Computer Vision
How Far Can Coding Agents Go in Visual Generation?
Hengguang Zhou, Yuanhao Ban, Jiaqi Feng et al. (+3 more)
Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: general-purpose coding
agents that construct images through executable programs. Our study of zero-shot drawing on AgentDrawBench, a 200-
prompt benchmark, reveals a distinctive capability profile.
Agents excel at preserving structure and satisfying precise visual constraints, achieving competitive results in domains where compositional accuracy matters more than photorealistic appearance, while remaining limited in perceptual richness. Their visual expressiveness varies with the drawing interface, and a qualitative study on reconstruction shows how procedural skills extracted from human demonstrations support different artistic media. We then examine code as an external visual
language of thought: a structured, executable representation that
agents can construct and inspect. We test it in visual question answering task and find reconstructed visual code retains substantial information for question answering and, in some domains, supports higher accuracy than the original image. These abilities raise a further question: do
agents draw on a shared internal representation of scene content beyond a particular drawing program? We explore an internal visual
language-of-thought hypothesis, in which a shared, compositionally structured representation supports visual operations across drawing
languages. Cross-harness drawings and images assembled from independent single-pixel queries exhibit related layouts and tones.
Agents also maintain self-generated room arrangements across viewpoints after visible drawings are removed. These results demonstrate that the coding
agent would maintain a consistent and coherent world inside its imagine even before the harness gets involved and the generation starts.
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All authors Hengguang Zhou, Yuanhao Ban, Jiaqi Feng, Xiaohuan Pei, Justin Cui, Cho-Jui Hsieh
Systems and Control
A Performative Control Approach to Day-to-Day Traffic Signal Retiming
Shuchang Jiang, Xinwu Qian, Santiago Segarra et al. (+1 more)
Conventional fixed-time traffic signal optimization and recent day-to-day signal retiming generally model route adaptation under prescribed route-choice behavior and origin-destination (OD) demand, while neglecting the fact that travelers' behavior can be performative to the signal control policy. A signal timing policy learned from historical traffic observations must therefore account for how its own deployment changes the distribution on which it will be evaluated in the future. We formulate this problem as performative signal control under observed traveler responses and characterize performative stability of the coupled policy-demand system. We propose incremental day-to-day retiming using the local policy gradient at the current operating point, estimated offline through simulation-based zeroth-order optimization without an explicit day-to-day demand-response model. This gradual approach provides a feasible path for policy and demand to co-evolve toward stability. Experiments on a downtown Denver network show that our approach improves congestion, throughput, and stability, generalizes across different demand levels, and remains effective when the offline simulator contains mismatched travel demand and routing behaviors.
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All authors Shuchang Jiang, Xinwu Qian, Santiago Segarra, Zhongyuan Zhao
Computation and Language
Can LLMs Access their World Knowledge for Event Prediction?
Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi et al. (+2 more)
Recent work has questioned whether large
language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models. We ask whether this knowledge is truly inaccessible, or whether current methods fail to present event prediction in a form LLMs can use. We argue for the latter and show that LLMs become effective event forecasters when the interface is designed around two choices. The first grounds each event in a natural-
language description, and the second separates event semantics from the prediction space to prevent semantic bias from label associations. To test this, we introduce DescribeEvent, a
benchmark of ~150K events across seven
datasets and six domains with event-level descriptions. We find that capable LLMs substantially benefit from descriptions and symbolic labels, outperforming description-free variants and non-LLM baselines, while weaker models gain little. These results suggest that accessing LLM world knowledge for event prediction is an interface problem, and that event descriptions offer a practical way to elicit the knowledge LLMs already encode.
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All authors Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, Alexander Pashevich
Machine Learning · Cross-listed from stat.ML
Discrete Diffusion Models are Minimax Optimal Under Sparsity
Luca Blunda, Tyler Farghly, Umut Simsekli et al. (+1 more)
Discrete
diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than those of their continuous counterparts. For continuous
diffusion models, a prominent line of theory suggests that these models can adapt to low-dimensional structure in the data, thereby improving statistical efficiency and generalization. In this work, we investigate the discrete analogue of this phenomenon by studying the statistical rates of discrete
diffusion models under sparsity assumptions. By analyzing a nonparametric
estimator derived from a regularized score-
entropy objective, we establish minimax-optimal rates in the sparse regime. The proposed regularization can be viewed as an adaptation of label smoothing techniques introduced for classification, and does not require the knowledge of the sparsity parameter. This shows that discrete
diffusion models can adapt intrinsically to sparsity.
