Artificial Intelligence · Cross-listed from cs.LG
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
Midhun Parakkal Unni and Samuel Kaski
Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of generated data with human beliefs regarding the target physics significantly mitigates risk. GMHF operationalizes this insight by employing a Conditional Neural ODE (cNODE) as a generative digital twin, coupled with a Reinforcement Learning (RL)
agent. The
agent iteratively refines the latent physical parameters of the generated trajectories based on feedback, effectively steering the meta-learner toward the unobserved target distribution. Empirical validation on a nonlinear Duffing oscillator shows that GMHF substantially reduces deployment loss as expert reliability increases, and that the divergence between generated and target data falls under reliable feedback, directly corroborating the divergence-minimisation mechanism predicted by our theory. Further experiments on a non-dynamical probabilistic model confirm that the framework extends beyond ODE-governed systems, establishing human-AI collaboration as a rigorous catalyst for robust generalisation under distribution shift.
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Artificial Intelligence
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination
Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal et al. (+1 more)
Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded
reasoning. Standard large
language models often produce fluent but weakly traceable responses to open-ended materials design problems, making it difficult to determine whether final answers are supported by coherent intermediate
reasoning. We develop Graph-PRefLexOR, a family of graph-native
reasoning models fine-tuned with Group Relative Policy Optimization (GRPO) to organize
reasoning into explicit phases for mechanism exploration, graph construction, pattern extraction, and hypothesis synthesis. This design links neural
language generation with symbolic relational structure, enabling causal connections to be constructed, inspected, and reused. On 100 open-ended questions from materials science and mechanics literature, Graph-PRefLexOR achieves 40-65% improvements over corresponding base models, with the largest gains in
reasoning traceability.
Embedding analyses show broader semantic exploration and approximately 2-3 times greater semantic diversity than baselines. Semantic backtracking and layer-wise hidden-state analyses further show stronger
alignment between structured
reasoning and final answers. Finally, test-time graph expansion reveals that additional compute primarily increases long-range conceptual recombination within a bounded semantic space, rather than simply expanding semantic coverage. These results establish graph-native reinforcement learning as a pathway toward interpretable AI systems for scientific hypothesis generation in materials design and other scientific applications.
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All authors Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal, Markus J. Buehler
Artificial Intelligence · Cross-listed from cs.CL
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
Chloe Ho, Aayush Aluru, Muhammad Hammouri et al. (+4 more)
Although large
language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form stories. In this work, we introduce MAGNET, a multi-
agent goal-driven narrative engine for storytelling, which generates stories with persona-grounded character
agents that propose actions based on a shared structured state and evolving story goals. We evaluate MAGNET on 20 and 100 page stories using LLM based editor annotations and rubric scoring. At both narrative lengths, MAGNET significantly reduces editor annotations and improves rubric scores compared to single-model prompting, StoryBox, and StoryWriter (p<0.05). Ablation experiments also show that the critic module, structured state, and goal sequencing each contribute significantly to performance (p<0.05). These results suggest that long-form narratives can emerge from explicit structured states, critic-based action refinement, and goal-driven multi-
agent generation with loosely specified character personas, providing a foundation for controllable and structurally coherent long-form narrative generation.
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All authors Chloe Ho, Aayush Aluru, Muhammad Hammouri, Kerry Luo, Ryan Lagasse, Arjun Bahuguna, Vasu Sharma
Artificial Intelligence · Cross-listed from cs.LG
Valdi: Value Diffusion World Models
Christopher Lindenberg and Kashyap Chitta
World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive enough to represent uncertain futures.
Diffusion models offer a natural mechanism for modeling uncertain dynamics, yet their iterative inference procedure makes them difficult to use for low-latency latent planning. We bridge this gap with Value
Diffusion World Models (Valdi), combining end-to-end online training for MPC with a latent
diffusion dynamics model. In preliminary experiments on the CarRacing environment, we show that Valdi, using a single
diffusion step at both training and inference, matches a deterministic MLP baseline. Our experiments expose a trade-off between predictive multimodality and control performance in this setup. Code is available at https://github.com/Kit115/ValueDiffusionWorldModels.
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Artificial Intelligence
Two AI Metrics Diverged: Will it Make All the Difference?
Alex Fogelson, Zachary A. Brown, Hans Gundlach et al. (+2 more)
As exponential compute scaling continues, will the capabilities of frontier AI models outstrip what is accessible to developers on a small fixed budget? Or will capabilities converge, with "meek models inheriting the earth"? Building on Gundlach et al. (2025b), we show that the answer depends on how we value and measure AI capabilities. We discuss conventional performance measures and show that, while validation loss shows a shrinking gap, on other metrics frontier models grow their lead forever. Classifying performance metrics by their functional forms in relation to training (and inference) compute, we provide tight mathematical conditions for determining which metrics favor meek models, and show that bounded performance metrics always do. But careful interpretation of performance metrics is essential: we show that many common bounded metrics have closely-related counterpart metrics that are unbounded (and vice versa). Determining the apt metric in a domain is a prerequisite for policy, since bounded and unbounded metrics may suggest opposing policy responses. If a particular capability – like software engineering, synthetic biology, or rhetorical persuasiveness – is unbounded when measured in the terms we care about, frontier-level capability will likely be concentrated in the hands of a few wealthy actors. Conversely, if that capability is instead bounded, frontier-level capabilities proliferate through meek models into the hands of the many.
