Robotics
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
Yuyao Jiang, Haichao Liu, Jiarui Zheng et al. (+3 more)
Tactile sensing is particularly valuable for contact-rich robotic manipulation. Recent work has made substantial progress in tactile representation learning for robotic manipulation. However, similar tactile observations can arise from interaction conditions with very different task-risk implications, such as sensor noise, task-necessary variations, and emerging undesirable contact. Without context-grounded risk information, these cases can be ambiguous to downstream policies, leading to unnecessary corrections to benign variations or delayed responses to genuinely risky contact. To address this limitation, we propose Risk-Aware Tactile Encoding (RATE), which learns tactile representations that encode task-conditioned interaction risk. Specifically, history-conditioned prediction captures interaction context, while alert supervision associates this context with task-conditioned risk. The learned risk-aware representation complements conventional tactile features through a lightweight residual adapter. Experiments in both simulation and the real world demonstrate substantial improvements in task success, with controlled ablations confirming the complementary benefits of alert-guided learning and predictive temporal modeling.
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All authors Yuyao Jiang, Haichao Liu, Jiarui Zheng, Zihan Ding, Weihao Yuan, Ziwei Wang
Robotics
Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access
Harry Robertshaw, Weijie Qi, Nikola Fischer et al. (+3 more)
Endovascular
brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This work presents the first demonstration of in vitro autonomous robotic
navigation for endovascular BCI access in the cerebral venous system. Soft Actor-Critic controllers were trained in silico for two sequential tasks spanning the right internal jugular vein to the superior sagittal sinus, using
geometric augmentation of one training anatomy.
Navigation was evaluated in a training anatomy and an anatomically unseen hold-out model over 250 in silico episodes and five fluoroscopy-guided in vitro robotic runs per task-anatomy condition, comprising 1,000 simulated episodes and 20 physical runs overall. Task recurrent predictors were also evaluated for online identification of impending
navigation failure. In silico success rates for Tasks A and B were 85.6% and 98.4% in the training anatomy and 42.0% and 91.6% in the hold-out anatomy, respectively. Fourteen of 20 physical runs were successful (70% overall), including 80% success for Task B in the hold-out phantom. In silico the predictors detected 99.3-100.0% of failures with false-alarm rates of 0.8-6.7%. During in vitro evaluation, predicted risk increased before failed episodes, but elevated probabilities during some successful runs showed reduced calibration after transfer. These results demonstrate the feasibility of autonomous cerebral venous access and show how online failure prediction could support human oversight, while also identifying anatomical generalization and sim-to-real calibration as priorities before preclinical translation.
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All authors Harry Robertshaw, Weijie Qi, Nikola Fischer, Alejandro Granados, Thomas C. Booth, Sam E. John
Numerical Analysis
Learning the Willmore flow with a lightweight neural operator
Elie Bretin, Roland Denis, Tokuhiro Eto et al. (+1 more)
We extend to the Willmore flow the phase-field neural operator approach proposed by Bretin, Denis, Masnou, and Terii (2022) for mean
curvature motion in two and three dimensions. The resulting lightweight, single-time-step architecture is motivated by a Bence-Merriman-Osher-type expansion of the Allen-Cahn
semigroup, and is trained using trajectories generated by a hybrid minimizing movement scheme. In our experiments with non-oriented interfaces, unconstrained convolution kernels produce anisotropic dynamics, whereas radial kernels enhance the
evolution process. We also construct three-dimensional kernels from trained two-dimensional kernels via their radial
Fourier profiles, which provides an effective initialization for the non-oriented 3D model. Finally, we show applications to curve and surface reconstruction from unoriented point clouds, including reconstruction using the transferred model without the need for additional 3D training.
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All authors Elie Bretin, Roland Denis, Tokuhiro Eto, Simon Masnou
Computation and Language
Author Representation Strategies for Zero-Shot Authorship Attribution: A Comparative Study of LLM-Based and Embedding-Based Approaches
Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi et al. (+2 more)
Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings where no task-specific supervision is available. In this work, we investigate the effect of author representations on ZS AA by evaluating a label-only prompting baseline together with three author representation strategies: representative writing samples, LLM-generated descriptions, and style
embeddings (LISA). The first three approaches perform attribution using LLM prompting, while the
embedding-based approach uses style
embeddings with cosine similarity. We investigate the influence of
prompt design and propose a two-stage
embedding-based attribution framework that combines candidate space reduction with
embedding-dimension selection. The results show that label-only ZS AA is ineffective, while incorporating author-specific representations consistently improves attribution performance. Among the evaluated approaches, the proposed two-stage LISA framework achieves the strongest overall performance, whereas LLM-generated style descriptions provide a substantially more compact representation of author style at the cost of some attribution performance. These findings demonstrate the importance of author representation in ZS AA, while indicating that current open-source LLMs remain insufficient for robust attribution without more effective representation learning.
