Artificial Intelligence
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
Avinash Kori and Fabrizio Russo
World Models (WM) are increasingly seen as a foundation for intelligent
agents that can predict, plan, and act beyond their training distribution. In this paper, we study WMs from a causal perspective across multiple levels of abstraction, ranging from perceptual observations to building a conceptual representation of the structure governing the environment dynamics. We argue that useful WMs must go beyond generative capabilities alone: they should also capture entity properties, entity-to-entity interactions, and entity-to-environment interactions that determine and explain the dynamics of a system. We provide a formal definition of Causal WMs (CWMs) grounded in the tasks they are intended to support, connecting world modelling with existing work in causal representation learning, object-centric learning, causal discovery, structural causal models, and model-based decision-making. Finally, we relate CWMs to the literature on identifiability, clarifying when the components of a WM can be recovered from data and up to which equivalence. With this, we ground WMs in representations and structures that support causal
reasoning and informed decision-making.
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Artificial Intelligence · Cross-listed from cs.CV
UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models
Yukun Dai, Mingzhe Dai, Tianshi Wang et al. (+3 more)
Vision-
Language-Action (VLA) models have emerged as generalist robotic policies capable of following diverse
language instructions and performing a wide range of manipulation tasks. However, their direct control over
embodied agents also exposes them to
adversarial interference that may cause unsafe physical behaviors. Existing attacks on robotic policies are typically optimized for a single task or instruction, leaving the cross-task vulnerabilities of multitask VLAs largely unexplored. We introduce UniTexture, a cross-task universal
adversarial texture attack that uses a single textured 3D object to induce targeted deviations in VLA action predictions across multiple tasks. UniTexture backpropagates gradients from the policy's action outputs to surface texture parameters through a differentiable renderer. It jointly optimizes the shared texture over a distribution of tasks, instructions, states, and viewpoints using a targeted action-space objective, steering predicted actions toward attacker-defined targets without optimizing a separate texture for each task. We evaluate UniTexture on OpenVLA and $\pi_{0.5}$ across diverse manipulation tasks and multiple evaluation settings. UniTexture reduces the mean task success rate from 90.0% under benign conditions to 48.4% under attack, induces target-aligned action shifts, and further exhibits cross-suite and cross-model transfer without re-optimization. Together, these findings reveal shared cross-task vulnerabilities in multitask VLAs that can be systematically exploited through a single
adversarial surface texture.
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All authors Yukun Dai, Mingzhe Dai, Tianshi Wang, Fengling Li, Jingjing Li, Lei Zhu
Artificial Intelligence
Academic League of Artificial Intelligence - An Integrative Perspective of Teaching, Research, and Extension
Alison R. Panisson, Maria Eduarda W. M. Vianna, Italo Firmino da Silva et al. (+9 more)
Academic leagues have become important mechanisms for promoting extracurricular education and strengthening the integration between universities and society. This paper presents the organizational framework adopted by the Academic League of Artificial Intelligence (LIA) at the Federal University of Santa Catarina (UFSC), designed to integrate teaching, research, and university extension through a student-centered, project-based approach. The framework combines democratic governance, collaborative learning, and dynamic project organization to foster both technical and transversal competencies. The framework is illustrated through representative initiatives, including competition teams, study groups, open lectures, knowledge repositories, and AI-powered applications with social impact. These projects demonstrate how diverse educational, scientific, and extension activities can be developed within a common organizational structure while promoting leadership, scientific production, community engagement, and knowledge preservation. The reported experience indicates that the proposed framework provides a flexible and replicable model for integrating the three university pillars into engineering and computing education, offering practical guidance for academic leagues and similar student organizations.
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All authors Alison R. Panisson, Maria Eduarda W. M. Vianna, Italo Firmino da Silva, Heitor Henrique da Silva, Rafaela Fernandes Savaris, Bernardo Pandolfi Costa, Martin Augusto Gagliotti Vigil, Jim Lau, Agenor Hentz, Andréa Sabedra Bordin, Alexandre Leopoldo Gonçalves, Roberto Rodrigues-Filho
Artificial Intelligence · Cross-listed from cs.RO
ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models
Gehan Zheng, Matthew Johnson-Roberson and Weiming Zhi
Contact-rich manipulation failures are often detected only after the
robot has committed to contact. This is especially limiting in wrist-camera setups: close gripper–object views help observe contact, but a poor approach may already push, miss, slip, or disturb the object before conventional detectors react. We introduce ContactGuard, a pre-contact execution monitor for chunked visuomotor policies. Given the policy's planned action chunk, ContactGuard predicts its short-horizon consequence in latent visual space and aborts if the predicted future latent indicates likely failure. Its latent world model is trained from unlabelled
robot trajectories to predict compact multi-view visual
embeddings under planned actions, avoiding pixel-level
video prediction. A lightweight failure probe is then trained from a small labelled set of pre-contact clips. At deployment, ContactGuard anchors prediction before an imminent contact event, rolls the model forward under the policy's own actions, and verifies the predicted post-contact latent. Across real-world contact-rich manipulation tasks, ContactGuard predicts failure more accurately than direct and corrupted-action ablations, and transfers to live
robot as a pre-contact abort signal without modifying the underlying policy.
