Machine Learning
Uncertainty-Normalized Margins for Direct Preference Optimization
Sadegh Khorasani, Petrus Mikkola and Matthias Grossglauser
Direct preference optimization (DPO) models binary preferences through a Bradley-Terry model with a common noise scale, without explicitly accounting for preference strength or
prompt-dependent uncertainty from human feedback. We introduce uncertainty-normalized margin DPO (UNM-DPO), which combines strength-dependent margins with a learned
prompt scale. Motivated by a heteroskedastic Bradley-Terry model, we develop two training objectives. Both compare the implicit rewards of preferred and rejected responses, derived from response log-probability ratios to a reference policy. Advantage-only (AO) divides this reward difference by the
prompt scale before subtracting the margin; whole-residual (WR) subtracts the margin before dividing by the scale. For the WR comparison model, we establish a necessary and sufficient condition under which known margins make the
prompt scale identifiable. We introduce a practical procedure for learning the scale. Building on WR, we introduce ULNM-DPO-WR, which normalizes each response's implicit reward by its length. We evaluate our methods against DPO and related baselines on HelpSteer2 and HelpSteer3, using the Skywork reward model as a judge. With Llama-3.1-8B-Instruct, ULNM-DPO-WR achieves tie-adjusted win rates against matched DPO of 68.00% and 65.31% on evaluation panels, with higher mean rewards and shorter responses on average. On AlpacaEval with a GPT-4.1 judge and GPT-4-Turbo reference answers, the same 8B policy achieves a length-controlled win rate of 21.62%, compared with 16.39% for DPO and 15.30% for SimPO. These results demonstrate the potential of combining preference-strength margins, learned
prompt scales, and length normalization for policy optimization.
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Information Retrieval
Exploring Forum Post Retrieval with Generative Modeling
Yang Li, Yaguang Liu, Heng Liu et al. (+14 more)
Generative recommendation (GR) has emerged as an alternative to
embedding-based
retrieval, building on the success of generative models in
language and
vision. We are exploring GR on Facebook Forum, a standalone application for medium-to-heavy users of Facebook Groups. Because Forum is a new surface, its own interaction data are too sparse to train a GR model from scratch. We address this with transfer along two axes: we train on a broader corpus of Facebook Groups engagements rather than Forum sessions alone, and we reuse hierarchical, prefix-based semantic IDs (SIDs) learned from cross-platform Facebook Feed data instead of fitting a Forum-specific tokenizer. A 3B-parameter instruction-tuned
language model is then supervised-fine-tuned to generate SIDs directly from user context. We systematically ablate the design choices that matter most in practice, including SID construction, the composition and length of user history, and the inclusion of user-profile features. Our results show that cross-platform SIDs transfer to a new recommendation surface, and offer practical guidance for teams deploying GR on real-world social platforms.
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All authors Yang Li, Yaguang Liu, Heng Liu, Samson Komo, Jane Kou, Yulian Zhou, Gang Yang, Shubhojeet Sarkar, Gaurav Chakravorty, Yujie Liu, Haipeng Chen, Yonghuan Yang, Deepti Chheda, Yamin Wang, Mike Plumpe, Rish Tandon, Shengbo Guo
Machine Learning
Alignment via Training Against Probes Without Losing Monitorability
Lena Libon, Alexander Panfilov, Ben Rank et al. (+3 more)
Models are usually aligned based on their observed outputs, using demonstrations, preference data, or reward signals. These objectives reward responses that look aligned. More capable models may learn to satisfy them without internalizing the intended behavior, for example by faking compliance during training. Such superficial compliance could be harder when the objective is defined on model internals rather than outputs. Therefore, we study probe-guided fine-tuning, using probes that detect undesired properties in model activations as a direct training signal. We evaluate linear and non-linear probes with different numbers of probes per layer across two
alignment objectives: harmlessness and honesty. We find that training against probes that do not update during training is an easily exploitable objective, while continuously updated probes substantially reduce harmfulness and improve honesty while preserving utility. Probe-guided fine-tuning achieves better safety-utility trade-offs than DPO and inference-time steering, while being substantially more robust against jailbreak and abliteration attacks. Moreover, the concepts stay linearly encoded after fine-tuning, meaning oversight is not lost by our method. Training against probes thus offers a way to shape what models represent rather than only what they output, which may become increasingly important as models get better at making their outputs look aligned.
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All authors Lena Libon, Alexander Panfilov, Ben Rank, Xin Chen, Jonas Geiping, Maksym Andriushchenko
Computer Vision · Cross-listed from eess.IV
Joint Supervised and Self-Supervised Training with Acquisition-Robust Techniques for Accelerated 4D Flow MRI Reconstruction
Mengyuan Xue and Bochun Mei
4D flow MRI measures time-resolved, three-directional blood velocity but requires long acquisition times, and its diagnostic signal is carried by the phase difference between velocity encodings, not by image magnitude. Recent work has developed a per-encoding variational network to address image reconstruction in this field. In this work, we incorporate a joint supervised and self-supervised training regime and utilize both magnitude and velocity data during supervision. At the same time, we add multiple acquisition-robust and conditioning strategies based on the acceleration factors. On the CMRx4DFlow~2026 aortic
dataset (1.5 and 3T), our model lowers RelErr by $38$–$50\%$ and AngErr by $7.0$–$8.7^\circ$ against a training-matched baseline across $R{=}10$–$50$, improving on every held-out subject at every acceleration. Our model also shows strong generalization ability to transfer on out-of-distribution data by employing the joint training scheme, with an increase of $7.2\%$ in SSIM and decrease of $38\%$ and $31\%$ in AngErr and RelErr respectively.