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All authors Nudrat Habib, Tosin Adewumi, Sana Sabah Al-Azzawi, Marcus Liwicki, Elisa Barney
Robotics
DR-IPC: Disturbance-Resilient Integrated Planning and Control for LiDAR-Based Quadrotor Navigation
Peng Liu, Jingyan Wang, Qipeng Ye et al. (+5 more)
LiDAR-based quadrotor
navigation in cluttered environments remains challenging under external disturbances, particularly when obstacle-aware motion generation and disturbance-rejection control are handled in separate layers. This article presents disturbance-resilient integrated planning and control (DR-IPC), which combines lightweight path guidance with nonlinear model predictive control (NMPC) to directly generate angular velocity and thrust. An interconnected extended Kalman filter and nonlinear disturbance observer jointly provide filtered state estimates and reconstructed disturbances for NMPC prediction. The resulting formulation unifies nonlinear quadrotor dynamics, actuator constraints, local motion generation, and penalised safe-flight-corridor residuals without requiring a separate trajectory-optimization stage. Gazebo and MARSIM simulations, together with indoor and outdoor experiments, validate DR-IPC under wind, suspended payloads, narrow passages, ball impacts and reactive avoidance of a dynamic obstacle. In multi-goal
navigation with disturbances, DR-IPC increases the number of completed missions from 1/10 to 9/10 in Gazebo and reduces the altitude RMSE from 0.34 to 0.01 m in experiments. The complete system operates onboard at 100 Hz. Supplementary
videos are available on the project page https://drpp316.github.io/DR-IPC-Page/, and the source code will be released.
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All authors Peng Liu, Jingyan Wang, Qipeng Ye, Wen Li, Jinya Su, Zuo Wang, Shihua Li, Yunda Yan
Machine Learning
Nicolas Zucchet and Scott W. Linderman
Distillation has become a core primitive of large
language model training, but its properties are not yet well understood. We take an entropic perspective, studying how the
entropy of the student depends on the data and the divergence that define the
distillation objective. We prove that forward KL inflates the
entropy of the student above that of the teacher. Since cross-
entropy training is a special case, this yields an identity that we verify quantitatively in pretraining and supervised finetuning. Other divergences come with no such guarantee: reverse KL deflates
entropy until the gap between student and teacher gets too large, and interpolating between the two changes
entropy smoothly early in training but abruptly at convergence. The lower
entropy of on-policy
distillation comes from
token-level reverse KL, not from on-policy sampling. The divergence therefore acts as an implicit
entropy regularizer, whose role is clearest in self-
distillation: as conditioning on privileged information deflates
entropy, the divergence hyperparameters that work best are those that compensate for it.
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Machine Learning
Beyond Trained Models: Compiling GNNs for a Sound Explainer Benchmark
Steve Azzolin, Francesco Paolo Nerini, Stefano Teso et al. (+4 more)
Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined ground truth, such as a motif planted in the data. This protocol implicitly assumes that a GNN trained on such data relies on the intended motif. Although prior work has questioned this assumption, plausibility remains widespread. First, we show that the assumption is violated on several widely used
benchmarks, where, e.g., degree statistics alone suffice to solve the task. Then, we remove this confounder by replacing training with compilation. We achieve this by introducing $\mathsf{Gracr}$, the first compiler translating graded modal logic formulas into GNN weights, yielding models that replicate the behaviour of the corresponding formulas. Since the behaviour of the model is now known by construction, we can define its ground truth explanation formally and compute it exactly. Building on this, we introduce $\mathsf{Gracr}\mathsf{Bench}$, a
benchmark of compiled GNNs for the evaluation of explainers against this exact ground truth. Experiments on eleven explainers across six tasks show its effectiveness for fine-grained diagnostic evaluation: notably, we discover that most explainers are not robust to indirect influences or alternative implementations of the same formula. These results position $\mathsf{Gracr}\mathsf{Bench}$ as a novel, rigorous evaluation setting for graph post-hoc explainability.
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All authors Steve Azzolin, Francesco Paolo Nerini, Stefano Teso, Francesco Bonchi, Bruno Lepri, André Panisson, Andrea Passerini
Computation and Language
Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation
Teng Lin, Xinyu Liu and Nan Tang
Table-to-report generation refers to the task of automatically generating article-level analyt- ical reports from relational tables and is an essential capability for automated data science and decision support. Its central challenge lies in systematically discovering verifiable com- posite insights across tables, attributes, and analytical perspectives, and organizing them into coherent, complete, and traceable evidence chains. Existing methods primarily rely on sequential, reactive data
agents or direct Large
Language Model(LLM) generation. They suffer from exploration bias: early local observations constrain subsequent actions, causing models to focus prematurely on local analyzes and miss cross-table or cross-dimensional evidence. We propose ComInsight, which reformulates insight discovery as the composition of atomic evidences. We first define an atomic insight as the smallest executable analytical unit conforming to a predefined analysis pattern and enumerate all valid atomic insights from database schema and content. These atoms are then organized into a multi-relational insight graph, where nodes represent verified data facts and edges encode logical, temporal, or hierarchical relations. Finally, a set of composition operators systematically fuses atomic nodes into higher-order composite conclusions. Every composite output is accompanied by executable SQL and fine-grained provenance, ensuring full verifiability. Across three
benchmarks InsightBench, DDR-Bench, and T2R-Bench, ComInsight consistently outperforms strong baselines in factual correctness, novelty, and structural completeness. We believe ComInsight offers a reliable, efficient, and explainable path toward table-to-report generation.
