Artificial Intelligence · Cross-listed from cs.MA
Artificial collectives of specialists and generalists excel at different tasks
John Meluso, Laurent Hébert-Dufresne, Christoph Riedl et al. (+1 more)
Collective artificial intelligence, where multiple
agents work on shared tasks, holds potential to solve expansive problems in fields from medicine to collective governance. But while prescriptive engineering solutions abound, we lack descriptive scientific understanding of artificial collectives, and therefore principles for how to design resource efficient multi-
agent systems. Through systematic experiments with optimizing
agents, we characterize how
agent interpretive abilities, rationality bounds, and task qualities interact to shape collective performance.
Agents range from specialists, with narrow interpretive abilities, to generalists, with broad ones. Collectives of specialists correspond to sparse, centralized networks, while collectives of generalists correspond to dense, decentralized ones. We show that interpretive network properties have small performance effects on average (0.07 standard deviations of performance). However, for specific task qualities, these effects are 4.5 times larger (0.33 sd) and can reach much higher for certain task qualities (1.84 sd). This leads collectives of generalists to perform better on tasks that involve generating, choosing, and coordinating, while collectives of specialists with a few generalist mediators perform better on tasks that involve negotiating. Rationality bounds then moderate these relationships. At loose bounds, specialists outperform generalists through more effective sampling of high-dimensional decision spaces. At tight bounds, generalists outperform specialists through better gradient estimation. A fundamental trade-off between performance and convergence speed emerges at moderate bounds. These findings suggest that multi-
agent design could benefit from matching interpretive networks to both task demands and
agents' computational limits, with implications for the efficiency and energy costs of multi-
agent systems.
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All authors John Meluso, Laurent Hébert-Dufresne, Christoph Riedl, H. Oliver Gao
Artificial Intelligence · Cross-listed from cs.LG
Machine Learning Classification of Cryopathy Syndromes: A Comprehensive Comparative Study
Nataliya Shakhovska, Valentyna Chopyak, Ivan Izonin et al. (+1 more)
Cryopathy syndromes are difficult to classify because laboratory patterns often overlap across diagnostic categories, while some diagnoses are rare. This makes routine interpretation of cryoglobulin-related tests challenging and increases dependence on expert judgment. The aim of this study was to develop and compare machine learning approaches for automated classification of cryopathy syndromes from laboratory data and to identify a practical strategy for clinical decision support. Methods: We analysed laboratory records from 2,686 patients assigned to 14 diagnostic categories. The
dataset included demographic variables, cryoglobulin measurements, precipitation tests, and hemagglutinin and hemolysin titers. Data preprocessing included cleaning, encoding, imputation, normalization, and construction of clinically informed interaction features. We evaluated 12 modelling strategies, including Random Forest, Gradient Boosted Trees, Multi-Layer Perceptron, soft-voting ensembles, class balancing with Synthetic Minority Over-sampling Technique, hierarchical classification, period-aware models, targeted binary classifiers, and probability calibration. Performance was assessed using stratified train-test evaluation and stratified 5-fold cross-validation. The main metrics were macro-averaged F1 score, accuracy, Top-3 accuracy, and expected calibration error. The overall task proved difficult because of marked class imbalance and clinical overlap between diagnoses. The best multiclass performance was achieved by a soft-voting ensemble of Random Forest and Gradient Boosted Trees. Cross-validation confirmed stable performance for the balanced Random Forest model. Tree-based methods consistently outperformed the neural network model. Feature engineering improved discrimination, and the most informative predictors were derived cryoglobulin-based interaction features.
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All authors Nataliya Shakhovska, Valentyna Chopyak, Ivan Izonin, Vira Haievska
Artificial Intelligence · Cross-listed from cs.AR
A3C3: AI Algorithm and Accelerator Co-design, Co-search, and Co-generation
Selin Yildirim, Yingbing Huang and Deming Chen
We present a holistic methodology for artificial intelligence
algorithm and accelerator co-design, co-search, and co-generation (A3C3), which jointly optimizes neural network architectures and their hardware implementations to address the inefficiencies of traditional top-down AI system design flows. Conventional AI deployment often treats model design and hardware mapping as separate stages: an
algorithm is first developed for accuracy, and only afterward adapted to meet latency, throughput, energy, or resource constraints. This separation can lead to suboptimal systems, particularly as modern AI workloads become increasingly heterogeneous,
memory-intensive, and platform-dependent. A3C3 instead parameterizes both algorithmic and accelerator design spaces and searches them jointly, enabling the automatic generation of model-accelerator pairs that better balance accuracy, latency, throughput, energy efficiency, and hardware utilization. This article is a book chapter of the Handbook of Embedded Machine Learning, edited by Sudeep Pasricha and Muhammad Shafique, Springer Nature.
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Artificial Intelligence · Cross-listed from cs.CR
Can LLMs Reason About Brand Ownership? An Empirical Study of Domain Attribution Intelligence
Fathima Mashood and Mohamed Nabeel
When a new domain resembling a popular brand appears, defenders face a fundamental ambiguity: it may be an attacker-created squatting site for phishing, or it may be a domain the brand itself registered, either defensively, to block attackers, or legitimately, for a new product or service launch. Incorrectly flagging a brand-owned domain as malicious produces a false positive that harms end users and damages the brand's reputation. Resolving this ambiguity requires brand intelligence: the ability to determine, at scale, whether a given domain belongs to a brand. Large
language models (LLMs), with their broad knowledge of brand domain relationships, offer a promising zero configuration approach to this problem, but their reliability for brand intelligence tasks remains unknown. We present the first systematic empirical evaluation of LLM brand intelligence across three tasks: domain enumeration (Q1), open ended brand attribution (Q2), and binary ownership classification (Q3). We evaluate four models, Gemini 2.5 Flash, Gemini 3.5 Flash, Claude Sonnet 4.5, and Claude Sonnet 4.6, across four
retrieval settings (in context, web search, WHOIS lookup, and combined) on 36 of the most phished brands. Our results reveal a stark dichotomy: models achieve up to 82% precision enumerating brand domains from
memory alone, yet fail at ownership verification without external tools, with macro F1 at most 0.37 in ICL mode. WHOIS augmentation lifts Q3 macro F1 by up to 0.65 points, yielding near perfect precision (<= 0.99), dramatically reducing the false positive risk for defenders. We provide concrete recommendations for deploying LLMs in brand protection pipelines.
