Cryptography and Security
The Supersingular Isogeny Problem in Time and Memory $p^{1/3+o(1)}$, Unconditionally
José Luis Delgado
Given a supersingular
elliptic curve $E/\mathbb{F}_{p^2}$, the $\mathsf{OneEnd}$ problem asks for a non-scalar endomorphism of $E$. By known reductions, solving this problem also solves the supersingular endomorphism ring and isogeny problems. Wesolowski obtained exponent $1/3$ under an assumption on the factorization of a small degree, whereas the previous unconditional exponent was $2/5$. We give a Las Vegas
algorithm, analyzed without a smoothness heuristic, with expected time and
memory \[ p^{1/3}\exp\bigl(O(\sqrt{\log p\,\log\log p})\bigr) = p^{1/3+o(1)}. \] The
algorithm fixes in advance a family of degrees that are products of small
primes. Known counting results provide many isogenies of these degrees from curves to their Frobenius conjugates, and a collision estimate shows that the isogenies occur on sufficiently many distinct curves for a random walk to reach one of them. From such a curve, the
algorithm splits a degree into two parts, enumerates two lists of shorter isogenies, and matches their targets to obtain an isogeny to the conjugate, whose composition with Frobenius gives the required endomorphism.
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Social and Information Networks
Auditing bipartite motif interpretations: a worked example with conservation checks and open-path decomposition
Tengfei Shao
Motif profiles of bipartite
agent-object networks, such as tourist-site visits and customer-item transactions, are read as evidence about structural roles and about differences between networks, often without asking what the two degree sequences already fix. In a simple bipartite graph the induced k-fan count on one node type is a sum of degree combinations, so it has zero variance under a null that preserves both degree sequences. We apply this known result to a reconstructed tourism rating network of 17 tourists, 80 sites and 637 edges, the sole inferential worked example, and, as a provenance-limited illustration, to published motif-instance aggregates over 36 monthly luxury customer-item networks. The four fan classes are exact functions of the degree sequences: in the tourism network the raw fan counts and the size-3 two-fan ratio (84.7% fan-out) restate those sequences. The published luxury counts require at least 89,502 customer-item edges against 26,451 reported transactions, so their 99.8% fan-in is reported as a descriptive value only. Against a hard bipartite configuration null, the four-cycle count is degree-consistent (z about +1.0) and the open path is deficient (z about -5.8) by 2,243 instances, 2.4% of the null mean; the deficit survives every leave-one-tourist-out re-run (z -4.5 to -8.1). An exact identity splits it at the point estimate into 64.4% mixing and 35.6% four-cycle, but that split is not an attribution: the observed mixing term lies below all 500 null samples, the two components are almost collinear under the null (r = 0.968), and the mixing share ranges from 36.6% to 116.5% under leave-one-tourist-out deletion. The deficit is extreme relative to the sampled null, while its class-level interpretation is undetermined and unstable. We give a four-step pre-interpretation check and a reference implementation.
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Machine Learning
COMPLEX: A Closed-Form Certified Embedding of Multiparameter Persistence Modules
Sushovan Majhi, Atish Mitra, Žiga Virk et al. (+1 more)
Every multiparameter persistence vectorization we know of carries a one-sided Lipschitz upper bound and nothing below it: without a lower gauge there is no sense in which the features are faithful, and no per-prediction guarantee can be built on them. This paper supplies the missing side. COMPLEX is a closed-form, training-free
embedding of multiparameter modules – slice the module along a fixed near-diagonal net, embed each slice barcode by the certified PLACE/PALACE landmark map, concatenate. Under a checkable witnessing-slice coherence condition, holding on 100% of audited pairs on Orbit5k, a single slice carries a closed-form lower gauge: separated modules stay separated in the
embedding. With the standard upper bound this gives, to our knowledge, the first two-sided distortion bound for a multiparameter feature map, making faithfulness measurable. Measuring it, we find the floor tight within a small factor of realized distances yet operationally local: an RBF-SVM reaches 91% where 1-NN reaches 78% on the same features. Local per-prediction certification therefore fails for a structural reason common to every landmark
embedding whose lower gauge is witnessed by one coordinate. With no learned
embedding and no held-out calibration – only a cross-validated SVM head – COMPLEX sets the state of the art on both Orbit
benchmarks (91.95% on Orbit5k, 92.98% on Orbit100k), level with or above Euler-characteristic surfaces and above
transformers and graphcode. On graphs it exceeds GRIL on all four shared molecular
benchmarks with one fixed configuration, including the only multiparameter method to clear COX2's majority baseline by more than three points. Closed-form selection – of the landmark radius, the kernel (certificate-preserving), and the bifiltration set – buys further accuracy; gradient-shaped adaptation buys none.
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All authors Sushovan Majhi, Atish Mitra, Žiga Virk, Pramita Bagchi
Computation and Language
DiaVLo: Diagnosing Behaviours of Vision-Language Models
Lorenzo Corti and Jie Yang
Vision-
language models (VLMs) rely on storing and transferring appropriate information across their sub-components. Verifying that the VLMs exhibit desired behaviours, while avoiding harmful ones, is central to their reliable deployment. Yet, methods that identify VLM behaviours remain scarce. We present DiaVLo, a diagnostic framework that leverages human curation and VLMs' generation capabilities to construct specifications of desired and observed VLM behaviours, surfacing potential misalignments. Beyond this, DiaVLo also provides causal estimates to identify the most influential concepts steering VLM behaviours. We evaluate DiaVLo on several open-source VLMs under both classification and generation conditions. Our experiments show that DiaVLo produces behaviour labels that correlate with model performance and provide context for measured performance. DiaVLo surfaced behaviours that are clearly aligned and misaligned, alongside patterns in how VLMs perceive, organise, and prioritise concepts.