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Multiagent Systems
Recursive Organization Improvement: A Modeling Specification for Human–Agent Organizations
Zilong Wang
Stronger AI
agents do not automatically produce better organizations: teams must also learn which work arrangements to retain and when to reconsider them, while those arrangements determine what evidence the team can learn from. We propose a modeling specification for recursive organization improvement and evaluate it through an executable checker, a public-record mapping, and two simulation studies. The specification connects actor-visible histories, organizational
memory, decision rights, and the evidence used to retain changes. Study 1 crosses six decision rules, three
memory conditions, and three task environments under fixed resource ceilings. In a stationary environment, cumulative evidence raises balanced evaluation's net value per task from 0.452 to 0.480, while selection regret falls from 0.029 to 0.002. After a workflow-ranking reversal, retained evidence delays adaptation. In post hoc acquisition-matched controls, the remaining contribution of program replacement depended on the
memory rule: small and uncertain under an eight-round window, positive under cumulative
memory after a reversal, and negative under reset evidence. Study 2 models human-
agent review with shared errors, copying, and decision rights. It identifies a two-actor case in which public records conceal whether agreement comes from shared errors or copying, and shows how dependence changes the preferred placement of human judgment under a fixed budget. Paid observation can lower decision error while costing more than the deployment gain. Assessments of organizational improvement should track the evidence acquired and retained, using acquisition-matched comparisons to isolate the contribution of evaluator revision.
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Artificial Intelligence
ChartRevise: A Dataset and Evaluation Protocol for Exact Chart Editing via Code
Jiaxiang Tang, Yi Zhou, Chad DeLuca et al. (+5 more)
Chart editing requires cross-modal edit grounding, realizing a requested visual change in the code that draws it, with necessary related updates and without altering unrelated content. Existing
benchmarks emphasize either code executability or chart quality, but their metrics do not clearly distinguish request completion from missed coupled updates and gratuitous changes. We introduce ChartRevise, a structured
dataset and evaluation protocol for exact program-grounded chart editing. For
dataset construction, we build on the grammar of graphics to systematically cover chart-editing operations, using source-program checks to verify their applicability across chart types and libraries. To improve edit exactness, our pipeline checks individual requirements and guides repair or exclusion when they are unmet. The resulting
dataset contains 92,438 records covering 344 edit types across 20 chart types and three plotting libraries. For evaluation, our reference-free protocol separately measures atomic requirement completion, identifies gratuitous changes, and detects missed coupled updates. These checks are combined with successful execution and rendering to determine exact-edit success. Across five models and four external
benchmarks, fine-tuning yields relative gains of 16% in mean requirement recall and 22% in mean exact-edit rate.
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All authors Jiaxiang Tang, Yi Zhou, Chad DeLuca, Rogerio Feris, Ahmed Khalil Omran, Zhi-Li Zhang, Pengyuan Li, Ali Anwar
Computer Vision
Kai Yan, Xiangyu Chen, Yulong Cao et al. (+9 more)
Vision-
Language-Action (VLA) foundation models have recently emerged as one of the prevailing solutions for autonomous driving, as they can utilize knowledge acquired during
vision-
language pretraining for accurate and interpretable driving. However, VLAs can take only a limited number of frames as visual input due to the high
token cost of an image, which is problematic for
memory-dependent tasks such as determining the arrival order at all-way stops and long-horizon driving scene understanding. Existing solutions use latent vector
memories accessed through cross-
attention, which are neither interpretable nor portable. In this paper, we propose AD-Memo, a general-purpose VLA driving
agent with
language-based
memory. The
agent outputs
memory as an extension of its Chain-of-Thought (CoT) to record surrounding objects critical to driving; this
memory becomes part of the
agent's future input. We curate
memory-based
datasets and train VLAs with a two-stage recipe: Supervised Fine-Tuning (SFT) and Da Capo, a novel semi-closed-loop Reinforcement Learning (RL)
algorithm which uses trajectory-level advantage for
memory and step-level advantage for driving, leading to better credit assignment. Across scenarios such as all-way stops and general driving, AD-Memo improves driving quality, enables better question answering on driving scenes, and produces plug-and-play
memory for other models.