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Artificial Intelligence · Cross-listed from cs.FL
Algebraic Decomposition Theory for Transformer Length Generalization
Andy Yang, Blerta Veseli, Corentin Barloy et al. (+5 more)
Transformer-based
language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization. It is not even known which regular
languages transformers length-generalize on – and this is a foundational class of
languages. Our contributions are to establish the first complete characterization of which regular
languages transformers length-generalize on and provide a decision
algorithm running in
polynomial time in the size of the
language's syntactic monoid. These results rely on an effective characterization of the regular
languages in C-RASP, a recently-established formalism that expresses which
languages transformers length-generalize on. This characterization is challenging because classical tools like Krohn-Rhodes decomposition theory for finite
semigroups are insufficient for C-RASP. Firstly, the basic building blocks of Krohn-Rhodes theory – flip-flop and simple groups – are not expressible in C-RASP. Secondly, the basic building block of C-RASP (unbounded counting) is not expressible by the finite
semigroups of Krohn-Rhodes theory. Thus, length generalization on regular
languages is controlled by an algebraic property that is invisible to classical finite decomposition theory. We generalize classical decomposition theory from finite
semigroups to the infinite additive group on the integers, allowing us to characterize C-RASP in terms of iterated wreath products of the integers and derive a provable
polynomial-time decision
algorithm for regular
language membership. Experiments across a broad test suite of regular
languages confirm that our theory captures
transformers' length-generalization behavior more accurately than existing classifications.
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All authors Andy Yang, Blerta Veseli, Corentin Barloy, Michaël Cadilhac, Andreas Krebs, Charles Paperman, Howard Straubing, Michael Hahn
Artificial Intelligence · Cross-listed from cs.CL
Are You Sure You're Sure? On the Impact of Instruction Tuning on Confidence and Lexical Diversity
Irina Proskurina, Mayank Kumar and Oyindolapo O. Komolafe
Instruction-tuned
language models achieve strong performance across a range of generation tasks but have recently been shown to exhibit verbalized overconfidence, which may manifest in less diverse supporting rationales for incorrect answers. However, whether such overconfidence is associated with rationale consistency remains an open question. In this paper, we study whether changes in the lexical diversity of generated answer rationales accompany changes in model confidence induced by instruction tuning. We evaluate three matched base and instruction-tuned models across question-answering
benchmarks and find that instruction tuning consistently increases answer confidence, despite limited changes in predictive accuracy, while degrading likelihood-based calibration. Secondly, we observe a non-uniform effect of instruction tuning on rationale diversity: cross-rationale diversity consistently decreases, whereas surface-level lexical diversity varies in both direction and magnitude across models and
benchmarks. Finally, we find that these differences persist after controlling for answer selection and rationale length, confirming that confidence and rationale diversity capture distinct effects of instruction tuning.
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Artificial Intelligence
RAIL: An Automatic Classifier of the Artificial Intelligence Readiness Level
Juan Irving Vasquez, Juan Terven and Laura-Ivoone Garay-Jimenez
Assessing the maturity of artificial intelligence technologies is essential for investment decisions, project management, and policy monitoring, yet the available readiness frameworks are heterogeneous and difficult to apply automatically: the adaptation of Technology Readiness Levels to AI lacks AI-specific gating criteria, the Machine Learning Technology Readiness Levels presuppose access to internal process artifacts, and AI/data readiness dimension models employ scales that resist direct comparison. This paper makes two contributions. First, we unify these three frameworks into the Unified AI Readiness Level (AIRL), a nine-level ordinal scale built on an environmental evidence ladder and complemented by dimensional caps (covering specification, data existence, data quality, data legality, expert knowledge, and algorithmic maturity) together with a generality-anchoring rule and explicit assignment disciplines, so that a readiness level becomes decidable from a natural-
language description of the work alone. Second, we propose RAIL (Readiness Assessment via Independent LLM-experts), a panel-of-experts classifier that operationalizes the scale: one evidence
agent and six independent dimension
agents, each a large
language model with a narrowly scoped mandate, deliver verdicts that a deterministic minimum rule aggregates and a chief expert reviews under asymmetric authority, confirming or lowering the panel's recommendation but never raising it above the caps. The method was tested in the analysis of several research works showing consistency and avoiding overestimation from monolithic LLM classifiers.
