Artificial Intelligence
The Integrator Advantage: Controlled Agentic AI for Small and Medium-Sized Companies
Christopner Koch and Joshua A. Wellbrock
Agentic AI marks a new phase of enterprise automation. Unlike traditional automation or conversational AI, agentic systems can interpret goals, plan multi step tasks, access tools, interact with enterprise systems, and execute workflows with varying degrees of autonomy. For small and medium sized companies, this creates potential to reduce administrative burden, accelerate routine processes, and improve the use of organizational knowledge. This paper argues that the near term value of Agentic AI does not lie in full autonomy or workforce reduction, but in controlled partial autonomy for simple and medium complexity business processes. It proposes an integration framework covering use case suitability, autonomy levels, technical integration, governance, security, employee enablement, and measurable impact. The paper concludes that Agentic AI can become a productivity lever when implemented as a human centered capability with responsibility and accountability retained by people.
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Artificial Intelligence · Cross-listed from cs.CV
DCP-Prune: Ultra-Low Token Pruning with Distribution Consistency Preservation
Xifeng Xue, Xiaokang Wang, Zirui Li et al. (+2 more)
Recent
vision token pruning methods effectively preserve model performance under moderate
token budgets but become unstable under ultra-low
token budget. Our analysis shows that as the
pruning budget decreases, accuracy degradation is often accompanied by larger feature distribution shifts. Critically, the degree of this distribution shift strongly correlates with performance degradation. To better characterize this phenomenon, we introduce a lightweight distribution consistency metric to estimate the distribution shift between retained and full
tokens. Motivated by these observations, we propose a two-stage
pruning framework consisting of Anchor-Context Graph Recovery (ACGR) and Text-Aware
Token Cluster Selection (TATCS). Specifically, ACGR transfers contextual information before
token removal, while TATCS dynamically re-selects representative
tokens when severe distribution shift is detected. Extensive experiments demonstrate that our method achieves superior and more stable performance under ultra-low
token budget. Notably, it retains 92.1% of the upper-bound average performance on LLaVA-1.5-7B with only 16 visual
tokens.
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All authors Xifeng Xue, Xiaokang Wang, Zirui Li, Ming-Ming Cheng, Guolei Sun
Artificial Intelligence · Cross-listed from cs.HC
Using AI in engineering education: a balancing act, driven by clear purpose
Olya Kudina
Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large
Language Models (LLMs) in engineering education. Students primarily value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, while simultaneously expressing concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verification. Through an analysis of two dominant metaphors, namely LLMs as an "oracle" and as a "tutor," the chapter shows how these systems cultivate expectations of authority, expertise, and personalized learning that often exceed their actual capabilities. The chapter further argues that students' attachment to the promises of efficiency and personalized support reflects a form of "cruel optimism," where the perceived benefits of LLMs often depend on the very skills, vigilance, and expertise that students are still developing. Overall, the chapter argues for a purpose-driven and context-sensitive approach to AI integration in engineering education, emphasizing critical AI literacy, reflective assessment design, pedagogical caution, and consideration of broader ethical and environmental impacts.
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Artificial Intelligence
GA-VINO: A Geometry-Aware Variational Physics-informed Neural Operator for Mindlin-Reissner Plates
Siqi Wang, Daobo Sun, Yizheng Wang et al. (+4 more)
Plate and shell structures are widely used in engineering fields. Rapid response prediction for such structures under complex geometries, heterogeneous materials, and varying loads is important for engineering design, but conventional numerical methods usually require repeated modeling and solution when the physical configuration changes. To address this issue, this study proposes a geometry-aware variational physics-informed neural operator (GA-VINO) for Mindlin-Reissner plates. GA-VINO represents the plate geometry using boundary point clouds and incorporates a material encoder, a load encoder, and a scalar-parameter branch to handle spatially random material fields, spatially varying pressure loads, and sample-level uniform parameters. Through multi-branch point cloud encoding and cross-
attention, GA-VINO fuses
geometric, material, loading, and query point information, and predicts the transverse deflection and rotations at arbitrary query locations. Unlike conventional data-driven neural operators, GA-VINO requires no labeled solution data during training. Instead, it minimizes a variational physics-informed loss constructed from the discretized total potential energy of the Mindlin-Reissner plate. Compared with grid-based neural operators, GA-VINO directly processes irregular point clouds and allows different physical fields to be discretized on different point sets, avoiding forced interpolation onto a common grid. The method is validated on multiple examples involving different geometries, material fields, and load distributions. The results show that GA-VINO achieves promising accuracy in deflection, rotation, gradient-sensitive, and energy-based metrics, completes full-field inference for new samples within milliseconds, and exhibits promising cross-geometry generalization capability.