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All authors Kai Yan, Xiangyu Chen, Yulong Cao, Alex Naumann, Peter Karkus, Yan Wang, Jef Packer, Alex Schwing, Yuxiong Wang, Boris Ivanovic, Wenjie Luo, Marco Pavone
Robotics
Yggdrasil: a Layer-First 3D Scene Graph for Real-Time Querying
Arshia Akhavan, Ermanno Bartoli, Afnan Algharbi et al. (+3 more)
Robotic
agents use 3D scene graphs (3DSG) to perform tasks ranging from scene understanding to scene interaction. Although an extensive body of work addresses scene graph generation, little
attention has been paid to optimizing the graph for consumption, which leaves state-of-the-art perception pipelines to work around their own scene graph and to pay a latency cost that does not fit the real-time budget a perception loop runs on. We present Yggdrasil, the first 3D scene graph designed to be efficient for both generation and consumption: a layer-first hierarchical graph built from generic nodes, edges, and layers, which expresses the representations existing pipelines already produce, indoor or outdoor, flat or hierarchical, while natively answering the positional and semantic queries downstream tasks issue. Against a published DSG baseline, Yggdrasil answers queries up to $121\times$ faster, and every query we measure falls between 2 and 127 microseconds, three to five orders of magnitude inside the 200 microsecond keyframe budget a 3DSG consumer lives in, on both a workstation and embedded class device. We integrate Yggdrasil into three published pipelines spanning human trajectory prediction, object-goal
navigation, and human-aware motion planning, where it removes up to 99% of the time each spends on its scene graph. The implementation,
benchmark harness, and all three integrations are available online.
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All authors Arshia Akhavan, Ermanno Bartoli, Afnan Algharbi, Alireza Hoseinpur, Iolanda Leite, Bryan Donyanavard
Artificial Intelligence
Component-Aware Feedback for Self-Evolving Programs
Ethan Lin, Jinming Nian and Yi Fang
LLM-guided evolutionary search can discover complex programs, but existing methods mostly only save candidate programs and fitness scores while discarding which component edits produced which fitness metric changes. Existing methods force the mutator LLM to infer the effect of prior edits from cluttered histories, making program search slow and unstable. This is especially true for locally servable LLMs to evolve multi-component systems. We introduce component-aware feedback, which compares each evaluated program with its parent, identifies the components that changed, and logs them with the associated metric differences into an attribution
memory that later mutations read. The
memory keeps each change in two reference frames, local against the parent it came from and global against the seed program, which shows both the immediate effect of a change and the cumulative progress made since the seed. We study this on LLM reranking, a multi-objective optimization problem where a multi-stage pipeline must balance quality against serving cost. Across twelve Bright
datasets, our method reaches the strongest baseline's final quality after a median of one third of the search budget and ends 7.2% higher in held-out nDCG@10, and under a cost-aware objective it finds pipelines that are on average more accurate while using 11% fewer
tokens per query, showing component-aware feedback to be a promising direction for more efficient self-evolving systems.
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Computational Engineering
SHIFT-Truck: A High-Fidelity Aerodynamics Dataset and Benchmark for Pickup Trucks
Riddhiman Raut, Yin Yu, Aashwin Anand Mishra et al. (+4 more)
Pickup trucks account for 14% of new light-duty vehicles produced in the United States, yet are among the least aerodynamic. Their open cargo bed adds a flow absent from existing automotive aerodynamics
datasets such as DrivAerML and SHIFT-SUV: the shear layer leaving the cab roof passes over a recirculating bed flow before separating again at the tailgate. The resulting drag lowers fuel efficiency, raises emissions and limits the range of electric trucks. Scale-resolved Computational Fluid Dynamics (CFD) is too costly for broad design exploration; neural surrogates can predict flow features at a fraction of that cost, provided they are trained on large-scale, high-fidelity, domain-specific data. We introduce SHIFT-Truck, the first such
dataset for pickup trucks. It comprises 1,000 Spalart-Allmaras delayed detached-eddy simulations (SA-DDES) of a reference pickup geometry morphed across 17 shape parameters. Each case is run on a mesh of about 100 million
cells at a Reynolds number of $1.4 \times 10^7$ and released with time-averaged surface pressure, wall shear stress, volumetric pressure and velocity. The setup is verified by grid refinement and repeated runs, and checked against wind-tunnel measurements. We define geometry-grouped splits and
benchmark four neural surrogates, DoMINO, GeoTransolver, AB-UPT and SMART, on surface and volume tracks. SHIFT-Truck also introduces controlled distribution shifts in the operating point, the input surface discretization and the vehicle archetype. Models with strong in-distribution performance can degrade substantially under these shifts: operating-condition changes expose failures to infer speed dependence, while tessellation and cross-vehicle shifts reveal markedly different robustness across architectures. SHIFT-Truck is thus a
benchmark not only for surrogate accuracy but also for generalization across physical and numerical distributions.
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All authors Riddhiman Raut, Yin Yu, Aashwin Anand Mishra, Michael Emory, Thomas Economon, Peter Lyu, Juan J. Alonso
Computer Vision
Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani et al. (+1 more)
Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor availability or operation may change over time, creating a substantial representation gap between template and search frames. Unlike conventional multi-modal tracking where paired modalities are simultaneously available, cross-modal tracking requires localization when template and search frames originate from different active modalities. Accordingly, we introduce TSDA-Track, a Template-Search Domain Adaptation framework to reduce modality discrepancy during training. We investigate two feature
alignment strategies. Pre-AFA TSDA-Track applies
adversarial alignment before
transformer's template-search interaction to suppress modality-specific bias. Enc-CFA TSDA-Track applies contrastive
alignment to encoder representations after interaction to strengthen target-level cross-modal correspondence. Both variants retain a shared inference pipeline without modality-specific branches. Experiments on LasHeR, and zero-shot evaluations on RGBT234 and GTOT under multiple cross-modal protocols demonstrate improvements over representative state-of-the-art trackers. For instance, under the modality-switch protocol on RGBT234, Pre-AFA TSDA-Track achieves an SR/PR of 43.2/56.0, compared with 36.8/50.0 for ToMP-101 baseline. In addition, a study on Anti-
UAV-024 further verifies the applicability of TSDA-Track to aerial tracking. Our study highlights the effectiveness of feature
alignment domain adaptation for cross-modal tracking.