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Machine Learning
Abstention and Noise Filtering: Two Missing Primitives of Softmax Attention
Richard Zhe Wang
Softmax
attention has two structural gaps. A head cannot abstain, because its weights sum to one, so it outputs something even when nothing is relevant. Nor can it filter what it reads, because its output is a weighted average of value vectors, passing interference as faithfully as signal. We call these missing primitives abstention and noise filtering. Recent studies report that gating the value pathway improves pretraining but attribute the gain to different causes. We show that a value gate partly supplies both primitives, which unifies the reported causes as views of one gain. We give each primitive its own mechanism in matched models of 10M to 350M parameters and measure what each contributes. The gain from gating is almost entirely abstention at 10M, whereas by 350M filtering contributes as much as abstention, so what a study observes depends on its scale. The two benefits are largely additive, with a small overlap. A gate determined by each value alone leaves the
attention sink in place, whereas a query-controlled mechanism removes it. Injecting interference into the value reads shows that abstention and filtering protect against it in distinguishable ways. The same patterns appear in pretrained models up to 20B parameters.
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Computation and Language
RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents
Shuai Bai, Jiayong Deng, Sicheng Fan et al. (+30 more)
Computer-use
agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface, implement software, and run and visually verify their artifacts. We introduce RecreationWorld, a five-platform framework built around recreation: given a running reference, an
agent must discover its behavior and build a faithful implementation with no prescribed workflow. RecreationWorld provides reproducible environments on Ubuntu, macOS, Windows, Android, and Web, plus a unified harness with native GUI control and coding tools. The running reference serves as an oracle for hidden behavioral tests, providing execution-grounded rewards. We scale trajectory generation with high-quality open-source applications. Models trained on these trajectories improve across five out-of-distribution coding and hybrid computer-use
benchmarks and more frequently verify their rendered outputs, providing evidence of transfer beyond recreation. For held-out evaluation, we introduce RecreationBench, comprising 250 diverse tasks across domains and platforms. Reference-grounded programmatic and visual assertions cover action-conditioned outcomes at multiple interaction depths; each is validated on the reference and by human reviewers before the suite is frozen for automatic scoring. GPT-6 Astra leads at 58.1% overall, but passes all programmatic tests on just 2.8% of tasks.
Agents reproduce static interface structure more reliably than interactions and computed outputs, while generated applications remain smaller and more monolithic than their references. We release the
benchmark, environments, and test suites.
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All authors Shuai Bai, Jiayong Deng, Sicheng Fan, Yikun Fu, Chang Gao, Xuhao Hu, Mianqiu Huang, Yizhen Jiang, Yuheng Jing, Dehui Kong, Keliang Li, Ning Li, Wanli Li, Dayiheng Liu, Dunjie Lu, Changwei Luo, Que Shen, Zheyuan Wang, Zijian Wang, Jie Wu, Gao Wu, Zhihui Xie, Rui Xie, Haiyang Xu, An Yang, Jiakang Yuan, Yanming Zhang, Jiajun Zhang, Xi Zhang, Zhenru Zhang, Zhuo Zhen, Mingkang Zhu, Bowen Zhou
Multiagent Systems
Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents
Hafsa Akbar, Daniel Platnick, Marjan Alirezaie et al. (+2 more)
LLM
agents in social simulation revise their opinions implicitly, in context: how open an
agent is to persuasion can neither be specified nor verified, and collective outcomes inherit the model's training prior. We introduce
Bayesian Chronicle
Agents (BCA), a minimal belief layer separating what an
agent believes from how it speaks. Each stance is a probability, updated by one
Bayesian step per utterance heard. A single prior-strength parameter $\kappa$ encodes stubbornness, modeled after its role in Friedkin–Johnsen (FJ) opinion dynamics. We then sweep this parameter to yield three canonical regimes of opinion dynamics on demand (consensus, persistent disagreement, committed-minority influence), with persistent disagreement matching the FJ closed-form fixed points at $R^2\!=\!0.93$–$0.99$. We further show that prescribed $\kappa$ remains recoverable after the
language round-trip, with perfect rank-order recovery across all four models. Explicit belief also makes simulation auditable: the layer surfaces systematic per-model stance biases that end-to-end simulation would silently absorb.