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All authors Alex Fogelson, Zachary A. Brown, Hans Gundlach, Jayson Lynch, Neil Thompson
Artificial Intelligence · Cross-listed from cs.CV
DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors
Seok-Young Kim, Abdelrahman Elskhawy, Taewook Ha et al. (+4 more)
We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often struggle to construct reliable 3D scene graphs due to unstable 3D object representations and missing relations caused by frame-wise inference. DeWorldSG addresses these issues by estimating instance-level
geometric 3D Gaussian distributions through depth-guided filtering and representing each object as a probabilistic 3D node rather than a single projected point. To mitigate relational sparsity from frame-wise inference, our framework further aggregates spatiotemporal evidence across object pairs and refines relations using contextual priors derived from a world model (V-JEPA 2). Experiments on the 3DSSG and ReplicaSSG
datasets demonstrate state-of-the-art (SoTA) performance in both object and predicate prediction, while producing temporally consistent scene structures. In particular, our method improves triplet recall by 77.4% and predicate recall by 23.2% over prior SoTA approaches, making it suitable for robotic manipulation and AR applications. Our code and models are open-sourced.
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All authors Seok-Young Kim, Abdelrahman Elskhawy, Taewook Ha, Dooyoung Kim, Eunjae Shin, Benjamin Busam, Woontack Woo
Artificial Intelligence · Cross-listed from cs.CV
Improving Sparse-View 3DGS Generalization via Flat Minima Optimization
Kangmin Seo, Sangeek Hyun, MinKyu Lee et al. (+1 more)
Recent advances in neural rendering have established 3D Gaussian Splatting (3DGS) as a highly efficient representation for novel view synthesis, enabling fast training and real-time rendering with strong fidelity. However, when supervision is limited to sparse input views, 3DGS tends to overfit to the observed images and generalize poorly to unseen viewpoints. We address this challenge from the perspective of flat minima (FM) optimization, which seeks solutions that remain stable under small parameter perturbations. Viewing Gaussian parameters as trainable weights, we adapt FM principles to the
geometric and dynamic nature of 3DGS with a lightweight training framework. Our method regularizes optimization with controlled Gaussian perturbations that account for each Gaussian's anisotropy and the training progress, preserving fine details while improving robustness to sparse-view overfitting. To further stabilize this flat minima optimization process, we introduce periodic reinitialization, which temporarily returns non-positional parameters to their initial states for a short window. Together, these techniques integrate seamlessly into existing 3DGS pipelines without architectural changes. Experiments on LLFF and Mip-NeRF360
datasets demonstrate improved quantitative metrics and perceptual quality under sparse-view supervision, producing reconstructions that are sharper, more stable, and better generalized to novel viewpoints.
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All authors Kangmin Seo, Sangeek Hyun, MinKyu Lee, Jae-Pil Heo
Artificial Intelligence
Self-Evolving Agents with Anytime-Valid Certificates
Biswa Sengupta
Self-evolving
agents violate the assumption behind most learning-theoretic guarantees: the data, evaluator, components, and hypothesis space are produced by the policy being updated. We present SEA, an architecture that confines self-modification to a small steering adapter and a versioned harness around a frozen base model and admits each modification only through an anytime-valid gate that emits an auditable certificate against a fixed error budget. Five loop controllers compose published guarantees; because such gates can only select among behaviors the frozen base already produces, five verifier-in-the-loop mechanisms – best-of-$N$, micro-step search, self-authored reproduction oracles, search-layer control, and self-repair – supply the dense, grader-free signal the gates require, computed from the issue text alone. On a $52$-instance SWE-bench Verified subset across four base models, base capability is the dominant, confound-free effect, and on two strong base models a deliberate no-op-composite control isolates the suite's contribution at $+4$ and $+5$ (Glm~5.2 $24\to28$; Gpt $29\to34$, the $65\%$ best), with event logs confirming that its mechanisms fire and prevent regressions. Results are single-run on expensive evaluations; confirming run-to-run variance and adapting the per-task
algorithm mix are future work.