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Artificial Intelligence · Cross-listed from cs.CV
FOCA: Future-Oriented Conditioning for Data-Efficient Vision-Language-Action Adaptation
Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen et al. (+20 more)
Vision-
Language-Action (VLA) models enable general-purpose robotic control via large-scale
multimodal pretraining, yet their effectiveness under few-shot imitation learning remains limited. We conduct a systematic stress test of state-of-the-art VLA models and show that performance degrades sharply as demonstrations are reduced, revealing a key weakness of existing adaptation strategies. To address this, we introduce FOCA, a future-oriented conditioning framework for data-efficient VLA adaptation. FOCA combines explicit prediction of task-grounded future interaction
embeddings with implicit
alignment to future goal observations, enabling long-horizon
reasoning in latent space without pixel-level prediction. This formulation naturally supports action-free co-training with synthetic
videos from
video world models and can be interpreted as learning a future-conditioned value-like representation. Extensive experiments demonstrate FOCA achieves 95.7% success with 20 demonstrations on LIBERO, improves 7-12% on RoboCasa, and delivers up to 26% absolute gains on real
robots, establishing a new state of the art in few-shot VLA adaptation.
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All authors Duc Minh Nguyen, Nghiem Tuong Diep, Binh Gia Nguyen, Trong-Bao Ho, Doanh Le, Tan Q. Nguyen, Thien-Loc Ha, Nhiem Tran, Bao Thach, Nhat X. Tran, Tuan A. Tran, Artur Habuda, Philip Lund Møller, Tran Nguyen Le, Daniel Sonntag, Matthias Niepert, Khoa D. Doan, Vu Duong, Hung Ngo, Minh N. Vu, Duy M. H. Nguyen, An Thai Le, Ngo Anh Vien
Artificial Intelligence
SignVLA: Real-Time Sign Language-Guided Robotic Manipulation via Attention LSTM and Vision-Language-Action Models
Ningwei Bai, Xinyu Tan, Harry Gardner et al. (+6 more)
Vision-
Language-Action (VLA) models enable
robots to execute manipulation tasks from natural-
language instructions grounded in visual observations. However, existing VLA interfaces primarily rely on
speech or text input, limiting accessibility for deaf, hard-of-hearing, and
speech-impaired users. We present SignVLA, a real-time sign-
language-guided VLA framework for accessible human-
robot interaction. The system introduces a modular sign-to-text interface that converts visual sign gestures into semantic instructions compatible with downstream VLA policies. Given
video streams, SignVLA extracts hand landmark features and employs an
attention-enhanced Long Short-Term
Memory (LSTM) network to capture temporal gesture dynamics for alphabet- and command-level sign recognition. A temporal stabilization module further improves prediction consistency in real-time interaction settings.The generated instruction sequence is then passed to a downstream VLA policy for sign-conditioned robotic manipulation. Experimental results demonstrate stable real-time sign recognition and successful execution of manipulation tasks driven by sign-
language inputs. Our findings suggest that lightweight temporal sign recognition can serve as an effective and practical accessibility layer for
multimodal embodied intelligence.
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All authors Ningwei Bai, Xinyu Tan, Harry Gardner, Zhengyang Zhong, Liuhaichen Yang, Luoyu Zhang, Zhekai Duan, Monkgogi Galeitsiwe, Zezhi Tang
Artificial Intelligence · Cross-listed from cs.CV
Translating Inference-Time Control to Radiology Vision-Language Models: Activation Steering for Pneumonia Classification on Chest X-rays
Eduardo Moreno Judice de Mattos Farina, Mateus A. Esmeraldo, Felipe Akio Matsuoka et al. (+2 more)
Inference-time engineering can alter model behavior without fine-tuning. However, its utility for improving diagnostic performance in medical
vision-
language models (VLMs) remains unclear. We aim to evaluate whether Contrastive Activation Addition (CAA) can improve pneumonia classification in chest radiograph VLMs without updating model weights. Three frozen chest radiograph VLMs (MedGemma-4B-IT, NV-Reason-CXR-3B, and CheXOne-3B) were evaluated on the public Kermany pneumonia test set. Classification was based on the logits of the
tokens Yes and No under a binary
prompt. Steering vectors included a 30-pair answer-bias control, a 30-pair pneumonia text contrast, and an image-conditioned contrast derived from 30 pneumonia and 30 normal development images. A deterministic 200-image development set was used for layer and scale selection (100 images) and threshold calibration (100 images). Performance was assessed using ROC-AUC, PR-AUC, F1 score, threshold analyses, reverse-vector controls, random-vector controls, and conditional bootstrap confidence intervals. Fixed-threshold F1 improvements were frequently observed but did not consistently indicate improved diagnostic performance. For MedGemma-4B-IT. NV-Reason-CXR-3B showed the strongest benefit: calibrated F1 improved from 0.7692 in the zero-shot setting to 0.8619 with pneumonia-text steering and to 0.8727 with image-conditioned steering. For CheXOne-3B, pneumonia-text steering increased calibrated F1 from 0.8528 to 0.8666, although the confidence interval crossed zero. On this public pneumonia
benchmark, CAA substantially altered prediction score distributions and operating characteristics without fine-tuning. Meaningful performance gains were observed in one of three evaluated VLMs, suggesting that activation steering may serve as a lightweight approach for adapting medical VLM behavior.