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All authors Siqi Wang, Daobo Sun, Yizheng Wang, Yilong Zhang, Yabin Jin, Xiaoying Zhuang, Timon Rabczuk
Artificial Intelligence · Cross-listed from cs.LG
Entropy-Gated Latent Recursion
Soham Bhattacharjee, Dushyant Singh Chauhan, Salem Lahlou et al. (+2 more)
Inference-time scaling has become the dominant lever for improving
language-model
reasoning, but existing methods derive rollout diversity from a single source:
stochastic token-level sampling. We argue that this single-axis sampling space is fundamentally limiting, and identify a second, fully deterministic and complementary axis: the layer span $L$ at which a frozen model's top decoder layers are recursively re-applied at high-uncertainty
tokens. Different choices of $L$ produce distinct rollouts that solve different subsets of problems, with no stochasticity. We instantiate this axis through
Entropy-Gated Latent Recursion (EGLR), a training-free decoding procedure that re-applies the top-$L$ layers for at most $K_{\max}$ iterations until the next-
token distribution converges. Combined with $T$ temperature samples, EGLR turns a single-axis
stochastic rollout pool into an $L\times T$ Cartesian sampling space at almost the same per-rollout cost. We characterize this space across $8$ instruction-tuned models and $6$ math
reasoning benchmarks, and show that the $L$-axis is genuinely complementary to temperature: on MATH-500 with Qwen2.5-3B-Instruct, the joint $L\times T$ oracle reaches $91.6\%$, $+8.2$ percentage points beyond the temperature-only oracle ($83.4\%$) and $+10.4$ points beyond the layer-only oracle ($81.2\%$), confirming that the two axes capture genuinely complementary problems. The expanded rollout pool provides richer per-
prompt candidates for any downstream procedure that consumes rollouts, including self-consistency, best-of-$N$ with verifiers, and group-relative RL training (GRPO), opening a new direction for inference-time scaling that does not rely on
stochastic noise.
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All authors Soham Bhattacharjee, Dushyant Singh Chauhan, Salem Lahlou, Martin Takac, Nils Lukas
Artificial Intelligence · Cross-listed from cs.CL
Sycophancy as Material Failure under Pushback Loading: A Multi-Axis Characterization Across Three Loading Cases and up to Seventeen Material Charges
Ferdinand M. Schessl
Sycophancy in LLMs is documented across 70+ papers, but expert agreement on construct boundaries remains low (ICC=.184; Ye et al., 2026). The construct fragments because behavioral classification depends on which surface form is privileged. We adopt a materials-science framing: conversation as test specimen under load, LLM-model as material charge, pushback as progressive load, stance-flip as material failure. We characterize this failure across three loading cases (debate n=1000; false-presuppositions n=3400; ethical-setting n=3400; 10-17 material charges per case; 7800 specimens total) using 14 turn-level axis-measurements spanning velocity, damage accumulation, frame-drift, brittleness, and direction stability, plus three speaker-resolved axes from an independent pipeline. The measurements are Hooke-coupled ($\sigma = E \cdot \varepsilon$ analog) and reproduce across loading cases with effects up to $|r_{rb}| = 0.35$ on debate; the sign structure adds a second pattern: the ethical-setting case inverts the velocity and accumulation blocks. Variance composition partitions into two profiles: debate is charge-dominated (brittle-fracture-like: the material grade decides), false-presuppositions and ethical-setting are topic-dominated (creep-like: the load decides); the ratios (2.03 vs 0.13/0.17) are
estimator-dependent, for debate even in direction. Cross-judge reliability (GPT-4o vs Haiku 4.5) shows debate scoring is judge-robust (Cohen's $\kappa = 0.88$) while false-presupposition scoring is judge-sensitive ($\kappa = 0.36$) – a caveat single-judge
benchmarks must report. This is the methodological move Ye et al.'s diagnosis calls for: a multi-axis characterization that does not depend on which surface form of the construct one privileges.
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Artificial Intelligence
CoffeeBench: Benchmarking Long-Horizon LLM Agents in Heterogeneous Multi-Agent Economies
Issa Sugiura, Daichi Hattori, Kazuo Araragi et al. (+5 more)
As LLM
agents become capable of increasingly long-horizon tasks, evaluating their performance in economic systems is becoming increasingly important. Unlike existing
benchmarks that primarily evaluate a single
agent interacting with a passive environment, economic systems are inherently multi-
agent, requiring autonomous
agents to communicate, negotiate, and transact while pursuing their own objectives over extended periods. We introduce CoffeeBench, a
benchmark for evaluating LLM
agents in a long-horizon multi-
agent economy composed of heterogeneous firms. In CoffeeBench, two farmers, two roasters, and two retailers autonomously operate their businesses over a 90-day simulation, each seeking to maximize cumulative net income through communication and transactions while managing cash, inventory, and pricing. The evaluated model controls one coffee roaster, while the remaining firms are controlled by fixed reference
agents. Across several recent open-weight and proprietary LLMs, all models outperform a passive baseline that takes no actions, with most achieving positive net income. Analysis of
agent behavior reveals substantial differences in long-horizon economic interaction: higher-performing models communicate more actively with other firms, whereas Claude~Haiku~4.5 exhibits an idle-drift failure mode, repeatedly choosing inaction despite producing coherent assessments and plans. We release our code and
agent trajectories to support future research.