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Artificial Intelligence · Cross-listed from cs.CL
CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models
Qizhi Jiang, Shuo Wang, Pei Ke et al. (+2 more)
Large
Reasoning Models (LRMs) have achieved remarkable success on complex tasks by leveraging long chain-of-thought (CoT) trajectories, yet they frequently exhibit overthinking on simple queries, resulting in significant
token overhead and reduced inference efficiency. However, existing compression methods predominantly apply uniform length reduction or rely on coarse-grained difficulty estimation, often leading to performance degradation on difficult problems. To address this limitation, we propose Confidence-Adaptive Thinking (CAT), a framework that incorporates the model's intrinsic self-certainty signals as confidence into the preference optimization process, which autonomously modulates
reasoning lengths based on problem difficulty. Experimental results show that CAT consistently outperforms state-of-the-art baselines on
reasoning accuracy across multiple
benchmarks on different base models. Our work enables LRMs to effectively compress confident responses while deliberating on uncertain ones, offering a potentially robust solution for balancing accuracy and latency in practical industrial scenarios.
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All authors Qizhi Jiang, Shuo Wang, Pei Ke, Yuhang Song, Ke Qin
Artificial Intelligence · Cross-listed from eess.SP
Meta-Transfer Learning for mmWave Beam Alignment
Ahmet Nuri Cevik and Sinem Coleri
Millimeter-wave (mmWave) beam
alignment is critical for next-generation wireless systems, but existing approaches either update all parameters during adaptation or restrict updates to a subset of layers without adapting intermediate feature representations. We propose MTL-BA, a meta-transfer learning framework for multiple-input single-output (MISO) beam
alignment that freezes a pre-trained convolutional backbone and meta-learns lightweight Scale-and-Shift (SS) adapters and a classifier head. Under an outdoor-to-indoor shift in DeepMIMO, MTL-BA achieves the highest Top-1 accuracy among the evaluated methods at 35 dB while updating $17\times$ fewer parameters than full fine-tuning and meta-training $6\times$ faster than MAML.
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Artificial Intelligence · Cross-listed from cs.CL
Recovering Input Text from Hidden States: Study of Gradient-Based Inversion of Decoder-Only Language Models
Mikołaj Słowikowski and Maciej Witold Majewski
This work studies the hidden-state inversion problem: recovering the original input
token sequence of a decoder-only
language model from its last-layer hidden states. Rather than treating inversion as a one-shot reconstruction, we study it as a continuous
embedding-space optimisation in which a soft proxy is driven towards the leaked target without any hard-
token projection during the search, and a
token is committed only once, at the end of the inner loop. This design choice has two consequences which are the main focus of this paper. First, keeping the optimisation entirely in continuous space exposes a rich set of internal signals: rank trajectories of the ground-truth
token, per-position loss curves, and a discrete loss measured at commit time. Second, the discrete loss allows assessing the correctness of recovery via cumulative discrete loss. We further analyse which
tokens break the reconstructions and find a sharp categorical asymmetry: space-prefixed, high-frequency function words in dense regions of the
embedding matrix dominate the failures, while content-bearing
tokens are recovered almost perfectly. On 10-
token C4
prompts the exact-match rate rises from 66.9% to 97.5% (mean similarity 0.994) as the candidate window is widened, confirming that most errors are recoverable near-misses rather than genuine ambiguities. A comparison with the released SIPIT reference situates these findings: per-step hard projection is faster, but the continuous formulation is what makes the optimisation observable and its failures detectable. The results show that last-layer hidden states of GPT-2 are as sensitive as the original text.
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Artificial Intelligence · Cross-listed from cs.RO
From World Models to World Action Models: A Concise Tutorial for Robotics
Xiaoxiong Zhang, Xiong Zeng and Wei Zhang
Rather than providing an exhaustive survey, this paper presents a concise tutorial on world models and world action models for robotics. After reading the tutorial, readers should have a clear understanding of what constitutes a "world", how world models and world action models are defined, and what roles they play within robotic AI systems. The tutorial also develops a unified perspective for comparing representative approaches, such as World Labs' spatial intelligence models, Yann LeCun's JEPA framework, and NVIDIA's Cosmos platform, and clarifies how these models differ in their representations, predictive capabilities, and interaction mechanisms.
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Artificial Intelligence · Cross-listed from cs.CV
Pano2World: End-to-End 3D Generation via Unified Multi-View Sequences
Zhenjia Li, Jinrang Jia and Yifeng Shi
A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration. Converting a single panorama into a persistent, renderable 3D representation for free-viewpoint
navigation has attracted growing interest; existing methods either adopt iterative per-view completion that propagates inpainting results to update the underlying geometry, leading to progressive error accumulation and cumbersome multi-step pipelines, or leverage the temporal consistency priors of
video generation models, yet the continuous-trajectory constraint intrinsic to such models limits their flexibility in covering scenes from multiple directions simultaneously. We present Pano2World, which takes a single indoor panorama as input and directly outputs a persistent, explorable 3D Gaussian scene. Given the source panorama, Pano2World first reconstructs a coarse 3D Gaussian proxy and renders it at adaptively sampled nearby poses to obtain geometrically aligned guidance panoramas; a panoramic
diffusion model then jointly denoises all target views via View-Aware
Attention Routing, where each target view simultaneously receives
geometric constraints from its corresponding guidance panorama and global semantic guidance from the source panorama, naturally enforcing cross-view consistency. To avoid the information loss incurred by decoding the multi-view hidden features formed during joint denoising back to the pixel domain via VAE, we introduce Latent Feature Adapter, a geometry-aware bridge module that directly distills these hidden features into a scene latent, subsequently decoded into the final 3D Gaussian scene. Experiments demonstrate that Pano2World significantly outperforms existing methods on the multi-position panoramic novel-view synthesis
benchmark.