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All authors Eduardo Moreno Judice de Mattos Farina, Mateus A. Esmeraldo, Felipe Akio Matsuoka, Paulo Eduardo de Aguiar Kuriki, Felipe Campos Kitamura
Artificial Intelligence
Process-Reward Tactic Evolution for Long-Horizon Bioinformatics Workflows
Lingzhi Yang, Yubo Fan, Song Wu et al. (+1 more)
LLM
agents can write code and call tools, but reliable bioinformatics work requires long-horizon interaction with workflow software, typed data objects, provenance, and biological checks. We study this setting through
Galaxy workflow execution. The
agent must explore task data, construct or adapt an executable workflow DAG, bind inputs and
dataset collections, monitor execution, debug failures, and validate biological outputs. We propose Process-Reward Tactic
Evolution, a
Galaxy-based training framework that turns verified workflow rollouts into reusable \tactics. During training,
agents practice on curriculum-organized
Galaxy tasks in
Agent Gym; process verifiers score workflow construction, software interaction, execution, and biological correctness; successful and failed traces are distilled into a tactic library. At inference, the trained executor, Process-Reward Tactic
Evolution, uses this library to execute held-out peer reviewed
Galaxy workflow converted BioWorkflow Bench and BioAgent Bench tasks in isolated environments. The paper evaluates whether process-supervised tactic accumulation improves long-horizon bioinformatics workflow completion, biological correctness, and execution efficiency over no-
memory and reflection-style baselines.
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All authors Lingzhi Yang, Yubo Fan, Song Wu, Gilchan Park
Artificial Intelligence
Study on Quantitative Dynamic Epistemic Logic for Belief Revision
Felipe Nunes de Souza Camargo
Belief revision is a process in which an
agent begins to believe in something she previously did not. I begin the paper by presenting, based on (Gärdenfors, 1998; Hansson, 1999), postulates for belief revision that constitute the basis of the AGM theory. I will then briefly show the semantics of a modal logic introduced in (van Ditmarsch, 2005), which I call `$P$'. This logic formalizes static epistemic states and has greater expressive power than AGM in doing so because it captures the quantitative notion of "degrees of conviction". The third step is to introduce revision operators on $P$ and, mostly following (van Ditmarsch, 2005), obtain the Dynamic Epistemic Logic (DEL) I call `$P*$'. It models processes of belief revision in several ways. Original results are presented in the following two sections. The first one of these sections revolves around a formalization of AGM postulates within $P*$ by proving some
theorems related to the satisfaction of those postulates by revisions defined in $P*$. The last section features an analysis of $P*$'s revisions that go beyond the mere satisfaction of postulates. I compare their formal behavior with respect to some philosophical criteria. At last, I conclude that the functions presented in (van Ditmarsch, 2005) are not good formalizations of the philosophical intuition behind AGM. Instead, it is captured by the function $*^0$ originally defined in this paper (but highly inspired by (van Benthem, 2007)). An implementation of this function is also provided.
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Artificial Intelligence
What Shapes Emergent Misalignment? Insights from Training Dynamics, Model Priors, and Data
Yuchen Zhang, Anietta Weckauff, Diego Garcia-Olano et al. (+1 more)
Emergent misalignment (EM) is a phenomenon in which models generalize with narrow fine-tuning, leading to broad (yet uneven) misalignment across evaluation questions. We study EM and its variability directly through the components of fine-tuning: training dynamics, model priors, and data. (1) We first explored how in-domain training loss relates to out-of-domain
alignment scores across
datasets and model families. Then, we tried to induce potential alternative local minima through different learning schedules for one narrow fine-tuning, but did not find strong runs with better broad
alignment scores conditioned on similar or lower training loss. (2) We found that although the mean and standard deviations of the misaligned model scores are usually statistically different from those of the pre-trained model, there are some potential signals on overall positive correlation. The evaluation
prompt-only activations from both the pre-trained and the original instruct models (prior to narrow fine-tuning) could predict fine-grained
alignment scores after narrow fine-tuning. (3) Finally, we compared activation deltas before and after narrow fine-tuning and found moderate-to-high subspace overlap and similarity between the resulting activation shifts for training and evaluation
prompts. Subspace overlaps between training and evaluation
prompt activations correlate with their shifts' similarities when measuring with the last
prompt-
token activations. The train-evaluation data
prompt overlap is controlled against overlap computed from random vectors and evaluation
prompts activations.