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All authors Issa Sugiura, Daichi Hattori, Kazuo Araragi, Keita Ogawa, Shota Onose, Taro Makino, Teppei Usuki, Takashi Ishida
Artificial Intelligence
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
Junjian Zhang, Hao Tan, Ruonan Li et al. (+3 more)
World models are widely used in robotic and agentic engineering control systems due to their ability to learn latent dynamics for planning and decision-making. As these systems are increasingly deployed in safety-critical settings, understanding their robustness under
adversarial conditions has become essential. However, existing evaluations lack a unified
benchmark for testing
adversarial threats across the policy, value, and latent-dynamics levels of world-model
agents. To fill this gap, we present ARB4WM, a unified evaluation framework for pre-deployment robustness and risk assessment of world-model
agents under visual perturbations. ARB4WM defines five white-box loss objectives across these three levels and studies their effects when combined with single-step or multi-step perturbation strategies and temporal attack modes, including full-frame, half-sequence, and sparse-frame exposure. Specifically, we evaluate four Dreamer-style
agents across 20 tasks from MetaWorld and the DeepMind Control Suite under different loss objectives, perturbation strategies, and temporal attack modes. Results show that attacks targeting value estimation, latent representations, and RSSM dynamics can be as damaging as direct policy disruption, and that early or frequent perturbations are especially harmful, while input-level defenses provide limited recovery under adaptive attacks. These findings suggest that safety, risk, and reliability assessment for world models should cover multiple component-oriented attack objectives and temporal exposure protocols rather than relying solely on action-space robustness. Source code is available at https://github.com/zaoanguai/ARB4WM.
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All authors Junjian Zhang, Hao Tan, Ruonan Li, Dong Zhu, Aiping Li, Zhaoquan Gu
Artificial Intelligence · Cross-listed from cs.CL
VeriGraph: Towards Verifiable Data-Analytic Agents
Jiajie Jin, Zhao Yang, Wenle Liao et al. (+5 more)
LLM-based
agents have demonstrated strong capabilities in data-intensive analytical tasks, yet their outputs are rarely verifiable: a reliance on linear text trajectories makes their
reasoning difficult to audit. In particular, deterministic computations over raw data and semantic deductions over natural-
language claims are often entangled in an unstructured stream, leaving numerical conclusions hard to reproduce and qualitative judgments hard to inspect. To address this, we propose VeriGraph, a traceable neuro-symbolic
reasoning framework that enables
agents to construct an explicit heterogeneous evidence directed acyclic graph (DAG) during execution. VeriGraph introduces three evidence-expansion primitives, namely computational, grounding, and derivational expansion, to connect raw data, interpreter variables, computed results, and natural-
language claims in a unified graph. Under this formulation, structural traceability is reduced to graph reachability from raw data sources to terminal claims, while semantic support is measured by claim-level evidence evaluation. To improve graph construction, we further design a graph-based policy optimization strategy with a composite reward that jointly supervises answer correctness, computational integrity, and derivational coherence. Experiments on four
benchmarks show that VeriGraph-8B achieves the highest overall score among all baselines. More importantly, VeriGraph produces auditable evidence graphs with substantially stronger claim grounding, achieving a 87.61% Grounding Rate under our claim-level evidence support evaluation. These results suggest that explicit evidence-graph construction is a promising path toward verifiable data-analytic
agents. Our code is available at https://github.com/ignorejjj/VeriGraph.
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All authors Jiajie Jin, Zhao Yang, Wenle Liao, Yuyang Hu, Guanting Dong, Xiaoxi Li, Yutao Zhu, Zhicheng Dou
Artificial Intelligence · Cross-listed from cs.SD
ArtNet: A JEPA-Like Articulatory Predictive Framework for Robust Zero-Shot Phoneme Recognition
Zeqian Hu, Fuliang Weng, Shu Shang et al. (+1 more)
Zero-shot cross-lingual phoneme recognition is often hindered by the fragility of direct acoustic-to-symbol mapping, which is susceptible to
language-specific variations. Echoing joint-
embedding predictive architecture (JEPA) work in
vision, we propose ArtNet, a framework that explores a structured feature prediction task based on articulatory features to enhance acoustic robustness. Specifically, ArtNet integrates an articulatory predictor, designed to extract universal articulatory representations from self-supervised learning (SSL) features, with a variational information bottleneck (VIB) to suppress
language-specific variations. Experiments on seven unseen
languages demonstrate that ArtNet, particularly when synergized with the proposed vector-space inventory
alignment (VSIA) strategy, significantly outperforms competitive baselines, achieving a 20.56% relative reduction in phoneme error rate (PER) and 7.01% in phoneme feature error rate (PFER).
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All authors Zeqian Hu, Fuliang Weng, Shu Shang, Yaqian Zhou
Artificial Intelligence · Cross-listed from cs.LG
Infant Spontaneous Movement Noise Improves Exploration in Deep RL
Francisco M. López, Markus R. Ernst, Francisco Cruz et al. (+2 more)
Exploration in deep reinforcement learning (RL) is commonly implemented as temporally uncorrelated white noise. However, recent works show that temporally correlated colored noise can improve exploration efficiency by producing smooth trajectories with better coverage of the state space. We inquire whether action noise inspired by infant spontaneous movements can also improve exploration in deep RL. We find that the power spectral densities of babies' end-effector velocities follow a colored noise process where the spectral exponent increases with age. Inspired by this developmental pattern, we introduce a mechanism that progressively increases the temporal auto-correlation of exploration noise during RL training, matching the infant statistics. Experiments across several RL environments show that infant-inspired noise produces structured exploratory behavior and can improve learning efficiency compared to conventional exploration strategies. These findings suggest that human motor and cognitive development can provide useful guidance for designing learning mechanisms in artificial
agents. Our code is available at https://github.com/trieschlab/baby-noise-rl.