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All authors Fereshteh Aghaee Meibodi, Amir Mehdi Soufi Enayati, Shadi Alijani, Homayoun Najjaran
Computer Vision
STEPS: Scene Text Editing with Preserved Style Using Diffusion and Contrastive Style Encoding
Nicolas Thiebaut, Nameer Hirschkind, Xiao Yu et al. (+1 more)
We introduce Scene Text Editing with Preserved Style (STEPS), a novel
diffusion model architecture for quality text replacement in images. Scene Text Editing (STE), also known as Visual Text Editing, consists of changing the textual content in an image while conserving the original style, e.g. font, colors, orientation, background, etc. STEPS advances the state of the art in STE through directed focus on improved style preservation. We introduce a style encoder for visual text that captures style independently of textual content, and a model architecture that combines the style encoder with multiple semantic conditions (target text characters encoding and rendered glyphs). STEPS achieves superior results to previous STE methods in style preservation, output readability, and subjective quality.
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All authors Nicolas Thiebaut, Nameer Hirschkind, Xiao Yu, Kyle Spence
Computer Vision · Cross-listed from eess.IV
TSGL: Teacher-Student Graph Learning for 3DGS Compression
Matin Bani Saedi, Matthew Kyan and Gene Cheung
3D Gaussian Splatting (3DGS) is a popular representation for novel view synthesis. However, 3DGS contains millions of Gaussian primitives, each with rich attributes, resulting in large file sizes. We propose a novel 3DGS compression method based on Teacher-Student Graph Learning (TSGL) that operates directly on a trained model, without 3DGS retraining or access to training images. Specifically, for each block of Gaussian primitives, using decoded positions and DC spherical harmonic (SH) coefficients as predictors, we learn a signal-dependent geometry graph G encoding the pairwise similarities between neighbouring Gaussians via a teacher-student model. Given G, we perform Graph
Fourier Transform (GFT) on the remaining attributes, so that signal energies are predominantly projected into the low-frequency coefficients for compact representation. On three standard
benchmarks, the method reaches 27x to 33x compression with less than 0.6 dB of PSNR loss, improving on recent post-training compression methods in both size and rendering quality.
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Machine Learning
Proper Scoring Rule-based Diffusion for Probabilistic Weather Forecasting
Joonhyeong Park, Giung Nam, Hyungi Lee et al. (+3 more)
Recent probabilistic weather forecasters train
stochastic predictors with the continuous ranked probability score (CRPS) to generate each ensemble member in a single forward pass. These models learn the predictive distribution from the forecast context alone, which becomes difficult at longer forecast horizons where uncertainty is high. To learn the predictive distribution more effectively, we introduce auxiliary conditional denoising tasks that predict the same future state from the context and its corrupted version, which provides partial future information that can reduce prediction ambiguity. Building on distributional
diffusion models, we learn the conditional distributions of these tasks with a single
stochastic predictor by minimizing a proper scoring rule across noise levels. At inference, the predictor can still generate each ensemble member in a single forward pass at the fully corrupted endpoint. Standard CRPS training is recovered as the endpoint-only special case of our formulation, so our framework extends existing CRPS-based forecasters with only additional conditioning inputs. Controlled experiments show that the auxiliary tasks improve one-step
forecasting across architectures, with larger gains at longer forecast horizons. The gains extend to high-dimensional global weather
forecasting under both training from scratch and fine-tuning, along with improved calibration and potential benefits for generalization under distribution shift.
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All authors Joonhyeong Park, Giung Nam, Hyungi Lee, Kyunghyun Cho, Byoungwoo Park, Juho Lee
Computation and Language
Jonathan Graehl
Privacy redaction must reliably remove personal information while preserving the relationships expressed in the text. We develop a general-purpose
multilingual named-entity tagger with distinctions fine-grained enough to support a broad range of redaction policies, and methods for cheaply learning additional distinctions. We fine-tune the
multilingual encoder XLM-R-large on text in 35
languages annotated by prompted frontier
language models, plus existing human-annotated corpora with labels mapped onto our entity types. We evaluate on human-annotated test sets in seven
languages and on LLM-annotated text in 35
languages. On the human-annotated sets, at operating points fixed on development data, our tagger reaches 88.2 redaction F1, against 69.1 for published GLiNER2 and 61.4 for Microsoft Presidio, and its architecture runs 4.9 times faster than GLiNER2's on CPU. Compared with frontier-model annotations alone, adding the mapped human-annotated corpora to training raised redaction F1 on those sets by ten points, with essentially no change on the LLM-annotated text. Open-weight LLMs fitting on one 96-GB GPU were less accurate than our tagger, whether prompted to tag text or used as encoders beneath a trained tagging layer, and roughly 30 to 1,100 times slower. We release code,
prompts and training recipes, with data-acquisition scripts and source links.