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Artificial Intelligence · Cross-listed from cs.LG
Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference
Zixuan Lan, Yanhong Li and Jiawei Zhou
Transformer-based
language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional
matrix multiplications. We propose Reduced
Matrix Multiplication (RMM), a training-free, input-adaptive inference method that reduces
Transformer matrix products by selecting informative slices along their contraction dimensions, without modifying model weights. Under a simple retention-ratio control, RMM provides a smooth and predictable accuracy-efficiency trade-off. Across
language models ranging from 1B to 70B parameters, we find that reduction tolerance depends on the model family, task, component, and retention ratio, although it often improves with model scale. Under moderate reduction, RMM remains robust across the evaluated discriminative, autoregressive generation, and long-context settings. We further show that the same principle extends to
multimodal vision-
language inference. Mechanistic ablations reveal a structural asymmetry within
Transformers:
attention-side computations are substantially more reducible than MLP components. Finally, wall-clock
benchmarks with custom kernels on an NVIDIA A100 show that these computational savings can translate into practical runtime gains, especially at longer sequence lengths. Together, these results position RMM as a scalable direction for input-adaptive inference-time optimization.
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Artificial Intelligence
Enhancing Virtual Agents through SLMs and Edge-Computing: An Exploratory Evaluation of Think and Memory Processes
Aimilios Hadjiliasi and Louis Nisiotis
Embodied intelligent virtual
agents are expected to operate as persistent, adaptive, and context-aware entities within complex virtual and Metaverse worlds. However, implementing cognitively capable
agents in such environments is conceptually and technologically challenging. Among a range of blueprints and development approaches, the Cognitive
Embodied Agent Architecture (CEAA) has been developed as an implementation-oriented framework for architecting components of perception,
memory,
reasoning, planning, and
embodied action. Considering the recent advances in edge computing and generative AI
language models, this paper explores the use of Small
Language Models (SLMs) to support edge-based operation of selected CEAA components, focusing on "Think" and "
Memory" as processes central to cognitive orchestration and persistence of virtual
agents in interactive virtual worlds. An edge-based virtual
agent gateway system was developed and evaluated on an NVIDIA Jetson Orin NX using Qwen2.5 models of different sizes, exploring the system's capability to process service requests and handle
memory-driven conversations. A series of simulation experiments evaluated routing accuracy,
memory-read performance, and latency, demonstrating an SLM-driven prototype
agent system that partially implements selected CEAA processes to support the development of
embodied agents whose cognitive "
brain" can operate efficiently and contextually for interactive experiences in immersive virtual worlds.
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Artificial Intelligence
Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Yiwei Li, Wanli Yang, Hexiang Tan et al. (+10 more)
Autonomous
agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this capability, however, evaluation must go beyond final scores, which neither reveal where progress is gained or lost nor indicate whether accumulated experience improves later decisions. We therefore present a systematic evaluation of seven frontier models on 36 long-horizon tasks based on a new framework that uses rule-based metrics to characterize within-run behavior through Solution Framing, Execution, and Feedback Control and controlled comparisons to assess experience reuse within and across tasks. The results show that current
agents operate more like engineering optimizers than fully autonomous researchers: they can formulate and implement practical solutions, but their performance varies substantially across runs, their strongest solutions mainly adapt or combine established techniques, and genuine methodological novelty remains rare. Detailed analysis reveals that observed performance is shaped by multiple factors, including distinct process bottlenecks behind similar final outcomes, experience reuse that can help or mislead subsequent decisions, and harness designs that affect performance stability. These findings suggest concrete directions for improving model training, inference-time strategies, experience management, and harness design.
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All authors Yiwei Li, Wanli Yang, Hexiang Tan, Xiangzhou Huang, Zhengyu Chen, Ziran Li, Borun Chen, Shanglin Lei, Huaisheng Zhu, Hao Tian, Fei Sun, Xunliang Cai, Jingang Wang
Artificial Intelligence · Cross-listed from cs.RO
Deliberate Practice: Learning Robot Skills under a Budget
Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut et al. (+2 more)
We consider the problem of autonomously learning
robot skills under a limited practice budget for sequential tasks. We propose an active skill learning
algorithm, Deliberate Practice (DP), that computes a provably budget-optimal allocation—practicing skills that maximize expected cumulative reward while being learnable within the budget. DP estimates both the time needed to master skills and the cumulative reward of the task plans that the skills unlock. Computing a budget-optimal allocation is challenging as it requires
reasoning about combinatorially many skill plans over a large practice budget. Our key contribution is a bilinear program that can compute this exactly using off-the-shelf solvers. Through simulated and real-world experiments on long-horizon manipulation tasks, we show that our approach allows
robots to optimally use limited practice time to acquire useful policies and improve long-horizon planning.