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Artificial Intelligence
From Benchmarks to Production: A Text-to-SQL System for Complex Financial Data
Arijit Sehanobish, Bruno Gomes Coelho, Guillaume Michel et al. (+3 more)
General-purpose Text-to-SQL systems achieve strong performance on academic
benchmarks like Spider and BIRD, where schemas are relatively shallow and column values are often human readable. In production financial databases, where concepts are stored as opaque integer keys rather than human-readable strings, these methods fall below 50%, as even simple queries require multiple joins and filter predicates reference opaque IDs. We present Financial LINking Text-to-SQL (FLINT), a domain-specialized Text-to-SQL system that closes this gap through three key components: (1) a lookup
agent that dynamically resolves natural-
language concepts to question-specific reference table constraints, (2)
embedding-based
retrieval of structurally similar query templates from a compact, expert-authored bank, and (3) schema linking that prunes a large table schema to the relevant subset by traversing foreign-key chains, rather than relying on name similarity alone. We evaluate on two
datasets totaling 359 questions over production financial schemas. FLINT outperforms various state-of-the-art baselines using the same LLM. The system is deployed in production as part of a financial data
retrieval service.
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All authors Arijit Sehanobish, Bruno Gomes Coelho, Guillaume Michel, Sophia Zhi, Valerie Faucon-Morin, Kristen Howell
Computer Vision
Feedforward Novel View Synthesis for Heterogeneous Cameras
Meng Wei, Cheng Zhang, Boying Li et al. (+4 more)
Feed-forward novel view synthesis has recently shown promising results from sparse posed images, but most existing methods assume that context and target views share a fixed camera family. This homogeneous-camera assumption breaks in practical multi-sensor systems, where perspective, fisheye, and panoramic cameras may coexist and where the target projection may be unseen during training. We study feed-forward NVS across heterogeneous central cameras and identify a key ambiguity introduced by tokenization: a visual
token aggregates a projection-dependent bundle of pixel rays, while existing camera encodings mainly expose absolute rays or
token-center relations. To address this, we combine
token-center relative Camera Positional Encodings and proposed local raymaps, a
token-level representation that explicitly describes the intra-patch ray distribution summarized by each
token. We further propose projection-aware 2D RoPE, which replaces raw image-grid coordinates with ray-induced angular coordinates so that relative positional
reasoning is aligned across camera projections. Together, these components treat diverse cameras as calibrated samplings of a shared ray space rather than separate visual domains. On ScanNet++ with heterogeneous-camera system, our method improves over camera-conditioned baselines under mixed-camera evaluation and demonstrates zero-shot generalization to panoramic views.
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All authors Meng Wei, Cheng Zhang, Boying Li, Yihang Chen, Jianmin Zheng, Hamid Rezatofighi, Jianfei Cai
Artificial Intelligence
Reasoning Models Are Accurate but Unsound on Identification
Arman Behnam and Binghui Wang
A
reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers a nonidentifiable one. The latter is more consequential, as no observational data can validate the claimed formula. Measuring this failure requires queries that are provably non-identifiable, which prior evaluations lack, and grading that accepts correct formulas in any equivalent form, which string matching cannot provide. We build CERTID, a formal identification pipeline that addresses both limitations. CERTID uses the sound and complete causal identification
algorithm ID to certify whether an effect is identifiable from a given graph and query, and verifies returned formulas against structural causal models whose interventional distributions are known exactly. CERTID further develops theoretical results to mitigate structural leakage, repair non-identifiable queries, and establish grading guarantees. We evaluate three frontier
reasoning models (Gemini Flash, Gemini Pro, and GPT5.5) on 1,200 certified instances spanning 4 to 50 vertices. Accuracy proves a poor proxy for soundness: on identical instances, the false-claim rate on non-identifiable queries varies by seventeen-fold across models. We also find that models decide identifiability with 97-100% accuracy on graphs generated after the strongest model's training snapshot. Instances, the certification procedure, the verifier, and per-instance records are available at https://anonymous.4open.science/r/certid-D718.
