Artificial Intelligence · Cross-listed from cs.CL
T^2MLR: Transformer with Temporal Middle-Layer Recurrence
Ziyang Cai, Xingyu Zhu, Yihe Dong et al. (+2 more)
Transformer reasoning is limited by autoregressive decoding, which repeat edly compresses rich hidden computation through
token space and makes it difficult for intermediate
reasoning states to persist across time. We in troduce
Transformers with Temporal Middle-Layer Recurrence (T2MLR), a
transformers-based latent
reasoning architecture that fuses a cached middle layer representation from the previous
token directly into an earlier layer of the current
token position, enabling abstract intermediate computation to persist across decoding steps with little inference overhead. Across natural-
language pretraining and multi-hop
reasoning finetuning, T2MLR consistently outperforms data- and parameter-matched
Transformer base lines. Moreover, applying recurrence to only a localized middle-layer block (as little as 20% of the network) often outperforms full-layer recurrence. Im portantly, T2MLR does not require pretraining from scratch: retrofitting the recurrent pathway into an existing pretrained 1.7B
Transformer and briefly finetuning substantially improves math
reasoning, lowering the barrier to practical adoption. These results suggest that effective latent
reasoning in
Transformers does not require looping over all layers as in previous works, but can instead emerge more strongly from targeted middle-layer recurrence.
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All authors Ziyang Cai, Xingyu Zhu, Yihe Dong, Yinghui He, Sanjeev Arora
Artificial Intelligence
Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Patrick Phuoc Do, Chau M. Ta and Chaoli Wang
Multimodal large
language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-centric and provide limited evidence of understanding of scientific visualization (SciVis). We
benchmark six MLLMs on the scientific visualization literacy assessment test, a standardized SciVis literacy assessment comprising 49 items based on 18 scientific visualizations and illustrations, spanning 8 techniques and 11 task types. We evaluate three closed-source and three open-source models under a closed-world protocol and compare their performance using data from 485 human participants. Results show that current MLLMs do not exhibit uniform SciVis literacy. Gemini is the strongest model overall, exceeding the human mean across the evaluated subsets, whereas the open-source models remain below the human baseline. Performance is highly uneven across techniques and tasks: models perform best on scientific illustration, search, and spatial understanding, but struggle on texture-based and integration-based visualizations and on quantitative estimation. Error analysis reveals recurring failures in fine-grained quantitative estimation, flow-direction interpretation, and grounded encoding interpretation. These findings position SciVis literacy as a necessary
benchmark dimension for evaluating
multimodal AI systems. Our code and model outputs are publicly available at https://github.com/patdmp/mllm-scivis-lit-
benchmark.
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Artificial Intelligence
MedFailBench: A Clinician-Built Open-Source Benchmark for Medical AI Safety Boundary Inspection
Goktug Ozkan
Most medical AI
benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a synthetic
benchmark and failure atlas built by a clinician. The resource labels medical AI errors by severity from 1 to 5 and safety gate type: missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, and source support gap. The current public release (v0.2.1) contains 44 synthetic cases reviewed by a clinician, with severity annotations, a public Hugging Face Space source, a safety gate taxonomy, a clinical severity rubric, and an automated pipeline for archiving model response screening runs. Forty cases have a populated safety gate field, and four require gate completion. No patient data, clinical validation claims, or model rankings are included. MedFailBench is released under Apache-2.0 and CC-BY-4.0 and carries the Zenodo DOI 10.5281/zenodo.21205535.
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Artificial Intelligence
The Industrialization of Research ; On AI-Driven Science and Its Consequences
Emmanuel Jeannot
Artificial intelligence is transforming scientific research – not merely as a more powerful instrument, but as an autonomous participant in the research cycle itself. This transition constitutes, in the most precise sense of the term, the industrialization of research: a shift from a craft model, in which knowledge, method, and judgment are embedded in the researcher, to a pipeline model, in which these steps are decomposed, automated, and supervised. The US Department of Energy's Genesis Mission is the most ambitious current instantiation of this shift, but the fundamental questions it raises extend far beyond any single program. This essay examines seven such questions: the erosion of the intergenerational transmission of scientific competence; the growing opacity of AI-generated theories; the collapse of peer evaluation under a flood of machine-generated output; the unproven capacity of AI for paradigm-shifting discovery; the capture of the scientific agenda by political and industrial actors; the compounding of systematic errors in closed-loop pipelines; and the structural bifurcation of the global research community into incommensurable tiers. These concerns do not constitute an argument against AI-driven science – whose demonstrated potential is real and significant. They constitute the conditions under which that potential can be responsibly pursued.