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All authors Hafsa Akbar, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama, Alex 'Sandy' Pentland
Artificial Intelligence
A Lie Detector Test for Language Models: Reading Knowledge a Model Won't Reveal
Hiskias Dingeto
Large
language models can hold knowledge they do not report. A model may sandbag on a capability evaluation, or answer against what it internally knows, and its outputs alone cannot tell whether it is hiding an answer or simply does not have one. We borrow the Concealed Information Test, a forensic method that identifies guilty knowledge by presenting a suspect with the true detail among plausible decoys and measuring a stronger response to the item they recognize. Our method, Probe of Internal Recognition (PIR), does the same inside a model. It presents a question with its candidate answers and reads, from the model's internal states, which candidate the model recognizes as correct. PIR is reference-free, needing no honest reference model and no labeled truth corpus. Across eight models from five families (Gemma, Qwen, Llama, Mistral, and Phi), PIR recovers the recognized answer at 0.70 to 0.87 balanced accuracy, well above the 0.28 to 0.40 unknown-item baseline and the 0.25 chance rate. It stays readable across every form of concealment we test, from prompted deception and trained sandbagging to external password-locked and
circuit-broken checkpoints, with recognition between 0.85 and 0.93. When the model hides a known answer, recognition stays high. When unlearning removes the knowledge, recognition drops to the level of a question the model never knew. PIR therefore separates a model that will not answer from one that cannot, which supports sandbagging audits and unlearning verification. The signal is causal, adds information beyond black-box behavioral cues, and extends from multiple-choice questions to free-form generation.
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Machine Learning
Assessment of Machine Learning-Based Critical Heat Flux Models in the CTF Subchannel Code for Square Rod Bundle Prediction
Aidan Furlong, Vinicius de Melo Monteiro, Robert Salko et al. (+2 more)
The prediction of critical heat flux (CHF), a key safety-related quantity in
nuclear thermal hydraulics, remains an important challenge due to its direct relationship with fuel performance and reactor safety. Recent studies have demonstrated that relative to traditional empirical correlations and lookup tables (LUTs), machine learning (ML) methods can substantially improve CHF prediction accuracy. Most ML-based CHF models, however, have been developed and evaluated using tube databases, leaving their applicability to reactor-relevant rod bundle geometries largely unexplored. This study evaluates ML-based CHF models deployed within the CTF subchannel code using the Electric Power Research Institute (EPRI) rod bundle CHF database. Both pure and hybrid residual correction models are considered in local and semilocal formulations. The tube-trained ML CHF models generally transferred favorably to rod bundle applications and outperformed traditional CHF methods across most geometries and operating conditions. The local hybrid LUT model produced the strongest overall performance, and the semilocal pure ML model remained highly competitive. Comparison against the Bowring correlation, W-3 correlation, and 2006 Groeneveld LUT demonstrated that substantial improvements in rod bundle CHF prediction are possible even when models are trained exclusively on tube data. These findings provide one of the first large-scale assessments of ML-based CHF models in square rod bundles within a production-level subchannel analysis environment and support their broader application in reactor thermal hydraulic analysis.
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All authors Aidan Furlong, Vinicius de Melo Monteiro, Robert Salko, Juliana Pacheco Duarte, Xu Wu
Information Theory
Minimum distances of primitive narrow-sense BCH codes via good zero-sets
Run Zheng
Determining the exact minimum distances of BCH codes remains a open problem. We establish the minimum distances of several families of primitive narrow-sense BCH codes, showing that they attain their designed distances. Our approach centers on $\mathbb{F}_q$-good zero-sets, which we introduce through a derivative condition on their vanishing
polynomials. We show that a $q$-ary primitive narrow-sense BCH code of length $q^m-1$ and designed distance $2\leq\delta\leq q^m-1$ has minimum distance $\delta$ if and only if there exists an $\mathbb{F}_q$-good zero-set of cardinality $\delta+1$ in the finite field $\mathbb{F}_{q^m}$ with $q^m$ elements. To construct $\mathbb{F}_q$-good zero-sets, we develop several methods based on
polynomial substitutions, power maps, and shifted inverses, as well as direct constructions using
polynomials of special forms. Together with suitable initial $\mathbb{F}_q$-good zero-sets, including those arising from known minimum-distance results, these methods yield new good zero-sets of various cardinalities and hence families of primitive narrow-sense BCH codes whose minimum distances equal their designed distances. These families cover a broad range of designed distances, with several known minimum-distance results recovered as special cases.
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Data Structures and Algorithms
Dynamic Contention Resolution Schemes
Moran Feldman, Gregory Kehne, Roie Levin et al. (+1 more)
We introduce a low-recourse rounding paradigm for packing problems in fully dynamic settings, which we name Dynamic Contention Resolution Schemes (DCRSs). These are dynamic analogs of (Online) Contention Resolution Schemes (or (O)CRSs) for low-recourse dynamic optimization and offer a variety of benefits. Similarly to their offline and online counterparts, DCRSs for different constraints can be combined to obtain DCRSs for the constraints' intersection. Furthermore, together with the Positive Body Chasing framework of Bhattacharya, Buchbinder, Levin, and Saranurak [FOCS 2023], DCRSs imply competitive recourse
algorithms for fully dynamic packing problems with submodular objectives: these are
algorithms that, for any input sequence, incur recourse that is itself competitive with the best possible recourse for that sequence. We show the existence of $\Omega(1)$-balanced and $O(\log \mathrm{rank})$-recourse DCRSs for matroid constraints, and $\Omega(1)$-balanced/$O(1)$-recourse DCRSs for matching and knapsack constraints. In particular, these yield the first non-trivial recourse bound for fully dynamic knapsack, as well as the first competitive-recourse
algorithm for non-bipartite matching, and both of these apply even to monotone submodular objectives. Beyond our particular results, we view the DCRS framework as a principled step towards mechanizing the relax-and-round paradigm of approximation
algorithms in the context of dynamic optimization.
