Artificial Intelligence · Cross-listed from cs.CL
Hui Ren, Zihan Li, Chang Liu et al. (+2 more)
Discrete
diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional
reasoning and global constraint satisfaction. Yet they share a structural bottleneck: when decoding in parallel, each
token is sampled independently from its marginal, severing the statistical dependencies among the
tokens decoded together. Continuous
diffusion language models avoid this by denoising a shared continuous state, but their denoiser sees only that state, so nothing ties it to a valid
token configuration until it is finally decoded. To address this, we propose Hierarchical Continuous
Diffusion Language Models (HC-DLM), which couple discrete
token generation with a continuous latent trajectory in a single, principled denoising process, whose training objective is derived from a variational bound on the
token likelihood. In contrast to recent methods that attach continuous context to a self-contained discrete chain, HC-DLM makes the latent the only persistent generative state:
tokens are read out from it at every step and feed back as a scaffold for the next latent update. On structured
reasoning (Sudoku), mathematical planning (Countdown) and
language modeling (LM1B), HC-DLM improves over discrete and continuous
diffusion baselines at matched model size, in puzzle accuracy on Sudoku and Countdown and in generative perplexity on LM1B. Project page: https://hc-dlm.github.io/.
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All authors Hui Ren, Zihan Li, Chang Liu, Huidong Liu, Alexander Schwing
Artificial Intelligence · Cross-listed from cs.CV
DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation
Zhengming Yu, Junkun Yuan, Haotian Yang et al. (+8 more)
Distribution Matching
Distillation (DMD) trains a few-step student from the difference between separately estimated target and student scores, so it must keep an auxiliary
diffusion model fitted to the student's evolving distribution at extra
memory and computation cost. We introduce DMAD, Distribution Matching as
Adversarial Distillation, which recasts distribution matching as classification and learns the required log-density ratios directly. Two discriminator heads on a shared backbone distinguish real data and teacher samples from the student's, and linear losses on their logits train the student without auxiliary score fitting. We prove that at the discriminator optimum these losses recover the distribution-matching gradient underlying DMD, through the classical identity linking discriminator logits to log-density ratios. We further introduce gap-based reweighting, which adapts teacher supervision across noise levels from the real-data head's empirical logit gap between real and teacher samples. DMAD reaches a Fréchet Inception Distance (FID) of 1.04 with one-step generation on ImageNet-64x64, 14.47 with four-step SDXL on COCO-10K, and a VBench total score of 85.15 with four-step Wan2.1-T2V-14B, the best values among the compared few-step methods and the multi-step teachers. On MiniMax-H3-33B, our four-step student achieves overall human preference rates of 79.1% over DMD2 and 84.6% over rCM for joint audio-
video generation, excluding ties. Our code, models and demos are available at https://yzmblog.github.io/projects/DMAD.
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All authors Zhengming Yu, Junkun Yuan, Haotian Yang, Gordon Guocheng Qian, Yizhi Wang, Angtian Wang, Yiding Yang, Bo Liu, Xin Li, Wenping Wang, Chongyang Ma
Artificial Intelligence · Cross-listed from cs.LG
Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry
Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi et al. (+4 more)
Molecular learning models are strongly shaped by their underlying representations. Yet standard sequential and graph formalisms struggle to explicitly encode higher-order
topology, such as ring systems and recurring motifs. Existing higher-order representations can capture these structures directly, but they are often computationally demanding and difficult to decode into valid molecules. Here, we introduce Higher-order Grammar Representation (HGR), a principled,
topology-aware framework that lifts molecules to combinatorial complexes and parses each complex into a compact sequence of production rules under a context-free higher-order grammar. By serialising higher-order
topology into rule sequences, HGR makes these structures directly compatible with standard sequence models, avoiding the computational overhead of explicit higher-order encodings while preserving topological expressiveness. To reduce
benchmark bias towards simple ring systems, we construct RingDiv, a ring-enriched
benchmark containing 1.18 million molecules, including the curated RingDiv300k subset, and introduce the ring diversity index (RDI) to quantify ring-system coverage. In molecular generation, HGR-based models uniquely combine 100% validity by construction with leading distributional
alignment, ranking first in FCD on all five generation
benchmarks. In representation learning, HGR-FM achieves the highest mean AUC across seven MoleculeNet
benchmarks under both transfer protocols, improving on the strongest baseline by 8.3 and 3.3 AUC points under probing and full fine-tuning, respectively. Collectively, these results establish HGR as an efficient higher-order representation for molecular generation and transferable representation learning.