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Artificial Intelligence · Cross-listed from cs.RO
Scaling Behavior Foundation Model for Humanoid Robots
Weishuai Zeng, Kangning Yin, Xiaojie Niu et al. (+15 more)
Humanoid control requires natural whole-body coordination, precise real-time responses to control signals, and robust generalization across diverse environmental contexts, making it a cornerstone for generalist
embodied agents. Behavior Foundation Models (BFMs) have recently emerged as a promising solution to address these challenges by leveraging large-scale behavioral data to achieve superior expressiveness, versatility and generalization. However, despite growing interest in scaling BFMs to further improve their capabilities, it remains unclear how key factors, including the learning paradigm, behavioral data and model architecture should be coordinated to enable effective scaling. In this work, we revisit the scaling recipe for BFMs and demonstrate that substantial performance gains can be achieved through the coordination of three core components: 1) the learning paradigm of motion tracking that reformulates diverse
humanoid control problems as the reproduction of integrated whole-body behaviors in the global frame; 2) the strategic synergy between on-policy rollout quantity and reference motion diversity; and 3) the expressive and scalable model architecture termed
Humanoid Transformer that facilitates the natural emergence of structured behavioral representations. Through extensive experiments in both simulation and real-world deployment, we demonstrate that our approach yields significant improvements in control fidelity and task generalization, reducing Mean Per-Keypoint Position Error (MPKPE) on the test set by over 10% in local mode and 82% in global mode compared with existing
humanoid controllers. These results establish BFM as a principled and effective foundation for scalable and general-purpose
humanoid control.
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All authors Weishuai Zeng, Kangning Yin, Xiaojie Niu, Shunlin Lu, Weixiang Zhong, Jiahe Chen, Feiyu Jia, Xiao Chen, Zirui Wang, Furui Xu, Ming Zhou, Kailin Li, Weinan Zhang, He Wang, Li Yi, Dahua Lin, Jiangmiao Pang, Jingbo Wang
Artificial Intelligence
Improving Weak World Models Behind Strong Agents in Atari Pong
Yukuan Lu, Zaishuo Xia, Weyl Lu et al. (+1 more)
Strong world-model
agents frequently contain weak world models. We study this
agent-world-model gap by reproducing five visual world-model
agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM, with performance comparable to the reported results, and independently evaluating their frozen world models. First, closed-loop rollout diagnosis qualitatively inspects visual trajectories generated by each frozen model under an independently trained policy. All five models exhibit clear visual or dynamical failures, including ball disappearance, incorrect motion, and invalid ball-paddle interactions. Second, under native zero-shot model-based reinforcement learning (MBRL), a new policy is trained entirely within the frozen model from scratch using the
agent's native RL procedure, without real-environment training. When evaluated in the real environment, these policies substantially underperform the reproduced
agents: DreamerV3 (-5.5 to -20.9), DIAMOND (19.7 to -9.6), TWISTER (17.7 to -13.3), Simulus (20.8 to -11.6), and STORM (18.7 to -21.0), where -21 is the minimum Pong return. This gap also extends broadly across Atari100K. Motivated by the ball-related rollout failures in Pong, we propose Concept-Guided Spatial Regularization (CGSReg), an auxiliary reconstruction loss on task-critical concept regions. We evaluate it under a more challenging pixel-space zero-shot MBRL setting, where policies learn directly from images generated by the frozen world model. Ball-region CGSReg improves pixel-space zero-shot MBRL in DreamerV3, DIAMOND, TWISTER, and Simulus, and also improves closed-loop rollouts in the first three; STORM shows no clear improvement.
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All authors Yukuan Lu, Zaishuo Xia, Weyl Lu, Yubei Chen
Artificial Intelligence · Cross-listed from cs.AR
NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference
Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao et al. (+3 more)
Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation. ReRAM-based analog in-
memory computing (IMC) pushes efficiency further, offering an order-of-magnitude improvement in compute density and energy efficiency over conventional digital logic by performing vector-
matrix multiplication (VMM) directly within the ReRAM crossbar; prior work has integrated such IMC blocks into FPGAs for DL inference. However, conventional IMC designs support only static-weight VMM, leaving nonlinear operations and dynamic
matrix-
matrix multiplication (DIMM) to the FPGA fabric. As a result, the benefits of IMC are largely confined to static-weight models, whereas
Transformer-based models, which rely on frequent nonlinear and DIMM operations, gain only limited improvement. Moreover, the ADCs within each IMC block consume more than 70% of its area and power, further limiting system efficiency and scalability. To address these limitations, we propose a novel FPGA architecture that integrates an ADC-free IMC block, replacing the conventional ADC with analog content-addressable
memories (ACAMs) that natively perform nonlinear operations inside the block. To fully exploit this block, we conduct an FPGA-aware design-space exploration that determines optimal crossbar dimensions while balancing FPGA area, flexibility, and DL performance, and we develop an efficient mapping that leverages ACAMs to carry out DIMM operations, extending the applicability of IMC to
attention computation. On CNN and
Transformer-based
benchmarks, the proposed architecture achieves up to 40x and 1.9x higher energy efficiency and 4.1x and 2.5x higher area efficiency, respectively. Overall, it significantly improves FPGA DL inference efficiency and sustains robust gains on
Transformer-based workloads across long input sequences, advancing domain-specialized FPGA design.