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All authors Shivam Vats, Sudarshan Harithas, Mete Tuluhan Akbulut, Arvind Raghunathan, George Konidaris
Artificial Intelligence
Who Speaks Matters: Authority-Aware Multi-View RAG over Italian Parliamentary Proceedings
Mirko Tritella, Riccardo Pozzi and Matteo Palmonari
Parliamentary proceedings are a primary record of democratic deliberation, yet their volume and fragmentation make multi-perspective access difficult for citizens, journalists, and researchers. Applying
Retrieval-Augmented Generation (RAG) to parliamentary transcripts introduces three specific risks: dominance of the most frequent speakers, inability to weight speakers according to topical expertise, and citation misattribution in politically sensitive text. We present ParliamentRAG, a RAG system for the Italian Chamber of Deputies that addresses these risks jointly. Its core contribution is a topic-dependent authority model that estimates each speaker's authority as a function of the current query, combining interpretable components such as profession, education, and previous interventions. Given a user query, the system retrieves relevant
speech chunks, identifies topic-relevant experts across parliamentary groups, and generates a summary synthesizing their perspectives, accompanied by supporting quotations. ParliamentRAG is evaluated against Google NotebookLM on 15 policy topics via a two-level protocol combining automated metrics and blind A/B human evaluation by six domain experts. The system achieves higher coverage across political groups (0.97 vs. 0.95), perfect quotation faithfulness (1.00 vs. 0.95), and stronger expert preferences on source-related dimensions, while NotebookLM remains stronger on prose-oriented dimensions.
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Artificial Intelligence
Jointly Predicting Courses and Grades Using a Transformer-Based Model
Paul Savala
Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester. This simplification can lead to inaccurate performance predictions, particularly for students with heavy or challenging course loads. This paper introduces a
TRansformer for Academic Course-grade Estimation (TRACE) that addresses this limitation by jointly predicting both the set of courses a student will take and their corresponding grades for an upcoming semester. Our approach encodes courses on a per-semester basis to capture the effects of course concurrency and utilizes a novel loss function combining course-set prediction with grade prediction. We demonstrate that predicting courses taken in addition to the grades in those courses leads to significant improvements in prediction quality. Trained on ten years of institutional data, our joint prediction model reduces mean absolute error by nearly 50% compared to an identical architecture that predicts grades alone. The model also outperforms traditional LSTM-based sequential models, as well as graph neural network-based approaches, and offers natural ways to incorporate student attribute data. This work demonstrates the utility of modern neural architectures for creating interpretable models that can be adapted to new institutions via retraining and recalibration, as well as the importance of key techniques, such as predicting courses taken during training. We discuss how this model could be incorporated into early detection systems at institutions of higher education.
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Artificial Intelligence · Cross-listed from eess.SP
Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks
Muhammad Hannan Akram, Muhammad Abubakar Rashid, Wassi Haider Kabir et al. (+3 more)
6G networks will not be serving as communication infrastructures only; rather, they are expected to evolve into intelligent systems, where thousands of autonomous artificial intelligence (AI)
agents are interconnected. The
agents are deployed across a wide range of platforms including low Earth orbit (LEO) satellites, high-altitude platforms (HAPs), unmanned aerial vehicles (
UAVs), edge servers, and terrestrial devices. These
agents continuously observe their environment and exchange information. Semantic communication provides an efficient mechanism for exchanging meaningful information instead of raw data. However, its effectiveness depends on the communicating
agents having sufficiently aligned beliefs to correctly interpret and decode the transmitted messages. This assumption becomes difficult to satisfy in the 6G network where heterogeneous AI models operate under diverse computational constraints and continuously acquire different knowledge from their local environments. This article presents a heterogeneity-aware belief synchronization framework for 6G AI-native networks. It uses latent translation models deployed on multi-access edge computing (MEC) servers. These models translate belief updates from one
agent to
agent-specific knowledge without requiring joint training and a homogeneous architecture of models. By exchanging compact belief updates through a latent translation model only when necessary, the framework preserves
privacy, reduces synchronization cost, and minimizes local knowledge drift. We validate the framework through a case study on a multi-layered terrestrial/non-terrestrial network. Results demonstrate that it maintains low synchronization cost, measured by the number of parameters transmitted, and low belief
alignment error across the heterogeneous
agents in the case study.
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All authors Muhammad Hannan Akram, Muhammad Abubakar Rashid, Wassi Haider Kabir, Haejoon Jung, Kapal Dev, Syed Ali Hassan
Artificial Intelligence
TopoIntent: Compiling Security Intent into Executable, Compliance-Checked Network Topologies
Xiaokang Qu, Jianliang Ma, Zao Fan et al. (+3 more)
Enterprise security
topology design requires translating business intent, regulatory requirements, and risk assumptions into zones, boundary devices, inter-zone paths, and access-control policies. Existing NetOps automation tools mainly operate after this design is fixed, providing limited support for generating structured security
topologies from underspecified natural-
language requirements. We present TopoIntent, a system that compiles security intent into executable, compliance-checked network
topologies. It uses a schema contract to constrain generation, retrieves reference architectures from a curated template library via dense-vector search, and applies staged fusion for intent-template
alignment and security completion. The generated
topology is checked against CIS Controls v8.1.2 safeguards visible at the
topology layer, while unresolved cases are marked for manual review. Structural gaps are repaired through additive schema-preserving edits. The final
topology is exported to Mininet scripts with kernel-level iptables ACLs, enabling executable reachability and allow/deny tests. Because no public
benchmark exists for this requirement-to-
topology task, we construct an evaluation set from reference security architecture diagrams. The
retrieval set contains 22 templates and 44 synthetic intents across five scenarios, while the held-out set contains 7 templates and 14 intents from finance and government scenarios excluded from
retrieval. On the held-out set, additive repair improves
topology-visible CIS satisfaction from 0.78 to 1.00 in fewer than 1.5 rounds on average, and one feedback round raises the post-ACL policy pass rate from 0.78 to 0.88.