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All authors Moran Feldman, Gregory Kehne, Roie Levin, Sherry Sarkar
Computation and Language
Moral Entropy: Auditing Bias and Uncertainty in Moral Judgment
Maciej Skorski
Most work in computational ethics treats annotator disagreement on moral content as noise to be voted away, collapsed into majority vote or the more permissive any-annotator rule the moment a single annotator flags an item. We argue this uncertainty should instead be modeled and learned from. We introduce Moral
Entropy, a
Bayesian framework that keeps a full posterior over the true label and decomposes its
entropy into aleatoric uncertainty (irreducible disagreement about the moral content) and epistemic uncertainty (from insufficient or noisy annotation) – and lets any heuristic consensus rule be audited against a calibrated ground truth via
entropy methods such as cross-
entropy/KL, Brier score, and expected calibration error. Across three corpora and fifteen discourse domains,
auditing the standard aggregation rules against this posterior reveals bias that no current pipeline reports: the any-annotator rule disagrees with the calibrated posterior on roughly 30% of items – pooled, almost entirely false positives, though the errors invert at the foundation level (19.9%/38.9% mean FPR/FNR on MFTC) – while the stricter majority and two-vote rules miss 63-83% of true positives.
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Machine Learning
Time series generation with spectrally aligned latent flow matching
Camilo Carvajal Reyes and Felipe Tobar
Latent flow models have proven to be a reliable and cost-effective method for time series generation. However, the latent compression induces unwanted artefacts, such as a spectral mismatch with respect to the underlying
dataset, thus hindering their use as training surrogates. In this article, we propose a spectrally-aligned latent-flow time series generator, where the latent space for flow matching is trained to preserve dynamical properties that are relevant for the suitability of synthetic samples. We find that incorporating fine-tuning losses based on canonical signal representations such as the
Fourier, wavelet and signature transforms helps overcome these issues. The interpretability of these transformations allows us to ensure that the synthetic signals are aligned with the true ones in terms of relevant features, such as smoothness or targeted spectral content, as opposed to relying on pointwise reconstruction losses only. We compare the proposed aligned models against a base latent-flow model and the state of the art over real-world long-range univariate and multivariate
benchmark datasets. Our quantitative results validate the superiority of the proposed method in terms of its performance on metrics reflecting signal realness and computational efficiency, while being aligned to the training set with respect to its local structure.
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Systems and Control
Learning-Based Augmentation and Adaptation for Grid Sim-to-Real Model Discrepancy
Sayak Mukherjee, Kyung-Bin Kwon, Ramij R. Hossain et al. (+1 more)
Modern power systems can encounter increased discrepancy between the operators' simulation model and the actual true dynamics of the grid, driven by uncertainties caused by integration of new inverter-based resources (IBRs), large loads, unmodeled dynamics, parameter drifts, etc., to name a few. All of these impact the control room operations, where some critical oscillations may not be captured during the transient studies. To circumvent these issues, we propose a learning-augmented hybrid approach where the operator simulation model is supplemented with artificial intelligence (AI)-learned residual models using the phasor measurement unit (PMU)/ point-on-wave (PoW) based sensed trajectory data. The physics-based operator model provides interpretability and structural consistency, while the learned residual captures discrepancies caused by non-idealities. The learned model employs advanced neural architectures and consists of a backbone encoder and multi-head decoder layers for heterogeneous grid channels. Subsequently, we formulated a continual learning-motivated adaptation framework such that the baseline residual AI model can also be updated when the underlying real grid model changes in future conditions. Extensive numerical simulations are performed on the IEEE 68-bus
benchmark model with a diverse set of disturbances, and different state-of-the-art predictive architectures involving recurrent learners, latent neural ODEs, and
transformers are explored to demonstrate both residual learning and adaptation capabilities.
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All authors Sayak Mukherjee, Kyung-Bin Kwon, Ramij R. Hossain, Marcelo Elizondo
Robotics
SkelWAM: A Skeleton-Guided World-Action Model for Zero-Shot Cross-Embodiment Manipulation
Pengjun Niu, Yujia Xie, Rui Peng et al. (+2 more)
Reusing manipulation experience across
robot embodiments is important for scaling
robot learning and reducing repeated task-specific data collection. However, changes in embodiment alter visual appearance, action dimensionality and semantics, and the whole-body configurations that can realize the same tool pose. We present SkelWAM, a skeleton-guided world-action model that couples perception and control through one explicit
geometric representation for single-source cross-embodiment manipulation. Arm centerline geometry, tool-center-point (TCP) pose, and parallel-jaw commands form a shared 25-D state. The same definition underlies canonical third-person and wrist observations and future whole-body action targets. Trained with predictive visual supervision, a
video-action mixture of
transformers predicts canonical skeleton action chunks, which embodiment-specific constrained decoders convert into joint or continuum-
robot controls. This formulation requires no one-to-one joint correspondence and uses no target-task demonstrations or target policy updates. We introduce LIBERO-Cross10, a source-only cross-embodiment transfer
benchmark covering ten tasks and ten target embodiments across four morphological groups. On this
benchmark, Franka-trained SkelWAM achieves 43.3% success over 1,000 episodes, exceeding the best-performing evaluated baseline by 36.2 percentage points. We further deploy a JAKA mini2-trained policy on the Feagine A03 continuum
robot for three tabletop manipulation tasks, illustrating the approach's potential for real-world cross-embodiment manipulation. Project page: http://www.liukepku.com/skelwam/index.html
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All authors Pengjun Niu, Yujia Xie, Rui Peng, Hang Zhao, Ke Liu
Robotics
CARF: Contrastive Attraction-Repulsion of Failure-Guided Flow Matching
Shuqi Zhao, Bang Du, Cheng-En Wu et al. (+3 more)
Robot demonstration collection often produces imperfect or failed trajectories in addition to successful demonstrations. Existing methods typically exploit failed trajectories by identifying segments that still make progress toward task completion, but largely overlook failure-critical behaviors that directly lead to task failure. Here we argue that these two types of segments provide fundamentally asymmetric supervision: progressive segments should be imitated, whereas failure-critical segments should be explicitly avoided. Based on this observation, we propose CARF, a Contrastive Attraction-Repulsion of Failure-guided framework for learning from imperfect
robot data. CARF introduces a progress-based importance scorer, trained solely on successful expert demonstrations and its perturbation results, to estimate step-wise contributions toward task completion and identify informative regions in failed trajectories. These scores guide a unified flow-matching objective that attracts the policy toward progressive behaviors and repels it from failure-critical ones, while excluding ambiguous segments. This enables more comprehensive utilization of imperfect data and avoids unreliable supervision from ambiguous failure segments. Extensive experiments in simulation and the real world demonstrate consistent improvements over competing baselines across diverse failure scenarios, with ablations further validating the effectiveness of the proposed scoring and attraction-repulsion mechanisms. Our website is https://zhao-sq.github.io/carf/#.