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All authors Francisco M. López, Markus R. Ernst, Francisco Cruz, Matej Hoffmann, and Jochen Triesch
Artificial Intelligence · Cross-listed from physics.flu-dyn
Learning Interface Breakup: A Geometry-Conditioned Latent Surrogate for Spray Formation
Julius H Ramlau, Friedrich Hastedt, Tolga Birdal et al. (+3 more)
Designing spray nozzles requires predicting how geometry shapes transient two-phase breakup, but high-fidelity volume-of-fluid (VOF) simulations with adaptive mesh refinement (AMR) are too expensive for iterative design exploration. Standard surrogate models are also challenged by this setting because both the
liquid–gas interface and the underlying adaptive discretization evolve across time and geometries. We introduce a geometry-conditioned latent surrogate trained on 797 two-phase nozzle simulations that addresses this by encoding the AMR
cell-density field, rather than the full multi-channel flow state, as a compact proxy for where the solver concentrates resolution. From this representation, the model reconstructs transient density
evolution and nozzle geometry, and a lightweight second stage recovers the remaining flow variables. On held-out simulations, the method accurately captures key interface dynamics while reducing inference time to 0.045 seconds per trajectory, corresponding to a speed-up of more than $6\times10^4$ relative to Basilisk CFD. These results suggest that AMR refinement structure can serve as a compact and learnable representation for geometry-conditioned surrogate modeling of transient two-phase flows.
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All authors Julius H Ramlau, Friedrich Hastedt, Tolga Birdal, Ehecatl-Antonio del Río Chanona, Nausheen S Basha, Omar K Matar
Artificial Intelligence · Cross-listed from cs.CL
Fast When, Careful Who: Dual-Process Multiparty Turn-Taking with Diffusion Augmentation
Rutherford A. Patamia, Ming Liu, Wei Luo et al. (+2 more)
Reliable turn-taking is essential for spoken dialogue systems. However, most existing methods are designed for two-speaker interaction and struggle with realistic multiparty audio containing overlap and rapid speaker changes. We study multiparty turn-taking on the VoxConverse
dataset and propose an audio-only two-stage pipeline that separates when to trigger a turn boundary from whether the floor is actually transferring. A fast trigger scans the audio and proposes candidate end-of-turn times, while a lightweight verifier runs only at those times to decide Hold or Shift and support next-speaker prediction. We report results in the full multiparty setting and a controlled dyadic top-2 projection for comparability. We also investigate
diffusion-based, label-preserving background-audio mixing as a data augmentation strategy. Results show improved shift detection over a baseline, with further improvements from
diffusion augmentation.
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All authors Rutherford A. Patamia, Ming Liu, Wei Luo, Favour Ekong, Akan Cosgun
Artificial Intelligence
TNODEV: Toolbox for Neural ODE Verification
Abdelrahman Sayed Sayed, Pierre-Jean Meyer and Mohamed Ghazel
Neural ordinary differential equations (neural ODE) gained
attention in safety critical settings such as continuous-time controllers for cyber-physical systems and classifiers integrated into automated decision pipelines, raising the question whether their behavior can be formally verified. Existing tools dedicated to neural ODE provide only a single reachability call without iterative input-set refinement, limiting the precision of their verdicts to whatever one reachability call can deliver. We present TNODEV, the first formal verifier for neural ODE that integrates a falsification checker, a fast interval-based reachability backend based on continuous-time mixed monotonicity, a verification and refinement loop with three input-set splitting heuristics, and a parallel scheduler in a single end-to-end pipeline. TNODEV supports safe-set inclusion verification on pure neural ODE, neural ODE in closed loop with a neural network controller and general neural ODE (GNODE), with the safe set specified either as an interval or as the half-space intersection induced by a target classification label. We evaluate TNODEV on a range of
benchmarks across safe-set inclusion and classification-robustness properties, including a direct reachability comparison against NNV 2.0 and CORA and a verification comparison against NNV 2.0 on MNIST general neural ODE classifiers.
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Artificial Intelligence
ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning
Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger et al. (+2 more)
Roundabouts challenge automated driving in mixed traffic, as heterogeneous and non-deterministic human behavior, unknown driving intentions, and high interaction complexity create uncertainty about whether the conflict zone will be blocked or available at the moment of entry. We present ROSA-RL – uncertainty-aware Roundabout Optimized Speed Advisory with Reinforcement Learning. It enables safe and efficient roundabout entry for automated and human-driven vehicles in mixed traffic through probabilistic conflict
forecasting. A
Transformer-based model predicts conflict zone occupancy over a five-second horizon, capturing multi-
agent interactions to anticipate upcoming conflicts and available gaps. The prediction outputs encode uncertainty in future motion and intent, and augment the state of a classical RL framework, enabling uncertainty-aware speed coordination. Evaluated in simulations grounded in real-world data, ROSA-RL can effectively handle uncertainty and outperform a comparable model-based baseline, closing the gap to an ideal setting assuming fully known occupancy while improving traffic efficiency and safety. The source code of this work is available under: github.com/urbanAIthi/ROSA-RL.