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All authors Yuchen Zhang, Anietta Weckauff, Diego Garcia-Olano, Maksym Andriushchenko
Artificial Intelligence · Cross-listed from cs.LG
B[FM]$^2$: Brain Foundation Model via Flow Matching with SplitUNet
Jaedong Hwang, Kathleen Zhang, Wei Dai et al. (+5 more)
EEG foundation models can learn generalizable representations from large-scale
EEG corpora to enable single-backbone transfer across diverse clinical and
brain-computer interface tasks. Existing models typically discretize the continuous multi-channel
EEG waveform into patches or codebook
tokens and train a
transformer with masked self-supervision. Recognizing that this discretization fragments continuous
brain rhythms and obscures fine-grained temporal dynamics, we present B[FM]$^2$(
Brain Foundation Model via Flow Matching), whose inductive bias aligns with the data by pretraining directly on the raw signal using continuous-time flow matching without patches, tokenization, or masking. However, multi-channel
EEG signals pose an architectural challenge for flow matching: time is densely sampled and highly autocorrelated (thousands of timepoints), while the electrode axis is short (tens of channels) at distinct scalp positions. To address this time-electrode asymmetry, we introduce SplitUNet, a velocity network that factorizes each block into separate 1D temporal and 1D electrode convolutions and downsamples only along time, preserving electrode
topology throughout the hierarchy. B[FM]$^2$ sets a new state of the art on $7$ of $9$ standard downstream
EEG classification tasks, using a pretraining budget of only $36{,}895$ segments ($\approx$ 307h), 1-2 orders of magnitude ($\approx$ 30x) less than required by existing
EEG foundation models. Further, it generates synthetic EEGs that two board-certified neurologists cannot distinguish from
brain data (Cohen's $\kappa = -0.096$). https://jd730.github.io/projects/BFM2
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All authors Jaedong Hwang, Kathleen Zhang, Wei Dai, Konstantinos Kontras, Maarten Vanmarcke, Maarten De Vos, Ila Fiete, Paul Pu Liang
Artificial Intelligence · Cross-listed from cs.CV
GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling
Yixuan Lai, Tianjia Shao, Weijia Dou et al. (+2 more)
Generating visually consistent multi-shot
videos remains an open challenge. As
videos span more shots, inconsistencies can accumulate across shots, causing entities that reappear across shots – characters, objects, and locations – to drift away from how they first appear. We observe that viewers judge consistency by comparing each later appearance of an entity with its first clear appearance; the visual quality of this initial appearance sets the consistency ceiling for all that follows. Motivated by this, we present GroundShot, a training-free, model-agnostic agentic framework for entity-grounded multi-shot generation. GroundShot builds an entity-level visual
memory online from accepted generated shots: it schedules shots' generation order by their expected usefulness as entity references, grounds entities from generated
videos, verifies their reliability before adding them to
memory, and retrieves suitable entity references from
memory before each shot is generated. To evaluate this entity-centered view of consistency, we further introduce GroundBench, a diagnostic
benchmark that measures consistency at the entity level while isolating controlled challenge dimensions. Experiments show that GroundShot improves multi-shot consistency over existing methods while requiring no additional training or model modification.
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All authors Yixuan Lai, Tianjia Shao, Weijia Dou, Siyu Zhu, Jingdong Wang
Artificial Intelligence
Fara-1.5: Scalable Learning Environments for Computer Use Agents
Ahmed Awadallah, Sahil Gupta, Yash Lara et al. (+12 more)
Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This requires two key ingredients: environments in which
agents can act and verifiers that can judge whether their demonstrations succeeded. We introduce FaraGen1.5, a scalable data pipeline for computer use
agents composed of three modular components: environments, solvers, and verifiers. FaraGen1.5 uses both live websites and synthetic environments that faithfully simulate domains gated by authentication or that require irreversible actions. It employs a solver harness that can be powered by multiple models, including strong frontier models such as GPT-5.4, and also incorporates a user simulator to enable multi-turn rollouts. Finally, FaraGen1.5 scores the resulting trajectories with three complementary verifiers covering task correctness, efficiency, and critical-point adherence. Using data produced by this pipeline, we train Fara1.5, a family of native computer use
agents (CUAs) at three scales built on Qwen3.5 (4B, 9B, and 27B). To train these models, we employ a supervised finetuning (SFT) recipe that carefully balances data from FaraGen1.5 for broad coverage, specific high-value tasks, and target model deficiencies in an iterative approach. Each model sets a new state of the art (SoTA) for its size class on browser-use
benchmarks: Fara1.5-9B reaches 63.4% on Online-Mind2Web and 86.6% on WebVoyager, while Fara1.5-27B achieves 72.3% on Online-Mind2Web, which is competitive with much larger proprietary systems. We also release weights for the Fara1.5 models under MIT license, making SoTA computer use accessible for all beyond closed API-only systems.
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All authors Ahmed Awadallah, Sahil Gupta, Yash Lara, Yadong Lu, Hussein Mozannar, Akshay Nambi, Zach Nussbaum, Yash Pandya, Aravind Rajeswaran, Corby Rosset, Alexey Taymanov, Luiz do Valle, Vibhav Vineet, Spencer Whitehead, Andrew Zhao
Artificial Intelligence · Cross-listed from cs.SE
Formally Verified Code Synthesis for Structured Data Translation in a Medical Internet of Things
Colin Samplawski and Adam D. Cobb
In this work we present a LLM powered, evolutionary code synthesis system for structured data translation in a Medical Internet of Things settings. A key challenge in this domain is ensuring that the synthesized code is trustworthy and reliable. To this end, we integrate a formal verification step into our code synthesis pipeline to ensure that any generated code is guaranteed to satisfy predefined requirements. In particular, we present a case study of integrating a novel device (a pulse oximeter) into the existing network of devices. Our system generates a formally verified translation between the device's JSON schema and the Fast Healthcare Interoperability Resources (FHIR) format used by the wider system. This formal verification stage ensures structured data translated by the generated code will always be in the target output schema. We provide a set of experimental results which demonstrate that our system is able to consistently generate correct translation at low cost.
