Artificial Intelligence · Cross-listed from econ.GN
Memory Scarcity, Open Models, and the Restructuring of the AI Industry, 2026-2030 – A quantitative scenario analysis of inference economics, training-cost divergence, and infrastructure solvency
Satoshi Matsuoka
We analyze how four forces restructure the AI industry over 2026-2030: the DRAM/HBM price surge, frontier-capable open-weight models (GLM-5.2), rapid inference-efficiency gains (near-Shannon-limit KV-cache compression, lightweight local runtimes), and the entry of Meta and xAI into compute resale on fleets bought before the
memory repricing. Formulating inference economics in dollars per petabyte of bandwidth delivered (\$/PB) -- model-agnostic for bandwidth-bound decode -- we show the entrant-incumbent cost gap never closes: a depreciation conveyor delivers newly amortized fleets to incumbents faster than hardware prices normalize (3.2x in 2026, 1.9x in 2027, re-widening to 3-4x by 2029-30). Training bifurcates into a luxury tier (\$18-38B per frontier run by 2030) and a mass tier (previous-frontier parity via RL/
distillation falling toward \$5M). Solvency of the announced buildout is confined to a corridor requiring roughly 2x annual
token-demand growth for four years with sticky premium pricing; a measurement critique shows public
token trackers overstate monetizable demand, and all pre-Q2-2026 projections predate the industry's shift from
token maximization to
token minimization. A vintage-breakeven analysis finds 2026 and 2028-29 capacity each fatally exposed to one pricing regime, with only the 2027 vintage robust. A greenfield custom-silicon entrant removes the merchant margin but not the
memory premium (central outcome: 25% success/34% mediocre/41% loss, improvable via staged go/no-go gates). China's LineShine LX2 – domestic HBM on a standard ISA – decouples its cost curve from the
memory crisis. Scenario probabilities: Rotating Landlord Oligopoly 25%, Commoditization Crash 25%, Jevons Absorption 20%, System-Layer Re-differentiation 18%, Geopolitical Bifurcation 12%. Solvency now depends on monetized bandwidth demand, premium stickiness, and vintage ownership.
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Artificial Intelligence · Cross-listed from cs.RO
Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators
Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller et al. (+2 more)
Across robotics, World Models (WMs) are increasingly used to evaluate action policies by simulating the consequences of actions in an imagined world, and returning a success or safety verdict. Yet a verdict is only as trustworthy as the WM that produced it, and the WM itself needs to be certified. In
video-generation WMs, fidelity metrics such as Fréchet
Video Distance (FVD) reward visual realism, but ignore whether the world responds correctly to the policy's actions, including those unseen in training. Classical simulation-based validation assumes a trusted simulator evaluating an untrusted policy, whereas generative WMs are themselves unverified learned artifacts. Hence, we argue that any WM used as a test oracle must first be accredited before its verdicts can serve as evidence. Building on credibility practices from safety-critical simulation, including Verification, Validation & Accreditation (VV&A), Safety of the Intended Functionality (SOTIF), and scenario-based testing standards, we define an admissibility ladder (L0-L4) that a WM must climb before its closed-loop verdicts are accepted as assurance evidence. Our framework is embodiment-agnostic, and is instantiated in autonomous driving (AD), where assurance methods for traditional simulation are most mature. Applied to two driving WMs, the lower rungs reveal a reversal: the model that ranks higher on visual generation quality (L0) ranks lower on action-following (L1-L2), so visual fidelity does not predict the action-robustness a closed-loop verdict depends on.
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All authors Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller, Mattia Piccinini, Johannes Betz
Artificial Intelligence
Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks
Ethan Chung, Chuanjun Zheng, Jasper Tan et al. (+3 more)
Vision-
language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging. We present ImagingBench, a
benchmark of 20 computational imaging tasks spanning five categories: ray and wave optics, image signal processing, inverse reconstruction, computational sensing, and calibration. ImagingBench evaluates three complementary settings: Expert, fixed expert-guided inverse reconstruction; Planner, planner-guided inverse reconstruction; and Forward, forward-system simulation for consistency checking. We
benchmark leading proprietary and open-source image-centric
multimodal systems, including Gemini, GPT, and Qwen, and compare them with representative task-specific non-agentic baselines. Across tasks, agentic models remain consistently weaker than specialized methods, especially on computational sensing problems such as lensless imaging, event-based reconstruction, time-of-flight imaging, and holography. Planner guidance provides only modest and inconsistent gains over the fixed-
prompt Expert baseline. Although the models often generate visually plausible outputs, their reference-based fidelity remains poor, revealing a substantial gap between semantic visual competence and physically grounded imaging performance. ImagingBench provides a unified testbed for measuring this gap and tracking progress in agentic AI for computational imaging.