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Cryptography and Security
PrivDev: Mapping Static-Analysis Data Types to DPV
Simon Bernbeck, Ricardo Ramalho, Matheus Amendoeira et al. (+1 more)
Static-analysis scanners can identify personal-data types in source code, but they lack mechanisms to connect these findings to standardized
privacy vocabularies. PrivDev maps 122 Bearer CLI data types to Data
Privacy Vocabulary Personal Data (DPV-PD) categories and links them to potentially relevant GDPR provisions. Our approach combines deterministic mapping for 43 exact-label matches with a
retrieval-grounded Large
Language Model (LLM) to resolve the remaining 79 non-trivial mappings. The resulting RDF knowledge graph contains 118 ODRL policy resources that were structurally validated using SHACL. The artifact passed five complementary validation gates that cover structural correctness, query consistency,
retrieval quality, LLM-based assessment, and human evaluation. In human evaluation, nine annotators produced 711 judgments, yielding a raw agreement of 0.72, Gwet's AC1 of 0.68, and Gwet's AC2 of 0.88. Our results indicate that the proposed mappings are plausible and reproducible, while also revealing ambiguities in scanner-defined data-type labels and coverage gaps in DPV-PD.
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All authors Simon Bernbeck, Ricardo Ramalho, Matheus Amendoeira, Juliana Alves Pereira
Robotics
XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation
Tingting Du, Ziyao Wang, Guoheng Sun et al. (+1 more)
World action models (WAMs) have advanced
robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and action based future prediction does not explicitly address the spatial understanding needed for
robot manipulation. Existing efforts often add a limited set of spatial prediction tasks through specialized heads or branches, leaving both the range of spatial supervision and the model architecture fragmented. We introduce XGenAct, a world action model that represents RGB observations,
robot actions, metric depth, surface normals, and functional role
segmentation as RGB
videos through deterministic codecs. By sampling perception and action tasks during training, XGenAct uses one
video diffusion transformer and one objective to learn temporal prediction across these spaces without modality specific learned heads. On held out RLBench tasks, structured perception training improves average closed loop success over RGB only training, and XGenAct achieves 52% success in the five task external comparison, versus 26% for the strongest evaluated baselines. It also predicts future depth and
segmentation more accurately than the evaluated pipelines that generate RGB first and then apply a frozen perception expert.
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All authors Tingting Du, Ziyao Wang, Guoheng Sun, Ang Li
Computation and Language
Learning from Repaired Reasoning: Root-Cause-Guided On-Policy Distillation
Chenglei Shen, Haoyang Yao, Weijie Yu et al. (+3 more)
On-policy self-
distillation (OPSD) uses reference solutions as privileged hindsight to supervise student-generated
reasoning trajectories. However, reference-based guidance may explain a correct solution without addressing why the student's own
reasoning fails. This
reasoning mismatch between the guidance provided and the correction needed can encourage the student to borrow correct conclusions while leaving its
reasoning errors unresolved. Moreover, applying the same hindsight throughout the trajectory risks a
distillation trap, where unnecessary constraints on valid
reasoning compete with correction of substantive errors. To address these issues, we propose Root-Cause-Guided On-Policy
Distillation (RC-OPD), which uses repairs of the student's own
reasoning to provide guidance that addresses its specific errors while building on valid progress. For each failed attempt, RC-OPD locates the earliest substantive error, develops a local correction, and uses the corrected intermediate result as an anchor for the valid prefix. An iterative diagnosis–repair–continuation process tests the repairs through student continuation, identifying further errors within a fixed repair budget. For repair chains that reach a correct answer, root–cause–guided
distillation uses failure diagnoses and corrective goals to supervise the erroneous segments, while anchor-guided
distillation supports the corresponding valid prefixes with
reasoning chains leading to the repaired intermediate results. We evaluate RC-OPD across multiple
datasets and model scales. Extensive experiments and analyses show that it mitigates
reasoning mismatch and the
distillation trap, yielding substantial performance gains.
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All authors Chenglei Shen, Haoyang Yao, Weijie Yu, Song Jin, Xiao Zhang, Jun Xu
Computer Vision
ProgressNet: Sketching and Prompting with a Frozen Text-to-Image Model
Arkaprabha Basu, Chaitat Utintu and Yi-Zhe Song
Humans draw progressively: a few strokes, a look at the result, a stroke erased, a
prompt revised. Image generators do not work this way. They typically take a finished sketch and produce the image in a single pass, so every edit starts the picture again, and the models that do keep state across turns are driven by text, cannot take a stroke, and are too slow to draw with. We present ProgressNet, a training-free framework that lets a frozen text-to-image model follow a drawing session as it unfolds: strokes are added and erased, the
prompt is revised, and the image keeps up at about a second per turn. It needs no new parameters because the frozen model already has what a progressive generator needs, a pathway through which the previous turn can be remembered, layers that can carry appearance forward without freezing structure, and an internal signal of how far to trust an unfinished sketch; three inference-time mechanisms (Previous-Concept
Memory, Layer-Selective K/V Injection and Banded Adaptive Control) use each in turn. As a sketch fills in, every existing method degrades, the FID of the FLUX+ControlNet baseline doubling between 10% and 100% completion on FS-COCO, while ProgressNet's barely moves; it maintains strong fidelity and progressive coherence across three sketch domains and is preferred by users over five competitors, most widely on erasure.