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All authors Xiaokang Qu, Jianliang Ma, Zao Fan, Tianshu Chu, Tianlong Fan, Linyuan Lü
Artificial Intelligence · Cross-listed from cs.CV
Sign Language Video Synthesis via Loss-Guided Multi-Expert GANs
Dingzhan Nong, Zhihao Ren, Ziqi Li et al. (+1 more)
This preliminary technical report presents a framework for sign
language video synthesis using a loss-guided multi-expert Generative
Adversarial Network (GAN) to enhance communication for individuals with hearing impairments. Three specialized discriminators–global, hand, and head–each guide a corresponding expert branch in the generator toward a distinct visual region, enabling implicit feature specialization without explicit diversity losses. To stabilize this multi-discriminator system, whose early-phase training otherwise exhibits chaotic dynamics, we introduce a United Loss consensus mechanism that regularizes each discriminator toward the ensemble average at a 10% weight. Each branch further adopts a dual-pathway convolutional-
transformer design with learnable AdaptiveFeatureFusion, balancing the stability of convolutions against the detail of windowed self-
attention. The generator is trained using an alternating three-mode schedule (discriminator, holistic generation, branch-specialized generation). On a custom 156GB
dataset with a filtered evaluation set that removes easy and repetitive samples, our 0.2B-parameter variant achieves 29.78 PSNR (0.9593 SSIM), the 0.66B variant reaches 30.52 PSNR (0.9631 SSIM) after 7.37M steps, and the 1.3B variant achieves 30.72 PSNR (0.9650 SSIM). The gain from 0.66B to 1.3B is only +0.20 PSNR despite nearly doubling the parameters, demonstrating sharply diminishing returns. Inference VRAM footprints are 1.5 GB, ~5 GB, and 8 GB respectively, enabling deployment on consumer-grade hardware. Full ablation studies remain ongoing due to the 2-3 month training cycle on a single GPU. The system was showcased at the 2025 Hong Kong Frontier Technology Summit.
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All authors Dingzhan Nong, Zhihao Ren, Ziqi Li, Tim Lo
Artificial Intelligence
Rules or Character? Scaling Laws for AI Safety Design
Satoshi Takahashi, Nobuji Kouno, Masaaki Komatsu et al. (+1 more)
Artificial Intelligence (AI) safety systems combine character shaping (e.g., Reinforcement Learning from Human Feedback [RLHF], Constitutional AI), which modifies behavioral distributions at training time, with rule enforcement (e.g., output filters, safety classifiers), which blocks harmful outputs at inference time, yet little formal analysis exists on how their optimal balance should change as deployment scales increase. We introduce a stylized comparative-statics model that parameterizes safety design as a resource allocation alpha in [0,1] between these two approaches, incorporating scale-dependent filter degradation, common-mode failures, and character fragility – the risk that shaped behavior degrades or collapses under novel conditions. Under a multiplicative Pareto damage model, we derive closed-form expected harm and supplement it with tail-risk (CVaR) analysis via Monte Carlo simulation. Across three scenarios (optimistic, moderate, pessimistic), the optimal alpha* is interior or at the rules-only boundary and shifts weakly toward character shaping as deployment scale T grows, from negligible (Delta alpha* = +0.01) to pronounced (Delta alpha* = +0.21) depending on scenario. The dominant parameter is the baseline character fragility rate p^(0)_frag, which shifts alpha* by 0.50 across its range – far exceeding the effect of tail severity, filter quality, or common-mode failure probability. CVaR and expected-harm optima converge at large T. These results suggest that safety architecture decisions depend less on deployment scale per se than on the reliability of character shaping under distributional shift.
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All authors Satoshi Takahashi, Nobuji Kouno, Masaaki Komatsu, Ryuji Hamamoto
Artificial Intelligence
LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoning
Yupan Ding, Jing Xiao, Zhenyuan Zhang et al. (+4 more)
Long-horizon Earth observation
reasoning requires models to organize multi-stage geographic
evolution, localize spatial changes, detect temporal anomalies, and infer future from extended image sequences. However, existing remote sensing
vision-
language models mainly focus on isolated images, image pairs, or short sequences, limiting reliable grounding in the relevant frames and regions. We introduce LongEarth-Bench, a
benchmark containing approximately 120k question-answering samples derived from 117k unique images. Its sequences average 15.14 frames and extend to 30 frames, covering 12 tasks across
evolution summarization, spatial
reasoning, anomaly identification, and logical prediction. A 30k-sample subset further provides structured
reasoning traces linking key frames and changed regions to final answers. We develop LongEarth through supervised fine-tuning with explicit sequence identifiers and structured chain-of-thought supervision. Building on LongEarth, LongEarth-R1 applies group relative policy optimization with format, temporal, and spatial rewards. LongEarth-R1 achieves the best results on all 12 long-sequence tasks while remaining competitive on standard remote sensing
benchmarks.