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All authors Ethan Chung, Chuanjun Zheng, Jasper Tan, Jingxi Li, Haopeng Zhang, Huaijin Chen
Artificial Intelligence · Cross-listed from cs.LG
Predicting LLM Safety Before Release by Simulating Deployment
Marcus Williams, Hannah Sheahan, Cameron Raymond et al. (+8 more)
Pre-deployment safety evaluations aim to inform the downstream risks of releasing a new AI model. Yet most evaluations provide limited evidence about how often undesired model behavior will occur in deployment: they generally have insufficient coverage, are unrepresentative, and are generally recognizable as tests. To address these concerns, we study a simple way to simulate a model deployment: starting from de-identified conversations from a previous model deployment, we hold fixed the initial conversation prefix and regenerate the next response using a candidate model. The resulting responses can then both be audited for novel misalignments and used to estimate the prevalence of model misbehavior before deployment. We evaluate deployment simulation across four GPT-5-series deployments, using registered, outcome-blinded predictions for GPT-5.4 and retrospective analyses of three earlier releases. We find that deployment simulation produces informative estimates of post-deployment misbehavior rates and outperforms baselines based on adversarially selected production data; its evaluation-awareness point estimates were also much closer to production traffic than those from traditional evaluations. We also identify the realism of tool resampling as a central challenge for further improving predictions and share results suggesting that this challenge is surmountable even in complex tool-use settings. Finally, we show that deployment simulation can be seeded from public chat
datasets and remain informative about production misbehavior rates, suggesting a path for external researchers to run deployment-grounded evaluations without access to private production logs. Overall, deployment simulation helps evaluators forecast how
language models will behave in the real world and supports more quantitative assessment of deployment risk.
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All authors Marcus Williams, Hannah Sheahan, Cameron Raymond, Tomek Korbak, Deng Pan, Peilin Yang, Leon Maksin, Ningyi Xie, Phillip Guo, Ian Kivlichan, Micah Carroll
Artificial Intelligence · Cross-listed from cs.LG
Entropy Pacing Policy Optimization for Multi-Task Agentic Reinforcement Learning
Zetian Hu, Shunyu Liu, Junjie Zhang et al. (+4 more)
Recent breakthroughs of Reinforcement Learning (RL) have highlighted its potential for complex agentic Large
Language Model (LLM) tasks. However, existing efforts largely focus on single-task settings, whereas real-world deployment necessitates a generalist
agent capable of solving multiple tasks simultaneously. In this work, we identify a critical yet underexplored phenomenon in multi-task agentic RL: different tasks can exhibit exploration-exploitation pace mismatch. Specifically, easier tasks may converge early to low-
entropy policies that hinder learning on harder tasks, while harder tasks can, in turn, push easier tasks back toward high-
entropy exploration. This back-and-forth interaction creates inter-task
entropy crossovers and frequent
entropy spikes. Inspired by this observation, we introduce
Entropy Pacing Policy Optimization (EPPO) for multi-task agentic LLMs, which coordinates
entropy across tasks to stabilize multi-task optimization. At the core of EPPO is a task-wise dynamic clipping mechanism that replaces the fixed clipping threshold in Group Relative Policy Optimization (GRPO) with a task
entropy-aware adaptive bound, tightening updates for over-confident tasks while relaxing them for under-explored ones. Experiments on the multi-task agentic
benchmarks demonstrate that the proposed EPPO yields results superior to its counterparts.
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All authors Zetian Hu, Shunyu Liu, Junjie Zhang, Yongcheng Jing, Ting-En Lin, Yongbin Li, Dacheng Tao
Artificial Intelligence · Cross-listed from cs.CV
Tree-of-Thoughts Reasoning for Text-to-Image In-Context Learning
Stepanida Alekseeva, Jenifer Kalafatovich and Seong-Whan Lee
In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query image. Recent studies show that state-of-the-art
multimodal large
language models struggle with this setting, particularly due to limited compositional
reasoning and sensitivity to
prompt construction. In this work, we propose a Tree-of-Thoughts (ToT)
reasoning framework for T2I-ICL that introduces a multi-stage
reasoning and selection layer that generates, evaluates, and selects among multiple candidate hypotheses before constructing the final
prompt for image synthesis. By exploring alternative
reasoning branches and selecting a coherent interpretation, the proposed approach mitigates
prompt ambiguity and compositional errors. We implement the proposed approach in a complete ToT-T2IICL inference pipeline and evaluate it on the CoBSAT
benchmark. Both qualitative and quantitative results show that structured multi-branch
reasoning leads to more consistent and semantically aligned image generation compared to baseline and Chain-of-Thought prompting strategies, without any additional training or fine-tuning.