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All authors Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi, Simone Foti, Jianmin Wang, Jure Leskovec, Tolga Birdal
Artificial Intelligence · Cross-listed from cs.LG
SoftServe: A Scalable Quasi-Newton Method for Deep Learning
Joohwan Ko, Tetiana Parshakova, Diana Cai et al. (+1 more)
Quasi-Newton (QN) methods have long been among the most effective methods for large-scale unconstrained convex optimization. Two obstacles have limited their use in deep learning: non-convexity and enormous parameter sizes. We introduce SoftServe, a family of QN methods designed to overcome these obstacles without line searches or ad hoc
curvature corrections. SoftServe derives positivedefinite
curvature estimates from the variational objective of Berglund et al. (2025), even in the presence of negative
curvature. We develop diagonal and Kroneckerfactored variants that preserve positive definiteness by construction and scale to massive neural networks. Finally, SoftServe relies on the stable coupled Newton-Schulz iteration for the required
matrix operations, replacing costly
matrix decompositions with GPU-friendly
matrix multiplications. SoftServe excels on problems that are severely ill-conditioned, including tasks such as recurrent networks, deep autoencoders, physics-informed neural networks, and a 136M-parameter physics-informed
diffusion model, often achieving lower losses than established baselines including Adam,
Muon, and SOAP.
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All authors Joohwan Ko, Tetiana Parshakova, Diana Cai, Robert M. Gower
Artificial Intelligence · Cross-listed from cs.CV
Generative Cinematographer: Composing Camera and Object Motion in 3D
Jiahan Zhang, Chaohao Yang, Namitha Guruprasad et al. (+4 more)
Current controllable
video generation systems often rely on 2D motion trajectories or sparse drag signals for object motion. These controls are ambiguous because the same 2D trajectory can correspond to different 3D motions, especially when the camera and objects move simultaneously. We present Generative Cinematographer (GenCine), a system that lifts a single image into an editable 3D scene scaffold where artists jointly author camera and foreground motion. Artists specify a camera path and move selected foreground regions using local 3D motion handles. Several handles can move different parts of a subject independently, providing a piecewise-rigid approximation to non-rigid motion without a physics simulator or category-specific prior. To communicate these controls to a pretrained
video model, we project them into guidance maps. These maps record where the controlled regions appear in each frame, assign each handle a fixed color across frames and encode the current 3D positions of its controlled points in the same world coordinate system as the background. This lets us describe object motion relative to the scene even as the camera moves. For training, we recover controls from the motion observed in real
videos and use ground-truth geometry and trajectories from synthetic
videos. We train a lightweight guidance branch and LoRA adapters on a pretrained Wan model to follow these controls. Our experiments show consistent camera-relative motion, improved
geometric consistency under viewpoint changes, and strong controllability across diverse real-world scenes.
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All authors Jiahan Zhang, Chaohao Yang, Namitha Guruprasad, Vivekjyoti Banerjee, Trong-Tung Nguyen, Alan Yuille, Anand Bhattad
Artificial Intelligence · Cross-listed from cs.RO
Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination
Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi et al. (+5 more)
Robots operating in the physical world will increasingly need to coordinate with other
robots, particularly in manipulation tasks where an object may be too large or heavy for a single
robot to carry alone. Physical limitations caused by hardware degradation or actuator faults can restrict the actions a
robot can reliably execute, yet these limitations may be unknown to its partner. We study whether a helper can infer a
robot partner's physical constraints from observing it coordinate with another
robot, then use the inferred capability to coordinate with the same partner on a new task. This is difficult because a demonstration shows what the constrained
robot did, but not what it could have done. In physically coupled tasks, the other
robot may also compensate for its limitations, making those limitations difficult to identify from the constrained
robot's behavior alone. Our key insight is that these constraints shape the joint behavior of the team, making the actions of both
robots informative about the constrained partner's capability. We introduce Watch, Infer, Coordinate, a
benchmark spanning three physically coupled manipulation settings, together with an inference approach that scores candidate constraints using observed joint behavior. Across all three settings, our method substantially improves constraint inference and zero-shot coordination, approaching an oracle with access to the true constraints.