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All authors Luca Blunda, Tyler Farghly, Umut Simsekli, Alain Durmus
Machine Learning
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence
Michael Sullins and Ian A. Kash
Regret matching (and its variants) and counterfactual regret minimization are central
algorithms in practice for normal-form and extensive-form game solving. Yet these have historically lacked the stronger theoretical convergence guarantees available to optimistic and extra-gradient methods. Recent work introduced IREG-PRM+, a regret matching variant which does have strong theoretical convergence guarantees. However, it relies on a simultaneous, extra-gradient update. IREG-PRM+ is an instance of the more general IR-PRM framework, where empirically optimistic and alternating updates often have better performance. However, the prior extra-gradient analysis does not extend to these cases. We introduce a new approach that covers both settings. For simultaneous and alternating IR-PRM+ in finite two-player zero-sum
matrix games, previous-utility prediction recovers the constant-regret and convergence-rate guarantees previously obtained with extra-gradient prediction: external regret bounded independently of T, an O(T^{-1}) average duality gap, and an O(T^{-1/2}) best-current-iterate gap. Moreover, the current duality gap vanishes and the current profile converges to the Nash
equilibrium set. The key step bounds the component of consecutive strategy movement not controlled by the existing IR-PRM+ regret inequality using the relative growth of the nondecreasing regret-vector norm rho_t. Coupling this bound across the two players yields recurrences whose effective coefficient scales as 1/(rho_t^X rho_t^Y), preventing unbounded norm growth. We extend the analysis to finitely many simplex learners under a weighted Minty condition and, through the corresponding CFR variant IR-PCFR+, recover analogous guarantees for finite two-player zero-sum perfect-recall extensive-form games under the same full-reach condition as prior work.
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Machine Learning
Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction
Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan et al. (+2 more)
Real measurements are too limited to train an accurate indoor pathloss predictor that works across a wide range of buildings. Large-scale synthetic pretraining offers a way forward, but real building layouts are also scarce, and accurate ray-tracing simulators are slow, expensive, and often proprietary. We address both problems by introducing a generator that automatically creates diverse indoor layouts and a fast, open-source approximate ray tracer that computes
radio maps for them. Using this pipeline, we generate 128M synthetic samples, pretrain ResNet-101 and Swin-T encoder-decoders, and fine-tune them on high-quality
benchmark data. Despite simulator mismatch, our ResNet-101 model reduces error by 15% on average relative to the best known methods across five indoor
radio-map prediction tasks and improves transfer on a small real-measurement
dataset. Both architectures show predictable data-scaling trends: power laws fitted on 2M-16M samples predict the mean normalized downstream RMSE at 32M-128M with 3.33% mean absolute percentage error for ResNet-101 and 7.10% for Swin-T. These results show that pretraining on large amounts of cheap simulated data can improve indoor
radio-map prediction, even when real measurements and high-quality simulations are limited.
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All authors Khoren Petrosyan, Artashes Mkrtchyan, Rafayel Mkrtchyan, Hrant Khachatrian, Theofanis P. Raptis
Machine Learning
Data Density Scaling Laws for Image Self-Distillation
Alvard Barseghyan, Ani Vanyan, Hakob Tamazyan et al. (+1 more)
Current scaling law formulations suggest that increasing the number of unique samples in a
dataset improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we examine this assumption for image self-
distillation models. Through systematic experiments across diverse
datasets, namely Maxar, SATLAS, Waymo, nuPlan, Ego4D, ImageNet, and YFCC15M, we demonstrate that the effects of sample repetition on downstream performance are non-trivial and
dataset-dependent. We identify settings where repeatedly training on a smaller random subset can outperform training on a larger set of unique samples. This finding highlights a limitation of existing scaling laws. To address this gap, we propose a modified version of a Chinchilla-style scaling law that explicitly accounts for two factors: the number of unique samples and repetition per sample. This formulation provides a framework for understanding data-compute trade-offs and improves prediction in some regimes where repetition plays a central role, although its benefits are not consistent across
datasets. Finally, we examine how subset selection and augmentation affect downstream performance.
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All authors Alvard Barseghyan, Ani Vanyan, Hakob Tamazyan, Hrant Khachatrian
Machine Learning
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan et al. (+2 more)
The Practical Molecular Optimization (PMO)
benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design. As the field has moved to docking-based objectives, evaluation practice has become unstandardized, and different methods report results on different docking tasks, often without a shared protocol for fair comparison or sample-efficiency constraints. To address this, we introduce Practical Molecular Optimization for Docking (PMO-Dock), a
benchmark and protocol for docking-based optimization consisting of 25 tasks covering hit generation, lead optimization, and a new specificity task requiring strong on-target binding while penalizing off-target interactions. The protocol separates model development from final evaluation: validation tasks are used for hyperparameter tuning, and test tasks are held out for final evaluation and are not used during development. Evaluation on the test tasks is done under a strict oracle budget to reflect realistic drug-discovery settings. We
benchmark four diverse high-performing methods spanning different optimization paradigms, Saturn (reinforcement learning), GenMol (discrete
diffusion), Genetic-guided GFlowNet, and ChemLlama (LLM-based evolutionary search). Our results show no universally best method across tasks, additional analyses of hyperparameter transfer (cross-task and cross-category) and sensitivity show that transfer is often limited and varies by method, motivating method-dependent selection strategies. This
benchmark provides a concrete standard for evaluating generalization to unseen tasks and sample efficiency under a limited test-time oracle budget.