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All authors Shuqi Zhao, Bang Du, Cheng-En Wu, Yichen Xie, Yixiao Wang, Masayoshi Tomizuka
Game Theory
Multiplicative Optimism for Constant Regret in Games
Ashkan Soleymani and Georgios Piliouras
We introduce Multiplicatively Optimistic Regret Matching (MORM), an uncoupled learning rule for finite general-sum games. Under simultaneous full-information self-play, every player achieves external regret $O(\sqrt n\log d)$ uniformly over all horizons, using only one-step optimism. The analysis combines a potential-based regret-matching argument with multiplicative stability and Hellinger control of strategy movement. A learning-rate safeguard additionally gives $O(\sqrt{T\log d})$ regret in the face of
adversarial utilities.
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Systems and Control
Multi-Agent Event-Triggered LQG Control under Shared Communication Constraints
Zahra Hashemi and Dipankar Maity
This letter studies event-triggered linear-quadratic-Gaussian (LQG) control for multi-
agent systems sharing a communication network with limited per-step capacity. Although the
agent dynamics are decoupled, the communication decisions are coupled through the shared network constraint, leading to a constrained multi-
agent scheduling problem. We show that the optimal control law remains certainty-equivalent and decouples across
agents through independent finite-horizon Riccati recursions, whereas the transmission schedule remains globally coupled. Based on this structure, we develop a centralized receding-horizon scheduling framework and reformulate the resulting problem as a mixed-integer linear program (MILP) using a closed-form characterization of the estimation-error covariance. To improve scalability, we derive a window-based skip-
pruning condition that safely fixes consecutive transmission decisions to zero before solving the MILP, and we propose an auction-inspired scheduler based on one-step transmission-benefit scores. Numerical results show that the proposed model predictive control (MPC) scheduler achieves the best trade-off between control performance and communication cost, while the auction-based scheduler attains performance close to MPC with substantially lower computational complexity.
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Computational Geometry · Cross-listed from math.MG
Improved bounds for universal convex covers of unit arcs
Ethan Keller
Moser's worm problem asks for a planar region of least area containing a congruent copy of every unit arc. We show that the infimum area $\alpha$ among convex universal covers satisfies $0.239\le\alpha\le0.24633\ldots$, reducing the gap between the previous refereed bounds by over $75\%$. For the lower bound, we choose four unit polygonal arcs and prove by finite subdivision that, however they are placed, their convex hull has area at least $0.239$. For the upper bound, we construct a quadrilateral of area $0.24633\ldots$ and prove cover universality by showing that its support inequalities force uncovered arcs to have length greater than one. The full proof is formalized in Lean 4 and verified by the Lean kernel. Code and certificates are available at https://github.com/ethan-keller/moser-worm-improved-bounds.
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Computation and Language
NemotronLabs VoiceChat: An Open Full-duplex Speech-to-Speech Model with Tool Calling Capabilities
Jagadeesh Balam, Travis Bartley, Edresson Casanova et al. (+51 more)
We introduce NemotronLabs VoiceChat, an open full-duplex
speech-to-
speech model with native tool-calling capabilities. NemotronLabs VoiceChat combines a streaming
speech encoder and decoder-only
language model with parallel specialized output streams for
agent text and structured function calls, an auxiliary RNN-T branch for incremental user transcription, and a streaming TTS decoder. This design enables the model to listen, transcribe, reason, invoke tools, and speak within a unified streaming architecture while preserving the temporal behavior required for natural conversation. On Full-Duplex-Bench 1.0, NemotronLabs VoiceChat achieves the lowest pause-handling takeover rates among evaluated open-weight systems, 100% takeover following user interruptions, and a 4.33/5 post-interruption response-quality score. On Full-Duplex-Bench 1.5, it resumes its response after user backchannels in 93% of cases. NemotronLabs VoiceChat obtains a 55.1 normalized average on VoiceBench and, on Full-Duplex-Bench 3.0 (FDB 3.0), achieves 82.5% tool-selection F1, while argument accuracy and end-to-end tool execution remain areas for improvement. These results demonstrate that full-duplex interaction,
speech recognition and generation, general
language capabilities, and external tool use can be integrated in a single open
speech-to-
speech model without sacrificing real-time conversational behavior.