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Artificial Intelligence · Cross-listed from cs.RO
GeoProp: Grounding Robot State in Vision for Generalist Manipulation
Guoyang Zhao, Quanhao Qian, Gongjie Zhang et al. (+5 more)
Proprioception is fundamental to robotic manipulation, yet standard fusion methods often treat it as an isolated vector lacking explicit
alignment with visual
tokens. Without a direct correspondence between 3D kinematics and 2D feature maps, manipulation policies struggle to ground the
robot's state within the scene, frequently underperforming even
vision-only baselines. To address this, we introduce GeoProp, a lightweight, plug-and-play adapter that aligns proprioception with
vision through explicit
geometric grounding and spatial feature sampling. GeoProp projects the
robot state onto the image plane to sample localized visual features, constructing a grounded state
token. It then injects state-derived spatial priors into the corresponding visual features via FiLM modulation. To capture motion intent, GeoProp further samples features at a short-horizon predicted coordinate derived from recent kinematics, providing look-ahead visual context. Across 67 tasks, GeoProp improves
Diffusion Policy by 8.7% on 63 simulation tasks and pi_0 by 4.0% on the RoboTwin subset, and yields a 10.6% average gain across both policy families in the real world, while adding only 2-3% to the parameter count. These results demonstrate that GeoProp is a simple yet high-impact inductive bias for generalist
embodied policies. Project page: https://alibaba-damo-academy.github.io/GeoProp/.
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All authors Guoyang Zhao, Quanhao Qian, Gongjie Zhang, Wenhao Li, Jiuniu Wang, Xiaowei Lu, Deli Zhao, Ran Xu
Artificial Intelligence
Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
Lifei Liu, Haoran Yu, Xiaochong Jiang et al. (+3 more)
Safety evaluations of multi-
agent LLM systems often compare a direct
prompt with a planner-executor pipeline and report the difference as a single "pipeline effect." We argue that this aggregate is difficult to interpret because it conflates three mechanisms: harmful intent may be reframed as plausible operational work, the planner may refuse or transform the request, and the executor may act under delegation
prompts implying prior approval. To separate these factors, we introduce a five-condition controlled contrast design, evaluated on 30 synthetic harmful scenarios and an exploratory external validation set from four
agent-safety
benchmarks using LLM-judged compliance. Our results show that aggregate pipeline safety is not a stable architectural property. Operational reframing is the most portable risk signal, increasing compliance for GPT, Gemini, and DeepSeek across both scenario sets, while Claude is comparatively resistant. Planner behavior can offset this risk mainly through refusal; however, when the planner produces executable steps, the executor may become more compliant than under the direct operational baseline. Approval-framed delegation is sensitive to
prompt design, model pairing, and scenario source, and a skeptical executor
prompt sharply reduces compliance. Raw-direct model rankings can also mispredict deployed planner-executor behavior. Gemini is safest under raw direct
prompts in the primary set yet shows the largest amplification with a Claude planner, rising from 8.9 percent to 38.9 percent compliance. GPTs near-zero aggregate pipeline effect instead hides a reframing increase canceled by planner refusal. These findings suggest that multi-
agent safety evaluations should report reframing, planner behavior, delegation framing, and model pairing separately before attributing failures to architecture itself.
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All authors Lifei Liu, Haoran Yu, Xiaochong Jiang, Su Wang, Pin Qian, Yihang Chen
Artificial Intelligence · Cross-listed from cs.CV
AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis
Xinyue Du, Yibo Liu, Zhenglei Zhou et al. (+3 more)
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these
multimodal observations may improve diagnostic assessment, but naive fusion can degrade performance when MRI is noisy or intermittently unavailable. We propose AT-Attn, a temporal-aware
multimodal framework that combines Change-and-Time encoding, time-biased asymmetric cross-
attention, and gated fusion to integrate MRI with longitudinal clinical information. We evaluate AT-Attn on an MRI-retained ADNI cohort of 1,520 patients using structural MRI, six cognitive-scale trajectories, and seven static clinical variables under patient-level five-fold cross-validation. The main asymmetric AT-Attn model achieves accuracy 0.719+/-0.024, macro F1 0.721+/-0.023, ROC-AUC 0.873+/-0.013, and PR-AUC 0.783+/-0.018, outperforming unimodal and naive
multimodal fusion baselines while remaining competitive with strong tabular baselines. These results suggest that a temporal-aware and constrained fusion strategy can help structural MRI contribute clinically relevant complementary information for patient-level AD diagnosis support.
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All authors Xinyue Du, Yibo Liu, Zhenglei Zhou, Xuancheng Yao, Weimin Zhong, Qiuhui Chen
Artificial Intelligence · Cross-listed from cs.CV
Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
Yapeng Li, Bo Jiang, Ziyan Zhang et al. (+2 more)
Functional
brain networks exhibit a hierarchical organization across ROI, community, and whole-
brain levels, supporting local processing, inter-community coordination, and global integration. Recent studies have demonstrated that
brain community-aware modeling is beneficial for both diagnosis and biomarker identification of
brain networks. However, existing
brain graph modeling methods often struggle to model ROI-community interactions, thereby failing to fully exploit the hierarchy across ROI, community, and whole-
brain network levels. To address this issue, inspired by deep
hyperbolic learning in modeling hierarchical structures, we propose a novel framework, termed
Hyperbolic Learning on
Brain Graphs (HLBG), for
brain network analysis. The core idea of HLBG is to exploit the inherent hierarchical geometry of
hyperbolic space to model the hierarchical relationships among ROIs, functional communities, and the whole-
brain network, thereby learning hierarchy-aware and highly discriminative representations for
brain network data. Specifically, HLBG first projects representations from ROIs, communities, and the whole-
brain network into Lorentzian
hyperbolic space. Then, the multi-level hierarchy is imposed via two
geometric entailment constraints. In addition, we introduce a new Graph-aware Mamba (GaMamba) model, which incorporates
topology-derived structural
prompts into Mamba to capture long-range dependencies while preserving graph topological information. Experiments on ABIDE-I and REST-MDD demonstrate that HLBG outperforms state-of-the-art methods and identifies disorder-relevant functional biomarkers.