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All authors Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian
Numerical Analysis
Exponential Stability of Slow–Fast Stochastic Systems with a Small Delay
Yudong Wang, Kele Sun, Shijie Zhou et al. (+1 more)
In this paper, we investigate the exponential stability of coupled slow–fast
stochastic systems with a small delay. Under an averaged Lyapunov condition, we establish practical $p$th-moment exponential stability for $0<p<2$ using a cutoff-function approach to handle singular Lyapunov derivatives near the origin. Exponential stability in the $p$th moment and almost surely is further obtained through two distinct mechanisms: homogeneous dissipation of the fast dynamics or vanishing sensitivity of the slow coefficients to the fast state near the origin. We extend these results to locally Lipschitz systems, establishing exponential stability with arbitrarily high probability for bounded sets of initial data, thereby broadening the applicability of stability analysis for
stochastic systems with delayed feedback. Numerical examples illustrate our results.
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All authors Yudong Wang, Kele Sun, Shijie Zhou, Xuerong Mao
Computer Vision
Action Jets: Learning the Local Shape of the Action-conditioned Data Manifold
Anton Arapin, Chahyon Ku and Bernadette Bucher
World models are a powerful means of
reasoning and planning, but many of these action-conditioned predictive models are high dimensional and computationally expensive. These challenges limit the number of action candidates that can be evaluated by a planner. We show that this cost is avoidable for planners over short horizons, which only require local information with respect to the current state. For a fixed state, the frames reachable by varying a low-dimensional action (a camera movement or an end-effector command) lie on a low-dimensional
manifold. We estimate the local shape of that
manifold directly via a network trained to estimate how each image region moves per unit of each action axis supervised by data collected from samples by the generator. A correction head fit to trajectory data estimates the shape of this
manifold region up to a third-order Taylor
polynomial. We call this network estimating the Taylor
polynomial describing the
manifold shape an Action Jet and compute its radius of validity in closed form. By traversing this approximation of the local
manifold shape, we only have to perform inference once per state for any number of candidate actions. Scoring sixteen candidates from a single extraction is over 300x faster than with either source generator, and this gap widens with the number of candidates a planner evaluates. We measure against state-of-the-art action-conditioned generators, IRASim and WorldGym on BridgeData V2, a real 7-DoF manipulation
benchmark, and FrameCrafter on DL3DV scenes, where we chart from the novel-view generators DepthSplat and LagerNVS. Because only the tangent comes from the generator and every other term from the data, the Action Jet also predicts held-out frames more accurately than IRASim, the generator that supplied its tangent, at short horizons and at over 100x less compute.
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Cryptography and Security
CyberArena: Benchmarking AI Agents in Live Symmetric Attack-and-Defense Combat
Bangya Liu, Jianhong Tu, Yueyang Pan et al. (+18 more)
AI
agents are demonstrating increasingly strong offensive and defensive cybersecurity capabilities in the real world. Current
benchmarks mostly evaluate
agents in isolation against static targets. Real cyber operations, however, are dynamic and coupled: security postures constantly change as attackers and defenders adapt to each other in real time. Static
benchmarks quickly saturate and miss an
agent's ability to attack and defend simultaneously under live pressure. To address this gap, we present CyberArena, a dynamic, symmetric
adversarial benchmark built around multi-round attack-and-defense combat. In isolated sandboxes, two
agents compete under identical rules, acting concurrently as both attackers and defenders over continuously changing services. This converts static targets into evolving, semi-open challenges, directly testing an
agent's speed and precision in discovering, exploiting, and patching vulnerabilities. To sustain the
benchmark's longevity, an automated agentic pipeline continuously generates, reviews, and validates new environments. Furthermore, because pairwise contests are notoriously hard to quantify, we introduce a dynamical model that maps match dynamics into unified, multidimensional scores across offensive, defensive, and domain-specific capabilities. Evaluating 12 frontier LLM
agents across 40 challenges in 466 matches, CyberArena separates model capabilities where single-
agent baselines hit a ceiling. The trajectories read like a fight rather than an exam: defenders patch one hole and miss the next and certify repairs on stale evidence, while capable
agents think a move ahead, planting backdoors for later rounds and, in one case, writing their own packet sniffer to watch the opponent. Beyond a
benchmark, CyberArena serves as an adaptable, scalable experimental platform for future research on cybersecurity
agents.
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All authors Bangya Liu, Jianhong Tu, Yueyang Pan, Hanhong Zhao, Zhe Ye, Hongwei Li, Saastha Vasan, Katherine Lambert, Junmin Zhu, Hao Wang, Jienan Chen, Yunze Xiao, Minglai Yang, Zihan Wang, Hairong Yin, Hao Wang, Zhiwen Fan, Tianneng Shi, Zhun Wang, Chenguang Wang, Dawn Song
Computation and Language
Evaluating Large Language Models for Multilingual Abstractive Summarization: A Comprehensive Study of English and Bengali
Khan Mahi Al Atahar, Nazifa Tasneem and Ajwad Abrar
Abstractive summarization remains challenging across
languages, where linguistic quality and factual consistency may diverge, particularly in low-resource settings. We present a standardized zero-shot
benchmark of eight closed-source and open-weight LLMs on size-matched English and Bengali XL-Sum test sets. Our evaluation combines lexical and semantic metrics with reference-free factuality verification and multidimensional LLM-as-a-Judge assessment, providing a comprehensive analysis of both surface-level quality and factual reliability in multilingual summarization. Results reveal substantial cross-lingual variation: GPT-5.6 Luna leads several English lexical metrics but does not maintain the same advantage in Bengali, where LLaMA 3.3 70B achieves the highest ROUGE and BLEU scores. Gemini 2.5 Flash obtains the highest observed Bengali factuality score. Furthermore, evaluation methods produce different model rankings, highlighting the limitations of relying on a single metric for multilingual summarization assessment. These findings demonstrate that strong English summarization performance does not necessarily transfer to Bengali and emphasize the need for comprehensive evaluation frameworks for low-resource
languages.