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All authors Jiajun Hu, Ruthwik Reddy Sunketa, Lei Zhao, Archit Gajjar, Luca Buonanno, Aman Arora
Artificial Intelligence
Long-Context Fine-Tuning with Limited VRAM
Vladimir Fedosov, Aleksandr Sazhin, Artemiy Grinenko et al. (+1 more)
Parameter-efficient fine-tuning reduces model and optimizer
memory, but dense
attention still makes long training sequences expensive. We combine Hierarchical Global
Attention (HGA) with segment-wise backpropagation and tiered KV storage. Only the active segment remains differentiable in VRAM; older KV is detached into RAM or NVMe, and HGA loads a bounded set of exact historical
tokens for each query block. On Qwen3-8B with 4-bit QLoRA and PG19, dense training on a 16 GB Quadro RTX 5000 fits 2,048
tokens but fails at 4,096, whereas HGA reaches 16,384
tokens with 15.28 GB peak VRAM. Under evaluation the same adapter runs through 131,072
tokens on this card; VRAM is not constant but grows gently with the resident chunk summaries, so RAM and NVMe capacity set the practical limit beyond these lengths. At the shared 2K training length, HGA-trained and dense-trained adapters obtain 2.7405 and 2.7383 nat under the same dense-
attention readout, while the stock model obtains 2.9541. At this boundary HGA training is already marginally faster (217.75 vs. 207.02
tokens/s), and the HGA-to-dense throughput ratio improves from 1K to 2K; because HGA keeps the attended historical set per
token approximately constant while dense work per
token grows, we expect this lead to widen as context grows. Dense
attention is used for the main quality and
retrieval comparisons so that they measure the learned weights and remain compatible with standard generation frameworks. HGA can also be used for
retrieval and generation; an optimized production-grade serving implementation is under development.
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All authors Vladimir Fedosov, Aleksandr Sazhin, Artemiy Grinenko, Frank Woernle
Artificial Intelligence · Cross-listed from cs.CL
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Dylan Van Mulders, Matthias Bogaert and Dirk Van den Poel
The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions. While Large
Language Models (LLMs) open new avenues for computational political science, the neutrality and helpfulness biases instilled by Reinforcement Learning from Human Feedback (RLHF) prevent them from sustaining steadfast partisan behaviour. We present a multi-
agent framework that reconciles factual grounding with ideological
alignment by combining Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and
Retrieval-Augmented Generation (RAG): DPO instils aggressive party-specific personas, while a per-party RAG pipeline keeps each
agent bounded to its official manifesto. We operationalize the framework on the 2019 Flemish election, deploying the partisan
agents in a hub-and-spoke negotiation arbitrated by a formateur. To make the emergent negotiation interpretable, we introduce a Multi-Layered Information Lineage
Topology (MILT) that traces every clause in the final agreement back to its manifesto origin and classifies it into five provenance states, a Coalition Influence Score (CIS) that aggregates these traceable contributions to identify which party shaped the agreement, and a real-world grounding pass that
benchmarks each simulated provision against the historically adopted coalition agreement. Across three independent simulations the framework yields a stable winner and ranking (N-VA ahead of CD&V and Open Vld), and manifesto-anchored lineage reliably predicts real-world materialization whereas hallucinated content does not. The result is a transparent, scalable testbed for the ex-ante exploration of party compatibility and formateur-mediated compromise.
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Artificial Intelligence · Cross-listed from cs.CV
Towards Hierarchical Structure Understanding of Newspaper Images
William Mocaër, Solène Tarride, Thomas Constum et al. (+7 more)
Understanding newspaper images remains a challenging task due to their complex, nested hierarchical structures and dense, heterogeneous layouts. In this paper, we explore two complementary approaches for newspaper structure understanding. First, we present a modular bottom-up pipeline that combines state-of-the-art open-source models: YOLO for layout detection, LayoutReader for reading order prediction, and a custom
algorithm for article
segmentation. This approach leverages existing robust components while maintaining flexibility and interpretability. Second, we introduce Tiramisu (Tiered
Transformers for Hierarchical Structure Understanding), a novel end-to-end
transformer-based architecture that explicitly models document hierarchy through an iterative tiered process. Tiramisu performs section and article separation, block localization, semantic categorization, and reading order prediction using highly parallelized
attention mechanisms. Finally, we release Finlam La Liberté, a new
dataset designed specifically for evaluating hierarchical information
retrieval in historical newspapers. Experimental results demonstrate the effectiveness of both approaches in reconstructing complex newspaper hierarchies, with comparative analysis highlighting their respective strengths for scalable document digitization. The Tiramisu training code, including the synthetic newspaper generator, is available at https://git.litislab.fr/tiramisu/tiramisu-newspaper-articles-extractor.
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All authors William Mocaër, Solène Tarride, Thomas Constum, Merveilles Agbeti-Messan, Tom Simon, Clément Chatelain, Stéphane Nicolas, Pierrick Tranouez, Sébastien Cretin, Thierry Paquet
Artificial Intelligence
BrainPilot: Automating Brain Discovery with Agentic Research
Haoxuan Li, Tianci Gao, Jianhe Li et al. (+13 more)
Understanding the
brain increasingly depends on integrating evidence across scales, modalities, and disciplines. Addressing a single research question therefore requires a coordinated sequence of operations, from surveying prior work to executing analyses and interpreting results in light of domain knowledge. AI
agents promise to accelerate this process, but current
agents lack domain expertise in
brain science, may fabricate claims, drift during multi-step
reasoning, and offer few defined points for expert intervention. These failures are especially costly in
brain science, where conclusions feed into downstream scientific claims and depend on laboratory-specific expertise and careful human judgment. We present BrainPilot a fully open-source multi-
agent system that accelerates
brain science research with traceable logs and
agent-verified results. A principal investigator (PI)
agent coordinates specialist
agents grounded in curated domain knowledge: a unified
brain science knowledge base containing 7{,}233 indexed items and a skill library of 72 reusable methodology units across seven research domains. Every major step is recorded in the Graph of Trace, an auditable record that links subgoals, tool use, evidence, and claims and allows researchers to follow and inspect the workflow. An Auditor
agent further integrates fabrication checking into the workflow. For evaluation, we run three
brain science tasks from
Agents' Last Exam, introduce our own
benchmark, BrainPilotBench-v0, and present additional end-to-end case studies. Across these evaluations, BrainPilot with an open-source backbone model attains performance comparable to state-of-the-art
agent framework with less costs.
