $\mathbb{F}_{q^m}$ is \[\#\left\{W\le\mathbb{F}_{q^m}^n:U\otimes_{\mathbb{F}_q}\mathbb{F}_{q^m}\subseteq W,\ \dim_{\mathbb{F}_{q^m}} W=k,\ W\text{ is MDS}\right\}=\frac{χ_{δ_GM(G)}(q^m)}{q^m-1},\] where $G$ is a generator matrix of $U$, $δ_GM(G)$ is its represented derived matroid, and $χ_{δ_GM(G)}$ is its characteristic polynomial. In particular, the formula determines existence over each extension field and the least extension degree for which such an MDS code exists. Examples show that these counts may distinguish different representations of the same column matroid, while monomially inequivalent codes may have identical counts over every finite extension.

We present BriLLM-MetaPred, the first engineering implementation of the MetaPred machine-learning framework established by a mathematical theory of AGI, instantiated for language modeling. The theory argues that MetaPred is both necessary and sufficient as a machine-learning primitive for general cognition. Taking this theoretical conclusion as a design premise, we report an implementation of the primitive for language modeling. A single continuously evolving dynamical state serves as both the context representation and the state from which predictions are read out. Continuous measurement of its own state reduces finite-depth inconsistency, maintains internal consistency, and provides an endogenous dynamical self-supervision signal in fixed-point form. The single dynamical state of MetaPred intrinsically couples training and inference. MetaPred therefore exhibits symmetry across almost all key elements of machine learning, an intuitive and observable property of the mathematical optimum over the space of all machine-learning paradigms. BriLLM-MetaPred is neither another recurrent language model nor an extension of BriLLM. It provides a bridge between two prior contributions: the construction and motivation of the MetaPred primitive provided by AGI theory, and the SiFu learning paradigm provided by BriLLM (Zhao et al., 2025). The latter's node-signal-flow design enables the primitive to run on available software and hardware accelerators. Our implementation of BriLLM-MetaPred achieves predictive performance comparable to a GPT baseline of the same size. This provides initial evidence for the engineering feasibility of the MetaPred framework established by the mathematical theory of AGI. MetaPred is the first machine-learning paradigm derived entirely from mathematical theory rather than assembled and incrementally improved through human design---and perhaps the last such paradigm.