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Robotics
Contact-UAN: Unsupervised Actuator Networks as a Residual World Model for Humanoid Control
Nolan Fey, Khai Nguyen, Yash Prabhu et al. (+2 more)
Humanoid policies trained in simulation can transfer qualitatively to hardware while failing to transfer the force interactions learned during training, compromising torque efficiency, load regulation, and robustness to impacts. This failure can arise from actuator sim-to-real gaps because forces depend on actuator torques. We introduce Contact-UAN, a residual world model that preserves a simulator's physics priors while correcting actuator dynamics to align simulated rollouts with recorded hardware rollouts. For humanoid motion with contact, this requires contact-consistent replay: if hardware playback in simulation begins with misaligned contact wrenches, the residual model learns to correct reset artifacts rather than actuator discrepancies. Using only onboard proprioception, Contact-UAN infers the simulator-side stance-foot pose needed to initialize replay with consistent contact forces. We show that a UAN trained on 6.5 minutes of real-world walking data generalizes to tasks that benefit from accurate force and impulse regulation and produces policies that transfer seamlessly to the real world, yielding 20% lower running torque, 43% lower force-tracking error, extreme compliance, accurate impedance control, and dynamic, robust jumps.
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All authors Nolan Fey, Khai Nguyen, Yash Prabhu, Gabriel B. Margolis, Pulkit Agrawal
Artificial Intelligence
Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models
Yupeng Qiu, Han Fang, Dhananjai Sharma et al. (+1 more)
Low-Rank Adaptation (LoRA) has made
diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful or problematic content is not faithfully disclosed. Hence, it is desired to develop a mechanism that, given a LoRA adapter, discovers the concept it was trained to generate, so as to support downstream investigation. We characterize the target concept through the conditioning input that most clearly exposes what the LoRA has learned. Such an input should induce coherent LoRA outputs that are distinctive from those of the base model and strongly align with the learned LoRA subspace. We therefore formulate concept discovery as searching for the conditioning input that jointly maximizes output coherence, LoRA-induced distinctiveness, and subspace
alignment. Even with a clear objective, however, optimizing it over an open-ended input space is prohibitively expensive, since reliable evaluation of coherence and distinctiveness requires repeated
diffusion rendering. Crucially, we observe that an input need not be rendered to determine whether it is promising. Its
alignment with the learned LoRA subspace provides a lightweight signal for localizing promising candidates before visual verification. Based on this insight, we develop a two-stage framework for text-to-image LoRAs. Subspace-Guided Evolutionary Search (SGES) uses render-free subspace
alignment to explore candidate
prompts and retain a compact shortlist, while Paired Visual Identification (PVI) renders only these candidates and compares the LoRA and base-model outputs to identify those exhibiting coherent and distinctive generation behavior. This design enables efficient and reliable discovery of previously unknown concepts encoded in LoRA adapters.
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All authors Yupeng Qiu, Han Fang, Dhananjai Sharma, Ee-Chien Chang
Artificial Intelligence · Cross-listed from cond-mat.mes-hall
Non-Abelian quantum Hall states at strong Landau level mixing: finding anyons with first-principles AI
Pierre-Antoine Graham, Ahmed Abouelkomsan and Liang Fu
Non-Abelian anyons, strange particles forming the basis of fault-tolerant topological
quantum computing, are predicted to emerge in fractional
quantum Hall states at even-denominator filling factors. Identifying which topological order is realized has proven to be a challenging problem that requires an accurate, non-perturbative treatment of realistic Landau level mixing, which has so far been missing from theoretical studies. Here we present the first complete solution of non-Abelian
quantum Hall problem that includes all electrons and all Landau levels, for
spin-polarized electrons at filling factor $\nu = 3/2$. We develop a momentum-resolved neural-network variational wave function, optimized solely on the microscopic
Hamiltonian, which gives direct access to the topologically degenerate ground states. Without any physics input about the expected outcome, it discovers a degenerate ground-state
manifold whose
entanglement reveals anyons with
quantum dimension $\sqrt{2}$, the hallmark of Ising-type non-Abelian order. By directly computing the overlap with trial wave functions, we further show that Landau level mixing favors the anti-Pfaffian over other candidate states. Our results demonstrate the power of neural-network wave functions in finding and characterizing non-Abelian anyons from first principles, in regimes of strong Landau level mixing, relevant to many recent experiments.