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Artificial Intelligence · Cross-listed from cs.DB
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
Jalal Mahmud and Eser Kandogan
Large
language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values. We present a cross-domain formative study of operationalization failures in
agent-generated analytical workflows. Across 236 analytical intents spanning finance, human resources, and public safety domains, we identify 153 recurring failures despite successful workflow generation and execution. Our analysis reveals five recurring classes of failures: comparative grounding, process
reasoning, quantitative
reasoning, role confusion, and policy grounding. These findings suggest a semantic gap between user-level analytical concepts and the information available to workflow-generation systems. More broadly, they raise questions about the admissibility of analytical operations and suggest that future agentic data systems may require richer semantic representations to bridge the gap between analytical intent and executable computation.
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Artificial Intelligence · Cross-listed from cs.LO
Streaming LRAT Certificates into Lean Theorems
Stefan Szeider
If the certificate produced by a SAT solver is checked by a verified checker, we get a verdict which convinces. But this verdict cannot be named, reused as a lemma, or composed with other formal developments. We propose the tool lrat-catcher, which turns a certificate into a Lean
theorem. It checks the certificate as a stream while the solver is still running. Hence the certificate is not required to be saved to a file. Additionally, our tool makes Lean core's verified LRAT checker resumable so that its state can be serialized. We prove that checking divided at such a state still properly refutes the original formula. We propose two import modes. The stream mode reads the certificate from a pipe in blocks and checks it on the fly in
memory. The file mode imports a stored certificate in chunks. If interrupted, it rechecks only the chunks it has not yet completed. The soundness
theorem for the stream mode guarantees that a garbled or
adversarial stream can only fail the check but not yield a false
theorem. We find that with compaction at chunk boundaries, the
memory required depends only on the live clause set, not on the certificate size. Our tool supports cube-and-conquer and formulas derived by preprocessing, to still form Lean proofs of the original formula. We provide several end-to-end case studies on well-known combinatorial problems. A larger scaling experiment on the empty-hexagon problem shows that 174 TB of certificates can be imported into Lean via streaming.
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Artificial Intelligence · Cross-listed from cs.CV
LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives
Lukas Kuhn, Giuseppe Serra, Randall Balestriero et al. (+1 more)
Vision-
language pretraining remains dominated by contrastive objectives, whereas
vision-only self-supervised learning has largely adopted non-contrastive methods. At the same time, the role of
vision-
language encoders has shifted: they are increasingly deployed not as zero-shot classifiers but as the frozen visual backbone of
vision-
language models and dense prediction systems, which consume the full grid of patch
tokens rather than a single pooled
embedding. We introduce LeVLJEPA, the first fully non-contrastive end-to-end
vision-
language pretraining method. LeVLJEPA learns through cross-modal prediction with stop-gradient targets and per-modality distributional regularization, without negatives, temperature, momentum encoder, or teacher-student schedule, and trains stably at large scale. We find that the resulting encoder provides markedly stronger dense semantic features for downstream use: as a frozen
vision-
language-model backbone, LeVLJEPA is the strongest of the evaluated encoders across GQA, VQAv2, and POPE under two distinct
language models, and outperforms contrastive baselines on semantic
segmentation, while remaining on par on global readouts such as linear probing. These results establish non-contrastive pretraining as an effective means of producing dense semantic
vision features.
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All authors Lukas Kuhn, Giuseppe Serra, Randall Balestriero, Florian Buettner
Artificial Intelligence · Cross-listed from cs.CV
Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
Eliott Thomas, Mickael Coustaty, Aurelie Joseph et al. (+3 more)
Table extraction from business documents relies on a cascaded pipeline where Table Detection (TD) first localizes tables and Table Structure Recognition (TSR) then recovers their internal layout. Building task-specific training sets for this pipeline is costly, particularly for TSR which requires fine-grained structural annotations. Active learning (AL) can reduce this annotation burden, yet most AL strategies are designed for single-model tasks and do not account for inter-stage dependencies in cascaded architectures. In this work, we present the first adaptation of Uncertainty Herding (UHerding), a hybrid coverage-uncertainty sampling method originally proposed for image classification, to cascaded object detection pipelines. We propose two pipeline-aware extensions that exploit the TD-to-TSR dependency: RankFusion adds dual-
manifold coverage over both detection and structure representation spaces, while CAPA further incorporates stage-dependent gating and per-task uncertainty calibration. Extensive experiments across two public (PubTables-1M and FinTabNet) and two private table extraction
datasets, with various annotation budgets (from 71 to 500 documents) show that UHerding generalizes well to table extraction, outperforming each baseline. Among pipeline-aware variants, RankFusion achieves higher expected gains but at the cost of greater variance, while CAPA emerges as the most consistent strategy, outperforming standard UHerding on three out of four
datasets.