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Human-Computer Interaction
Interactive Machine Learning Interfaces for Disease Risk Prediction: Effects on Risk Perception and Behaviour
Tiffany Ngai, Max Homm, Matthew Bradbury et al. (+1 more)
Machine learning risk models are increasingly being used in patient-facing health tools, but it remains unclear how well users understand the information these systems present. In this work, we study how people interpret an interactive Type 2 Diabetes (T2D) risk interface and whether interacting with it influences their attitudes toward behavioural change. Through an exploratory mixed-methods study with 15 participants, we compare participants' perceived understanding with their actual understanding and identify key themes from qualitative interviews. We find that participants often understood the interface better than they initially believed, but still faced important barriers related to unclear terminology, ambiguous risk framing, and limited explanations of model inputs. Finally, we propose relevant design guidelines and discuss broader issues surrounding trust and
fairness. Our findings highlight the importance of intuitive visual design, familiar presentation, and clear explanations in patient-facing ML interfaces.
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All authors Tiffany Ngai, Max Homm, Matthew Bradbury, Anamaria Crisan
Computer Vision
Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation
Ziyi Wang, Junchi Yao, Heqian Qiu et al. (+5 more)
Recent advances in autoregressive
video generation have improved temporal consistency over extended durations, yet interactive storytelling requires more than continuous scene extension: a new shot may combine characters and backgrounds from different historical shots. Whole
prompt retrieval can overlook the distinct reference needs of individual components, while directly combining all historical
memories may introduce unrelated visual content. To address these problems, we present Weave Forcing, a training-free framework for compositional
memory reuse in interactive long
video generation. First, we use an LLM for semantic slot routing to decompose user
prompts into character and background descriptions and explicitly select suitable historical references for each component. To isolate the required content, masked
memory weaving uses contrasting
attention maps conditioned on semantic slots to construct refined semantic masks, selectively exposing relevant
tokens from compressed historical KV
memories to guide the generation of the current shot. We further introduce coverage adaptive RoPE to adjust temporal offsets and
memory retention according to no, partial, or full reference coverage, addressing visual artifacts observed when incomplete historical references are positioned close to the current generation. Extensive experiments demonstrate that Weave Forcing improves cross-shot subject and background consistency while maintaining competitive visual quality and text
alignment.
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All authors Ziyi Wang, Junchi Yao, Heqian Qiu, Wenbo Shi, Chengjiu Wang, Jinyang He, Binkai Hong, Hongliang Li
Artificial Intelligence
Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability
Samuel Lewis-Lim, Xingwei Tan, Mario Sanger et al. (+2 more)
Chain-of-thought (CoT)
reasoning allows humans to inspect how large
language models reach their answers, and oversee model behaviour. This
reasoning comes at an increased inference cost, motivating efficient methods that train models to solve tasks using fewer
tokens. However, a common concern is that such training may cause models to skip important
reasoning steps, so the CoT no longer faithfully reflects the model's decision. It is unclear whether or when this occurs in practice, since different efficiency methods apply length pressure to models' CoT in distinct ways, and faithfully explaining a model's decision takes more
tokens on some tasks than others. To understand these dynamics, we fine-tune a variety of models with three methods that apply length pressure differently, namely a fixed generation budget, a per-example length target, and a group-relative length reward. We evaluate how efficient
reasoning affects CoT faithfulness (i.e., how well the CoT reflects model decisions on related inputs) and monitorability (i.e., whether the CoT reveals when input interventions alter the output). We find that it affects faithfulness and monitorability differently. Faithfulness falls in most settings, primarily because the trained models are less consistent. Monitorability is more robust, as models keep acknowledging the influence on their answer even when the CoT is much shorter.
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All authors Samuel Lewis-Lim, Xingwei Tan, Mario Sanger, Zhixue Zhao, Nikolaos Aletras
Systems and Control
Hierarchical Control via MPC-RL for Multi-Timescale Battery Systems
Rasa Pourjam, Ehecatl Antonio del Río Chanona and Paulina Quintanilla
Multi-timescale systems present a fundamental challenge, where fast operational decisions must coexist with long-horizon sustainability targets. In this work, we propose a new hierarchical control framework via Model Predictive Control (MPC) and Reinforcement Learning (RL) to separate decision-making on two distinct timescales. The high-level MPC optimizes long-horizon setpoints at the slow dynamic and on a fast timescale, a low-level pretrained RL
agent tracks these setpoints in real time to maximize short-term objectives. RL is introduced to learn nonlinear control policies, without relying on model linearizations or requiring the heavy online computation from solving repeated optimal control problems. The framework is applied to a Battery Energy Storage System (BESS) operating in frequency regulation
markets to balance fast profit opportunities (seconds) and slow battery degradation (weeks to months). The design employs a degradation-aware RL
agent trained offline to generate safe long-horizon setpoints, and a degradation-unaware
agent fine-tuned from it for fast runtime setpoint tracking. Compared to MPC baselines, the proposed approach successfully extends battery lifetime by 84% and increases operational profit by 34%.