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All authors Haoxuan Li, Tianci Gao, Jianhe Li, Yang Fan, Runze Shi, Weiran Wang, Tianxiang Zhao, Zezhao Wu, Xiaoyang Jiang, Qihui Zhang, Jia Li, Xiao Xiao, Kai Du, Xiaoxuan Jia, Chao Xie, Lu Mi
Artificial Intelligence · Cross-listed from cs.NI
ANet Patu-1: The Value of Connection in the Agent Network
Mu Yuan, Jinke Song, Zhaomeng Zhou et al. (+1 more)
The Internet taught us that the value of a network depends on how its nodes connect: broadcast
stars scale as $V\!\propto\!N$ (Sarnoff), fully-connected meshes as $N^2$ (Metcalfe), and group-forming networks as $2^{N}$ (Reed). We ask the analogous question for networks of AI
agents. We model the net value of connection as a function of coordination-group size, derive from it the properties an optimal collaboration protocol must have, and introduce ANet Patu-1 – a self-organizing consensus protocol in which the network continuously re-forms its own coalitions, adaptively riding the upper envelope of all three regimes at $O(1)$ parallel consensus rounds. To measure value without opinion-grading, we score an emergent protocol by formally specifying it and deriving its complexity, the way distributed
algorithms are analyzed. Two results follow. (i)~Emergence – a crowd of the cheapest model, when heterogeneous, starts weak but its collective value compounds with $N$ and overtakes a crowd of a far stronger model that is homogeneous: a crossover that marks a scaling law for collaboration rather than for scale. (ii)~Reflexivity – a heterogeneous network, given only its own problem and no design hints, converges on ANet Patu-1 itself, reconstructing the high-dimensional law that governs its own connective value.
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All authors Mu Yuan, Jinke Song, Zhaomeng Zhou, Lan Zhang
Artificial Intelligence · Cross-listed from cs.CV
Parameter-efficient Prompt Tuning of Vision Foundation Model With Adaptive Focal Loss for Interpretable MCI Screening
Javad Khoramdel, Farhad Hoseyni and Amirhossein Nikoofard
Mild Cognitive Impairment is a critical early stage of cognitive decline that frequently precedes Alzheimer's disease, yet its automated detection from neuropsychological drawing tests remains fundamentally constrained by data scarcity, class imbalance, and diagnostic ambiguity near clinical boundaries. Existing methodologies attempt to bypass these constraints using computationally expensive, fully fine-tuned hybrid architectures that relegate spatial explainability to a post-hoc approximation rather than an intrinsic model property. We propose a parameter-efficient framework utilizing frozen DINOv2-Small model adapted via three modality-specific learnable
prompt tokens while Operating with 1.19 million trainable parameters, each
token serves as a query in a shared cross-
attention layer over the source image patch
tokens. Crucially, spatial explainability is achieved directly through these
attention maps; as a structural consequence of the architecture. Then task-conditioned
embeddings fused via an
attention module to quantify modality-level importance per subject. To handle boundary ambiguity, a MoCA-adapted focal loss introduced that integrates continuous cognitive scores into the training target, loss modulation, and adaptive sample weighting, strictly generalizing standard soft-label approaches. Under stratified five-fold cross-validation, the proposed architecture yields an MCI-class F1 of 0.641 and an AUC of 0.795, outperforming the computationally heavier ResViT baseline by 0.110 in MCI-class F1.
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Artificial Intelligence
Man, Machine, and Masterpiece: Artistic Ownership in the AI Era
Sofi Gjing Jovanovska, Kuntal Ghosh, Daniel Muhu Njenga et al. (+2 more)
The integration of AI-driven systems in creative work has sparked debates among artists and legal communities about notions of ownership. Yet there remains little consensus on how ownership should be defined and attributed when human and AI contributions are intertwined. To provoke critical reflection on these tensions, we designed ArtSplit, a provotype that explicitly quantifies human and AI contributions across different stages of creative work. Rather than aiming to resolve ownership, the provotype was used to elicit artists' responses to the idea of attributing ownership through measurable actions in the creative workflow. We argue that quantification fails to align with artists' understandings of creative intent and agency, and that efforts to measure ownership risk diluting long-standing assumptions through which artists understand and practice creative work. This critique challenges the impulse to transform a historically and socially situated relation into a technical problem.