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All authors Suyu Ye, Zheyuan Zhang, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Tianmin Shu, Homanga Bharadhwaj, Nakul Agarwal
Artificial Intelligence · Cross-listed from cs.RO
DuoMind: Enabling Distributed Multi-Robot Coordination with Semantic Communication
Hanchu Zhou, Dechen Gao, Hang Wang et al. (+5 more)
Vision-
language models (VLMs) and
vision-
language-action models (VLAs) have recently driven rapid progress in general-purpose
robots, yet most progress has focused on single-
robot settings. Extending these capabilities to multi-
robot systems remains challenging because
robots must coordinate long-horizon behaviors while maintaining reliable, fine-grained execution. We introduce DuoMind, a distributed hierarchical framework for multi-
robot coordination through semantic communication. Each
robot uses a VLA-based action model for low-level execution and a VLM-based orchestrator for high-level
reasoning and inter-
agent coordination. At each planning step, the orchestrator at each
robot reasons over the task instruction, local observations, and messages received from other
robots. It then generates low-level instructions for the action model and semantic messages for peer
robots. This architecture exploits the complementary strengths of pretrained models by combining the semantic
reasoning capabilities of VLMs with the precise action-generation capabilities of VLAs. To address the scarcity of
benchmarks for multi-
robot coordination, we further develop RoboPoly, a
benchmark comprising long-horizon manipulation tasks that require coordinated, closed-loop execution under distributed control. Experiments on RoboPoly and RoboTwin demonstrate that DuoMind improves multi-
robot task performance, while ablation studies confirm the contributions of hierarchical orchestration and semantic communication. More details are available on our project page.
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All authors Hanchu Zhou, Dechen Gao, Hang Wang, Brendan Lynch, Boqi Zhao, Qiyao Ma, Raman Goyal, Junshan Zhang
Artificial Intelligence · Cross-listed from cs.CL
From Knowledge Access to Source Learning: Developing Source-Specific Competence
Lucheng Fu, Kejing Xia, Yiyang Wang et al. (+10 more)
Large
language model (LLM)
agents increasingly rely on persistent external sources to solve sequences of knowledge-intensive tasks. Existing methods improve how source content is accessed and organized, while
agent-
memory systems preserve reusable knowledge from prior interactions, but repeated use of the same source is still largely treated as repeated access rather than an opportunity to progressively improve understanding of that source. We study source learning: developing reusable source-specific competence over a persistent authoritative source. We represent this competence with a persistent source model that captures reusable understanding of the source, including how its knowledge is structured, interpreted, and applied. To construct and progressively refine such models, we propose SourceLearn, which combines two complementary learning mechanisms. Self-Directed Source Learning identifies what remains incompletely understood and adaptively revisits the source, while Task-Guided Source Learning uses downstream experience to reveal local representational gaps and recurring needs in how source knowledge should be organized. In both cases, learning signals determine what should be reconsidered, while persistent updates are reconstructed from the authoritative source. Across five
benchmarks and three LLM backends, SourceLearn achieves the best performance in 13 of 15 settings, with gains of up to 22.6 points over Hybrid RAG and substantial overall improvements over static source representations and experience-based
memory baselines.
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All authors Lucheng Fu, Kejing Xia, Yiyang Wang, Yiqiao Jin, Jinjin He, Xiyuan Yang, Haoxin Liu, Ye Yu, Haibo Jin, Yijia Xiao, Wenke Lee, B. Aditya Prakash, Haohan Wang
Artificial Intelligence · Cross-listed from cs.LG
Finetuning with Sampling: SFT Learns Better Than You Think
Aayush Karan, Sitan Chen and Yilun Du
Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and reinforcement learning (RL) to this end. Conventional wisdom dictates that RL enables strong generalization on new tasks without losing existing capabilities, while SFT is prone to weak generalization and catastrophic forgetting. At the same time, SFT can learn from off-policy expert data, whereas RL must rely on a model's ability to find successful trajectories with repeated sampling. In our work, we seek to leverage the strength of on-policy learning while utilizing the privileged information contained in off-policy data. However, rather than modifying the learning objective to accommodate this data, we instead tailor the data distribution to better suit the learner. We introduce a Markov chain Monte Carlo (MCMC) sampling
algorithm that progressively transforms off-policy traces to be more on-policy given a reference model for finetuning. Across tasks like scientific skill acquisition, mathematical
reasoning, and open-ended expertise, our sampling
algorithm enables SFT to rival prevailing posttraining techniques, often generalizing better and forgetting less than strong on-policy baselines. In addition, the resulting finetuned models exhibit strong distributional performance and are capable of learning beyond sharpening the base model distribution. At a higher level, our approach presents sampling as a model-native operator that shapes data for learnability, offering broader utility as a general-purpose primitive throughout the posttraining stack.