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Machine Learning
An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials
Rinkle Juneja, Viktor Reshniak, Richard K. Archibald et al. (+4 more)
Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and multiresolution character of modern fusion-materials campaigns. To address this challenge, we present a reproducible workflow, implemented in the
Galaxy scientific workflow environment, for automated crack identification and quantitative damage assessment from scanning electron microscopy images. The workflow processes SEM images and experimental metadata to identify cracks, quantify damage, and retain the intermediate products and processing history needed for reproducibility. Outputs include crack masks, skeletonized crack networks, quality-control visualizations, and scalar damage descriptors. The method is designed to operate without image-specific parameter tuning across tungsten grades, microstructures, magnifications, and damage states. We demonstrate the workflow on a sparse electron-beam thermal-shock
dataset containing 418 images from 114 experiments spanning five tungsten grades and three microstructural states. We define a crack-density descriptor, which provides standardized inputs for downstream machine-learning prediction and physics-based crack simulation. These predictive components are exposed in the same
Galaxy environment and are intentionally treated here as extensible workflow modules. The principal contribution is therefore an end-to-end, shareable, and computationally portable workflow that links experimental characterization, automated image analysis, preliminary damage prediction, and simulation-guided data acquisition for fusion-materials research.
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All authors Rinkle Juneja, Viktor Reshniak, Richard K. Archibald, John W. Duggan, Gregory R. Watson, Cory D. Hauck, Gary M. Staebler
Systems and Control
MagLearn 2: High-Fidelity, Saturation-Aware, and History-Efficient Sequence-to-sequence Modeling of Transient B-H Behaviour Under PWM Excitation
Jingrong Yang, Binyu Cui, Yuming Huo et al. (+4 more)
Accurate transient B-H modeling under pulsewidth-modulated (PWM) excitation is challenging since the
magnetic-field response depends on both the instantaneous flux density and the preceding magnetisation trajectory. Although sequence-to-sequence models enable waveform prediction, existing studies have not jointly addressed how much
magnetic history is required, how saturation region should be handled, and how trained models can be adapted to unseen materials. To address these gaps, this paper presents MagLearn 2, a condition-aware, short-window LSTM-based sequence-to-sequence framework for reconstructing the future H(t) response from the historical B(t) and H(t) trajectories. A saturation-aware auxiliary module selectively replaces saturation-dominated predictions within the complete waveform forecast. In a representative saturated 3C90 case, the auxiliary module reduces the sequence-level relative RMSE from 67.84% to 9.19%. To understand how much the history samples contributes to the predictions, an occlusion-based sensitivity analysis is conducted to quantifies the contribution of
magnetic history and reveals that that the information most relevant to prediction is concentrated near the forecast boundary. In the independent evaluation for MagNet Challenge II, the proposed framework's best-performing model achieved the lowest reported average RMSE to date on the sequence-prediction task at 2.2%.
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All authors Jingrong Yang, Binyu Cui, Yuming Huo, Lizhong Zhang, Alfonso Martinez, Song Liu, Jun Wang
Machine Learning
Getting Your Guidance Weights Right in diffusion and flow-matching posterior sampling
Liam Moroy, Jean-François Giovannelli, Yoann Altmann et al. (+3 more)
Training-free posterior sampling methods, also known as Plug-and-Play methods, leverage pretrained unconditional
diffusion or flow-matching models to solve inverse problems. Most existing approaches rely on guidance weights to balance, at each time step, prior information from the unconditional score or velocity network with measurement consistency, yet the tuning of these weights is often not discussed and is largely left to heuristics. We introduce a simple and principled offline strategy for automatically tuning these guidance weights. Our key observation is that, at each time step, the conditional denoising score-matching objective for
diffusion models, or the conditional flow-matching objective for flow-matching models, is a least-squares objective. Therefore, when the conditional prediction is expressed as a weighted sum of the unconditional network output and a measurement-guidance term, optimizing over these weights reduces to a two-dimensional linear least-squares problem. The resulting time-dependent guidance weights can be optimized offline for a given measurement operator, noise level and sampler at the cost of a single minibatch of sampling trajectories, without retraining or fine-tuning the pretrained generative model. Instantiated with the standard Tweedie-based measurement-consistency term, our approach improves posterior sampling and achieves state-of-the-art reconstruction performance across
diffusion- and flow-matching-based methods. Moreover, the optimized guidance weights enable
diffusion samplers to reduce the number of sampling steps from 1000 to 50 with no significant degradation in reconstruction quality. Code will be made available.