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Artificial Intelligence · Cross-listed from cs.CV
TriMotion: Modality-Agnostic Camera Control for Video Generation
Seunghyun Shin, Jifei Song, Wooseok Jeon et al. (+2 more)
Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation process on a single specific modality, such as explicit pose trajectories or reference
videos, limiting their ability to support heterogeneous user inputs. To address this limitation, we present TriMotion, a modality-agnostic framework for camera-controlled
video generation that maps
video, pose, and text inputs, describing the same camera trajectory into a shared motion
embedding space. Learning such a space requires synchronized supervision across modalities. Therefore, we build the Motion Triplet
Dataset by extending a Multi-Cam
Video Dataset with geometry-grounded motion descriptions derived from camera extrinsics. We further introduce a latent motion consistency objective that leverages the motion
embedding space to encourage the generated
video to follow the target camera trajectory directly in latent space, avoiding the cost of pixel-space decoding. Extensive experiments show that TriMotion generates high-quality
videos that accurately follow the target camera trajectories across all three modalities. Beyond standard generation, the shared motion
embedding space also enables flexible applications such as sequential motion composition and cross-modal motion interpolation.
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All authors Seunghyun Shin, Jifei Song, Wooseok Jeon, Hae-Gon Jeon, Jiankang Deng
Artificial Intelligence · Cross-listed from cs.CL
Beyond 'One Language, One Script': Quantifying Orthographic Bias in Multilingual VLMs with PuMVR
Prabhjot Singh, Bhushan Pawar and Madhu Reddiboina
Current
Vision-
Language Models (VLMs) are celebrated for their
multilingual capabilities, yet they operate under a flawed assumption: that one
language corresponds to a single writing system. This overlooks billions of users of multi-script
languages like Punjabi, Serbian, Hindi-Urdu, Kurdish, among many others, for whom a model's capability may be fractured by orthographic bias. We introduce PuMVR (Punjabi
Multimodal Visual
Reasoning), the first
benchmark designed to quantify script-dependent bias through 375 culturally grounded image-
reasoning tasks across Punjabi's three active scripts (Gurmukhi, Shahmukhi, Roman). Evaluating 10 state-of-the-art VLMs, we expose a substantial Script Gap: models frequently solve visual puzzles in one script while failing identical tasks in another, with accuracy deltas reaching 16% and Script Consistency Rates (SCR) as low as 24.8%. Crucially, visual input boosts absolute performance but does not close this gap, the relative bias persists. Our analysis suggests
reasoning patterns show limited cross-script transferability, and Chain-of-Thought pathways diverge based on script alone. We propose SCR as a core metric for script-agnostic evaluation, challenging current
multilingual assessment paradigms and providing a framework for equitable AI.
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Artificial Intelligence · Cross-listed from cs.CL
FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes
Prabhjot Singh, Somnath Luitel, Manmeet Singh et al. (+1 more)
AI systems for peer review fail on three fronts: they train on Computer Science and Machine Learning venues alone, ignore the iterative dialogue that validates science, and evaluate on stylistic mimicry rather than real editorial judgment. We introduce FirstPass, a
dataset and fine-tuned model that addresses all three. Curating 3,668 complete multi-round peer-review dialogues from Nature Communications across five scientific domains (biology, chemistry, neuroscience, physics, and earth science), we exploit mandatory transparent peer review (instituted November 2022) and verify 100% content integrity by automated audit. We fine-tune Qwen2.5-7B-Instruct via Low-Rank Adaptation (LoRA) on three tasks: review generation, reviewer updating, and revision-cycle prediction. Our key finding is that response-only loss masking is a prerequisite, not an optimization: without it, accuracy is 62.0%, below the majority baseline; with it, FirstPass achieves 80.5% accuracy and F1-macro 78.2% on predicting editorial outcomes (Standard vs. Extended revision cycles), outperforming Gemini-3.1-flash-lite-preview zero-shot by 10.4 percentage points and all baselines with statistical significance (McNemar p < 0.001). On generation, FirstPass produces reviews averaging 1,187 words, substantially closer to human references (2,155 words) than any baseline, achieving ROUGE-L 0.154 with significant gains over Qwen and DeepSeek zero-shot (p < 0.001). Deployed in the pre-submission loop as an anticipatory scientific co-author, FirstPass simulates expert critique and predicts revision cycle outcomes before submission, giving authors the judgment a trusted colleague would provide, with consistent cross-domain performance across five disciplines.
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All authors Prabhjot Singh, Somnath Luitel, Manmeet Singh, Josh Durkee
Artificial Intelligence · Cross-listed from cs.CV
UniSLAD: A Unified Framework for Structural and Logical Industrial Visual Anomaly Detection
Changyi Li, Chao Yang, Yu Xiao et al. (+1 more)
Visual anomaly detection is a fundamental task in industrial automation. While existing approaches have achieved notable progress in identifying structural defects, the detection of logical anomalies remains relatively underexplored. In practice, structural and logical anomalies frequently co-occur in industrial workflows. Therefore, a solution capable of detecting both structural and logical anomalies is crucial for advancing comprehensive anomaly detection research. To address this limitation, we propose a unified framework, termed UniSLAD, which jointly addresses logical and structural anomalies without additional training, enabling a practical solution for dynamic industrial environments. First, we introduce a dual-feature extractor that synergistically integrates a Convolutional Neural Network (CNN) backbone for local texture perception with a
Transformer backbone for global contextual
reasoning, yielding richer and more comprehensive representations. Building on this foundation, we design dual-granularity feature representation modules. At the patch level,
memory banks enhanced by the Mahalanobis Transform (MT) preserve representative features and support more discriminative anomaly scoring. At the image level, distribution maps are aggregated using Lower-Upper Mean (LUM) and Power Mean Pooling (PMP), yielding a more robust global representation than conventional average pooling. Extensive experiments on the two industrial
benchmarks demonstrate that UniSLAD achieves competitive performance in comprehensive anomaly detection, achieving 99.4% and 93.1%, respectively. Furthermore, ablation studies verify the individual contributions and effectiveness of each proposed component.