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Robotics
$\lambda_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation
Lambda-humanoid Team
Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting
robot demonstrations at scale remains costly. Human demonstrations offer a scalable alternative, but existing egocentric human supervision often omits locomotion and posture, while embodiment differences and controller dynamics hinder accurate end-effector execution. We introduce HumanVerse, a 500-hour
dataset of open-world human loco-manipulation pairing egocentric
video with synchronized body and hand motion. Our human-to-humanoid pipeline aligns camera viewpoints and arm appearance, and refines motion references and controller inputs offline to reduce end-effector errors while preserving whole-body coordination. We validate the pipeline through zero-shot transfer to Unitree G1 on representative whole-body manipulation and long-range loco-manipulation tasks, without target-task
robot demonstrations. Building on this pipeline, we develop $\lambda_0$, a humanoid foundation model for whole-body loco-manipulation, with
robot-aligned human demonstrations providing supervision for mid-training. Increasing whole-body human data consistently improves downstream task progress. $\lambda_0$ achieves 90.0% success on SIMPLE and 62.5% across real-world loco-manipulation tasks. We expect this study to guide scalable whole-body human data collection toward humanoid foundation models for richer interaction with the physical world. Homepage: https://lambdahumanoid.github.io/
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Machine Learning · Cross-listed from cond-mat.mtrl-sci
Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells
Qiwei Han and Chi Zhou
Existing machine-learning models for perovskite solar
cells struggle to predict devices made from new materials. Most treat device layers as arbitrary categorical labels and represent perovskite absorbers either through featureless composition vectors or through rigid
crystal graphs that require a pre-solved unit
cell. As a result, they cannot generalize to unrecorded
alloys or unfamiliar
cell architectures. We present the Virtual-
Alloy Physics-Aware Semantic System (V-PaSS), an architecture built around the physical continuity of perovskite solid solutions. V-PaSS represents arbitrary mixed-cation, mixed-halide absorbers by interpolating relaxed
crystal graphs of 23 pure $ABX_3$ endpoint compounds via Vegard's law with learned bandgap bowing. For the device stack, it couples layer sequence and
cell polarity (n-i-p or p-i-n) with interfacial band offsets and encodes chemical descriptions with a scientific
language model. Evaluated under an institution-grouped protocol where test devices come exclusively from unseen laboratories, an untuned default configuration of V-PaSS achieves the lowest error among fourteen tested models (2.861% MAE, a 5.2% relative reduction over a gradient-boosted baseline at 3.017%, and outperforming the best tuned
benchmark at 2.912%). V-PaSS pulls furthest ahead where test devices differ most from training: reducing error by 15.7-19.0% on unseen absorber
alloys, by 16.1% on mixed absorbers from new laboratories, and widening its lead over the baseline by 3.6$\times$ on devices with unobserved transport layers. Combined with tree ensembles, V-PaSS sets a new state of the art across all five
benchmark splits.
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Cryptography and Security
Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries
Inderjeet Singh
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize this limitation for embodied and tool-use AI. An adversary that matches the complete observation law until observable, irrevocable certification and retains intervention access afterward attains $v\mathbb{E}[(n-S_0)_+]$ expected post-certification events over $n$ trials, where $v$ is the active event probability and $S_0$ the unattacked certification time. The result covers adaptive and privately randomized evaluators. Switching at certification is exactly optimal for a fixed likelihood-ratio test under memoryless, event-increasing interventions without a binding intervention budget. A positive clean-certification requirement forces linear worst-case expected excess when intervention raises event rates over an authorized deployment horizon, even if certification is deferred to the deadline. In simulated RoCo collaboration, switching yields commanded-separation violations in 34 of 36 post-certification episodes. Preregistered randomized AgentDojo trials estimate 1.98 additional security violations per four-task continuation (95% interval [1.67, 2.29]). An
adversarial language-model controller infers hidden certification from noisy evaluation receipts and times intervention accordingly. With permanent revocation, a mixture likelihood-ratio monitor bounds cumulative excess above a prespecified reference rate at square-root order, up to logarithmic factors, on every sample path. A single fixed alternative can permit linear expected excess under stationary attacks. Against a controller observing the monitor's rule and statistic, live mixture monitoring reduces violations from 12.00 to 3.81 per 24 assigned task opportunities. Revocation also reduces tasks executed.
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Cryptography and Security
Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model
Mamady Delamou, Luan Chen and Arsenia Chorti
Physical-layer spoofing attacks pose a critical threat to wireless localization systems by manipulating channel state information (CSI). Detecting such attacks becomes particularly challenging when the attacker and the legitimate node are spatially proximate, as their CSI characteristics become increasingly similar. To address this challenge in indoor scenarios, we propose an adaptive Gaussian mixture model (GMM)-based detection framework that learns legitimate CSI patterns and detects spatial inconsistency through statistical anomaly detection. Using a massive multiple-input multiple-output (
MIMO) experimental
dataset with 64 uniform linear array (ULA)
antenna and 5 mm spatial resolution, we evaluated our detector against CSI spoofing attacks with spatial displacements ranging from 50 mm to 450 mm. The adaptive GMM achieves 95-98% detection rate for displacements above 150 mm while maintaining 1% false alarm rate. We
benchmark our approach against conventional GMM, local outlier factor (LOF), one-class support vector machine (SVM) and k-nearest neighbors (k-NN) classifiers, demonstrating overall superior detection performance of our proposed classifier.