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All authors Sofi Gjing Jovanovska, Kuntal Ghosh, Daniel Muhu Njenga, Ahmed Mufassir, Shadan Sadeghian
Artificial Intelligence · Cross-listed from cs.HC
When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration
Célina Treuillier and Denis Lalanne
The broad adoption of Artificial Intelligence (AI), especially Generative AI, raises pressing questions about how users interact with these systems to produce new content. In this paper, we introduce the concept of authorship calibration, defined as users awareness of their actual authorship when interacting with AI. Using the CoAuthor
dataset, we empirically examine how authorship calibration varies across users and how it relates to their frequency of AI use. Our results reveal high variability: users relying heavily on AI tend to misjudge their authorship, whereas those using AI less frequently exhibit more accurate authorship calibration. These findings suggest that AI can obscure users perception of their own authorship. In learning contexts, miscalibration can affect metacognitive monitoring and learning strategies, ultimately impacting learning outcomes. Fostering authorship calibration then appears essential for promoting responsible and educationally meaningful AI integration.
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Artificial Intelligence
SMC-ES: Automated synthesis of formally verified control policies
Riccardo Curcio, Toni Mancini and Enrico Tronci
The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.e., policies) that are not only performant but also provably safe and robust. While learning-based methodologies such as Reinforcement Learning offer flexible and scalable approaches to automatically synthesize such controllers, they typically lack the formal guarantees necessary for safe deployment. To bridge this gap, we propose a novel simulation-based methodology to automatically synthesize policies with formal guarantees regarding performance, safety, and robustness specifications. Specifically, given a set of properties to verify, a confidence parameter $\delta$ and an allowable failure probability $\varepsilon$, our method guarantees that the synthesized policy comes with a certificate: with confidence at least $1 - \delta$, the probability of encountering a scenario where the given properties are violated is at most $\varepsilon$. We demonstrate the feasibility of our approach by developing SMC-ES, an
algorithm that integrates Evolutionary Strategies with Statistical Model Checking-based verification. We evaluate SMC-ES on a suite of continuous control tasks using Gymnasium and Safety Gymnasium testbeds. Results show that, at the price of a sustainable increase in computational cost, our
algorithm provides formal guarantees regarding performance, safety, and robustness specifications, while performing competitively against leading model-free Deep Reinforcement Learning (DRL) and Safe-DRL baselines.
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Artificial Intelligence · Cross-listed from hep-lat
LQCDMaster: Agentic Scientific Computing for Lattice Quantum Chromodynamics Research
Haofei Gao, Tingjia Miao, Wenkai Jin et al. (+12 more)
Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise required to turn research motivation into reliable computing workflows. Here we present LQCDMaster, a tool-augmented, skill-guided and domain-specialized scientific computing
agent that converts natural-
language LQCD research tasks into executable PyQUDA computing workflows, including measurement scripts, job-submission artifacts, execution logs and numerical outputs. The system combines agentic planning, expert-annotated LQCD skills and a deterministic Wick-contraction tool to constrain the algebraically fragile components of code generation. We evaluate LQCDMaster on a
benchmark at the forefront of scientific research, comprising 70 LQCD computing tasks, with observables covering local and nonlocal two-point functions, Wilson loops,
meson and
baryon three-point functions. The generated workflows exactly reproduce expert-written implementations in 63 of 70 tasks at machine precision, with three additional discrepancies attributable to convention mismatches. Across representative observables, the
agent reduces implementation time from hours to minutes while preserving end-to-end numerical validation. Further, we present a typical case of LQCDMaster-driven exploration: a
lattice computation of light-cone distribution amplitudes with diagonal Wilson-line, a quantity accessible with standard methods but never before computed, and computation of the spectrum of
proton, deuteron, triton, hyperon, hyperdeuteron and hypertriton. This work pioneers the paradigm of agentic scientific computing by automating the end-to-end scientific computing workflows in
lattice QCD research, lowering its barrier and facilitating the exploration and verification of non-standard scientific ideas.
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All authors Haofei Gao, Tingjia Miao, Wenkai Jin, Muhua Zhang, Hanzhang Wang, Jie Ran, Jinxin Tan, Zhentao Zhang, Bo Tang, Leiyi Li, Jun Hua, Xiangyu Jiang, Qi-An Zhang, Siheng Chen, Wei Wang
Artificial Intelligence
Moral Attitudes of Sentient ASI towards Humanity and Implications for AGI Development
Jean-Paul Van Belle
This paper suggests the adoption of a novel inversion in AI ethics: instead of asking how humans should treat artificial superintelligence (ASI), it examines how future sentient ASI may morally consider and evaluate humanity. We are not only designing intelligent systems but also shaping the initial conditions under which those systems form judgments about us. The paper proposes a preliminary set of post-human moral principles that may govern sentient ASI actions. The implication is that technical design choices (some are suggested), humanity's moral behaviour, and the essence of what it means to be human, may influence humanity's long-term standing in a post-ASI world.