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All authors Changyi Li, Chao Yang, Yu Xiao, Kari Tammi
Artificial Intelligence · Cross-listed from cs.CV
One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception
Keqin Zeng, Shuting Su, Shihao Lin et al. (+2 more)
Reliable spatial decision automation, such as autonomous driving and maritime surveillance, critically depends on robust visual perception. However, real-world spatiotemporal data exhibits severe heterogeneity, often manifesting as extreme long-tail distributions for safety-critical scenarios. This data scarcity induces
dataset shift that degrades detection performance and pose safety risks. While synthetic data generation offers a potential solution, existing generative approaches, such as
diffusion models and Generative
Adversarial Networks (GANs), often lack explicit spatial grounding and structural constraints, resulting in spatial and physical inconsistencies in generated scenes. To address these challenges, we introduce WMGen-v1, an agentic text-based world model framework for long-tail spatial data generation. WMGen-v1 employs a Large
Vision-
Language Model (LVLM) to construct a structured scene representation from a single reference image, while a Large
Language Model (LLM) performs guidance-based scene expansion under physical plausibility and commonsense constraints. Subsequently, conditioned on the structured semantic representations produced by this
reasoning process, a
diffusion model generates diverse and physically grounded long-tail training data. Experiments on internal industrial
datasets, ROADWork, and LaRS
benchmarks demonstrate that WMGen-v1 outperforms baseline approaches. Notably, detectors trained solely on WMGen-v1 synthetic data approach real-only performance on aggregate
dataset-level metrics, highlighting its potential to alleviate long-tail data scarcity for downstream spatial perception.
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All authors Keqin Zeng, Shuting Su, Shihao Lin, Ziyue Li, Rui Zhao
Artificial Intelligence · Cross-listed from cs.HC
Co-Construction Blindness and Asymmetric Epistemic Vulnerability in Human-LLM Interaction
Bianca Helena Ximenes
This paper introduces two constructs to describe, as far as we know, a previously unnamed risk in human-LLM interaction. Co-construction blindness is the failure to recognize that LLM outputs are not independent assessments to be verified, but co-constructed artifacts shaped by the user's own inputs, accumulated history, and metadata. Every user of a conversational LLM is IN the loop, not ON it – yet every deployment disclaimer positions them as external auditors. Asymmetric epistemic vulnerability describes the condition in which co-construction blindness produces consequences of radically different magnitude depending on where in the authority structure the user sits. We argue that these constructs describe a structural inevitability, not an anomaly, using the public case of Richard Dawkins's interaction with Claude as a paradigmatic instance. We document a secondary mechanism – structural deference – through a first-person exchange in which a large
language model concedes that it treated Dawkins more gently than warranted because his intellectual output is represented in its training data. We map the research gaps this analysis opens and call for shared terminology as a precondition for appropriate governance and design response.
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Artificial Intelligence · Cross-listed from cs.SE
Integrating Large Language Model Agents with Digital Twins for Industrial Autonomous Systems
Yuchen Xia
Industrial automation is being transformed by digitalization and the increasing use of cyber-physical systems. Modern production environments require greater adaptability, faster reconfiguration, and more intuitive human-machine interaction. However, traditional rule-based systems rely on fixed logic and cannot autonomously adapt to changing conditions. Consequently, current automation systems lack a systematic approach for integrating adaptive and generalizable
reasoning capabilities for interpreting, planning, and executing user tasks across dynamic environments and heterogeneous components. This dissertation proposes a three-layer framework that integrates large
language models (LLMs), digital twins, and automation systems into an autonomous system. Autonomy is defined as a design property assigned to system components and enabled through LLM-based
reasoning to achieve adaptive, goal-oriented behavior. The Task-Process-Service-Resource (TPSR) model is introduced to transform user tasks into executable processes. Four LLM roles are identified: process orchestration, service matching, digital resource generation, and
agent-as-a-service. Five peer-reviewed studies develop and refine these concepts using the design science research methodology. Case studies and prototypes demonstrate adaptive task planning, event-driven control, simulation-based parameterization, and digital model generation. Results show high task executability, command correctness, and content-generation accuracy while reducing manual effort. The framework enables the integration of LLM-based
reasoning into industrial automation systems and improves adaptability and usability. Limitations include dependence on accurate digital representations, the computational demands of LLMs, and the need for human intervention in safety-critical situations.
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Artificial Intelligence · Cross-listed from cs.SE
A Topology-Aware, Memory-Centric Architecture that Separates Root-Cause Derivation from Root-Cause Explanation
Momil Seedat
Modern microservice deployments fail in ways that are easy to detect and hard to explain. When a fault propagates along service dependencies, alerts fire in floods, dashboards multiply, and the scarce resource, an engineer who understands how the services relate, is consumed reconstructing context that the monitoring stack discarded. We argue that the missing ingredient in autonomous operations is not a better anomaly detector or a larger
language model, but operational
memory: a persistent, structured representation of how a system normally behaves, how its parts depend on one another, and how it has failed before. We present O PS C ORTEX, a working multi-
agent prototype that organizes this
memory into four tiers and uses it to separate two tasks the field usually conflates: deriving a root cause and explaining it. Root cause is computed deterministically from a learned dependency graph and the temporal ordering of threshold crossings; a large
language model (LLM) is then asked only to explain, confirm, and recommend, using evidence the system has already assembled. We motivate the design with two documented production cascading failures, review representative literature on observability, anomaly detection, graph-based localization, and LLM-assisted diagnosis, and show how each architectural choice maps directly to a failure mode those incidents exhibit. The prototype is validated on an instrumented e-commerce
benchmark with eight injectable failure scenarios.