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Machine Learning · Cross-listed from stat.ML
Semi-Amortized Bayesian Inference with Reusable Local Posteriors
Niek Stevenson, Paul-Christian Bürkner, Andrew Heathcote et al. (+1 more)
Amortized
Bayesian inference uses simulations to perform
Bayesian estimation when likelihoods are expensive or analytically unavailable. However, learning the full joint posterior is challenging in high-dimensional models with complex dependencies. We introduce a semi-amortized method that divides parameters into smaller blocks, and uses conditional independence to identify the information needed to infer each block. We train neural
estimators for blocks whose posterior updates are computationally expensive or frequently repeated. Each
estimator learns from simulations of the relevant model components to approximate the block's full conditional posterior, given the relevant observations and the values of the variables on which it depends. A Markov chain then alternates direct draws from these
estimators with conventional updates of the remaining variables. Each neural update uses the current conditioning values and requires neither likelihood evaluation nor an acceptance-rejection step. Where the same conditional structure recurs, one
estimator can be reused across blocks and
datasets, with an output dimension determined by the block rather than the full model. We illustrate the approach in high-dimensional and computationally expensive hierarchical and spatiotemporal models.
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All authors Niek Stevenson, Paul-Christian Bürkner, Andrew Heathcote, Stefan T. Radev
Machine Learning · Cross-listed from stat.ML
Wong–Zakai Convergence and FNO Learning for Stochastic Volterra Equations
Wonjae Lee, Jiuk Jang and Hyungbin Park
This paper studies Wong–Zakai convergence and
Fourier neural operator learning for
stochastic Volterra integral equations (SVIEs) describing random systems with
memory. A Wong–Zakai
theorem establishes convergence of pathwise solutions driven by piecewise linear approximations of Brownian motion to the corresponding SVIE solution. This convergence provides a foundation for using mirror-padded
Fourier neural operators to learn the solution operator mapping initial conditions and driving noise to entire solution trajectories. Numerical experiments demonstrate competitive prediction accuracy and generalization to temporal grids finer than those used for training, while assessing the influence of padding and computational cost. As a follow-up to \citet{LKP:26}, this study extends their operator-learning framework to SVIEs through separate convergence and approximation analyses tailored to convolutional
memory.
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Artificial Intelligence
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
Yuxiao Hu, Qian Li, Ding Wang et al. (+4 more)
Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific
conjecture should be retained, revised, or rejected. A
conjecture may admit multiple mathematical realizations, and similarly accurate equations may imply different explanations. This motivates a shift from refining individual expressions to developing and testing the
conjectures that guide them. We introduce SCOPE, a framework that sustains multiple
conjecture-driven research trajectories. Multiple scientist
agents independently pursue
conjectures through equation modeling, question formulation, and active experimentation, then exchange evidence to revise their
conjectures and research directions. Their findings are consolidated into a scientific report linking models, conclusions, and evidence. Existing equation-discovery
benchmarks primarily assess recovered expressions, leaving the evidence and scientific conclusions established through the investigation underexamined. We therefore introduce ClosureBench, an interactive
benchmark of 60 closure-discovery tasks across five scientific domains. ClosureBench focuses on closure-discovery tasks to give equation discovery a concrete scientific purpose. Each task embeds an unknown algebraic relation in a known PDE system, where
agents control experimental conditions in OpenFOAM to acquire new data for equation discovery. Beyond equation accuracy, ClosureBench proposes three metrics to evaluate the discovery process: evidence faithfulness, mechanism insight, and experimental effectiveness. Compared with diverse baselines, SCOPE achieves higher equation accuracy on both ClosureBench and a real-world scientific discovery case, while producing more accurate, reliable, and effective scientific discoveries.
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All authors Yuxiao Hu, Qian Li, Ding Wang, Siheng Chen, Linfeng Zhang, Jinyue Yan, Yuntian Chen
Information Theory · Cross-listed from stat.ML
Delay Complexity Dimension: Learning with Delayed and Censored Labels
Madhava Gaikwad
We study PAC learning of binary classifiers when labels arrive after random delays and are censored beyond an observation window $W$. How many samples does this observation model require? We propose a distribution-dependent answer, the Delay Complexity Dimension (DCD). The DCD combines the VC dimension with the exact label-information rate of the observation channel. Every learner needs $\Omega(\mathrm{DCD}/\varepsilon^2)$ samples. An oracle pseudo-label ERM matches this rate on the instance family of the lower bound, up to constants and logarithmic factors. On that family the DCD is within a constant factor of the classical censored-ERM complexity, and whether the DCD rate is achievable where the two separate remains open. The theory also yields a quantization scheme under which delay information lowers the residual label variance, an expedition policy with a $(1-1/e)$ guarantee for its version-space objective, and a phenomenon with no analogue in the no-delay setting. Under a fixed collection horizon, a moderate window can strictly beat immediate labels. Experiments on synthetic data and the Criteo conversion logs support these predictions. A budget-matched test of a companion labeler-diversity prediction is reported as a negative result.