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All authors Yupan Ding, Jing Xiao, Zhenyuan Zhang, Chaofeng Chen, Liang Liao, Gui-Song Xia, Mi Wang
Artificial Intelligence · Cross-listed from cs.LG
Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples
Yusen Tan, Yixuan Chen, Zheng Fang et al. (+8 more)
Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or
datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental
datasets. Here we introduce UltraIR, a foundation model for IR spectroscopy with more than 100 million parameters that enables simulation-to-real transfer learning for chemical sensing and analysis from molecules to complex samples. UltraIR is pretrained on approximately 60 million simulated IR spectra using spectral reconstruction, molecular fingerprint similarity
alignment, and functional-group prediction, then adapted to downstream objectives with task-specific labels or targets. Across functional-group prediction, molecular structure elucidation, physicochemical property prediction, mixture-component identification and quantification, bacterial classification, medicinal-herb geographic origin traceability and constituent quantification, microplastics classification, and soil property prediction, UltraIR outperforms conventional machine-learning and task-specific deep-learning baselines. It performs strongly with limited labeled experimental spectra and in zero-shot inference for the same analytical task across
Fourier-transform infrared spectrometers and laboratories, providing a route to adaptable, data-efficient chemical sensing from complex real-world samples.
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All authors Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia
Artificial Intelligence
LLM-Guided Graph Generation for Structure-Based Local Improvement Methods
Hai Xia, Vaidyanathan Peruvemba Ramaswamy and Stefan Szeider
Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various problems, researchers tend to design variable selection strategies that take into account structural features across different domains. In this paper, we build an automatic pipeline that is problem-agnostic to all problems in the MiniZinc format. By prompting an LLM with our semantic guidelines, we guide the LLM to produce a graph generator that maps any instance of a problem type to a uniform weighted graph, where nodes represent decision variables and edges represent constraint relationships. These problem-agnostic graphs guide our structure-based local improvement (SLIM) framework for variable selection. Meanwhile, the weighted graph enables all problem instances to share the same generic graph representation, from which the same graph features can be extracted and used for configuration selection. We evaluated our pipeline on instances across 20 MiniZinc competition problems, finding that
algorithm selection achieves a 39.6% average problem-weighted win rate against a one-shot Gurobi baseline, more than doubling the best single configuration (19.3%). A post-hoc configuration and a feature ablation indicate a headroom of up to 44.0%, demonstrating that LLM-based semantic generation enables effective automated structure and feature extraction for constraint optimization.
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Artificial Intelligence · Cross-listed from cs.LG
Training AI Scientists to Replicate Research
Damon Falck, Samer Sabri, Anja Surina et al. (+8 more)
The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments. The act of replication typically illuminates details that were previously underspecified, and thus requires similar hypothesis-driven exploration to open-ended research. In this work, we develop Replica, a scalable task space for paper replication. To provide reward signal, we introduce an auto-generated rubric-based judge that has low noise and agrees with human assessment of replication quality. We post-train Faraday, a 27B-parameter "AI Scientist"
agent that leverages coding
agents as tools, surpassing the performance of Claude Opus 4.8 and GPT-5.5 on held-out replication tasks. Qualitative analysis of individual rollouts reveals that Faraday adopts a more scientifically-principled approach. We believe that our results provide a stepping stone towards AI
agents capable of long-horizon scientific innovation without requiring complex harnesses.
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All authors Damon Falck, Samer Sabri, Anja Surina, Thom Foster, Anya Sims, Sam Devlin, Dylan Rogers, Tantum Collins, Kaloyan Aleksiev, Louis Kirsch, Edward Hughes
Artificial Intelligence · Cross-listed from cs.CL
It's How You Ask: Gender-Associated Linguistic Bias in LLMs
Katherine Van Koevering and Anjalie Field
Professional communication is increasingly mediated by LLMs - but do these models serve all users equally? We show that when
prompts contain linguistic features more commonly used by women (hedges, tag questions, collective reference), they systematically elicit shorter, less sophisticated, and less formal responses across three document types and four models. These effects persist after controlling for
prompt complexity and feature carry-over. Explicit gender cues like sign-off names are encoded in the same representational space as linguistic dialect - suggesting shared underlying mechanisms - yet linguistic register is far more influential, producing large, consistent effects where names produce none. Our results further reveal that post-hoc mitigation is challenging: because these patterns are culturally embedded and outside conscious control, users cannot easily avoid them through strategic self-presentation, and mechanistic analysis reveals that linguistic features are encoded in early
transformer layers and entangled with other features. Our work calls for upstream consideration of the influences of linguistic variation to mitigate disparate impacts of LLM-mediated workplace communication.