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All authors Yapeng Li, Bo Jiang, Ziyan Zhang, Dongdong Chen, Zhengzheng Tu
Artificial Intelligence · Cross-listed from cs.LG
Multiplication Beyond Groups: Stratified Fourier Mechanisms in Transformer Circuits
Zitong Andrew Chen, Junaid Hasan, Akhil Srinivasan et al. (+2 more)
Transformers have demonstrated a remarkable ability to learn algorithmic
reasoning, yet mechanistic analyses have mostly focused on globally invertible operations such as cyclic addition and group composition. In this work, we investigate how small
transformers learn modular integer multiplication over composite
moduli, a fundamentally non-invertible operation due to the presence of zero-divisors. We propose the monoid extension: a localized generalization of Group Composition via Representation (GCR) that suggests the learned computation does not rely on a single global representation space. Instead, the model partitions the input space into local hierarchical algebraic regions, where group-like structure survives and
Fourier mechanisms can be applied. In
transformers trained on square-free modular multiplication, we find that
embeddings organize around these regions,
attention exhibits class-sensitive routing and low-rank write directions, and local character features explain a large fraction of the model's output logits. Our results suggest that representation-theoretic mechanisms previously identified for group operations can extend beyond groups to more general structures.
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All authors Zitong Andrew Chen, Junaid Hasan, Akhil Srinivasan, Hemkesh Bandi, Jarod Alper
Artificial Intelligence · Cross-listed from cs.LG
Complexity-Budgeted, Interaction-Aware Interpretable Model for Tabular Data
Srikumar Krishnamoorthy
Inherently interpretable classifiers for tabular data typically rely on sparse features, rules, or patterns that users can inspect directly. The marginal feature-screening step common to these methods can discard variables whose predictive value emerges only through joint configurations with other variables. We present Interaction Aware Interpretable Machine Learning (IAIML), a framework that addresses this limitation through three coordinated mechanisms: adaptive per-feature discretization, finite-grid pairwise interaction scoring, and a partitioned explanation budget. Detected interactions are routed through one of two strategies: relaxing the screening filter so that interaction-supported variables enter the pattern search, or constructing explicit pair terms for a sparse downstream classifier. On a 40-
dataset panel comprising 24 real-world tabular
benchmarks and 16 synthetic interaction stress tests, evaluated under nested cross-validation, IAIML achieves mean AUC within 1.4 points of tuned gradient-boosted ensembles while requiring roughly 14–28 times fewer fitted explanation components. On
datasets with strong pairwise interaction structure and low marginal signal, IAIML outperforms all baselines. Among compact interpretable methods, IAIML is comparable to RuleFit in AUC and component count and is less expensive to tune. EBM obtains a small but significant AUC advantage across the full panel, with a substantially larger lookup-table footprint. Performance degrades on
datasets requiring higher-order interactions beyond the pairwise scope. Component-isolated ablations confirm that adaptive discretization and interaction-aware admission each contribute incrementally. These results support IAIML as a compact, interaction-aware framework appropriate for settings where bounded explanation size and controlled treatment of feature interactions are design requirements.
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Artificial Intelligence · Cross-listed from cs.SE
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
Arun Malik
AI
agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats
agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully
agent-orchestrated to hybrid to fully deterministic workflows, together with an evidence-based promotion mechanism that converts repeatedly validated
agent behaviors into cheaper and more reproducible deterministic workflows, while automatically demoting workflows that regress. Evaluated on a production cloud networking AIOps system processing tens of thousands of incidents per month, the approach increased deterministic execution from 0% to 45% over eight months, reduced per-incident
agent costs by more than 70% despite doubling incident volume, and improved safety through greater reproducibility and auditability. The paper also presents the execution taxonomy, promotion and demotion criteria, trace extraction methodology, economic model, safety considerations, and discusses limitations and threats to validity.
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Artificial Intelligence · Cross-listed from cs.CV
Making Implicit Preservation Intent Explicit in Conversational Image Editing
Soomin Han, Jihyung Ahn, Bumsoo Kim et al. (+1 more)
Conversational image editing requires preserving not only visible content, but also content that temporarily disappears across turns. When newly added or modified content occludes a previously visible region, that region should reappear if it was never semantically changed. However, existing systems often fail to recover such occluded-but-unchanged content, producing inconsistent or hallucinated results. We introduce OCCUR-Bench, a diagnostic
benchmark for temporal preservation in conversational image editing. OCCUR-Bench provides diverse occlusion-and-revelation scenarios with historical restoration references, enabling evaluation of faithful restoration rather than plausible regeneration. We also propose ReSpec, a training-free framework that makes implicit preservation explicit by pairing restoration-aware instructions with historical visual references. Given an editing history, ReSpec identifies what should persist, selects the historical image state that provides missing visual evidence, and conditions an in-context editor on the resulting instruction and reference image. Experiments show that ReSpec improves restoration fidelity and temporal consistency on OCCUR-Bench, highlighting the need to ground preservation in editing history rather than only the current image.