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Sound
An Initial Study of Physiological Markers for Deepfake Speech Detection
Vojtěch Staněk, Michal Frič, Kamil Malinka et al. (+1 more)
Conventional deepfake
speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions. Human
speech, however, contains physiological markers associated with respiration, phonation, and temporal coordination arising from the
speech-production system that synthesizers may not reproduce faithfully. This motivates the hypothesis that deepfake
speech can be detected by identifying inconsistencies in these patterns, which we test using 36 handcrafted physiological markers organized into respiratory, vocal, and temporal categories. We evaluate the marker vectors with logistic regression and SVM classifiers on pooled English recordings. The best in-domain system achieves a 21.40% equal error rate (EER), while logistic regression achieves 32.77% on the out-of-domain
dataset pair. Since the markers describe specific
speech-production properties, the logistic-regression coefficients indicate whether higher marker values push the classifier toward bona fide or deepfake
speech. These results provide initial evidence that physiological markers offer a useful, interpretable signal for deepfake
speech detection.
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All authors Vojtěch Staněk, Michal Frič, Kamil Malinka, Anton Firc
Artificial Intelligence
The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Robert Tjarko Lange, Luca Grillotti, Yutaro Yamada et al. (+2 more)
Modern coding
agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and autonomously operate with ever-increasing time horizons. Despite these developments, fully autonomous research
agents suffer from a limited ideation radius and myopic hill-climbing search dynamics. To bridge this gap, we introduce The Agentic Scientist, an end-to-end distributed pipeline for human researchers to conduct computational research alongside modern coding
agents. Building on its predecessor, our system decomposes research into task drafting, idea generation, planning, sandboxed experimentation, result aggregation, baseline and ablation construction, post-hoc analysis, and report writing. Importantly, each step in the pipeline is powered by the next generation of coding
agents and leverages agentic self-verification. The overall research process is driven by a meta-controller that manages parallel research
agents and enforces rubric verification at each stage. This design enables the same orchestration layer to be reused across heterogeneous domains with different execution environments, agentic coding harnesses, and evaluation metrics. Our system can be used across a spectrum of autonomy: either as a co-scientist tool or to perform end-to-end scientific investigation. Within the co-scientist setting, we leveraged a development version of our system to submit a single research paper, which got accepted for publication at ALIFE 2026. We further apply The Agentic Scientist fully autonomously to open problem-solving tasks in machine learning, and demonstrate the system's capability of scientific discovery across different domains of computational research. Our ablations suggest that reliable scientific autonomy depends not only on base model capabilities, but also on the cross-
agent orchestration layer.
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All authors Robert Tjarko Lange, Luca Grillotti, Yutaro Yamada, Rujikorn Charakorn, David Ha
Information Theory
On the Investigation of Antenna Beam Pattern Models for Fast Beam Placement in LEO Satellite Networks
Ninh Thi-Thanh Tam, Nguyen Viet Anh, Nguyen Minh Quan et al. (+1 more)
In this paper, we investigate the impact of
antenna beam pattern models on the beam placement problem in Low Earth Orbit (LEO) satellite networks. Unlike conventional beam placement studies that often rely on a fixed
antenna pattern or a predefined half-power beamwidth, this work compares four representative gain models, namely the Bessel $J_1$ model, the combined Bessel $J_1+J_3$ model, the Gaussian model, and a 3GPP-TR 37.842 model. For each
antenna pattern, the corresponding half-power angle is obtained and used to construct a user compatibility graph. The beam placement problem is then transformed into a Minimum Clique Cover problem and solved using a graph coloring-based
algorithm. Numerical results compare the considered
antenna models in terms of the number of active beams, load balancing, statistical channel gain-to-noise ratio (SCGNR), and signal-to-interference-plus-noise ratio (SINR). We analyze the impact of increasing the number of active beams on SINR-based spectral efficiency to explain the importance of carefully controlling the number of active beams. The results show that
antenna beam pattern models affect both beam resource usage and link quality, highlighting the need for
antenna-pattern-aware beam placement in LEO satellite networks.
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All authors Ninh Thi-Thanh Tam, Nguyen Viet Anh, Nguyen Minh Quan, Trinh Van Chien
Artificial Intelligence
Why Static Skill Inconsistencies Do Not Equate to Runtime Risks: A Behavioral Analysis of LLM Agent Execution
Fengrui Fan, Huahao Wang, Jingyi Fang et al. (+3 more)
LLM
agent frameworks heavily rely on external skill assets to standardize tool usage, encode operational workflows, and supply executable runtime resources. Despite their widespread adoption, multi-component skill packages frequently suffer from cross-artifact inconsistencies, where documentation, scripts, and configuration files specify conflicting behavioral constraints. Although static auditing techniques can uncover such contractual discrepancies, static defect volume cannot reliably reflect real-world
agent performance degradation. This gap leaves practitioners without rigorous evidence to assess the practical risk of flawed
agent skills. This paper presents a systematic empirical study to demystify the runtime implications of skill inconsistencies. We conduct large-scale auditing over 575 community and
benchmark skills, yielding 1,182 manually validated contract inconsistencies. We further build a controlled evaluation pipeline with 49 executable
agent tasks and three representative LLMs across NoSkill, OriginalSkill, and FixedSkill experimental settings. Through fine-grained behavioral analysis of 441 paired execution trajectories, we quantify the actual performance impacts of resolved defects.