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All authors Eliott Thomas, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Vincent Poulain d'Andecy, Jean-Marc Ogier
Artificial Intelligence · Cross-listed from cs.CV
GaussianFusion: Unified 3D Gaussian Representation for Multi-Modal Fusion Perception
Xiao Zhao, Chang Liu, Mingxu Zhu et al. (+5 more)
The bird's-eye view (BEV) representation enables multi-sensor features to be fused within a unified space, serving as the primary approach for achieving comprehensive 3D perception. However, the discrete grid representation of BEV leads to significant detail loss and limits feature
alignment and cross-modal information interaction in
multimodal fusion perception. In this work, we break from the conventional BEV paradigm and propose a new universal framework for multi-modal fusion based on 3D Gaussian representation. This approach naturally unifies multi-modal features within a shared and continuous 3D Gaussian space, effectively preserving edge and fine texture details. To achieve this, we design a novel forward-projection-based multi-modal Gaussian initialization module and a shared cross-modal Gaussian encoder that iteratively updates Gaussian properties based on an
attention mechanism. GaussianFusion is inherently a task-agnostic model, with its unified Gaussian representation naturally supporting various 3D perception tasks. Extensive experiments demonstrate the generality and robustness of GaussianFusion. On the nuScenes
dataset, it outperforms the 3D object detection baseline BEVFusion by 2.6 NDS. Its variant surpasses GaussFormer on 3D semantic occupancy with 1.55 mIoU improvement while using only 30% of the Gaussians and achieving a 450% speedup.
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All authors Xiao Zhao, Chang Liu, Mingxu Zhu, Zheyuan Zhang, Linna Song, Qingliang Luo, Chufan Guo, Kuifeng Su
Artificial Intelligence · Cross-listed from cs.CV
Prototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
Huanwen Liang, Yuhao Huang, Xiliang Zhu et al. (+6 more)
Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal screening, accurate diagnosis remains challenging due to the low prevalence and high heterogeneity of anomalies. Existing deep learning methods for prenatal tasks rely on large-scale annotated
datasets, which are difficult to obtain in practice. Although few-shot learning alleviates data scarcity, it typically requires fine-tuning for new categories, limiting its practicality in resource-limited clinical settings. To address these challenges, we propose a training-free framework for multi-class prenatal US anomaly classification and localization that operates with only a few reference images per class, representing the first exploration of this setting. Our framework comprises three key components: (1) a
memory bank with multi-granular prototypes that explicitly models both class-level semantics and anomaly characteristics; (2) a prototype-driven soft merging mechanism that aggregates discriminative features to detect the anomaly region; and (3) a class-aware refinement strategy that leverages prototype consistency to improve category prediction. Extensively validated on a multi-center prenatal US
dataset containing 1,149 cases, with a total of 2,357 images and 9 categories, our proposed method outperforms the competitors.
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All authors Huanwen Liang, Yuhao Huang, Xiliang Zhu, Yuanji Zhang, Xuedong Deng, Xinru Gao, Guowei Tao, Yuhan Zhang, Dong Ni
Artificial Intelligence · Cross-listed from cs.DL
Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences
Mark Russinovich, Ram Shankar Siva Kumar and Ahmed Salem
Large
language models can generate polished scientific text that includes unsupported claims, allowing
hallucinations to enter the archival record. Assessing this risk via technical statements is difficult and often requires expert judgment, but citations provide a more auditable surface: a reference either resolves to a real scholarly work with compatible authorship, or it does not. We measure citation
hallucination in peer-reviewed proceedings using a conservative definition limited to identity-level failures: non-existent works and substantial author-list mismatches. We explicitly exclude ordinary bibliographic drift (e.g., venue/year differences, publication-status updates, minor name variants). To audit citations at scale, we build RefChecker, a verification pipeline that resolves bibliography entries against multiple bibliographic sources and escalates unresolved cases to web-search re-verification. We apply RefChecker to accepted camera-ready papers from ICLR, ICML, NeurIPS, and USENIX Security. Hallucinated citations have entered the archival record. While reference-level rates are usually below 1%, proceedings are large enough that paper-level failures are visible: in 2025, roughly one in twenty NeurIPS and USENIX Security papers contains at least two likely hallucinated academic-paper-like references under our strict definition. We also observe post-ChatGPT increases in several venues, including a tail of papers with 5+ failures in a single bibliography, and likely hallucinated citations even among award-winning papers. These results suggest peer review alone does not reliably enforce citation integrity, yet
auditing is tractable (about 0.04$ per paper in one venue-scale scan). We open-source RefChecker for routine, reproducible citation verification before publication (https://github.com/markrussinovich/refchecker).