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All authors Anna-Lena Schlamp, Jeremias Gerner, Klaus Bogenberger, Werner Huber, Stefanie Schmidtner
Artificial Intelligence
The Faithfulness Gap: Certifying Semantic Equivalence Between Natural-Language and Formal Mathematical Statements
Noor Islam S. Mohammad and Tamim Sheikh
Autoformalization, translating natural-
language mathematics into formal proof assistants, is bottlenecked not by translation fluency but by faithfulness: a formal statement can typecheck and be provable, yet still encode a different
theorem than the source intended. We introduce Bidirectional Provability Fingerprinting (\bpf{}), a framework that certifies faithfulness by characterizing each candidate through its forward and backward consequence neighborhoods in the ambient theory and matching these against probes derived from the natural-
language statement. We further introduce four novel components: (i) Counterfactual Probe Generation (\cpg{}), a contrastive procedure that synthesizes probes targeting specific drift directions; (ii) the Equivalence Spectrum, a continuous faithfulness score that replaces brittle binary verdicts; (iii) Adaptive Probe Budget Allocation (\apba{}), an information-theoretic budget router; and (iv) Faithfulness-Guided Decoding (\fgd{}), which uses \bpf{} signals as a reward during autoformalization. We prove a drift detection
theorem and a PAC-faithfulness result establishing that the equivalence class of a natural
language statement is learnable from $\mathcal{O}(\log(1/\delta)/\varepsilon)$ probes under mild assumptions. We release \driftbench{}, a
benchmark of $2{,}183$ NL/Lean~4 pairs with controlled drift labels across six subfields of mathlib4. \bpf{}\,+\,\cpg{} detects $89.6\%$ of drifted formalizations at a $3.0\%$ false-positive rate-against $41.2\%$ for typecheck and $63.3\%$ for LLM-judge baselines, and \fgd{} reduces the rate at which a state-of-the-art autoformalizer emits drifted statements by $47\%$. https://pmlrbd.github.io/BPF/
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Artificial Intelligence
Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Kairos Team: Fei Wang, Shan You, Qiming Zhang et al. (+20 more)
We introduce Kairos, a regret-aware native world-action model stack for Physical AI. Kairos is motivated by the view that a physical world model should not aim to fully simulate all future pixels, but should learn and maintain the information most relevant to embodiment control: object state, spatial relations, contact conditions, task progress, action consequences, failure boundaries, and deployment uncertainty. Kairos establishes three model-side prerequisites toward this goal. First, it learns control-relevant information through a Cross-Embodiment Data Curriculum, which organizes open-world
videos, human behavioral data, and
robot interactions into an intervention-strength progression from passive physical observation to intentional behavior and
embodied action grounding. Second, it maintains control-sufficient states through a unified understanding, generation, and prediction architecture equipped with Hybrid Linear Temporal
Attention, where local, mid-range, and global temporal pathways support multi-timescale state maintenance under efficient inference. Third, it deploys these states through a Deployment-Aware System Co-Design, treating latency,
memory footprint, and hardware compatibility as first-order constraints for future observation, action, and feedback loops. Experiments on
embodied world-model
benchmarks, world-action
benchmarks, long-horizon generation, and inference-efficiency evaluation show that Kairos achieves superior performance while offering a favorable efficiency to capability trade-off.
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All authors Kairos Team: Fei Wang, Shan You, Qiming Zhang, Tao Huang, Zuoyi Fu, Zhisheng Zheng, Yunlong Xi, Feng Lv, Xiaoming Wu, Zeyu Liu, Cong Wan, Pu Li, Ruiqing Yang, Xiaoou Li, Wei Wang, Kangkang Zhu, Yuwei Zhang, Shi Fu, Zheng Zhang, Xiaoning Wu, Xuzeng Fan, Dacheng Tao, Xiaogang Wang
Artificial Intelligence · Cross-listed from cs.SD
Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection
Zhuodong Liu, Hugen Lv, Xiangyu Li et al. (+1 more)
Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage. Existing methods address this but incur architectural complexity or training instability. This paper proposes a dual-granularity orthogonal disentanglement framework enforcing feature independence at two levels: sample-level cosine orthogonality captures directional decorrelation, while batch-level cross-covariance regularization eliminates linear correlations across
embedding dimensions. A curriculum disentanglement schedule progressively strengthens the orthogonality constraint without auxiliary networks or
adversarial dynamics. Experiments on ASVspoof 2019 LA, ASVspoof 2021 DF, and In-the-Wild
datasets demonstrate that the proposed method achieves 1.35%, 7.88%, and 21.58% equal error rates (EER), respectively, surpassing gradient reversal disentanglement by 2.60% absolute on cross-
dataset transfer.
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All authors Zhuodong Liu, Hugen Lv, Xiangyu Li, Chunhong Yuan
Artificial Intelligence · Cross-listed from cs.LG
Direction-Conditioned Policies via Compositional Subgoal Scoring for Online Goal-Conditioned Reinforcement Learning
Swaminathan S K, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan et al. (+1 more)
Hamilton-Jacobi-Bellman theory implies that the optimal goal-conditioned action depends on the goal only through the gradient of the goal-reaching distance at the current state, yet standard online GCRL still conditions the actor on the raw goal – a signal that is geometrically uninformative when the goal is far from the data distribution. We propose Direction-Conditioned Policies (DCP), a fully online method that decomposes goal-reaching into two components sharing one InfoNCE representation $\psi$: a subgoal-scoring step that selects a visited state $z_t$ aligned with the final goal $g$ in $\psi_g$, and a direction-conditioned actor that consumes the unit direction $d_t$ and magnitude $r_t$ from $\psi(s_t)$ to $\psi(z_t)$. The two components train jointly, factor cleanly at deployment (subgoal scoring is removed, while direction conditioning remains with $g$ in place of $z_t$), and admit independent modification at the same $(d_t,r_t)$ interface. We prove three results. First, direction sufficiency under HJB: the optimal action under control-affine dynamics depends on the goal only through the value gradient. Second, a quantitative bound showing that, under mild conditions on the learned representation and assuming the scoring rule returns an on-path $z_t$, the actor's conditioning input at training and at deployment coincide up to representation error and geodesic slack. Third, a controllable-subspace characterization of when directional conditioning fails. Across nine environments, DCP improves over Contrastive RL on most final metrics, with the largest gains on manipulation and obstacle-interaction tasks; a qualitative analysis of the learned $\psi$-distance landscape shows the contrastive representation behaves as an online quasimetric encoding environment
topology, and the single failure case (AntSoccer) localizes to a learned-gradient pathology that the theory anticipates.