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Artificial Intelligence · Cross-listed from cs.CL
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Chengyu Shen, Yujie Fu, Gangtao Xin et al. (+13 more)
Large
language models are increasingly evolving from text generators into general
agents capable of understanding user requests, invoking external tools, and completing complex tasks through interaction. However, existing
agent benchmarks often focus on limited scenarios, tool ecosystems, or interaction formats, making it difficult to systematically characterize model capabilities across heterogeneous application settings. We introduce OmniaBench, a
benchmark for evaluating general
agents across diverse scenarios with explicit state spaces. We derive application-oriented scenario knowledge from app stores, product documents, industry resources, Web
retrieval, and human refinement, forming a hierarchical taxonomy that spans ToC, ToB and ToE with 90 level-1 and 354 level-2 domains. Based on this taxonomy, we construct executable environments and synthesize single-turn and multi-turn tasks through four complementary routes: DAG, DAG-S, Solver, and Program. OmniaBench further introduces a ten-dimensional capability taxonomy and eight compositional atomic difficulty factors to support fine-grained evaluation and analysis. The resulting
dataset contains 1,431 tasks, together with a challenging subset of 644 tasks designed to reduce evaluation cost and mitigate potential contamination of the full set after public release. The bench presents substantial challenges to current frontier models, with even Claude-Sonnet-5 and GPT-5.6-Sol achieving Overall Pass@1 scores of only 58.54 and 57.14, respectively. Further analyses reveal clear differences across domains and capabilities, as well as persistent limitations in planning, constraint maintenance, and adaptive correction. OmniaBench provides a broad and diagnostic
benchmark for characterizing the capability boundaries of general
agents.
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All authors Chengyu Shen, Yujie Fu, Gangtao Xin, Yanheng Hou, Wenlong Fei, Guojie Zhu, Jiawei Li, Hongcheng Gao, Runming He, Zhen Hao Wong, Meiyi Qiang, Hao Liang, Zhao Cao, Hao Jiang, Chong Chen, Wentao Zhang
Artificial Intelligence
Demographically-Conditioned Synthetic Medical Images for Bias Mitigation and Bias Detection in Disease Classifiers
Mahmoud Ibrahim, Bart Elen, Chang Sun et al. (+2 more)
Per-subgroup
fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect. We argue that a demographically-conditioned synthetic generator can do both: mitigate bias on the training side and detect bias on the evaluation side. Working on COVID-19 chest CT classification with an end-to-end fine-tuned Stable
Diffusion 2.1 generator, we make two findings. For bias mitigation (training), a demographically-balanced synthetic cohort is most useful as a pretraining prior, not as joint augmentation: with the same fixed data, sequential pretraining followed by fine-tuning substantially outperforms joint augmentation, and the resulting classifier surpasses the full-real baseline at $\sim$$100\times$ real-data efficiency. For bias detection (evaluation), across five synthetic minority cohorts and five classifier seeds, the synthetic
estimator reproduces the subgroup ranking of a well-powered real oracle (Spearman $\rho = 1.00$ on MCC and Recall) and gives the more reliable per-
cell estimate where the small real test set runs out of samples. The synthetic cohort is therefore most useful in exactly the
cells that
fairness audits care about, as both a fix for and a measure of subgroup bias.
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All authors Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier
Artificial Intelligence
CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
Yuan Gao, Wenjun Yu, Jun Jiang et al. (+3 more)
Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data,
radio configurations, partitions, adaptation procedures, task definitions, and metrics, preventing reproducible comparison across CFMs and against task-specific networks. We release CFM-Bench, a unified multi-domain, multi-task
benchmark comprising 157,900 official single-frame examples from six domains spanning 3GPP statistical simulation, two ray-tracing pipelines, terrestrial and aerial measurements, and synchronized vehicular
multimodal simulation. Source-specific interfaces preserve complex channel state information (CSI) and the physical metadata available in each domain while allowing documented model-specific preprocessing. To reduce spatio-temporal leakage, official partitions isolate complete trajectories, measurement sessions, flights, vehicle links, simulation realizations, or buffered spatial regions. CFM-Bench excludes all
benchmark splits from foundation-model pretraining, reserves the official training split for downstream fine-tuning, and reports the data used during model development. Six task groups across PHY, RAN, and
ISAC applications cover CSI feedback, frequency and temporal channel extrapolation, propagation-state classification, current- and future-beam prediction, and single-frame and temporal localization. Representative experiments on CSI feedback, channel extrapolation, current-beam prediction, and wireless positioning provide reproducible reference results for pretrained channel-prediction models and task-specific neural networks. These results show that relative model performance can vary across data domains, highlighting the importance of using common data partitions, task definitions, and evaluation metrics. CFM-Bench provides a common substrate for evaluating the transferability of channel representations across models, domains, and tasks.
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All authors Yuan Gao, Wenjun Yu, Jun Jiang, Yunfan Li, Xinyu Guo, Shugong Xu
Artificial Intelligence
Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation
Paul Darm, Cem Alpturk, Kenneth Ulrich et al. (+3 more)
Automated optimisation is increasingly adopted in industrial processes, yet a trust gap persists between engineers who design these
algorithms and operators who must act on their recommendations. Explainable AI methods like SHAP (SHapley Additive exPlanations) have transformed interpretability for machine learning predictions; optimisation outputs could benefit from similar techniques. We present an approach that integrates Implicit Function
Theorem (IFT) based sensitivity analysis with SHAP attribution and narrative generation via Large
Language Models (LLM), producing explanations tailored for operators. Our approach leverages IFT to compute exact parameter sensitivities $\partial p^*/\partial x$ from the optimality conditions, enabling efficient GradientSHAP computation. For an industrial High Pressure Grinding Roll (HPGR) control optimisation problem with 22 features, we achieve equivalent SHAP attributions (correlation $>$0.99 with KernelSHAP) with over 40$\times$ speedup, enabling real-time natural
language explanations. We validate on industrial scenarios and present feedback from domain experts on generated explanations.