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Artificial Intelligence · Cross-listed from cs.CV
ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition
Eliott Thomas, Tri-Cong Pham, Mickael Coustaty et al. (+5 more)
Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines. Accurate TSR depends on precise boundary localization: small errors in row or column boundaries can propagate into incorrect
cell assignments and structural inconsistencies. Yet detection-based approaches treat table elements as generic objects, ignoring a fundamental property of table layout: rows and columns play structurally distinct roles and their boundaries carry unequal importance. We propose an Edge-constrained Fine-grained Localization loss (EFL) that formalizes this structural asymmetry by encoding table-specific
geometric priors into the training objective: row-like elements are supervised with emphasis on their horizontal boundaries, while column-like elements prioritize vertical boundaries. Implemented within a real-time detector with distribution-based boundary refinement (D-FINE), EFL operates during training only and guides boundary refinement toward structurally meaningful adjustments with no change to the inference pipeline. The proposed approach, ConRTF, is also data-efficient, maintaining robust accuracy with as few as 2k–3k annotated tables. Experiments on PubTables-1M and two private
datasets show consistent improvements over the optimized baseline and several real-time detectors including RT-DETRv2 and YOLOv10-11, with gains of up to +1.6 GriTS points at equal inference speed.
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All authors Eliott Thomas, Tri-Cong Pham, Mickael Coustaty, Aurelie Joseph, Gaspar Deloin, Vincent Poulain d'Andecy, Jean-Marc Ogier, Antoine Doucet
Artificial Intelligence · Cross-listed from cs.LG
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
Hanning Yang, Meropi Karakioulaki, Lennart Purucker et al. (+3 more)
Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of underlying mechanisms, which is particularly valuable in clinical settings. However, in rare diseases, both the structure and parameters of the model are typically unknown, while individual-level data is scarce, noisy, heterogeneous, and subject to
privacy constraints. In such settings, population-level summary statistics provide a practical
privacy-preserving data representation, while capturing heterogeneity further requires modeling parameters as distributions rather than fixed values. Yet no existing method jointly discovers ODE structure and refines parameter distributions solely from summary statistics. We present AgentODE, an end-to-end framework that addresses this gap. An LLM proposes candidate ODE structures, while a tool-augmented inference
agent iteratively refines parameter distributions through a diagnosis–update loop, operating on population-level summary statistics alone. We evaluate AgentODE on three
benchmark problems across different fields and two clinical
datasets, including the rare disease recessive dystrophic epidermolysis bullosa (RDEB), with only 231 observations across 46 patients. AgentODE recovers functionally consistent ODE structures across all settings, and experiments on RDEB demonstrates that in sparse and noisy data settings
reasoning from summary statistics promotes mechanistically principled structure discovery, whereas baselines with individual-level data access recover implausible structures despite better predictive performance. AgentODE opens new possibilities for mechanistic modeling of rare diseases directly from population-level summary statistics, where data scarcity and
privacy constraints have traditionally limited such analyses.
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All authors Hanning Yang, Meropi Karakioulaki, Lennart Purucker, Tim Litwin, Cristina Has, Moritz Hess
Artificial Intelligence · Cross-listed from cs.LG
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
Seung Hun Han, Hyeongwon Kang, Jinwoo Park et al. (+1 more)
Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge. In large-scale industrial applications, labeling time series data is often prohibitively expensive and time-consuming, making unsupervised learning a practical and widely adopted approach. However, existing unsupervised methods frequently struggle to distinguish near-normal anomalies from normal patterns and are vulnerable to noise contamination within normal samples. To address these limitations, we propose a novel framework that leverages active learning to iteratively enhance the performance of unsupervised models. Our framework's core contributions are (1) a masked time-series reconstruction feedback strategy that forces the model to learn robust temporal dependencies, and (2) a minimax learning strategy that promotes robustness by differentially treating normal and abnormal samples. This process encourages the model to better capture the dynamics of subtle and noisy patterns. The proposed framework is evaluated across 28 test cases involving four multivariate time-series
datasets and seven unsupervised backbone models. Experimental results demonstrate a 12.39% improvement in AUC compared to the original models, confirming that our method can be readily integrated into existing unsupervised reconstruction-based anomaly detection systems to significantly enhance their performance.
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All authors Seung Hun Han, Hyeongwon Kang, Jinwoo Park, Pilsung Kang
Artificial Intelligence · Cross-listed from cs.CV
Partial Skeleton Visibility for Action Recognition: A Constrained Field-of-View Approach
Yingjie Dai, Tianyang Xu, Yanglin Deng et al. (+2 more)
Skeleton-based action recognition has achieved remarkable success by exploiting joint coordinates and their topological connections, yet prevailing methods overwhelmingly assume complete and clean skeleton inputs. In real-world deployments, such as
egocentric vision, crowded surveillance, wearable devices, or edge robotics, limited field-of-view (FoV) frequently causes substantial joint visibility dropout, leading to severe performance degradation that existing models are largely unprepared to handle. To bridge this critical yet underexplored gap, we introduce PartialVisGraph, a novel hypergraph framework tailored for robust skeleton action recognition under constrained FoV. We first construct highly expressive hypergraphs by introducing learnable virtual hyperedges that form a soft incidence
matrix, capturing flexible high-order dependencies beyond conventional pairwise graphs. We then propose the Single-Head Sample-Adaptive
Transformer, which adaptively aggregates joint features onto hyperedges while explicitly incorporating a visibility prior. This prior selectively gates information flow, preventing occluded or out-of-view joints from corrupting reliable feature propagation. We further establish rigorous evaluation protocols with realistic FoV simulation
benchmarks on NTU RGB+D 60 and 120. Extensive experiments demonstrate that PartialVisGraph consistently achieves state-of-the-art accuracy under partial visibility, with gains of up to 68.8% on subsets with severe FoV restrictions compared to recent strong baselines, while remaining superior on full-visibility settings. Our approach offers a principled and practical pathway toward deployable skeleton-based action understanding in unconstrained environments.