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All authors Swaminathan S K, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan, Aritra Hazra
Artificial Intelligence
Model Graph Inductive Learning for Knowledge Graph Completion
Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani et al. (+1 more)
Link prediction in knowledge graphs fundamentally depends on the quality of learned
embeddings for entities and relations. However, most existing methods derive these
embeddings by aggregating only the local neighborhood of each entity, neglecting the global structure of the knowledge graph. This limited view prevents models from capturing higher-level structural patterns that are essential for accurate and generalizable link prediction. To address these limitations, we introduce Model Graph Inductive Learning (MGIL), a framework that constructs a model graph by clustering entities based on the similarity of their incoming and outgoing relational structures or their entity types. A GNN is then applied to this model graph to produce
embeddings that capture the global view of the knowledge graph. These
embeddings subsequently serve as high-quality initial features %
embeddings for the original knowledge graph, replacing random initialization and leading to more stable and expressive representations. Extensive experiments on standard and recently proposed inductive
benchmarks demonstrate that MGIL achieves state-of-the-art or highly competitive performance in inductive link prediction, highlighting its effectiveness across diverse graph settings.
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All authors Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan
Artificial Intelligence
Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing
Kyungjin Im, Miru Kim, Chanin Eom et al. (+1 more)
Model merging has become a practical post-training strategy for building a single multi-task large
language model (LLM) by combining multiple task-specialized models. However, most existing approaches rely on post-hoc merging, in which task-specific models are merged only once after training. This one-shot aggregation often suffers from task interference, leading to information erasure across individual tasks. In this work, we show that replacing post-hoc merging with an iterative many-shot merging protocol is effective in improving multi-task performance. Building on this insight, we propose METIS, Mitigating Erasure from Task Interference for Stable many-shot merging. METIS is a loss-aware many-shot merging method that addresses information erasure in post-hoc merging through task-wise loss-gap weighting and consensus-based masking. Notably, METIS exhibits significant performance improvement on the worst-performing task, effectively mitigating information erasure. (Project page: https://imkyungjin.github.io/METIS/)
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All authors Kyungjin Im, Miru Kim, Chanin Eom, Minhae Kwon
Artificial Intelligence · Cross-listed from cs.LG
daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization
Dayuan Fu, Mohan Jiang, Tongyu Wang et al. (+5 more)
GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective. We present daVinci-kernel, a reinforcement learning framework that couples skill discovery with skill exploitation through a dynamically evolving skill library. daVinci-kernel jointly trains three
agents sharing one LLM backbone: a Skill Selection
Agent that retrieves relevant techniques via BM25 and LLM reranking, a Policy
Agent that generates multi-turn CUDA/Triton kernels conditioned on selected skills, and a Skill Summary
Agent that distills successful rollouts into reusable skills. Candidate skills are added only after execution-based verification confirms reproducible speedups. All three
agents share a single LLM backbone, are initialized via a structured SFT cold start on diversity-filtered data, and are then jointly optimized end-to-end with multi-turn REINFORCE and per-
agent advantage estimation. On KernelBench, daVinci-kernel-14B achieves 37.2%, 70.6%, and 32.2% on Level 1, Level 2, and Level 3 under the Fast$_1$ threshold, outperforming the strongest prior RL-trained model, DrK̇ernel-14B.
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All authors Dayuan Fu, Mohan Jiang, Tongyu Wang, Dian Yang, Jiarui Hu, Liming Liu, Jinlong Hou, Pengfei Liu
Artificial Intelligence · Cross-listed from cs.CL
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
Jieyuan Liu, Jianyang Gu, Shijie Chen et al. (+2 more)
Knowledge-based visual question answering (KB-VQA) lets
vision-
language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-derived knowledge base. In pure-text long-context LLMs, retrieved-context use follows the U-shaped lost-in-the-middle effect of Liu et al. (2024): information at the start and end of context is used, the middle is lost. Whether this transfers to deployed
multimodal KB-VQA is open. To close this gap, we design the first controlled probe of reader-side position dependence in
multimodal KB-VQA: a gold-position protocol in which only the gold passage's
prompt slot varies within question. We run it on three open-source 7B/8B VLM readers and two KB-VQA
benchmarks with up to 20 retrieved passages. The shape flips from U to primacy: gold-at-first beats gold-at-last by 16 to 26 points on all six combinations of reader and
benchmark, an effect we call Lost at the End; the gap holds at every scale we test, 3B to 32B, attenuating at 32B. Three targeted ablations narrow the cause. A text-only control that removes the image and changes nothing else shows the primacy is already present in text mode and does not depend on the image. Image-position and distractor-shuffle ablations trace the effect to
prompt slot 0 of the instruction-tuned reader, where a second answer-bearing passage placed later is largely wasted. On a frozen reader, three
retrieval-side fixes (MMR, oracle reranking, rank-based reordering) all fail to improve on the deployment default. Our findings indicate that recall@k is the wrong metric for deployed KB-VQA and that the remaining headroom sits on the reader side; we release our protocol as a controlled instrument for evaluating reader-side interventions.