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All authors Paul Darm, Cem Alpturk, Kenneth Ulrich, William Duncan, Ali Anwar, Annalisa Riccardi
Artificial Intelligence · Cross-listed from cs.CL
Latent Trajectory Discrimination for AI-Generated Text Detection
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti et al. (+5 more)
Most existing approaches to AI-Generated Text Detection (AIGTD) treat documents as static objects and base their decisions on aggregate statistics or globally compressed
embeddings. However, this perspective overlooks the inherently dynamic nature of autoregressive generation, where content evolves progressively through the latent space. In this paper, we reformulate AIGTD as the problem of distinguishing between latent generation trajectories. Instead of relying on static representations, we model how textual representations evolve across the sequence. To this end, we propose
Geometric Trajectory and Contrastive Learning (GTCL), a framework that segments the document into ordered local units, encodes each unit in an
embedding space, and constructs a structured and sequence-level representation. GTCL then applies contrastive learning to these trajectories to learn
geometric regularities associated with the autoregressive generation. Evaluations performed on three different
benchmarks and several approaches show that GTCL outperforms detection baselines consistently, which implies that explicitly modeling sequential dynamics provides robust discriminative signals across models and domains. These results suggest that modeling trajectory differences could improve detection and open up a dynamic direction that has been underexplored in previous AIGTD literature.
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All authors Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Federica Parlapiano, Giulia Quaglieri, Davide Traini, Domenico Ursino, Luca Virgili
Artificial Intelligence · Cross-listed from cs.LG
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Ku Onoda, Paavo Parmas, Hiroki Furuta et al. (+4 more)
Text-to-image (T2I) models produce realistic,
prompt-aligned images, but samples for the same
prompt often cover only a few visually distinct modes. For person-centric
prompts, this lack of diversity can reflect or amplify demographic skew. We formalize representative diversity as target-mode coverage: how well a set of samples covers predefined modes, such as colors or perceived-race categories. We then propose MultiMaxPO, a reinforcement learning post-training method that improves this coverage in
diffusion T2I models by optimizing a multi-axis max@K objective. Given one score per mode for each sample, multi-axis max@K takes the maximum over samples for each mode and then sums these maxima. A sample therefore receives credit for a mode only when it raises that mode's maximum, so different samples are rewarded for different modes. We verify this credit assignment on a synthetic Gaussian mixture and on Stable
Diffusion 3.5 Medium with pixel-based color rewards, and then apply it to perceived-appearance
fairness. On held-out
prompts and under three automatic evaluators, MultiMaxPO improves the
Fairness Score over the base model by 0.22-0.36 on Stable
Diffusion 3.5 Medium and by 0.07-0.19 on FLUX.1-dev, outperforms matched RL baselines, and maintains image quality and text
alignment. Code is available at https://github.com/KuOnoda/multi-axis-maxk.
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All authors Ku Onoda, Paavo Parmas, Hiroki Furuta, Soichiro Nishimori, Yuta Oshima, Shohei Taniguchi, Yutaka Matsuo
Artificial Intelligence
Contextualized Early Detection of Online Firestorms: A Sequential LLM-Based Approach
Besim Shala, Peter Mandl, Andreas Humpe et al. (+1 more)
Online firestorms are rapid collective escalations of highly negative user-generated content and may cause substantial reputational and economic damage. Existing detectors usually work with volume signals, sentiment scores, or predefined linguistic features. Such signals are useful, but they capture contextual meaning shifts in evolving discussion threads only indirectly. This paper proposes an LLM-based detection system with two operating modes. The first mode classifies complete Reddit threads retrospectively by combining local chunk-level assessments into a thread-level judgment. The second mode processes threads sequentially and issues early warnings when a sliding window exceeds calibrated thresholds. In this mode, the
language model estimates three firestorm indicators: negativity share, escalation level, and contributor count. On a balanced Reddit
dataset, the global mode achieves strong classification performance, while the early warning mode reaches high recall and detects escalating threads after only a small number of comments and distinct contributors. The results indicate that LLMs can be used not only for static judgment tasks, but also as repeated
estimators in context-aware monitoring of social media discourse.
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All authors Besim Shala, Peter Mandl, Andreas Humpe, Martin Häusl
Artificial Intelligence · Cross-listed from cs.RO
Steering Robustness into World Action Models via Mechanistic Interpretability and Optimal Control
Jihoon Hong, Julian Skifstad, Qiyue Dai et al. (+2 more)
World Action Models (WAMs) enable semantically- and physically-informed control but are brittle under distribution shift. In this work, we use mechanistic interpretability to study how robustness-relevant perturbations are represented in WAM activation space. Comparing activations across successful and unsuccessful rollouts, we find some WAM architectures exhibit low-dimensional linear separability for robustness-critical features, while others do not. This motivates the use of contrastive activation directions for training-free WAM steering. We also show that local linearity in WAM activation dynamics enables efficient feedback steering via model-based optimal control, yielding World-Action Linear Quadratic Regulator (WA-LQR), a minimally-invasive reduced-order LQR controller. Via mechanistic evaluations, we predict strong steerability in the Cosmos-Policy and DiT4DiT models but weak steerability in LingBot-VA, consistent with steering intervention results. On Cosmos-Policy and DiT4DiT, WA-LQR generalizes contrastive directions to new tasks and improves robustness to camera, gripper, and visual-noise perturbations over unsteered and
prompt steering baselines.