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Artificial Intelligence · Cross-listed from cs.CV
MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI
Negin Kafee Hernashki and Soumick Chatterjee
Unsupervised anomaly detection (UAD) methods for
brain MRI are ranked by a single score, yet that score rests on choices that are rarely reported: how each anomaly map is aligned with the reference, how and on which data the threshold is set, and which false-positive budget, metric, aggregation and lesion definition are used. We present MIRTO, an evaluation protocol that makes these choices explicit and measures their effect. It gates the geometry of every comparison with a registration check and label-free diagnostics of known power, sets thresholds on validation data alone and reports the false-positive volume actually realised on test, repeats each comparison over 15,552 defensible evaluation pipelines, and attaches paired subject-bootstrap intervals with multiplicity control. Applied to four UAD methods trained on the same healthy data and tested on 312 BraTS 2020 subjects, MIRTO showed that an axis-order mismatch between stored maps and the reference lowered a
diffusion model's voxel AUROC from 0.873 to 0.583 whilst barely moving its slice-level AUROC. Within each metric, the method explained at least 0.95 of the variance in voxel AUROC and AUPRC and 0.77 in Dice, but only 0.14 in lesion sensitivity, where the lesion definition and hit criterion dominated. A Dice advantage that was significant at validation thresholds vanished at equal realised false-positive burden, and an exact identity attributes it to threshold transfer. A training-free change to REFLECT's latent aggregation raised Dice at equal burden by 0.052. Nine hypotheses were tested against explicit criteria; because the same cohort served to develop the protocol, all inference is exploratory.
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Artificial Intelligence · Cross-listed from cs.LG
Local Support Learning
Assaf Ben-Kish, Akarsh Kumar, James Glass et al. (+1 more)
We explore catastrophic forgetting in the context of large pre-trained models. By considering forgetting as a
geometric problem in the input space of each weight
matrix, we uncover a natural retention objective under which updates produced by gradient-based optimizers are suboptimal. Following this observation, we propose Local Support Learning (LSL), a general-purpose framework that augments gradient-based training for retention of prior capabilities without access to prior data. During a new learning phase, LSL pairs two components with distinct roles: a standard weight adapter, trained as usual to minimize the loss, and a gating function that enables the adapter only on input activations from its own training distribution, making the update local to that distribution. The key challenge is that this gate must route data from all learning phases while training only on data from the current one. We address this with a gate based on a Gaussian Mixture Model (GMM), whose likelihood decays rapidly away from its training data, giving it a natural tendency to stay closed on data from prior phases. We show that this post-training approach can resolve forgetting in LLMs of up to 7 billion parameters, retaining both pretrained and finetuned capabilities across multiple training phases, while being efficient in
memory and compute, robust to hyperparameter choice, and showing scaling potential.
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All authors Assaf Ben-Kish, Akarsh Kumar, James Glass, Raja Giryes
Artificial Intelligence · Cross-listed from cs.CL
Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows
Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing et al. (+2 more)
Real-world enterprise data science and analytics workflows require
reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL
benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real enterprise warehouses are too sensitive to release, these
benchmarks are built on public
datasets where a business event fits in a single table. We introduce Argo-Bench, an evaluation framework comprising 210 data science and analytics tasks. Drawing on public data, peer-reviewed industry literature, and regulatory filings, we simulate a food delivery platform in New York City at true scale, with 81 million orders in 2024, grounded economics, fraud patterns, and marketplace incentives. We export this world to an ERP warehouse of 235 tables and 7.5 billion rows, modeled on the Oracle E-Business Suite schema. The simulator's ground-truth state is withheld from the warehouse the
agent sees, so tasks require reconstructing facts by navigating the warehouse before acting on them. Argo-Bench goes beyond text-to-SQL: the
agent files actions such as banning fraudulent accounts, allocating courier incentive budgets, or issuing back pay, and the grader scores each by its consequences in the simulator. Every task has an executable reference solution that demonstrates solvability using only the warehouse. The strongest of 14 frontier and open-weight models scores 95 or higher on only 34.8% of tasks and averages 59.5 points. We hope Argo-Bench drives progress toward
agents that understand, navigate, and act within real data environments.