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Artificial Intelligence
StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems
Yanwen Peng, Delvin Ce Zhang, Xi Wang et al. (+1 more)
Large
language model based multi-
agent systems usually communicate in text, i.e., using discrete
tokens. However, text introduces a discrete bottleneck. Converting the sender's continuous hidden states into discrete
tokens discards information that
token identities alone cannot capture. Recent work proposes latent communication as an alternative, where
agents transmit hidden representations directly without converting them to text. However, existing latent methods either inject working
memory layer by layer across the
transformers, or require trained projectors that limit portability. We propose StateBridge, a training-free latent communication approach that aligns the sender's final-layer hidden states to the receiver's input space via a closed-form orthogonal transformation. Lightweight norm calibration and vocabulary anchoring ensure compatibility with the pretrained input distribution. The aligned states are prepended to the input of the receiver
agent as a continuous prefix. We evaluate StateBridge on math
reasoning, code generation, and question answering with four models from two families. StateBridge achieves the best or tied-best score on 22 out of 26 model-task pairs, consistently outperforming the strongest baseline.
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All authors Yanwen Peng, Delvin Ce Zhang, Xi Wang, Nikolaos Aletras
Artificial Intelligence · Cross-listed from cs.GT
Keep, Customize, or Exit: Default Design and Token Pricing in LLM Reasoning Services
Ahmet Bugra Gundogan, Yigit Turkmen and Melih Bastopcu
We study a large
language model (LLM) service in which a provider chooses a per-
token price and a default
reasoning-
token allocation, while a user may accept the default, customize the allocation, or exit. Larger allocations can improve accuracy but increase
token cost and latency. We model this interaction as a Stackelberg game and derive the user's unique optimal customized allocation in closed form. For any price, the acceptable defaults form either an empty set or a compact interval. We characterize the provider's optimal default through a three-regime rule, reduce
equilibrium computation to a one-dimensional price optimization, and prove the existence of the
equilibrium. We further show that defaults affect the implemented
reasoning allocation only when users value the convenience of avoiding customization; otherwise, every service-providing outcome implements the user's optimal customized allocation. Experiments with two compact open-weight
reasoning models on five mathematics and science
benchmarks support the accuracy-
token model and show how model and task characteristics determine
equilibrium prices, defaults, and
reasoning allocations.
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Artificial Intelligence · Cross-listed from stat.AP
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Žan Gorenc, Žiga Gradišar, Felix Mütter et al. (+2 more)
Estimating the distribution of relaxation times (DRT) fromelectrochemical impedance spectroscopy (EIS) is an ill-posed inverse problem that is highly sensitive to regularisation choices. We propose a physics-informed convolutional autoencoder that estimates DRT directly from EIS data without spectrum-specific tuning. A discretised relation between impedance and the DRT is embedded in the training process, constraining the network to produce impedance-consistent distributions. The model resolves overlapping relaxation processes in synthetic two-ZARC spectra and accurately reconstructs measurements from three independent solid
oxide fuel and electrolysis
cell datasets, with range-normalised errors below 1.1%. Decoder-probe analysis shows that the learned latent representation is organised according to relaxation timescale. Distances in this latent space capture operating changes, hydrogen-shortage events, and long-term degradation. The same lightweight architecture is applied across all
datasets without modification, providing consistent DRT estimation and an interpretable basis for condition monitoring.
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All authors Žan Gorenc, Žiga Gradišar, Felix Mütter, Vanja Subotić, Pavle Boškoski
Artificial Intelligence · Cross-listed from cs.LG
Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
Wojciech Zarzecki and Jarosław Arabas
Existing global optimization
benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization methods. We argue that the tasks related to the black-box
adversarial attack (BBAA) can serve as valuable global optimization
benchmark in many-dimensional space. We demonstrate the efficiency of several types of evolutionary
algorithms and other metaheuristics in solving example BBAA problems. Thus, we take a step towards convergence of global optimization methods to the challenges and needs that arise in the modern machine learning field.
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Artificial Intelligence
NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
Eleftherios Mylonas, Angelos Kouprizas, Michael Birbas et al. (+1 more)
Edge AI deployment demands neural architectures that are simultaneously accurate, computationally efficient, and hardware-deployable - a challenge addressed by hardware-aware Neural Architecture Search (NAS). While recent works incorporate quantization directly into the NAS loop, these approaches expand search complexity and tightly couple architecture and quantization design. The simpler post-search quantization strategy has received little analytical
attention: the effects of Post-Training Quantization (PTQ) on the NAS-discovered Pareto structure remain uncharacterised, and no framework combines quantized architecture mapping onto reconfigurable accelerators with automated hardware exploration. This paper addresses both gaps. First, a three-stage pipeline is proposed: a hardware-agnostic Pareto rank surrogate frontend on NAS-Bench-201, a quantization bridge with Pareto-aware filtering and feedback control, and an evolutionary Domain Space Exploration (DSE) backend on CGRA4ML for optimal hardware mapping. Second, an empirical study characterises how INT4 PTQ perturbs the NAS-Bench-201 Pareto space through formal stability metrics on ground-truth data for all 15,625 architectures, and demonstrates that an FP32 zero-shot surrogate outperforms a dedicated INT4-trained surrogate in Pareto space coverage across two standard search strategies.