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Discrete Mathematics · Cross-listed from math.CO
A rainbow partition theorem for trees and connected maximin share allocations of chores
Marcin Anholcer, Maciej Bartkowiak, Bartłomiej Bosek et al. (+3 more)
Xiao, Qiu, and Huang (AAMAS 2023) and independently Lonc (personal communication) asked whether indivisible chores located at the vertices of a tree can always be allocated to $n$
agents in connected bundles so that the cost of every
agent is at most its connected maximin share; for goods, this is a
theorem of Bouveret, Cechlárová, Elkind, Igarashi, and Peters. We answer the question affirmatively, even for monotone costs. The answer follows from a combinatorial
theorem: if $\mathcal P_1,\ldots,\mathcal P_k$ are partitions of the vertex set of a finite tree, each into at most $k$ connected parts, then the vertex set can be split into disjoint sets $B_1,\ldots,B_k$, some possibly empty, such that each nonempty $B_i$ is connected and contained in a part of $\mathcal P_i$. Equivalently, if each of $k$ colours occurs on at most $k-1$ edges of a tree, then the vertices can be partitioned into connected sets labelled by distinct colours, none containing an edge of its own colour; in particular, one can choose for every colour a component of the forest obtained by deleting that colour so that the chosen components cover the tree. The bound is already best possible for paths, and for additive costs, the
theorem is equivalent to the fair-division statement. The proof reduces the problem to inward partitions of oriented trees, which we obtain from the colourful KKM
theorem on a simplex of edge weights, using a leaf-elimination labelling that remains compatible when weights vanish. We also give an
algorithm running in time $k^{O(k)}$ plus
polynomial time.
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All authors Marcin Anholcer, Maciej Bartkowiak, Bartłomiej Bosek, Jarosław Grytczuk, Zbigniew Lonc, Paweł Rzążewski
Computation and Language
Marking Contour Tones in Yorùbá: A Typographic and Computational Proposal
Kólá Túbòsún
Yorùbá is a tonal
language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical items whose conventional spellings avoid vowel lengthening that would otherwise provide a host syllable for the second tone. A particular concern is a class of names in which the conventional spelling does not just omit tonal information but inverts the meaning of said name, sometimes asserting the opposite of what the name intends. This paper describes the problem, illustrates the inadequacy of current solutions, and proposes the adoption of the caron and circumflex marks. These are symbols with precedent in Yorùbá phonological scholarship since Olmsted (1951), used as orthographic conventions on single vowels to encode rising and falling contour tones, making them accessible for the first time through standard keyboard input and computational text processing. The proposal is supported by an implementation in the WriteYoruba keyboard and the TTSYoruba
speech synthesizer, whose architecture and listener evaluation are reported separately (Tubosun, Oluokun, et al. 2026).
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Networking
Enabling Efficient Client Selection in FL-as-a-Service for Multi-Application based Society 5.0
Prachi Nandi, Sonakshi Satpathy, Timam Ghosh et al. (+1 more)
The rapid development of the Internet of Things (IoT) has led to the generation of vast amounts of data from sensors, prompting the need for advanced learning models to analyze this data for personalized services.
Federated learning (FL) emerges as a solution, offering decentralized learning that preserves user
privacy by building models on local devices and sharing only aggregated insights. This paper explores FL-as-a-service (FLaaS) in IoT, highlighting its potential for collaborative learning across applications while addressing challenges like security,
privacy, and optimizing hierarchical architectures for efficient model convergence and accuracy. To enhance the effectiveness of FL in IoT environments, this study focuses on selecting optimal nodes for model processing through the evaluation of parameters such as delay, energy consumption, and link status. The proposed method aims to identify suitable client nodes for model training. Performance evaluation is conducted using a Human Activity Recognition
dataset under simulated IoT network conditions. The proposed approach is compared against randomized and Q-learning based client selection strategies. Experimental results shows improvements in delay and energy efficiency while maintaining communication performance. The findings highlight the potential of efficient client selection mechanisms for enhancing FLaaS in dynamic IoT ecosystems and supporting future intelligent services in Society 5.0.
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All authors Prachi Nandi, Sonakshi Satpathy, Timam Ghosh, Arijit Roy
Machine Learning
Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations
Hanwei Zhang, Tianma Hu, Gaojie Jin et al. (+2 more)
Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remains unclear, with existing studies reporting mixed and sometimes contradictory findings. We argue that these discrepancies arise from conflating different robustness notions and perturbation regimes, rather than from fundamental disagreements about CBMs themselves. To disentangle these factors, we introduce a generator-based evaluation framework that enables controlled comparisons between standard classifiers and CBMs under two distinct perturbation types: continuous
geometric perturbations in latent space and discrete semantic interventions in concept space. Within this framework, we evaluate robustness both empirically, via prediction and concept-level sensitivity metrics, and certifiably, using randomized smoothing in latent and concept spaces. Across experiments, we reconcile previously conflicting findings by clarifying when, and in what sense, concept bottlenecks do or do not improve robustness. By further analyzing robustness under varying task conditions, including class semantic similarity and concept vocabulary size, we show that interpretability does not inherently confer robustness. Instead, concept bottlenecks shift where and how sensitivity manifests, revealing a nuanced interpretability robustness trade off that depends critically on the perturbation regime and task structure. Together, our results show that interpretability and robustness are distinct objectives: interpretable intermediate representations do not uniformly improve robustness, but instead redistribute sensitivity across perturbation spaces and model families.