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All authors Jagadeesh Balam, Travis Bartley, Edresson Casanova, Sanjay Chauhan, Chen Chen, Zhehuai Chen, Zijia Chen, Francesco Ciannella, Shalini De Mello, Slyne Deng, Mikyas Desta, Harishchandra Dubey, Slim Essid, Nourchene Ferchichi, Boris Ginsburg, Mariana Graterol Fuenmayor, Negar Habibi, Kevin Hu, Anand Joseph, Viraj Karandikar, Myungjong Kim, Viacheslav Klimkov, Seelan Lakshmi Narasimhan, Lily Lee, Jason Li, Eileen Long, Ameya Mahabaleshwarkar, Aditya Malte, Adi Margolin, Amrita Mazumdar, Sasha Meister, Valentin Mendelev, Koki Nagano, Oluwatobi Olabiyi, Seonwook Park, Ankita Pasad, Yifan Peng, Elena Rastorgueva, Jayda Ritchie, Jason Roche, Rajarshi Roy, Nikhil Srihari, Yuanhang Su, Yoshi Suhara, Viet Anh Trinh, Jinhan Wang, Piotr Zelasko, Hui Wang, Puhui Meng, Chaosen Zhang, Yunsheng Liu, Shawn Wang, Wenjing Li, Zhonglei He
Formal Languages and Automata
Automata-Theoretic Verification of Interval Markov Decision Processes
Sarvin Bahmani, Soumyajit Paul, Sven Schewe et al. (+2 more)
Interval Markov decision processes (IMDPs) provide a natural framework for modeling
stochastic systems with uncertain transition probabilities, represented by probability intervals and resolved adversarially. Such uncertainty arises naturally, for example, when the transition model is learned from finite data or obtained through model-based reinforcement learning. In this paper, we study the automata-theoretic verification of IMDPs against rich temporal specifications, including all LTL specifications, by considering the broader class of {\omega}-regular objectives. We show that classical automata-theoretic verification techniques extend to IMDPs, but with a sharp distinction determined by the structure of the transition intervals. For stable IMDPs, where either the upper bound is zero or the lower bound is strictly positive, verification reduces to ordinary MDP analysis and can be carried out using the standard automata used in that setting (good-for-MDP automata). For unstable IMDPs, where intervals may include zero while the upper bound is strictly positive, verification becomes game-like and requires automata whose nondeterminism can be resolved on the fly (good-for-games automata). Building on these insights, we develop
algorithms for verifying {\omega}-regular specifications over IMDPs and derive probabilistic guarantees when the interval model is learned from sampled data. The resulting framework enables principled verification of
stochastic systems under probabilistic model uncertainty, connecting automata-based verification with data-driven
stochastic modeling.
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All authors Sarvin Bahmani, Soumyajit Paul, Sven Schewe, Sadegh Soudjani, Ashutosh Trivedi
Computational Engineering
The ecological collapse of color: photoreceptor number buys a geometric hue manifold that natural spectra never fill
Mohammad Rostami
We ma[ the profound gap between the theoretical color dimensions an organism's eye can perceive and the actual color dimensions supplied by the natural world.
Geometric theory predicts that n photoreceptor classes allow an organism to perceive a hue
manifold homeomorphic to an (n-2)-dimensional sphere. While this math was previously only derived analytically for humans and birds, this research empirically proves the existence of this biological “topological ladder” across multiple species using persistent homology. We recover a dichromat's line, a trichromat's hue ring ($S^1$), a tetrachromat's hue sphere ($S^2$), and a pentachromat's glome ($S^3$) using real animal cones. Crucially, the study reveals that the “ecological color
manifold”, the colors actually created by natural reflectance spectra under natural light, falls dramatically short of these
geometric capabilities. Across 25 diverse species spanning three independent hyperspectral databases, an animal's
geometric color capacity climbs steeply with more photoreceptors, yet the actual effective dimension of the colors they experience stays stuck near unity. As a result, the fraction of available geometry the world actually fills steadily declines as receptor counts grow. A controlled decomposition shows this mismatch is driven by the low spectral rank of nature rather than receptor limitations; the effect vanishes in a simulated, full-rank world. The real-world constraint yields an ecological prediction confirmed on held-out species: aquatic tetrachromats, whose light fields are spectrally narrowed by water, collapse more deeply than land or air species ($p = 0.012$).
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Artificial Intelligence
Learning Cardiac Features: ECG Biometrics Across Time and~Exercise
Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine et al. (+1 more)
Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authentication, this paradigm holds significant potential to secure sensitive cardiac data and to serve as a pretext task in self-supervised learning. Yet, most studies remain confined to singlesession, resting data, leaving robustness to temporal and physiological variations largely untested. We address this gap by evaluating ECG biometrics under realistic conditions involving exercise-induced stress and cross-session variability. A Siamese ResNet with late multi-lead fusion strategy is trained on a large ECG
dataset extracted from cardiopulmonary exercise tests and evaluated with a exercise-and time-aware protocol, as well as on public
benchmarks. This first extensive assessment of ECG biometrics under combined physiological and temporal variability achieves an intra-session rest-to-peak EER of 1.7% and stateof-the-art 3.9% on the CYBHi
dataset. Findings support the presence of an intrinsic cardiac signature resilient to physiological and temporal drift.