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All authors Eleftherios Mylonas, Angelos Kouprizas, Michael Birbas, Alexios Birbas
Artificial Intelligence
Towards Context-Aware Clinical Motion Understanding in Daily Living at Home: Freezing of Gait Detection with Egocentric Vision
Vayalet Stefanova, Diwas Lamsal, Margot Genbrugge et al. (+5 more)
Understanding motion in daily living requires context beyond kinematics, because similar inertial patterns during activities of daily living (ADLs) can reflect intentional stopping, object interaction, or pathological movement impairment.
Egocentric vision provides task-related context that may help disambiguate these cases. We investigate this challenge through freezing of gait (FOG) detection in Parkinson's disease (PD), a symptom strongly influenced by contextual factors during ADLs. Using synchronized
egocentric video, wearable IMUs, and expert-annotated FOG labels collected from 13 PD participants in their homes, we evaluate frozen representations from pretrained ego-
video and time-series foundation models, alongside an IMU-based TCN trained from scratch, under leave-one-subject-out evaluation. The IMU-based TCN achieved the strongest event-detection performance, reaching 42.3 F1 and 83.0 AUROC, compared with 32.6 F1 and 77.2 AUROC for V-JEPA2 ego-
video features. Although ego-
video alone did not outperform IMU-based sensing, it showed above-chance discrimination, and qualitative analyses suggest that
egocentric vision may capture FOG-relevant information independent of IMUs. Together, these results support the use of pretrained ego-
video representations to add contextual information to wearable-sensor-based clinical motion understanding in daily living.
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All authors Vayalet Stefanova, Diwas Lamsal, Margot Genbrugge, Maxim Yudayev, Christian Schlenstedt, Moran Gilat, Bart Vanrumste, Benjamin Filtjens
Artificial Intelligence · Cross-listed from cs.CL
Mixture of Training: Recombining Small-Scale Scaffolded Pretraining Runs into a Larger Language Model
Mohammed Sabry, Sean Augenstein, Keith Rush et al. (+1 more)
We ask whether
language-model pre-training can be decomposed into smaller, independently trainable jobs that can later be recomposed into a coherent larger model. We introduce Mixture of Training (MoT), a scaffolded modular pre-training procedure that partitions a target
Transformer into contiguous layer blocks, trains each block inside a frozen pretrained aligner scaffold, and then recomposes the trained blocks with an optional short end-to-end adaptation pass. On a 1.3B-parameter Gemma-style model trained on C4, MoT provides a small-scale proof of mechanism: independently trained depth slices can be recomposed into a usable
language model, and a quality-parity schedule reaches the same reported perplexity as the monolithic baseline. This parity setting processes more aggregate
tokens and has a shorter idealized layer-equivalent critical path after aligner preparation; its effective compute advantage depends on reusing the aligner across runs. We therefore present MoT not as a general replacement for monolithic pre-training, but as a small-scale framework for studying whether scaffolded sub-runs can act as reusable training units.
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All authors Mohammed Sabry, Sean Augenstein, Keith Rush, Lucio Dery
Artificial Intelligence
Sovereign by necessity? Frontier AI export controls, cyber security, and the limits of national AI capability
Alan Woodward and Andrew Rogoyski
A small number of firms based in two states produce the most capable frontier AI models. The governments of those states have shown both the legal power and the political will to decide which other countries may use these systems. In June 2026 the United States required a leading developer to obtain licences before releasing its most advanced models to any foreign person, including foreign nationals resident in the United States. The affected models were withdrawn worldwide at short notice, partly because the restriction proved impractical to administer. This followed within months of the first documented case of a largely autonomous, AI-run cyber espionage campaign, and coincided with mounting evidence that frontier models alter the economics of both cyber attack and cyber defence. This article examines how these two developments interact, and situates them within the unusual
market dynamics now driving large-scale AI development. It argues that access to frontier AI is becoming part of national cyber defence, that such access can be revoked, and that the obvious remedy of sovereign capability remains only partly feasible for all but a handful of states. Drawing on evidence about training costs, the concentration of computing power and the support offered by national AI programmes, it asks what sovereignty can realistically mean for small and middle powers, and for large powers as well. The article proposes a layered strategy: negotiated access guarantees, sovereignty at the level of inference, hedging with open-weight models, pooled regional capability, sustained talent development and continued investment in basic cyber resilience. The open-weight hedge proves at once more capable and more politically exposed than is commonly assumed. Much of the near-term risk lies in how capable models are deployed and contained rather than in their apparent performance.
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