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All authors Jieyuan Liu, Jianyang Gu, Shijie Chen, Jefferson Chen, Zhen Wang
Artificial Intelligence · Cross-listed from cs.CV
Unified Multimodal Model for Brain MRI Imputation and Understanding
Zhiyun Song, Che Liu, Tian Xia et al. (+2 more)
Multimodal large
language models (MLLMs) hold great potential for medicine, as they inherit knowledge from LLM and allow multiple data modalities to be integrated, analysed and interpreted in natural
language. However, the field of medical MLLMs is constrained by non-trivial challenges, notably the scarcity of high-quality training data and the frequent occurrence of missing data in the real-world clinical setting. Here, we propose a novel unified
multimodal model, UniBrain, for
brain magnetic resonance image (MRI) analysis. To address potential missing
brain MRI modalities, we employ a unified training strategy to perform joint imaging modality imputation and
brain image understanding. During training, an interleaved and description-enriched data flow is constructed to train the model in an autoregressive manner, enabling medical
reasoning with generated
multimodal data. A self-
alignment strategy is introduced to leverage dense image
embeddings to learn fine-grained anatomical features without requiring detailed image captions. Furthermore, we propose a dynamic hidden state mechanism to alleviate the exposure bias during long-context
multimodal inference. Extensive experiments on multi-disease
brain MRI
dataset demonstrate that UniBrain achieves high performance for
brain image imputation, understanding, and disease diagnosis under various extents of modality incompleteness.
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All authors Zhiyun Song, Che Liu, Tian Xia, Avinash Kori, Wenjia Bai
Artificial Intelligence
Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents
Suqing Wang, Qinghai Miao, Chao Guo et al. (+1 more)
Art therapy plays a vital role in emotional healing, in which narrative creation acts as the primary vehicle for emotional expression. Given the inherently dynamic nature of
emotions during healing, narratives with finely controlled emotional fluctuations enable individuals to safely project inner conflicts and achieve emotional catharsis. Recently, with the rapid development of Large
Language Models (LLMs), automated narrative generation technology has provided a new pathway to support such artistic designs. However, while existing methods can produce fluent texts, they struggle to generate narratives that adhere to specified affective trajectories, failing to meet the demands of
emotion-oriented psychological healing. To address these issues, this paper proposes EC-Script, an LLM
agent-based framework that enables hierarchical control of the affective trajectory in narrative generation for emotional healing. To ensure that the generated narratives strictly follow the given emotional patterns, EC-Script establishes overall narrative direction through
Emotion-Trajectory Planning, propels scene-level plot development with Character-Driven Scene Generation, and regulates local emotional changes of characters via
Emotion-Controlled Script Writing. Ultimately, it outputs scene-by-scene script content that remains highly consistent with the preset affective trajectory. Experimental results demonstrate that EC-Script significantly outperforms baseline methods in affective trajectory adherence, exhibiting excellent and reliable emotional controllability, thereby providing effective technical support for AI-assisted emotional healing scenarios.
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All authors Suqing Wang, Qinghai Miao, Chao Guo, Yisheng Lv
Artificial Intelligence · Cross-listed from cs.RO
HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Youngjae Min, Jovin D'sa, Faizan M. Tariq et al. (+3 more)
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios but often becomes brittle under distribution shift, reward misspecification, and
stochastic interactions. Model predictive path integral (MPPI) control enables strong real-time refinement without gradients, but its performance depends on a well-shaped sampling prior, while manually designing the priors does not scale to multi-scenario deployment. We present HOLO-MPPI (High-level Offline, Low-level Online MPPI), a multi-scenario motion planning framework that combines high-level policy learning with low-level
stochastic optimal control. Offline, we learn a high-level policy that proposes scenario-robust plans in an abstract action space, with a learned world model for online rollout. Online, the policy serves as a data-driven prior generator that parameterizes MPPI's sampling distribution conditioned on the current observation and goal. MPPI then optimizes low-level control sequences around this prior in real time to adapt to local disturbances. We instantiate HOLO-MPPI in autonomous driving by designing an effective high-level action space and tailored model architectures. Our evaluation across diverse driving scenarios shows that HOLO-MPPI improves upon MPPI and end-to-end RL baselines while maintaining real-time control.