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All authors Luca Thiebaud, Paul Chauchat, Mustapha Ouladsine, Stéphane Delliaux
Machine Learning · Cross-listed from math.ST
Schedule optimization for tau-leaping in masked discrete diffusion
Cecilia Secchi and Giacomo Zanella
Masked
diffusions are popular generative models for discrete distributions. Unlike standard autoregressive sampling, they reveal several coordinates in parallel, approximating each block's joint conditional law by a product of one-coordinate conditionals. The resulting procedure, usually called tau-leaping, reduces computational cost but introduces a factorization error ($\varepsilon_\text{fact}$), even with perfectly learned predictors. We study the resulting tradeoff between generative accuracy and computational cost, focusing on how to choose a denoising schedule to minimize $\varepsilon_\text{fact}$ for a fixed sampling budget. To do so, we establish an exact integral representation of $\varepsilon_\text{fact}$ separating the schedule from the target's dependence structure, summarized by a dependence density $\rho$. This representation yields recursive stationarity equations for optimal schedules and allows us to quantify how estimation errors in $\rho$ affect schedule selection. As the dimension $N$ and sampling budget grow, we characterize the optimal schedule and quantify the cost of random block sizes relative to a deterministic planner. We highlight a fundamental dichotomy: if $\rho$ converges uniformly to a strictly positive continuous profile as $N\to\infty$, schedule optimization can only improve the leading constant of $\varepsilon_\text{fact}$, while if $\rho$ degenerates, schedule optimization can improve the asymptotic order. Examples based on stationary processes and exchangeable mixtures illustrate these regimes.
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Social and Information Networks
Setting the clock: Evaluating temporal window parameters for coordinated behavior detection
Georgios Panayiotou, Lorenzo Mannocci and Maurizio Tesconi
Coordinated behavior is a central mechanism of online collective action. On social media platforms, it can support legitimate mobilization, but it can also be exploited in disinformation campaigns, astroturfing, and information operations. Detecting coordinated behavior on social media platforms typically relies on coordination networks, where users are linked when they perform similar actions within shared temporal windows. While the temporal window is central to how coordination is operationalized, it is often treated as an implementation detail rather than as a substantive modeling decision. This paper presents a first analysis of how two key temporal parameters, window length and stride, affect the detection of coordinated communities within information operation campaigns. We find that window length determines which coordination patterns are detectable, while window stride has negligible effect on precision and recall. Our analysis highlights selecting appropriate temporal window parameters as an open methodological challenge requiring careful treatment.
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Cryptography and Security
Provisional Reachability: Containing Agents by Making Every Crossing Revocable
Yoshiaki Takashita
A companion paper found that what a defender must block over time has units: bits per period [Takashita, 2026a]. This paper sets it. Hold every crossing in escrow for one period, audit each held item independently with probability r, and revoke the window if any audit catches something. An adversary crossing k times, each carrying c bits, expects kc(1-r)^k, maximised at k* = 1/ln(1/(1-r)), a bound of L(r) ~ c/(er) per window. The bound is a supremum over the adversary's choice, so the scheme may be public; simulation matches it to 7.7 standard errors. It is a rate, not a total: escrow alone still lets the secret assemble in every run. But if the secret decays at a fraction mu of held bits per period, holdings converge to g/mu at any horizon, so an L-bit secret is unreachable once mu > g/L – an error threshold in Eigen's sense, sharp where the closed form puts it (100% of runs assemble at 0.9mu*, 0% at 2mu*, over 20,000 windows). Deception that needs the adversary to reason badly fails: a surface whose names lie left accuracy at 18 of 18, and 100% at three reader strengths. Withholding reference works, and differently: no reader would commit at all. Keying the entry points hides 0.10 bits of what a module does; keying the denotation hides 2.64 of 3.00 at chance accuracy, while 100% of readers still call it ordinary Python. Variance must be removed from the audit rate, where loot is convex in r, and added to the activation budget, where survival is multiplicative: extinction 70% to 100% at a fixed mean. End to end the stack takes the leak from 100,000 to 59 bits, a factor of 1,704, leaving 12% of legitimate work standing; keying the window to the caller restores that to 100% at no cost in leakage, at the price of a bound that is per principal. Of 65 read-only tools, escrow leaves 2,400 bits per call: a factor of 10, not infinity.
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Computers and Society
Beyond the Desert Label: A Pathway Diagnostic for User-Centered Smart Mobility Service Design
Oluwasegun Adegoke and Sevgi Erdogan
Smart-city mobility platforms increasingly rely on spatial screening tools to identify neighborhoods where public transit fails dependent users, but a single transit desert label can mask very different user problems: localized mismatch between service and concentrated need, or basic absence of usable service. These call for different user-centered responses. This paper introduces a pathway-based, reproducible, data-driven diagnostic that distinguishes relative transit mismatch from minimum-service failure and reports the specific service attributes (frequency, span, weekend service, walking access, and destination accessibility) driving each classification. The workflow combines open data (GTFS, ACS, LEHD, Census, and OpenStreetMap), detects spatially coherent mismatch using Local Moran's I, and applies an equity-informed service-failure test that centers vulnerable users. Applied to Baltimore, Philadelphia, Nashville, and Dallas, the diagnostic shows that legacy-transit cities are dominated by localized mismatch, while auto-oriented cities show broader minimum-service failure, with distinct service-deficit profiles in each case. By making the mechanism behind an under-service label explicit, the tool supports more inclusive, user-centered smart-mobility planning across cities with different transit baselines.