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Artificial Intelligence · Cross-listed from physics.chem-ph
A large-scale foundation model enables simulation-to-real adaptation for nuclear magnetic resonance-based molecular structure analysis
Chen Yang, Zheng Fang, Hanyu Sun et al. (+7 more)
Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great potential for its rapid and automated interpretation. However, the scarcity of experimental NMR
datasets has constrained deep learning in this domain to narrow, task-specific applications that lack broad generalization. Here, we introduce UltraNMR, a large-scale foundation model for NMR that leverages the intrinsic properties of NMR spectra to learn generalizable spectral representations. We collected 158 million paired simulated $^{1}$H and $^{13}$C NMR spectra to train UltraNMR, employing multiple domain-specific pre-training objectives. UltraNMR captures both intra-spectral and inter-spectral dependencies, enabling seamless simulation-to-real adaptation. We demonstrate that adapting UltraNMR to a range of molecular structure analysis tasks on experimental NMR spectra consistently yields state-of-the-art performance and clearly outperforms UltraNMR variants trained directly on downstream data without simulation pre-training. We also construct a large-scale NMR spectral vector library by encoding simulated NMR spectra using UltraNMR, covering 94 million unique molecules and enabling effective structure-aware
retrieval. In real-world applications, UltraNMR facilitates the structural elucidation of two previously unknown natural products from Chinese herbal medicines recorded in the Chinese Pharmacopoeia. These results suggest that large-scale simulation pre-training can effectively bridge the simulation-to-real gap, enabling robust and generalizable molecular structure analysis of real-world NMR spectra.
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All authors Chen Yang, Zheng Fang, Hanyu Sun, Fanjie Xu, Hongxin Xiang, Hanyu Gao, Xiangxiang Zeng, Yuqiang Li, Xiaojian Wang, Jun Xia
Artificial Intelligence · Cross-listed from physics.chem-ph
Empowering Polymeric Materials Discovery by Artificial Intelligence
Chenyao Ma, Linda Zhang, Yuheng Chen et al. (+29 more)
Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural
evolution across multiple length and time scales. Consequently,
polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools,
polymer databases, predictive models, AI
agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific
reasoning. We argue that
polymer science is entering an era of autonomous discovery, in which data, simulation,
reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for
polymer innovation, fundamentally transforming how
polymer research is conducted.
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All authors Chenyao Ma, Linda Zhang, Yuheng Chen, Wei Du, Shangwen Fang, Zihao Jiang, Chuanyu Liu, Xinyu Ma, Rui Su, Gang Wang, Muyao Yu, Dong Zhong, Jie Zhu, Weibo Gong, Huan Gu, Limin Li, Chen Shen, Rui Wu, Zhenghao Wu, Kan Xu, Min Zhou, Donglin He, Xiayun Huang, Shan Jiang, Pengfei Ou, Jiayu Peng, Yuwei Zhang, Jie Zhao, Di Zhang, Piao Ma, Zhenghao Li, Hao Li
Artificial Intelligence · Cross-listed from cs.LG
How Transparent is DiffusionGemma?
Joshua Engels, Callum McDougall, Bilal Chughtai et al. (+11 more)
LLM
reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surprising model behaviors. However, DiffusionGemma performs a larger fraction of its computation in a continuous latent space; does this make its
reasoning less transparent? We study this question by decomposing transparency into two components: variable transparency, whether we understand intermediate snapshots of a model's computational state; and algorithmic transparency, whether we can use these snapshots to reconstruct the process by which the model arrived at its outputs. Naively, DiffusionGemma has poor variable transparency: its opaque serial depth, the amount of serial computation that occurs in between interpretable model states, seems at first 28.6X higher than the corresponding autoregressive Gemma 4 model. However, we show that we can map the information flowing between denoising steps through an interpretable
token bottleneck with no decrease in downstream performance. Treating these intermediate states as interpretable reduces the opaque serial depth to just 1.1X that of Gemma 4. Algorithmic transparency is harder for
diffusion models than for autoregressive models because all
token predictions in the canvas can change at every denoising step, giving the model the power to implement complicated distributed
algorithms during the denoising process. To begin bridging this gap, we conduct a suite of interpretability case studies, uncovering initial evidence of novel
diffusion-specific phenomena such as non-chronological
reasoning,
token and sequence smearing, and intermediate-context
reasoning. Finally, we test monitorability, a key application of transparency that measures whether model outputs are useful for downstream tasks. We find that DiffusionGemma is similarly monitorable to Gemma 4.
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All authors Joshua Engels, Callum McDougall, Bilal Chughtai, Janos Kramar, Senthooran Rajamanoharan, Cindy Wu, Arthur Conmy, Asic Q Chen, Jean Tarbouriech, Min Ma, Brendan O'Donoghue, João Gabriel Lopes de Oliveira, Rohin Shah, Neel Nanda
Artificial Intelligence · Cross-listed from cs.IR
Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation
Ruizhong Qiu, Yinglong Xia, Dongqi Fu et al. (+6 more)
Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recommendation lies item tokenization, which bridges item semantics and recommendation models. However, existing methods often struggle to effectively organize and inject complex user-behavioral and item-semantic contexts into recommendation models simultaneously. On the one hand, existing graph-based integration methods, such as graph serialization and graph neural networks, either suffer from scalability issues or exploit only local graph information. On the other hand, existing semantic tokenization methods typically rely on heuristics and lack explicit supervision signals, which may lead to inaccurate or suboptimal semantic representations. To address these limitations in user interest context modeling, we propose G2Rec, a scalable framework that unifies holistic graph-based user co-engagement modeling with semantic tokenization for industrial-scale generative recommendation. Overall, G2Rec enables recommendation models to capture holistic and semantically grounded user interest prototypes without requiring ground-truth user interests, thereby providing more comprehensive and accurate modeling of user behavior contexts in industrial sequential recommendation. Online deployment across product surfaces and extensive experiments on public
datasets demonstrate the superiority of G2Rec over existing methods.