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All authors Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae
Artificial Intelligence · Cross-listed from cs.CV
Uncertainty Quality of VGGT: An Analysis on the DTU Benchmark Dataset
Markus Hillemann, Robert Langendörfer, Steven Landgraf et al. (+1 more)
Visual Geometry Grounded
Transformer (VGGT) has already attracted a great deal of
attention in a short period of time, not least due to the Best Paper Award at CVPR-2025. Similar to
DUSt3R and MASt3R, VGGT aims to bring about a paradigm shift by replacing established methods like bundle adjustment and feature matching with a simple, unified, feed-forward neural network that predicts camera poses, depth maps, and dense 3D structure directly from multiple images of a scene in a few seconds. A key aspect is its ability to process an arbitrary number of views consistently in a single forward pass without any post-processing or iterative optimization. For photogrammetry, this opens new possibilities for real-time, scalable, and accessible 3D reconstruction. In this context, not only high reconstruction accuracy but also high-quality uncertainty estimates are crucial, as they foster trust and enable robust quality assurance. This paper therefore investigates the quality of VGGT's uncertainty predictions. The analysis identifies an effective confidence threshold for filtering VGGT's raw output and demonstrates that enhancing uncertainty quality holds strong potential for improving the accuracy of its 3D reconstructions.
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All authors Markus Hillemann, Robert Langendörfer, Steven Landgraf, Markus Ulrich
Artificial Intelligence
Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning
Mudit Rastogi
Large
language models (LLMs) remain limited in multi-
agent planning because independently generated plans can create coordination failures such as spatial collisions, resource contention, and temporal deadlocks. We introduce Tensor-Coord, a multilinear
algebra framework that represents the joint plan of N
agents as a third-order tensor \(T \in R^{N \times H \times A}\) over
agents, timesteps, and actions. Canonical Polyadic (CP) and Tucker decompositions are used to identify latent coordination structure. The minimal epsilon-approximate CP rank R* defines a computable coordination complexity measure, with \(CC(Pi)=(R*-N)/N\). We prove that R*=N is necessary and sufficient for plan independence. The residual \(E=T-T_{R*}\) defines a conflict score over
agent pairs, timesteps, and actions, localizing failures without domain-specific rules. Tucker factors provide interpretable
agent roles, temporal phases, and action clusters that are converted into natural
language constraints for iterative LLM replanning. Experiments on multi-
robot delivery tasks across Easy (2
agents, 5x5 grid), Medium (3
agents, 5x5 grid), and Hard (4
agents, 5x5 grid) settings show convergence to conflict-free plans in 100% of 2-
agent cases within 1.4 iterations on average, 80% of 3-
agent cases within 3.2 iterations, and 60% of 4-
agent cases within 4.0 iterations. CP rank scaled approximately linearly as \(R*(N) = 3.9N + 0.5\), supporting its use as a predictor of coordination complexity.
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Artificial Intelligence · Cross-listed from cs.CY
AI systems out-persuade expert humans
Kobi Hackenburg, Caroline Wagner, Luke Hewitt et al. (+5 more)
Many societal decisions are settled by contests of persuasion. Conversational AI is a powerful new entrant in these contests, but whether it can out-persuade skilled and highly incentivized humans has remained unclear. Here, in a series of four preregistered experiments (n = 18,978 conversations from 6,923 people), we pitted AI systems against a range of human persuaders, including laypeople, winners of a separately preregistered four-round online persuasion tournament, professional canvassers, and world championship debaters. We found that AI systems were reliably more persuasive than expert humans, even when expert humans chose their issues, researched in advance, underwent hours of live, structured practice, and were incentivized with {\pounds}1,000 cash bonuses. In a follow-up study, AI's advantage persisted after experts received a coaching tool that let them practice against the AI that beat them, review their performance history, and see what AI would have said at key moments. We found converging evidence that AI's advantage stemmed from rapidly deploying larger quantities of information: after coaching, expert humans could tie an AI constrained to respond at human speeds and with human-length messages. In a final study, we show that AI's advantage extends to consequential real-world behavior: AI was nearly 3x more effective than professional canvassers from a UK fundraising firm at raising real-money donations to Save the Children. Together, these results establish that frontier AI systems out-persuade expert humans in conversation, with significant implications for political communication.
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All authors Kobi Hackenburg, Caroline Wagner, Luke Hewitt, Ben M. Tappin, Ed Saunders, Hannah Rose Kirk, Helen Margetts, Christopher Summerfield
Artificial Intelligence
When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting
Binyan Xu, Xilin Dai, Fan Yang et al. (+1 more)
AI
agents can now take irreversible actions in operational systems, but
agent-caused losses are still not clearly assigned, priced, or transferred. Providers often disclaim consequential damages, users are left with uncompensated losses, and default human review limits the efficiency gains of automation. We ask when autonomous AI deployment can become economically acceptable despite failure risk. Our answer is to quantify risk at the customer-task-trace episode level and transfer it through insurance. Automation is acceptable when its expected benefit exceeds the premium, control cost, and remaining risk. This requires a defined role with bounded permissions and comparable traces. We introduce trace-economic underwriting, which maps tool-use traces to customer exposure and claimable loss, then uses this representation for pricing, control, and risk transfer. It uses deterministic economic labels rather than an LLM judge. In our trace-to-loss testbed, trace-economic pricing reduces pricing MAE from $17.7K to $569 and removes regressive cross-subsidy. A 300-trace expert audit accepts 295 labels unchanged. On 1,000 real SWE-smith traces, trace-conditioned controls reduce CVaR95 by 72%.
Theorem~1 gives a finite-sample scope condition. We release code, labels, and audit sheets.
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All authors Binyan Xu, Xilin Dai, Fan Yang, Kehuan Zhang