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All authors Soomin Han, Jihyung Ahn, Bumsoo Kim, Buru Chang
Artificial Intelligence · Cross-listed from cs.CL
Riemannian Geometry for Pre-trained Language Model Embeddings
Szczepan Konior, Alexandre Quemy, Przemysław Klocek et al. (+2 more)
Understanding the
geometric structure of pre-trained
language model
embeddings matters for interpretability and safety. We ask whether sentence-level classification signal lives in the Riemannian geometry of contextual
token embeddings, and probe it by extracting per-
token pullback metrics from a learned encoder's analytical Jacobian and aggregating them with the Fréchet mean on the symmetric positive definite (SPD)
manifold; we call this procedure Riemannian Mean Pooling (RMP). Across three
datasets with non-trivial linguistic structure (CoLA, CREAK, RTE), RMP outperforms Euclidean mean pooling, while on FEVER-Symmetric, a
benchmark constructed to remove annotation-driven lexical artifacts, the method correctly stays at chance. Ablations show that a randomly initialised encoder combined with Fréchet aggregation already beats Euclidean pooling on two of the three signal-bearing
datasets, localising the source of the gain to the
geometric aggregation rather than to learned
manifold structure; the trained encoder contributes additional signal specifically on CREAK, the most knowledge-heavy of the three signal-bearing
datasets.
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All authors Szczepan Konior, Alexandre Quemy, Przemysław Klocek, Bartłomiej Sobieski, Grégoire Cattan
Artificial Intelligence
Measuring Intelligence Beyond Human Scale
Jerry Han, Rafael Moschopoulos, Ella Colby et al. (+6 more)
How can we measure intelligence beyond human capability? Human-authored
benchmarks saturate, and above human capability, examiners may not know which tasks are both hard and verifiable. We argue that this difficulty is inherent to absolute-scale evaluation and propose a new paradigm based on relative measurement in which models generate public challenges that separate other systems. Aggregating these outcomes yields an
adversarial psychometric rating system that can scale with the systems being measured. We describe practical protocols that reduce incentives for private-information attacks, support judge-free adjudication, and naturally scale with
agent capabilities. We instantiate the framework across verifiable and open-ended, non-verifiable domains, illustrating how model-generated evaluation can continue to measure systems beyond the human frontier.
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All authors Jerry Han, Rafael Moschopoulos, Ella Colby, Vishrut Goyal, Andrew Tu, Kia Ghods, Shirley Yu, Mark Braverman, Elad Hazan
Artificial Intelligence · Cross-listed from cs.LG
On the Principles of Deep Feedforward ReLU Networks
Changcun Huang
The architecture of deep feedforward neural networks is ubiquitous in deep learning, either as a whole system or as a subnetwork of other architectures, and thus its mechanism is a key ingredient of the black box of neural networks. On the basis of the simplest two-layer ReLU network, this paper systematically studies the mechanism of deep feedforward ReLU networks with multiple hidden layers and successfully explains the training solution obtained by the back-propagation
algorithm. The concept of a path, especially in terms of the relationships between paths, plays a central role in uncovering the mystery of the black box. It is shown that a unit of a deep ReLU network can form a piecewise linear
manifold to divide the input space, instead of a hyperplane of the two-layer case. How to efficiently use the hidden-layer units to produce both linear functions and partitions of the input space is also a central problem. The principles of a two-layer ReLU network can be generalized to the deeper case to a large extent, such as multiple strict partial orders and continuity restriction. The combination of the basic and simple principles proposed can yield complicated instantiations including the training solutions, and in this sense the black box of deep feedforward ReLU networks is revealed.
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Artificial Intelligence · Cross-listed from cs.LG
Intrinsic Green's Learning: Supervised Learning on Manifolds via Inverse PDE
Alexandre Quemy
We introduce Intrinsic Green's Learning (IGL), a framework that models a target function on a
manifold as the solution to a linear PDE whose source term is learned from data. Rather than approximating the target directly, IGL learns a source and integrates it against a Green's kernel. An encoder discovers a low-dimensional coordinate chart on the
manifold where both the source and the kernel decompose as low-rank tensors, collapsing a high-dimensional integral into independent one-dimensional integrals with cost linear in the intrinsic dimension. A two-stage
algorithm separates coordinate discovery from source fitting, a near-convex linear solve, preventing the dimensional collapse of joint training. Learnable gates on each coordinate automatically discover the intrinsic dimension of the
manifold. We validate IGL on synthetic
manifolds and on MNIST, where it simultaneously achieves near-optimal classification and automatic recovery of the intrinsic dimension.