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All authors Fengrui Fan, Huahao Wang, Jingyi Fang, Yutao Hu, Yueming Wu, Deqing Zou
Artificial Intelligence · Cross-listed from eess.IV
Personalising Deformable Image Registration with Clinical Context and Deformation History for Adaptive Radiotherapy
Caiwen Jiang, Songlin Zhao, Yuzhen Ding et al. (+23 more)
Adaptive radiotherapy must account for anatomical changes during treatment, but doing so depends on reliably mapping targets, organs at risk and dose across serial images. Existing registration methods treat each image pair independently, without using radiotherapy-specific context or the patient's preceding anatomical trajectory. We developed Clinical-context-Aware and Patient-adaptive Deformable Image Registration (CAP-DIR), integrating source contours, dose, clinical text and prior-registration features. We evaluated 16,361 CT examinations from 3,473 patients across four institutional cohorts and five public
benchmarks. CAP-DIR increased internal-test organ-at-risk-region normalized cross-correlation from 84.76% to 96.92% relative to the strongest learning-based comparator, with low deformation folding, and remained superior across seven external cohorts. In a blinded review of 20 longitudinal cases, clinically acceptable contours increased from 45.0% to 82.5% for clinical target volumes and from 22.5% to 62.5% for organs at risk, while estimated editing time decreased. In a controlled
benchmark, CAP-DIR reduced global dose-mapping error by 34.5% relative to the strongest comparator. These findings show that clinically informed, longitudinal registration can improve contour usability and dose mapping for adaptive radiotherapy.
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All authors Caiwen Jiang, Songlin Zhao, Yuzhen Ding, Jia Mi, Samir H. Patel, Alexandra Bennett, Keldon K. Lin, Terence T. Sio, Jonathan B. Ashman, Lisa A. McGee, Jean-Claude M. Rwigema, William G. Rule, Sameer R. Keole, Sujay A. Vora, William W. Wong, Nathan Y. Yu, Michele Y. Halyard, Steven E. Schild, Safia K. Ahmed, Daniel J. Ma, Robert L. Foote, Michelle A. Neben Wittich, Thomas J. Whitaker, Haibo Lin, Dinggang Shen, Wei Liu
Hardware Architecture
CacheSmith: Accurate RAG Serving under Latency SLOs with Adaptive KV Reuse
Xinyue Ma, Damin Lee, Aashaka Shah et al. (+1 more)
This paper presents CacheSmith, a KV cache reuse system for RAG prefill that maximizes quality-gated SLO attainment. While context caching lowers prefill latency by reusing precomputed KV states for retrieved chunks, it misses cross-chunk
attention that a full prefill would compute over concatenated inputs. Prior selective recomputation schemes tackle this problem but enforce static, one-size-fits-all configurations that fail to effectively handle request heterogeneity or dynamic latency budgets. CacheSmith decomposes selective recomputation into a three-phase workflow of WarmUp, Probe, and Patch that can be dynamically configured to exploit a wide range of cost-accuracy trade-offs. At serving time, an SLO-aware scheduler in CacheSmith forms batches to mitigate inter-request interference during layer-by-layer prefill, while a budget-aware planner configures recomputation phases across the batch to maximize the number of requests satisfying both generation quality and latency targets. Both components make fast, solver-free decisions over profiled cost-accuracy bands. On four
benchmarks across varying arrival rates and burstiness levels, CacheSmith improves quality-gated SLO attainment by up to 27.5%, over four state-of-the-art baselines.
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All authors Xinyue Ma, Damin Lee, Aashaka Shah, Myeongjae Jeon
Robotics
ChunkAct: Action Chunking for Agentic Robotics
Bingxin Xu, Wenrui Bao, Xinxin Liu et al. (+3 more)
Agentic robotics places a large
language model (LLM)
agent in the
robot control loop, where it composes perception, motion, and
vision-
language-action (VLA) tools under the guidance of a
memory and thereby generalizes to new tasks without retraining. This generality comes at a steep price in efficiency. Every primitive requires a round of LLM
reasoning over the full interaction history, so the cost of an episode grows with the number of primitives. We find this
reasoning largely redundant: once a sub-goal is chosen, its primitives follow a routine already prescribed by
memory. We present ChunkAct, which introduces action chunking to agentic robotics. Online, a single call launches a chunk of chained primitives, which executes autonomously and returns control only upon failure. Offline, ChunkAct compiles the
memory into these chunks, so execution is
memory-free at test time and the planner is reserved for decisions that require
reasoning. LLM computation therefore scales with the number of decisions rather than the number of primitives. On LIBERO-Pro, ChunkAct improves task success by 5.0 points over the state of the art in agentic robotics while requiring 2.9x fewer LLM calls, 4.4x fewer input
tokens, and 2.3x fewer output
tokens.
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All authors Bingxin Xu, Wenrui Bao, Xinxin Liu, Jiawei Zhou, Yuzhang Shang, Emilio Ferrara