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All authors Liam Moroy, Jean-François Giovannelli, Yoann Altmann, Steve McLaughlin, Frédéric Champagnat, Guillaume Bourmaud
Machine Learning
Certified Mechanistic Edits: Behavioral Guarantees for Skill Removal and Preservation
Md Sazid Uddin, Md. Khairul Alam Mazumder and M. F. Mridha
Mechanistic edits (ablations, weight edits, activation steering) are the standard tools for unlearning a harmful capability from a neural network while preserving useful ones. Current approaches validate their effects only by testing, which can never cover an entire continuous region of inputs. Prior work at the interpretability-verification boundary certifies descriptions of a model: what a
circuit computes, or whether it faithfully explains the whole. We instead certify the behavioral effect of an edit: that disabling a
circuit removes one skill and provably preserves another, for every input in a region; a feature non-interference guarantee in the information-flow-security sense. We demonstrate such certified edits from toy ReLU networks up to a standard softmax + LayerNorm
transformer, proving removal and preservation over continuous
embedding-space regions and reaching roughly 9x the input-perturbation dimension an exact solver can handle by switching to sound bound propagation. Furthermore, we prove that no finite deterministic black-box test can certify removal, exhibiting an edit that passes exhaustive testing yet provably fails on a survivor pocket that can be made arbitrarily small. Guarantees hold on small, standard-architecture networks and, like any removal claim, presuppose that the target skill admits a decidable specification, a property which real-world harms may not have.
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Machine Learning
Below what training size do deep tabular generators stop beating trivial baselines? A preregistered benchmark on a size ladder of clinical and standard datasets
Shivam Shrivastava
Deep tabular generative models are benchmarked on
datasets with tens of thousands of rows; clinical
datasets have hundreds. We preregistered and ran a size-ladder
benchmark to find where the two regimes diverge: 8 public
datasets subsampled from 200 to 20,000 training rows, seven generators (independent marginals, Gaussian copula, SMOTE, unconditional SMOTE, CTGAN, TVAE, TabDDPM) with a fixed 20-trial tuning budget and 5 evaluation seeds, plus 4 natively small clinical
datasets at true size, for 2,220 committed runs in total. The primary metric is the AUROC of fixed classifiers trained on synthetic and tested on real data. In 23 of 24 (
dataset, deep model) pairs no deep model ever beats the best trivial baseline by more than seed noise, at any training size we measured. The best baseline wins 40 of 49 (
dataset, size)
cells. Our preregistered prediction that the deep models' ranking would be unstable at small sizes is falsified: mean Kendall tau between adjacent rungs below 5,000 rows is 0.806, above our 0.8 threshold, and stability is highest at the smallest sizes rather than lowest. One caveat bounds all of this: in 81% of
cells the gap between the top two methods is smaller than the variation between seeds. Finally, method rankings on natively small clinical
datasets agree only moderately with rankings on subsampled large ones (mean tau 0.57 to 0.64), which questions whether a subsampled large
dataset can stand in for a small one. All 2,220 result files, the preregistration and its hash, and the code that regenerates every figure and number from those files are public.
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Robotics
Detect and Suppress: A Mechanistic Defense against Adversarial Patches in VLA Models
Yukiya Horiba, Koshiro Aoki, Shunsuke Yasuki et al. (+2 more)
Adversarial patches can disrupt
Vision-
Language-Action (VLA) models by manipulating visual observations, leading to failures in
robot control. However, it remains poorly understood which internal mechanisms underlie these failures and how targeted interventions can mitigate them. In this work, we mechanistically analyze VLA representations using a sparse autoencoder (SAE) and identify a feature whose activation strongly correlates with the presence of an
adversarial patch. Based on this analysis, we suppress the identified feature at inference time only when a linear probe detects an attack. This intervention improves robustness without the cost of fine-tuning the VLA. We evaluate our method against VLA
adversarial patch attacks on LIBERO-10. Conditional intervention improves success rate under intermittent attacks, whereas continuously applying the same intervention substantially degrades policy performance. These results show that attack-related internal representations can provide useful targets for VLA
adversarial defense and that controlling when to intervene is important for limiting disruption to nominal policy behavior.
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All authors Yukiya Horiba, Koshiro Aoki, Shunsuke Yasuki, Bum Jun Kim, Taiki Miyanishi
Machine Learning
Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting
Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak et al. (+2 more)
Long-term
forecasting models commonly process all patches in a look-back window using the same fixed stack. Older contextual patches and recent evidence therefore receive the same computational depth. Yet the information closest to the forecast and the more distant context do not contribute equally. Uniform processing leaves this distinction unexpressed in the architecture. We propose MARO, a Most-Recent Anchoring with Recurrent Ordering model that processes the look-back window from the most recent patch to the oldest. The most recent patch serves as the anchor. It initializes the latent state and conditions each subsequent step, so older patches are folded into a representation that remains centered on recent evidence. A single shared module is reused at every step, so extending the scan further into the past introduces no additional parameters. Intermediate states retained during the scan allow the forecast head to weigh short and long portions of the history separately. This expresses recency through the order of recurrent refinement. Extensive experiments across multiple real-world time series
datasets show that MARO achieves state-of-the-art performance on both long-term and short-term
forecasting tasks.Ablation studies examine the contribution of the main architectural components.