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All authors Hanwei Zhang, Tianma Hu, Gaojie Jin, Xu Cheng, Ronghui Mu
Machine Learning
Geometry-physics confounding impairs PDE learning across varying domains
Yinghao Cheng, Gengxiang Chen, Xu Liu et al. (+6 more)
Learning partial differential equation (PDE) dynamics across varying domains is central to predictive modelling and data-driven discovery of governing equations. However,
geometric variation alters both field representation and the governing differential operators, confounding
geometric effects with intrinsic physical properties in the observed dynamics. This work identifies geometry-physics confounding as a unified failure mechanism for PDE learning across varying domains. In forward operator learning, this confounding increases the burden of inferring geometry-dependent operator changes from finite data, reducing data efficiency and generalisation. In equation discovery, omitting geometry-induced operators misspecifies the candidate library, leading to biased parameters, missed governing terms and spurious terms. We propose a de-confounding framework that makes the known geometry-to-operator transformation explicit. Geometry-induced coefficient fields improve prediction and data efficiency across five operator-learning
benchmarks, while geometry-complete candidate libraries recover the generating equations and reduce held-out PDE residuals by more than two orders of magnitude in both evolving-domain systems. By separating known
geometric action from intrinsic physics, the proposed framework supports more reliable and data-efficient PDE learning across scientific and engineering problems with varying geometries.
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All authors Yinghao Cheng, Gengxiang Chen, Xu Liu, Qinglu Meng, Yixin Jing, Xiangguo Tang, Wenping Mou, Lihui Wang, Yingguang Li
Computer Vision
Eulerian Motion Reconstruction for Water Scenery
Chuhan Chen, Yen-Chi Cheng, Ayush Saraf et al. (+8 more)
Reconstructing and animating water scenery from nature produces compelling and immersive visual experiences. Previous work examined this task from the perspective of 2D
video textures, with the goal of creating a looping
video. In our work, we tackle the problem from a 3D perspective, creating a looping 4D dynamic reconstruction which can be interactively rendered from novel viewpoints from a single non-looping 2D source
video. We represent motion as a 3D static Eulerian motion field that advects canonical Gaussian splats that are cyclically reborn at fixed time periods, supervised using rendering losses. To model non-periodic and
stochastic dynamics present in real-world scenes, we add a non-periodic, time-varying residual term to capture deviations from the static Eulerian motion field. We show quantitatively and qualitatively that our framework enables photorealistic animation of water scenes better than prior art.
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All authors Chuhan Chen, Yen-Chi Cheng, Ayush Saraf, Rajvi Shah, Tuotuo Li, Johannes Kopf, Chen Gao, Hung-Yu Tseng, Deva Ramanan, Matthew O'Toole, Changil Kim
Artificial Intelligence
When Scientific Contradictions Are Lost in Translation
Tal Zeevi, Trey W. Jensen and Maxwell Strome
Two scientific findings can disagree without contradicting each other. Determining whether they conflict requires knowing whether they describe comparable measurements. We study how
language models behave at this decision point. In a controlled task, we generate an unsatisfiable XOR constraint system and translate its constraints into scientific reports from different laboratories. One assignment satisfies more constraints, while another satisfies fewer but better matches expected biology. This creates a simple dilemma: does the model choose the assignment that best fits the constraints, or the one that better matches biological expectations? When the constraints are stated directly, GPT-5.6 Sol and Claude Opus 5 recover the best-supported assignment in 90% and 96% of cases, respectively. In scientific prose, however, the models behave differently. Claude Opus 5 often prefers the biologically expected assignment. Removing that biological preference increases recovery of the better-supported assignment from 27% to 79% (p<.001); recovery reaches 92% when the same Biology-favored record is accompanied by a formalization request and an explicit paired-design cue (p<.001). GPT-5.6 Sol is less sensitive, with neither corresponding change reaching statistical significance. These results suggest that reliable scientific verification depends not only on formal
reasoning, but also on how models decide which findings should be compared and what relations they imply.
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Computer Vision
HIGS: Hierarchical Implicit Grids for Joint Geometric and Semantic Scene Understanding
Hanwen Cao, Wenqiang Wu, Kuang-Ting Tu et al. (+5 more)
Neural implicit representations have had a significant impact on scene reconstruction by enabling
robots to build continuous, differentiable, and high-fidelity 3D maps. Most existing works focus on
geometric reconstruction and lack semantic information for high-level spatial understanding and task planning. Also, as the scale and complexity of the environment increase, neural representations face the challenge of maintaining computational efficiency in back-end optimization. To resolve these two challenges, we introduce a hierarchical neural field that leverages multiresolution submaps to achieve an efficient and scalable implicit representation, and a unified query and decoding mechanism to support both
geometric and semantic features. More specifically, the learnable map features can be converted to the output with the query and decoding process for both training and inference. For large-scale representation, we decompose a scene into overlapping submaps and do hierarchical optimization within each local submap, thus enabling scalable computation. To further improve efficiency, we design feature encoders that predict initial hierarchical grid features to substantially reduce the time needed to optimize the submap features from scratch. To correct estimation drift among submaps, we align and fuse them entirely within the implicit feature space, leading to substantial acceleration by avoiding the need to decode the final output. Building upon this efficient hierarchical representation, we embed both
geometric features and
vision-
language latent features into the map, and demonstrate it on both Signed Distance Field (SDF) construction and open-vocabulary object grounding. Our approach significantly improves computation and
memory efficiency, maintains high estimation accuracy, and endows the
robot with spatial awareness on large-scale real-world
benchmarks.