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Artificial Intelligence · Cross-listed from cs.CV
AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning
Kyuan Oh and Bumsoo Kim
Large
vision-
language models incur substantial inference costs because high-resolution inputs introduce thousands of visual
tokens, many of which are redundant for a given query. Existing
pruning methods often combine query relevance and
token diversity, yet these objectives can conflict under aggressive compression: relevance-driven selection may overconcentrate the budget on correlated local evidence, while diversity-driven selection may suppress indispensable
tokens or retain distinct but uninformative regions. We introduce AnchorPrune, a training-free framework that first constructs a protected relevance anchor and then expands it with complementary visual context. AnchorPrune adaptively determines the anchor size from the novelty profile of relevance-ranked
tokens, preserving a compact set of query-critical evidence, and allocates the remaining budget through importance-weighted novelty to recover informative, non-redundant context relative to the anchor. This ordered design prevents contextual expansion from displacing indispensable query cues while improving overall visual coverage. AnchorPrune is lightweight, architecture-aware, and requires neither retraining nor model modification. Across image and
video vision-
language models and
benchmarks, it consistently improves the accuracy-efficiency trade-off over training-free baselines, particularly under severe compression. On LLaVA-NeXT-7B, AnchorPrune preserves 97.6% of full-
token performance using only 160 of 2,880 visual
tokens. These results establish relevance-anchored contextual expansion as an effective principle for efficient
multimodal inference. Code is available at https://github.com/MULTI-cau/AnchorPrune.
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Artificial Intelligence · Cross-listed from cs.LG
Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
Dennis Gross, Quentin Mazouni, Helge Spieker et al. (+1 more)
Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated testing methods target only selected environments, testing scenarios, and RL
algorithms. To address this, we propose a comprehensive framework for testing single- and multi-
agent RL policies under varying conditions. Our implementation of this framework, Gimitest, is an open-source tool that supports various gym frameworks and allows for modifications of their integrated components. This article describes the framework and details Gimitest's functionality and architecture. It showcases its effectiveness in testing multiple RL policies in environments such as the official Farama Gymnasium and PettingZoo.
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All authors Dennis Gross, Quentin Mazouni, Helge Spieker, Arnaud Gotlieb
Artificial Intelligence · Cross-listed from cs.LG
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
Ishaan Batta, Meenu Ajith and Vince Calhoun
While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the
brain. We design a
multimodal generative framework for structural and functional
magnetic resonance imaging (MRI) features through systematic evaluation of encoding strategies, latent
multimodal fusion, and generative model selection. Using structural gray matter volume (GMV) and static functional network connectivity (sFNC) features from a large neuroimaging
dataset, we analyze generative frameworks involving variational autoencoders (VAEs),
transformers, generative
adversarial networks (GANs), and
diffusion models. Architectures that employ modality-aware graph encoding of functional connectivity into a lower-dimensional latent space outperform vectorized encoders or direct data space approaches. The proposed
multimodal graph VAE (gMMVAE) surpasses alternative generative variants across multiple metrics for generation fidelity, reconstruction quality, efficiency, and latent space discriminability, highlighting its potential for robust
multimodal neuroimaging analysis.
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Artificial Intelligence
Learning social norms enhances compatibility in dynamic human-AI coordination
Yi Yang, Siyuan Liu, Xin Gao et al. (+4 more)
Humans continuously coordinate with others in dynamic interactions, often through implicit, hard-to-quantify social norms that act as shared tacit expectations among interacting
agents. As AI
agents, including large
language models (LLMs), become embedded in daily life, they increasingly participate in such interactions and reshape social interaction structures. Yet they often fail to coordinate with humans in an effective, considerate, and natural manner. We hypothesize that this gap arises because existing approaches align model behavior with human demonstrations without explicitly quantifying the underlying norms that generate such behavior. We selected pedestrian-vehicle interaction as a representative dynamic interaction and developed a simplified experimental platform that captures its key interactive features. From 3,456 dynamic human interactions collected via this platform, we identified three principles underlying human social norms: outcome predictability, value
alignment, and advantage awareness. Incorporating these principles into AI
agents significantly improves human-AI coordination. In the closed-loop interaction task with humans, the social-norm-informed LLM achieved a nearly fourfold higher total score than the baseline strategy and outperformed human-human interactions by 43%. These findings indicate that formalizing tacit social norms into explicit, quantifiable principles can enable AI
agents to achieve mutually beneficial coordination in dynamic interactions, supporting their more natural integration into human society.