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All authors Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Machine Learning
Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak et al. (+2 more)
Most long-term time-series
forecasting models map the look-back window directly to the full horizon in a single pass. While efficient, this design does not explicitly identify which historical states are most relevant to different future segments or exploit what followed those states. Analog
forecasting addresses this by retrieving past states similar to the present and using their observed continuations, but single nearest matches can be unreliable and overlapping patches may produce redundant candidates. We propose DuoTS, a Dual-Context Time Series
forecasting model that uses retrieved evidence without relying on it exclusively. DuoTS first produces a base forecast with a parallel patch encoder and linear prediction head, then progressively refines it one future patch at a time. Each refinement combines two views: a current context that attends to recent
tokens and captures the latest dynamics, and a detail context that provides distinct retrieved analogs together with their subsequent trajectories. Patch-wise refinement allows the model to balance these views across the forecast horizon and associate each future segment with evidence appropriate to its temporal distance from the present. Experiments on multiple real-world
datasets show that DuoTS achieves state-of-the-art performance, while ablations confirm the contribution of each context. The refinement mechanism is also model-agnostic, requiring only an encoded look-back window and the future-patch position, and can therefore be integrated into existing
forecasting models.
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All authors Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Artificial Intelligence · Cross-listed from stat.ML
AREX: Affine-Residual Exponential Integrator for Few-Step Sampling in Flow Matching
Shizheng Lin, Soon Hoe Lim and N. Benjamin Erichson
We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tractable part of the sampling dynamics. We show that the velocity field of the moment-matched Gaussian target is the $L^2$-optimal affine approximation to the marginal velocity field. This motivates decomposition of the learned dynamics into an affine component over the whole sampling path, determined by the first two target moments, and a neural residual term. AREX keeps the affine component and integrates it using an explicit
matrix-valued propagator. In turn, we only require to integrate over the residual term. This differs from scalar exponential integrators, which analytically handle only isotropic linear dynamics. Across image and text-to-image generation tasks, AREX consistently improves sample fidelity in the few-step sampling regime without retraining the underlying model.
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Computation and Language
Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models
Mehrdad Ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani et al. (+2 more)
Hallucination detection is particularly important for medical
language models, but repeated-sampling approaches are computationally expensive. A faster alternative is a single-pass uncertainty head that predicts
hallucination risk from a frozen generator's internal signals. Existing uncertainty heads consume backbone-specific features and tokenization, motivating adaptation when the backbone or
language changes. We study two Persian medical models, Gaokerena-V and Gaokerena-R, on a 168-question Iranian medical entrance examination. Across five generations per question, Gaokerena-V produces the same option on only 14 questions and Gaokerena-R on 37, compared with 168 for Med-Gemma, indicating substantial response variability in the Gaokerena models. We therefore adapt the LLM Uncertainty Head (LUH) framework to these models and construct two paired claim-level
hallucination datasets directly in Persian, with 1,600 responses per backbone. Lightweight heads are trained on frozen-backbone
attention maps and
token probabilities. On held-out test splits, the heads achieve PR-AUCs of 0.4820 and 0.4652, corresponding to 2.30 and 2.66 times their respective random baselines, and ROC-AUCs of 0.7852 and 0.7810. The resulting detectors require neither
retrieval nor repeated sampling at inference.
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All authors Mehrdad Ghassabi, Pedram Rostami, Hamidreza Baradaran Kashani, Sadra Hakim, Audrina Ebrahimi
Systems and Control
Learning in Inverse Games: Tractable Training with Probabilistic Guarantees
Arghya Mallick, Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani et al. (+1 more)
Inverse game theory seeks to learn
agents' unknown objectives from observed
equilibrium behavior. Existing residual-based approaches can lead to non-convex problems and need not ensure strong monotonicity of the learned game, limiting reliable
equilibrium prediction. We develop a tractable convex framework for learning static and dynamic non-cooperative games using first-order and Nikaido-Isoda (NI) loss. Specialized operator parameterizations, including a Helmholtz–Hodge decomposition in a reproducing kernel
Hilbert space (RKHS), enforce strong monotonicity for quadratic, nonparametric, non-quadratic, and linear-quadratic dynamic games. We establish finite-sample out-of-sample prediction guarantees, using Rademacher complexity to match existing bounds for the non-quadratic setting. For noisy sequential data, we develop a robust receding-horizon learning scheme whose adaptive regularization admits a maximum a posteriori (MAP) interpretation and is computed using randomized trace estimation. Numerical experiments, including a stylized autonomous-vehicle collision-avoidance application, demonstrate predictive accuracy and robustness to measurement noise.
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All authors Arghya Mallick, Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani, Sergio Grammatico