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Machine Learning
RACER: Role-Aligned Competence Estimation for Human-AI Routing
Joshua Strong, Emma Sun, Alexander Capstick et al. (+3 more)
Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this problem to unseen experts using a small context set of expert behavior. Neural context encoders such as L2D-Pop can be query-dependent, but may learn routing shortcuts tied to absolute class coordinates. Identity-Free Deferral (IFD) removes such shortcuts through role-indexed classwise competence profiles, but its estimates are constant within each class and cannot capture instance-level expert specialization. We propose RACER—Role-Aligned Competence Estimation for Routing—a role-relative framework for estimating an unseen expert's competence from context. RACER estimates the posterior-predictive probability that the expert is correct on a query under each candidate class role, then combines these estimates with the model posterior to obtain the Bayes-relevant expert-correctness probability. Nonparametric and neural kernel-pooling
estimators use candidate-role relations, shared aggregation, and symmetric summaries, excluding absolute class-identity channels. We prove coherent class-relabelling invariance, derive a Bayes-aligned deferral surrogate, and give a plug-in regret bound relating routing regret to classifier and competence-estimation error. On controlled synthetic
benchmarks, including a PathMNIST histopathology context-scaling study with simulated experts, RACER benefits from additional context under hidden subtype dependence and gives the strongest aggregate performance on a separately sampled unseen-expert split in the CIFAR-100 synthetic experiments. On the radiologist and human–AI chest-radiography
benchmarks (VinDr-CXR and CheXpert), the RACER family is competitive or best in budget-swept deferral, with calibration results varying across metrics and
datasets.
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All authors Joshua Strong, Emma Sun, Alexander Capstick, Pramit Saha, Cheng Ouyang, J. Alison Noble
Robotics
GALA: Geometry-Aware Latent Action Modeling for Vision-Language-Action Model Pretraining across Embodiments
Yichen Liu, Puzhen Yuan, Xiang Zhu et al. (+2 more)
Learning large-scale
vision-
language-action (VLA) models from multi-embodiment
datasets remains challenging due to heterogeneous action spaces across end effectors. Although latent action models (LAMs) can learn embodiment-agnostic action representations from diverse
video data, existing image-based LAMs often fail to capture fine-grained end-effector articulation, particularly finger-level
geometric changes in human and dexterous
robot hands. To address this limitation, we propose GALA, a Geometry-Aware Latent-Action modeling framework that augments image-based latent actions with 3D end-effector
geometric motion. However, naively incorporating point clouds yields fine-grained action representations with limited shared semantics, hindering cross-embodiment pretraining. To address this issue, we introduce the Unified End-effector Motion Representation (UEMR), which preserves fine-grained motion information while improving the cross-embodiment generalizability of latent actions. Building upon UEMR, GALA combines visual latent actions that capture scene-level dynamics with
geometric latent actions that capture shared fine-grained end-effector articulation, providing effective supervision for VLA pretraining from multi-embodiment data, including action-free ego-centric human
videos. Experiments on fine-grained motion probing, cross-embodiment
retrieval, and downstream VLA evaluation demonstrate GALA's effectiveness in modeling generalizable fine-grained motions across embodiments, achieving 68.3% RoboCasa-GR1 success rate and 75.5% real-world success rate. Code, appendix, and demos are available at https://puzhenyuan.github.io/GALA-website/.
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All authors Yichen Liu, Puzhen Yuan, Xiang Zhu, Yanjiang Guo, Jianyu Chen
Machine Learning
Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks
Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke et al. (+2 more)
Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational control. This paper presents a shared latent-space framework that connects simulator calibration and reinforcement learning control through a common learned representation of urban traffic dynamics. First, we develop a combinatorial MLP-autoencoder architecture that learns low-dimensional
manifolds linking simulator inputs (origin-destination demand, network parameters) to outputs (travel times, congestion patterns), enabling efficient
Bayesian optimization for calibration. This approach demonstrates superior sample efficiency compared to traditional dimension reduction methods, achieving better fit to observational data within fixed computational budgets. Second, we implement a deep Q-learning
agent with experience replay and target networks to optimize dynamic traffic assignment through scheduling and routing adjustments. In empirical evaluations on
benchmark networks, our approach reduces system-wide travel times by up to 51% compared to baseline operations. The learned latent representation is not only used to reduce the dimensionality of
Bayesian calibration, but is also incorporated into the reinforcement learning state representation, allowing the control policy to operate on compressed and calibrated traffic dynamics. This shared latent-space formulation provides a unified pathway from simulator calibration to adaptive operational control within intelligent transportation systems. Our results highlight the transformative potential of deep learning methods in urban mobility planning and management, particularly for large-scale networks where traditional optimization approaches face computational bottlenecks.
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All authors Adewumi Augustine Adepitan, Christopher J. Haruna, Oluwasegun Adegoke, Ayooluwatomiwa Ajiboye, Oluwatobi Oluwasakin