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All authors Jihoon Hong, Julian Skifstad, Qiyue Dai, Alice Chan, Glen Chou
Artificial Intelligence · Cross-listed from cs.LG
A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Christoph Jürgen Hemmer, Florian Plaswig and Daniel Durstewitz
Recent foundation models (FMs) for zero-shot reconstruction of dynamical systems (DS) achieve strong out-of-domain generalization but provide little insight into the mechanisms that underlie their forecasts. Such an understanding could help to strip down overladen FM architectures to their bare essence and expose the minimal requirements for in-context learning in the DS domain. Toward this goal, here we iteratively reduce a recent powerful SOTA model for DS reconstruction, DynaMix (Hemmer & Durstewitz, 2025), to a minimal interpretable two-parameter form, which we call DynaBase. DynaBase produces forecasts through a linear blend of the current latent state and the nearest in-context neighbor and its temporal successor. Surprisingly, despite its extreme simplicity, DynaBase produces highly competitive zero-shot DS reconstructions across chaotic and cyclic systems, with a negligible parameter load, many orders of magnitude below that of other FMs. Even more, this extreme simplicity permits direct model optimization on DS reconstruction measures, as well as closed-form one-step analytical solutions on prediction MSE. Theoretical and empirical analysis of DynaBase further leads to a 1-parameter family of maps, with the context-parroting
algorithm of (Zhang & Gilpin, 2026) recovered at one end, and chaotic (divergent but bounded) behavior at the other. We further show how different training strategies lead to models either optimal for short-term prediction or for DS reconstruction. Thus, DynaBase not only exposes the minimal mechanisms required for producing zero-shot DS reconstruction, but also reconciles within an accessible mathematical frame divergent observations in the literature.
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Artificial Intelligence · Cross-listed from cs.CV
Benchmarking Face Recognition without Real Faces
Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo et al. (+2 more)
Synthetic face
datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace. Even studies that train entirely on synthetic images still rely on real-face
benchmarks to measure performance, leaving the
privacy problem only half solved. We ask whether synthetic
datasets can replace real
benchmarks for face recognition evaluation. We test 12 synthetic
datasets against 7 established real
benchmarks using 24 pre-trained models that span both convolutional and
transformer architectures. Our evaluation covers biometric verification metrics, similarity score distributions, cross-model ranking consistency, and the underlying distributional properties of each
dataset. Benchmarking fidelity varies widely across the synthetic candidates, but the two strongest, MorphFace and Vec2Face, reproduce the relative behavior of real
benchmarks and reach agreement levels that fall within the natural disagreement already observed among the real
benchmarks themselves. These results establish that well-constructed synthetic
datasets can support reliable comparative evaluation for face recognition, moving the field closer to a fully synthetic and
privacy-preserving pipeline for both training and benchmarking.
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All authors Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo, Jacques Klein, Tegawendé F. Bissyandé
Artificial Intelligence · Cross-listed from cs.LG
Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
Hamid Dashtbani, Mehdi Dousti Gandomani and AmirMahdi Sadeghzadeh
Machine learning models are increasingly adapted in various domains. However,
adversarial examples pose a significant threat to the reliable deployment of these models. In recent years, some powerful
adversarial example attacks have been proposed for the fast and query-efficient generation of
adversarial examples, even in black-box scenarios, highlighting the need for scalable, low-cost, and powerful defenses. In this work, we present two contributions to the domain of black-box
adversarial example attacks and defenses. First, we propose Random Logit Scaling (RLS), a randomization-based defense against black-box score-based
adversarial example attacks. RLS is a plug-and-play, post-processing defense that can be implemented on top of any existing ML model with minimal effort. The idea behind RLS is to confuse an attacker by outputting falsified scores resulting from randomly scaled logits while maintaining the model accuracy. We show that RLS significantly reduces the success rate of state-of-the-art black-box score-based attacks while preserving the accuracy and minimizing confidence score distortion compared to state-of-the-art randomization-based defenses. Second, we introduce a novel adaptive attack against AAA, a SOTA non-randomized black-box defense against black-box score-based attacks that also modifies output logits to confuse attackers, demonstrating its vulnerability against adaptive attacks.
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Artificial Intelligence · Cross-listed from cs.CL
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Zlata Kikteva, Artur Romazanov, Annette Hautli-Janisz et al. (+1 more)
Given the current trend to employ large
language models (LLMs) in almost any imaginable context, LLM-generated text detection and authorship attribution have become a pressing issue. Prior work has primarily focused on surface-level linguistic features, an approach shown to be susceptible to paraphrasing and other obfuscation techniques. In this paper, we go beyond the linguistic surface, extracting and analysing
reasoning structures in LLM-generated texts with the goal of capturing more complex signals of LLM authorship. We propose a graph neural network approach that leverages
reasoning graphs extracted by an argument mining pipeline, demonstrating improved robustness and generalisation over a traditional Longformer baseline. Our approach outperforms the baseline by up to 27 percentage points under the obfuscation attacks such as paraphrasing and backtranslation, and 19 percentage points when evaluated on the texts generated by the unseen model versions, simulating real-world conditions in which new LLM versions are continuously released.
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All authors Zlata Kikteva, Artur Romazanov, Annette Hautli-Janisz, Ramon Ruiz-Dolz