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All authors Hanwen Cao, Wenqiang Wu, Kuang-Ting Tu, Mathias Otnes, Jeffrey Delmerico, Rui Wang, Yulun Tian, Nikolay Atanasov
Machine Learning
Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sadegh Khorasani, Ali Najar, Saber Salehkaleybar et al. (+1 more)
We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive the covariance of the observed variables and introduce marginal quasi-equivalence, which characterizes when different causal models share a full-dimensional subset of the observational distributions they can generate. We formulate structure learning as minimization of the Gaussian negative log-likelihood with a logarithmically scaled complexity penalty that counts directed edges and latent variables. For a fixed number of observed variables and a fixed upper bound on latent variables, we establish consistency of global score minimizers up to marginal quasi-equivalence under algebraic faithfulness, structural minimality, and model-overlap assumptions. We parameterize the inclusion of directed edges and candidate latent variables using Bernoulli gates, whose continuous probabilities are optimized jointly with the structural coefficients. Averaging the penalized negative log-likelihood over these gates yields an objective with a closed-form differentiable complexity penalty. We prove that this expected objective has the same global infimum as the corresponding discrete structure-learning objective. Experimental results show that our approach achieves lower recovery error than previous methods in several experimental settings.
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All authors Sadegh Khorasani, Ali Najar, Saber Salehkaleybar, Negar Kiyavash
Robotics
Dense Temporal Motion Retargeting for Legged Robots
Jaeryeong Kim, Taerim Yoon, Jin Cheng et al. (+2 more)
Legged
robots can learn expressive whole-body skills from the motions of humans and animals. Due to the morphology gap between the source and the
robot, however, the motion must be tailored to the dynamic properties of the
robot. In particular, dynamic motions such as a jump require careful adjustment, since their timing and control are interdependent. We propose dense temporal motion retargeting (DTMR), which jointly optimizes timing and control within a single optimization, where dense means that the timing is adjusted for every control step. This dense formulation enables DTMR to deform only the parts of the motion that need a change in timing. The problem is solved with sampling-based model predictive control (MPC) in parallel on a GPU. We evaluate DTMR against baselines on two hours of human motion with four
humanoid robots, where the results show that DTMR outperforms baseline methods, particularly on dynamic motions. We also show that allowing more temporal deformation yields more precise retargeting. We further compare DTMR with a baseline that optimizes the temporal dimension, where the result shows that DTMR retargets more precisely under the same deformation budget while being ~19x faster. Lastly, policies trained on our references transfer to a real
humanoid robot.
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All authors Jaeryeong Kim, Taerim Yoon, Jin Cheng, Sungjoon Choi, Stelian Coros
Robotics
Correcting WHERE, Preserving HOW: Compositional Generalization for Vision-Language-Action Models via Referential Guidance
Yanyan Zhang, Disheng Liu, Xinpeng Li et al. (+8 more)
While
Vision-
Language-Action (VLA) models enable flexible action generation, their generalization across diverse environmental elements, including manipulated objects, destinations, and backgrounds, is limited by the lack of diversity in robotic training data. Trained end-to-end on such data, VLAs tend to exploit visual shortcuts, associating actions with task-irrelevant visual features rather than the intended task semantics. These shortcuts block recomposition of elements already seen by the policy, that is, compositional generalization. Existing approaches mitigate such
entanglement through task-relevant perception or targeted data diversification, but offer no explicit mechanism for unseen recomposition and require backbone-specific modifications with retraining. We observe that under such recomposition, VLAs often fail at global grounding while retaining local manipulation skills that recover near the correct target in familiar configurations. Therefore, we propose Referential Guidance (ReGuide), a training-free wrapper that, given object poses from a grounding module, combines semantic and
geometric rebinding to guide the end-effector into demonstration-supported configurations of the instructed referent, where the frozen policy can resume execution. Experiments in simulation across multiple VLA backbones as well as on a real
robot show that ReGuide improves success rates under compositional shifts by up to 56.8 and 75.0 percentage points, respectively, while preserving standard-task performance.