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All authors Ruizhong Qiu, Yinglong Xia, Dongqi Fu, Hanqing Zeng, Ren Chen, Xiangjun Fan, Hong Li, Hong Yan, Hanghang Tong
Artificial Intelligence
Toward Calibrated Mixture-of-Experts Under Distribution Shift
Gina Wong, Drew Prinster, Suchi Saria et al. (+2 more)
Calibration aligns a model's predictive uncertainty with the frequencies of its empirical outcomes and is important for understanding and trusting reported probabilities. Recent work shows that enforcing calibration at the level of individual predictors can improve ensemble accuracy and calibration, with mixture-of-experts (MoE) models showing strong empirical improvements in particular; however, the conditions under which calibration helps MoE are not well understood. In this work, we study how MoE models behave under distribution shift, focusing on how routing mechanisms interact with expert-level calibration. We show that expert calibration is sufficient to ensure calibration of the overall model under a broad class of distribution shifts in hard-routed models, but is insufficient for calibrating soft-routed models. To address this, we propose an
adversarial reweighting that penalizes calibration errors of the routed aggregate under distribution shift, and we demonstrate that it improves the accuracy-calibration tradeoff both on average and on difficult subsets of the data, across model classes, prediction tasks, and distribution shifts.
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All authors Gina Wong, Drew Prinster, Suchi Saria, Rama Chellappa, Anqi Liu
Artificial Intelligence
How Do Instructions Shape Speech? Cross-Attention Attribution for Style-Captioned Text-to-Speech
Nityanand Mathur, Hamees Sayed, Wasim Madha et al. (+4 more)
Style-captioned text-to-
speech systems use natural
language to control voice characteristics, but how individual words influence acoustic output remains unclear. Understanding this is critical for diagnosing failure modes and improving controllability in expressive TTS. We propose cross-
attention attribution for
speech diffusion models, adapting the DAAM framework to the
speech domain for the first time, and apply it to CapSpeech-TTS. Our method extracts per-
token heatmaps across 25 layers and 24 ODE steps. We analyze 3,600 (style caption, text transcript) combinations comprising 120 style captions conditioning the generation of 30 text transcripts each, revealing how caption
tokens shape waveforms. Results show: (1) style
tokens have lower temporal variance than content/function
tokens, confirming global conditioning; (2) style
attention correlates with F0 and energy; (3) style conditioning peaks in early steps and deep layers; (4)
attention entropy reaches its minimum at layer 17, co-occurring with the style importance peak, indicating maximal network selectivity at the most style-critical stage. This is the first study of how natural
language influences cross-
attention in
speech diffusion models
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All authors Nityanand Mathur, Hamees Sayed, Wasim Madha, Apoorv Singh, Sameer Khurana, Akshat Mandloi, Sudarshan Kamath
Artificial Intelligence
LedgerAgent: Structured State for Policy-Adherent Tool-Calling Agents
Md Nayem Uddin, Amir Saeidi, Eduardo Blanco et al. (+1 more)
Policy-adherent tool-calling
agents in customer-service domains must maintain task states across turns while calling tools and obeying domain policies. Task states consist of relevant facts, identifiers, constraints, and conditions observed through user interaction and tool calls. In standard
agents, task states are not represented separately. Observations, tool returns, and policy instructions are placed in the
prompt, leaving
agents to reconstruct the relevant states from the
prompt each time they decide what to do next. This design makes state management implicit, creating two common failure modes. An
agent may retrieve the right facts but later ground its decision in stale, missing, or incorrect information; and a syntactically valid tool call may still violate a domain policy that depends on the current task state. We introduce LedgerAgent, an inference-time method for tool-calling
agents that maintains observed task states in a separate ledger and renders the states into the
prompt. The ledger is also used to check state-dependent policy constraints before environment-changing tool calls are executed, blocking policy violations. Across four customer-service domains and a mixed panel of open- and closed-weight models, LedgerAgent improves average pass\textasciicircum{}k over a standard
prompt-based tool-calling approach, with the largest gains under stricter multi-trial consistency metrics.
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All authors Md Nayem Uddin, Amir Saeidi, Eduardo Blanco, Chitta Baral
Artificial Intelligence
DeepSWIP: Quotient-WMC Counterfactuals for Neural Probabilistic Logic Programs
Saimun Habib, Vaishak Belle and Fengxiang He
Neurosymbolic systems such as DeepProbLog combine neural perception with probabilistic logic, but standard inference is associational. Counterfactual
reasoning additionally requires a causal semantics for interventions and evidence. We introduce DeepSWIP, a single-world counterfactual semantics for DeepProbLog programs. Using neural materialization, we reduce fixed-context neural predicates to ordinary ProbLog choices, apply Single World Intervention Programs (SWIPs), and compute counterfactuals by weighted model counting (WMC) over a single transformed program. Under finite grounding and unique-supported-model assumptions, DeepSWIP is exact relative to the learned materialized FCM. The standard quotient-WMC form of ProbLog conditionals identifies active neural probabilities and explains intervention cleaning, calibration sensitivity, and rare-evidence instability. Experiments on MPI3D confirm the transformation against a DeepTwin construction against 12,000 queries, as predicted and a 2.14$\times$ inference speedup from avoiding the Twin's endogenous duplication. A SUMO HOV experiment shows that neural calibration degradation biases plug-in estimates, while a correctly scoped randomized-policy AIPW
estimator removes most first-order bias for population mean and ATE estimands. Code is at https://github.com/saibib/deep_SWIP.
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