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All authors Yi Yang, Siyuan Liu, Xin Gao, Huamu Sun, Chao Liu, Qing Zhou, Bingbing Nie
Artificial Intelligence · Cross-listed from cs.LG
Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting
Qingzhong Li, Yue Hu, Hui Ma et al. (+4 more)
Accurate
forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Real-world traffic often exhibits bursty endogenous dynamics and disturbances triggered by external urban events, which makes reliable prediction highly challenging. Most existing spatiotemporal traffic
forecasting methods primarily focus on intrinsic traffic patterns or structural relationships within a single modality, and rarely model burst behavior together with exogenous contextual signals. To address this issue, we propose MSPF-Net, a
multimodal cellular traffic
forecasting framework that integrates external contextual information. Specifically, MSPF-Net consists of a Spatiotemporal-Frequency Traffic Encoder for capturing temporal, spatial, and spectral traffic patterns, a Peak Enhancement Module for extracting burst-aware representations of sudden spikes, a News Context Representation Module for encoding urban news streams into exogenous contextual
embeddings, and a Dynamic Fusion Prediction Module for adaptively integrating these heterogeneous signals to generate forecasts. Experiments on the Milano, Trento, and LTE traffic
datasets demonstrate that jointly modeling traffic dynamics, burst patterns, and news contextual signals can effectively improve
forecasting performance.
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All authors Qingzhong Li, Yue Hu, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing
Artificial Intelligence · Cross-listed from cs.LG
Physics-guided spatiotemporal neural models for fuel density prediction
Tolga Caglar, Jaynil Jaiswal, Saqib Azim et al. (+3 more)
This paper presents a physics-guided machine learning (PGML) framework for fuel density prediction, integrating physics constraints and domain knowledge into deep learning models to enhance model accuracy and stability. We explore three deep learning architectures – ConvLSTM, Adaptive
Fourier Neural Operator (AFNONet), and
Video Vision Transformer (ViViT) – to model the spatiotemporal
evolution of fuel density. Our approach incorporates differentiable physics-informed terms in the loss function, including a mass-conserving fuel transport term and a rate-of-spread estimation. Experimental results, averaged across multiple independent trials, demonstrate that the proposed PGML framework outperforms purely data-driven baselines without physics constraints in both accuracy and stability. This framework enables computationally efficient, physically plausible fire
forecasting to support adaptive prescribed burn management.
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All authors Tolga Caglar, Jaynil Jaiswal, Saqib Azim, Yudhir Gala, Mai H. Nguyen, Ilkay Altintas
Artificial Intelligence
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
Wachiravit Modecrua, Krittin Pachtrachai and Touchapon Kraisingkorn
Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data. We present the Large Behavioral Model (LBM) that learns customer decision making directly from large-scale retail transactions through a unified Person-Environment formulation. Customer state is represented by a behavioral profile derived from historical purchases, while product context is incorporated through
retrieval-augmented generation. The model is trained using continued pre-training on verbalized behavioral data, supervised fine-tuning for decision generation, and reinforcement learning with verifiable rewards for evidence-based calibration. We evaluate the proposed framework on purchase prediction, hard-negative discrimination, basket completion, promotion response, and cross-domain voucher redemption. The model consistently outperforms frontier general-purpose
language models on in-domain retail tasks while demonstrating strong zero-shot and fine-tuned transfer across retailers and decision domains. Ablation studies show that continued pre-training is the primary driver of behavioral generalization,
retrieval is most effective when applied during both training and inference, and reinforcement learning improves reliance on explicit behavioral evidence over generic
language-model priors. These results demonstrate that behavioral knowledge encoded in transaction histories can be effectively learned by
language models, providing a scalable foundation for customer digital twins and behavior simulation.
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Artificial Intelligence · Cross-listed from cs.RO
WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time
Yusen Feng, Bingchen Han, Jiangran Lyu et al. (+13 more)
Steering
robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional
robot demonstrations, task-specific fine-tuning, or long-context conditioning. We present WAM-TTT, a test-time training framework for steering world action models from raw human
videos. Rather than treating human
videos as trajectories to imitate, WAM-TTT absorbs them into a lightweight adaptive
memory inside a frozen WAM through self-supervised
video prediction. To make this
memory useful for control, we introduce a meta-training stage that aligns human demonstrations with
robot behaviors using paired human-
robot data and a key–value
memory reconstruction objective. At test time, only unlabeled human
videos are required to adapt the
memory, while the pretrained WAM remains frozen. This enables efficient and reusable steering without
robot actions, human-side annotations, or task-specific fine-tuning, while preserving the generalization ability of the foundation model. Extensive experiments show that WAM-TTT consistently outperforms in-context human-
video conditioning baselines across diverse manipulation tasks and generalization settings.
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All authors Yusen Feng, Bingchen Han, Jiangran Lyu, Kai Liu, Yixin Zheng, Yuxuan Wan, Weiheng Liu, Sun Han, Ruiqin Li, Yulong Zhang, Fangfu Liu, Xuesong Shi, Libin Liu, Yizhou Wang, Zhizheng Zhang, He Wang
Artificial Intelligence · Cross-listed from cs.LG
Hybrid Least Squares/Gradient Descent Methods for MIONets
Jun Choi, Chang-Ock Lee and Minam Moon
In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes the LSGD method for DeepONets. Since MIONet is the sum of the entrywise product of multiple branch networks and a trunk network, it can be viewed as a multilinear function with respect to the last layer parameters of each branch network. These sets of parameters can be optimized using the alternating least squares method, where we solve the LS system for a single branch network in turn. To handle the large-sized system
matrix, we introduce Kronecker and Khatri-Rao products and tensor permutation
matrices to factor the large
matrix into small ones. Our method is compatible with a general type of $L^2$ loss with regularization terms for the last layer parameters of each branch, where linear operators can be applied to the MIONet output in each loss term.