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All authors Yanyan Zhang, Disheng Liu, Xinpeng Li, Chaoda Song, Mohsen Hariri, Debargha Ganguly, Wang Yang, Kai Ye, Bryce Grant, Vipin Chaudhary, Yu Yin
Computer Vision
Exo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation
Hongjia Zhai, Xiyu Zhang, Haoran Zhang et al. (+5 more)
Egocentric videos of human manipulation provide valuable visual experience for
embodied intelligence, yet collecting such data at scale is costly. Exocentric-to-
egocentric video generation offers a scalable alternative by transforming abundant third-person manipulation
videos into first-person observations. However, existing methods often struggle to faithfully preserve demonstrated hand-object interactions (HOI) across large viewpoint changes due to insufficient fine-grained interaction guidance and weak object-centric anchoring. We present Exo2EgoHOI, an HOI-aware
video generative framework for interaction-preserving exocentric-to-
egocentric translation. To preserve fine-grained HOI, we introduce a unified 4D HOI prior that combines scene geometry, articulated hand renderings, and dense hand-object relation fields, together with a dual-branch residual adapter for injecting structural and relational cues into the
video generation backbone. To preserve object consistency, we introduce Decomposed Gated Cross-
Attention, which separately encodes object and background references and adaptively integrates global semantic and local appearance features as object-centric anchors. Experiments on ARCTIC-HOI and Ego-Exo4D demonstrate substantial improvements in object consistency and HOI preservation while maintaining competitive visual fidelity. In particular, on ARCTIC-HOI, Exo2EgoHOI improves object mIoU by 32.3% and reduces MPJPE and PA-MPJPE by 34.7% and 50.0%, respectively, relative to the respective best baseline results. Project page: https://rcl-robotics.github.io/Exo2EgoHOI/.
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All authors Hongjia Zhai, Xiyu Zhang, Haoran Zhang, Zhichao Ye, Haomin Liu, Guofeng Zhang, Ian Reid, Xingxing Zuo
Computation and Language
StreamDecisionBench: Evaluating Decisions in Force on Evolving Language Streams
Jhen-Ke Lin and Chung Chun Wang
Language models increasingly make real-time decisions in applications that apply the latest answer until a newer one arrives. A late answer can prolong an outdated decision, such as a call recorder still running while a customer reads out card details, an error offline accuracy misses. We make three contributions. First, we release StreamDecisionBench (SDB), a
dataset of eight streaming scenarios in four application families, with executable reference decisions derived from public rules. Second, we propose an evaluation protocol and a metric, in-force accuracy: the share of time the applied decision is correct across update intervals of 0.5-8 s. It reflects accuracy and latency jointly, attributing each error to judgment, latency or both. Third, we evaluate fifteen single-model settings, and this attribution separates speed-limited from judgment-limited models: slower, more accurate models lose 42-47% of the time to outdated answers, a fast model 35% to wrong ones. We therefore test hybrids in which a slow model corrects a fast one; with the right pairing and configuration, a hybrid outperforms every single model. However, even the best evaluated system keeps a correct decision in force only about two-thirds of the time, leaving a substantial gap for real-time use.
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Machine Learning · Cross-listed from physics.ao-ph
Methodological Changes to the Attention ResUNet Hourly Precipitation Postprocessor
Thomas M. Hamill
This note is a technical companion to a previously published preprint describing an
Attention Residual U-Net that postprocesses deterministic forecasts from The Weather Company's Global and Regional Atmospheric Forecast (GRAF) model into probabilistic hourly precipitation forecasts. It documents what has changed in that method since publication. Feature-wise Linear Modulation conditioning on calendar season and forecast lead time is used to produce a single trained model for each season, replacing 192 separately trained per-month, per-lead checkpoints. Lead time is extended from 48 to 72 h. Two new input channels are used, per-pixel local
solar hour and a static, monthly-varying precipitation climatology. During verification, the climatological reference against which the Brier Skill Score is computed now has an added diurnal dimension, on top of the monthly resolution it already had. Brier Skill Score and reliability are compared between the new vs. the previous training. Forecasts generated with the new training show a modest, consistent improvement of the current training over the original.
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Machine Learning
Learning-Enabled Estimation: Tight Characterizations under Sample Selection Biases
Vikram Kher, Jane H. Lee, Anay Mehrotra et al. (+1 more)
When can we learn from biased samples? We study regression when outcomes are observed only after passing through selection filters that depend on both
covariates and outcomes themselves, a ubiquitous challenge spanning clinical trials with patient dropout, labor
markets with self-selection, and auctions with strategic entry. Ignoring such selection yields systematically biased conclusions with real-world consequences. This challenge has a long history in econometrics and statistics, starting with Heckman's seminal two-stage model and followed by numerous generalizations. While these works provide various sufficient conditions for identification, a complete characterization of when such regression is possible has remained elusive. In this work, we provide a characterization for when regression is possible in the presence of sample selection bias. Our results establish the minimal assumptions required on the functional forms of selection processes under which regression remains possible, which are particularly relevant in modern settings where selection mechanisms are increasingly complex and opaque. As a corollary of our characterization, we show that there are settings where the regression function can be identified even when the selection filter itself cannot. This observation already goes beyond the “estimate selection filter, then debias regression” paradigm that is followed by virtually all existing approaches. Under natural strengthenings of our identification conditions, we also establish finite-sample estimation guarantees with explicit convergence rates and provide oracle-efficient
algorithms. This yields the first general-purpose estimation method for this broad class of selection problems. Finally, we explore the implications of our results for several well-studied econometric settings with complex selection mechanisms such as auctions with entry costs and labor
markets.
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All authors Vikram Kher, Jane H. Lee, Anay Mehrotra, Manolis Zampetakis