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All authors Yingjie Dai, Tianyang Xu, Yanglin Deng, Xiao-Jun Wu, Josef Kittler
Artificial Intelligence · Cross-listed from cs.CL
Self-conditioned Flow Map Language Models via Fixed-point Flows
Jaehoon Yoo, Wonjung Kim, Floor Eijkelboom et al. (+4 more)
Self-conditioning is a core technique that enhances continuous flow-based
language models, where the model learns to denoise generated text by conditioning on its own denoising estimate. While empirically successful, its performance improvements are poorly understood. Moreover, there is growing interest in the use of few-step generators based on flow maps, for which how to leverage self-conditioning is unclear. Here, we show that flow
language models with self-conditioning perform a fixed-point iteration that improves generation through iterative refinement. We use this viewpoint to formulate fixed-point flows, a two-dimensional class of self-conditioned flows, where the first dimension represents the flow process and the second represents the fixed-point iteration. We show that fixed-point flows define valid flow maps, and show that they can be distilled from self-conditioned flow models by compressing both fixed-point iterations and the flow process, the former with fixed-point
distillation and the latter with flow map
distillation. Our resulting flow map
language model, FMLM$^\star$, outperforms state-of-the-art self-conditioned models and few-step models in one- and few-step generation on OpenWebText. Code is available at https://github.com/Ugness/self-conditioned-fmlm.
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All authors Jaehoon Yoo, Wonjung Kim, Floor Eijkelboom, Chanhyuk Lee, Nicholas M. Boffi, Seunghoon Hong, Jinwoo Kim
Artificial Intelligence · Cross-listed from cs.SE
ClarifyCodeBench: Evaluating LLMs on Clarifying Ambiguous Requirements for Code Generation
Zheng Fang, Dongming Jin, Yihong dong et al. (+4 more)
Large
Language Models have emerged as programming assistants. However, the efficacy of code generation is constrained by the quality of input requirements, which are frequently ambiguous, incomplete, or underspecified. While LLMs excel at one-shot code synthesis, their ability to proactively clarify intent remains underexplored, as a critical trait for robust software engineering. Existing
benchmarks largely overlook this interactive bottleneck, assuming perfectly specified
prompts that do not reflect the iterative nature of requirement elicitation. To bridge this gap, we introduce ClarifyCodeBench, a novel interactive
benchmark for evaluating LLMs' capability in resolving requirement ambiguity. Constructed from real-world programming tasks, ClarifyCodeBench features high-quality manual annotations, including N unique ambiguity types, associated clarification questions, and corresponding ground-truth answers. Furthermore, we formalize two rigorous metrics to assess the interaction quality: Turn-discounted Key Question Rate, which penalizes inefficient questioning, and Optimal Round Adherence, which measures the precision of the elicitation process. We conduct a systematic evaluation of six state-of-the-art LLMs using ClarifyCodeBench. Our empirical results yield three critical insights: 1) Capability Decoupling: Strong code generation performance does not inherently translate to effective requirement clarification; 2) The
Reasoning Paradox: While increased computational thinking enhances code correctness, it yields marginal gains in identifying ambiguities; 3) The Multi-ambiguity Ceiling: LLMs' clarification performance degrades sharply as the density of ambiguities increases, revealing a significant bottleneck in handling complex, real-world specifications. Our work underscores the necessity for future AI4SE research to transition from static synthesis to interactive elicitation.