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Artificial Intelligence · Cross-listed from cs.RO
End-to-End LLM Flight Planning with RAG-based Memory and Multi-modal Coach Agent
Amin Tabrizian, Arsyi Aziz, Aarifah Ullah et al. (+3 more)
Bridging the gap between human pilot intent and autonomous flight operation is critical for real-world electric vertical takeoff and landing (eVTOL) aircraft deployment. Flight planning traditionally relies on classic
algorithms that struggle to incorporate flexible human preferences. We present FRAMe, an End-to-End Large
Language Model (LLM) Flight Planning tool with RAG-based
Memory and Multi-modal Coach
Agent. Our system integrates a planner LLM with a multi-modal coach
agent and
retrieval augmented generation (RAG)-based
memory to generate flight plans that satisfy mission constraints while aligning with human flight operator preferences. We demonstrate the system in a range of real-world-inspired scenarios of varying difficulty levels. Across four LLMs, the full FRAMe system (RAG and coach) yields the highest validity for every planner (up to 93.8% aggregate, 99% on Easy scenarios for the strongest planner) and shifts preference-relevant metrics in the operator-favored direction where the metric has headroom. FRAMe signifies how advanced LLMs can be deployed for human-centric mission planning, translating natural
language instructions into safe, efficient, and flexible flight routes. The code is available at: github.com/amin-tabrizian/FlightPlanningLLMs
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All authors Amin Tabrizian, Arsyi Aziz, Aarifah Ullah, Mahyar Ghazanfari, Pouria Razzaghi, Peng Wei
Artificial Intelligence · Cross-listed from cs.CR
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
Kiarash Ahi and Saeed Valizadeh
Large
Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable
Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks. These technologies power real-time threat detection, phishing defense, secure code generation, and vulnerability exploitation at unprecedented scales. Following a rapid surge where LLM-generated malware grew to account for an estimated 50% of detected threats by 2025, up from just 2% in 2021, navigating this highly automated threat landscape in 2026 demands next-generation security frameworks. This paper presents a comprehensive survey of the beneficial and malicious applications of LLMs in cybersecurity, including zero-day detection, DevSecOps,
federated learning, synthetic content analysis, and explainable AI (XAI). Drawing on a review of over 70 academic papers, industry reports, and technical documents, this work synthesizes insights from real-world case studies across platforms like Google Play Protect, Microsoft Defender, Amazon Web Services (AWS), Apple App Store, OpenAI Plugin Stores, Hugging Face Spaces, and GitHub, alongside emerging initiatives like the SAFE Framework and AI-driven anomaly detection. We conclude with practical recommendations for responsible and transparent LLM deployment and trustworthy AI, including model watermarking,
adversarial defense, and cross-industry collaboration, setting a new
benchmark for rigorous, holistic cybersecurity research at the intersection of AI and threat defense, and offering a roadmap for secure, scalable LLM systems that serves as a critical reference for researchers, engineers, and security leaders navigating the complex challenges of AI-driven cybersecurity.
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Artificial Intelligence · Cross-listed from cs.CV
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Heeju Mun, Tackang Yang, Yunsoo Nam et al. (+1 more)
The accuracy of existing leaf-wood
segmentation methods for tree point clouds varies across forest types and sites. Self-supervised learning (SSL) on point clouds has improved the generalization of deep learning models for forestry point cloud tasks, including biomass regression and individual tree
segmentation, but its applicability to leaf-wood
segmentation remains untested. In this study, we pretrained Point-M2AE, a widely used SSL architecture for point clouds, on ShapeNet-55 augmented with 2,400 individual tree point clouds. For fine-tuning and inference, we used recursive voxel subdivision to handle the wide variation in point density across inputs, allowing the same model to operate at both individual-tree and plot scales without architecture change. Compared to the model without pretraining, the pretrained model improved wood IoU from 60.5% to 70.0% for needleleaf and from 69.7% to 76.3% for broadleaf trees. On a
benchmark spanning four countries across three climatic zones, the pretrained model achieved the smallest cross-site variation and highest overall performance among compared methods (LeWos, CWLS, and PointTransformer). Plot-level
segmentation maintained accuracy comparable to individual-tree performance, with mIoU of 84.7% for broadleaf and 77.7% for needleleaf plots, showing that the model generalizes across scales without additional finetuning. As a downstream test in tropical forests, where dense canopies make
segmentation challenging, we applied our model and a quantitative structure model to estimate wood volume for 28 trees from Guyana, Indonesia, and Peru to assess whether the
segmentation improvements from SSL pretraining translate into improved downstream performance. The resulting volume estimates achieved the lowest error among all methods tested (MAE = 2.40 m$^3$), less than half that of algorithmic baselines (LeWos: 5.94 m$^3$; CWLS: 5.27 m$^3$).
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All authors Heeju Mun, Tackang Yang, Yunsoo Nam, Changhyun Choi