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All authors Zheng Fang, Dongming Jin, Yihong dong, Yongmin Li, Kechi Zhang, Zhi Jin, Ge Li
Artificial Intelligence · Cross-listed from cs.CV
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder et al. (+22 more)
Well-designed autonomous driving
datasets have fundamentally shaped research progress, yet existing literature primarily describes what
datasets contain rather than how to strategically design impactful ones. This is especially limiting for small and medium-sized labs and startups that cannot afford to misallocate scarce resources. We argue that impactful
dataset creation begins with a diagnosis: whether a research question is blocked by a data problem or an evaluation problem, and proceeds by selecting the minimal data operator(s) that closes the resulting gap, recording new data only when no cheaper operator(s) suffices. We analyze the
evolution of major autonomous driving (AD)
datasets through this lens and distill a strategic framework spanning gap identification, operator choice, sensor suite design, and annotation strategy. We ground the framework in a running case study of our KITScenes
dataset family. The
datasets are available at: https://kitscenes.com/
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All authors Richard Schwarzkopf, Jonas Merkert, Frank Bieder, Annika Bätz, Alexander Blumberg, Carlos Fernandez, Felix Hauser, Fabian Immel, Christian Kinzig, Hendrik Königshof, Fabian Konstantinidis, Martin Lauer, Willi Poh, Nils Rack, Kevin Rösch, Yinzhe Shen, Marlon Steiner, Gleb Stepanov, Dominik Strutz, Ömer Şahin Taş, Julian Truetsch, Kaiwen Wang, Royden Wagner, Jan-Hendrik Pauls, Christoph Stiller
Artificial Intelligence · Cross-listed from cs.SE
LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
Zhao Tian, Yingquan Zhao, Chenyao Suo et al. (+2 more)
LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large
language models (LLMs) have recently achieved remarkable success in issue resolution, their effectiveness on complex system-level LLVM compiler remains largely unexplored. To address this gap, we introduce LLVM-Bench, the first large-scale
benchmark for LLVM issue resolution, containing 423 real-world, validated tasks collected from the LLVM project. We further develop LLVM-Gym, a scalable evaluation platform that automates issue reproduction, patch application, compiler building, and test execution. Using LLVM-Bench and LLVM-Gym, we conduct a comprehensive study of four representative LLMs, six
retrieval configurations, and three
agents. Our results show that current LLM-based issue resolution techniques remain limited on LLVM-Bench, with patch invalidity and build failures as the dominant failure modes. We further reveal a strong complementarity among different LLMs and
agents, motivating LLVM-Ens, a lightweight ensemble approach that expands the patch space through integrating the patches generated by diverse techniques, filters incorrect and redundant candidates, and identifies the most promising solution. Our results show that LLVM-Ens achieves a resolution rate of up to 21.99%, further improving LLVM issue resolution.
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All authors Zhao Tian, Yingquan Zhao, Chenyao Suo, Meng Wang, Junjie Chen
Artificial Intelligence
Self-GC: Self-Governing Context for Long-Horizon LLM Agents
Xubin Hao, Hongjin Meng, Xin Yin et al. (+2 more)
Long-horizon LLM
agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix. Current systems mostly rely on in-run heuristics such as chronological
pruning and tool-output masking, or on final self-summary near a context limit. Heuristics are cheap but blind to future dependencies; summaries preserve narrative state but often hide exact evidence, locators, and editable artifacts. We present Self-GC, where GC denotes self-governing context while deliberately echoing garbage collection: the system does not merely reclaim unused
tokens, but governs the lifecycle of
agent context objects. Self-GC turns user turns, tool spans, and skill state into indexed objects; asks a side-channel planner to propose fold, mask, and prune actions; and lets the harness enforce recoverable sidecars, safe commit boundaries, and cache-aware commit. On a 33-session Hard Set, Self-GC prunes 43.95% of prefix
tokens while leaving 84.85% of future continuations unaffected, compared with no-impact rates of 54.55% to 69.70% for heuristic baselines. On a 332-session production-derived suite, three planner backbones reach no-impact rates of 91.27% to 94.58%, while baselines remain at 77.71% to 87.46%. In production, an online account-level split reduces daytime average input
tokens by 10% to 15%, with peak reductions near 20%. These results point to context management as runtime lifecycle control over indexed, recoverable objects rather than post hoc text cleanup.
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All authors Xubin Hao, Hongjin Meng, Xin Yin, Jiawei Zhu, Chenpeng Cao
Artificial Intelligence · Cross-listed from cs.CV
LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
Tobias Christian Nauen, Anosh Billimoria, Federico Raue et al. (+3 more)
Comparing
transformer backbones for image
segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself. We introduce the Lightweight Universal Mask Adapter (LUMA), a lightweight, backbone-agnostic mask-
transformer head that treats any backbone as a black-box feature extractor, letting a set of queries read from its features through cheap cross-
attention. LUMA matches the accuracy of EoMT, the state-of-the-art efficient ViT-segmenter, at cost parity, while attaching unchanged to hierarchical, convolutional, and mixture-of-experts backbones alike. Holding this head fixed, we
benchmark 20 backbones, 11 pretraining schemes and a range of resolutions on ADE20K, COCO, and Cityscapes under one modern recipe. We find that “efficient”
token mixers fail to deliver efficiency even at the high resolutions that motivate them, with plain ViT holding the throughput Pareto front at every resolution. Additionally, the pretraining objective, not the architecture, the lever the field has tuned hardest, governs
segmentation quality. Code: https://github.com/tobna/LUMA
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All authors Tobias Christian Nauen, Anosh Billimoria, Federico Raue, Stanislav Frolov, Brian B. Moser, Andreas Dengel