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All authors Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing, Duke Gand, Joseph J Ma
Artificial Intelligence · Cross-listed from cs.CV
Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc et al. (+2 more)
On-policy self-
distillation has recently emerged as an effective approach for improving
language-model
reasoning by supervising students with a frozen or EMA version of themselves that receives privileged information. Its application to
multimodal large
language models (MLLMs), however, remains largely unexplored. Recent approaches use privileged visual information, such as image crops corresponding to a question, to improve fine-grained perception, but their gains are confined to tasks that benefit from such visual zooming and require either human-annotated grounding data or external teacher models. We introduce a different form of on-policy self-
distillation for MLLMs that provides the teacher with textual, spatially grounded guidance identifying the visual elements relevant to a query. We use procedurally generated scenes with automatically available object identities and spatial coordinates, enabling scalable and annotation-free post-training. The teacher uses this spatial guidance to locate and integrate evidence from multiple relevant image regions, while the student learns to reproduce the resulting behavior from the image and question alone. Our approach consistently improves performance on counting, document and chart understanding
benchmarks across multiple models. Importantly, although post-training uses only synthetic scenes, the resulting improvements transfer to real-world perception
benchmarks, yielding a 3.23-point gain in average performance across CVBench, V*, ZoomBench, BLINK, HR-Bench, and MME-RealWorld. These results show that spatially grounded privileged information can induce broader perceptual capabilities through on-policy self-
distillation, enabling substantial synthetic-to-real transfer beyond the task and data distribution used for post-training. Project page: https://github.com/sirkosophia/Where-OPD
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All authors Sophia Sirko-Galouchenko, Monika Wysoczanska, Andrei Bursuc, Nicolas Thome, Spyros Gidaris
Artificial Intelligence
A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
Javier Diaz Esteban-Herreros, David Muñoz-Valero, Raquel Martínez-España et al. (+2 more)
A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disagreement problem in Explainable Artificial Intelligence, where different attribution methods produce divergent explanations for the same input and prediction. A natural
language inference engine is implemented over the Medical Abstracts corpus with five enriched hypotheses per diagnostic category and a balanced sample of one thousand texts per class. Five explanation methods are compared: SHAP and LIME as model-agnostic approaches, occlusion and Input x Gradient as deep-learning-specific approaches, and
Attention x Gradient as a
transformer-specific approach. Explanations are standardized through top-
token attribution, and pairwise agreement is quantified using the Jaccard index. High predictive accuracy is achieved across well-defined clinical domains, whereas performance degrades under high semantic ambiguity. Explanatory stability directly mirrors predictive certainty, exhibiting strong convergence in univalent categories and a marked drop under diagnostic uncertainty. Furthermore, qualitative error auditing uncovers three systemic failure mechanisms: lexical hypersensitivity, semantic overlap, and loss of attribution coherence. The results support the combined use of several explanation methods and quantitative agreement metrics when auditing
transformer-based models in medical text classification, and suggest prioritizing specific clinical ontologies over broad diagnostic labels.
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All authors Javier Diaz Esteban-Herreros, David Muñoz-Valero, Raquel Martínez-España, Jose M. Juarez, Juan Moreno-Garcia
Artificial Intelligence · Cross-listed from cs.CL
Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)
Zilin Du, Bowen Yang and Boyang Albert Li
Data selection is critical for training large
language models on massive and heterogeneous corpora. Meta-learning for Training-data Selection offers a principled alternative to heuristic scoring by learning data weights from a target validation objective, but existing methods face a trade-off between fine-grained valuation and transferability to unseen data. A natural solution is to replace per-sample weights with a selection network. However, we find that directly incorporating such a network into existing MTS objectives leads to unstable optimization and poor generalization, caused by weight suppression and persistent reliance on easy-to-learn features. To address these issues, we propose Transferable Example Scoring and Selection (TESS), a scalable data-selection framework built on a Pointwise Value Matching objective (PVM). Experiments on LLM safety and targeted instruction tuning demonstrate strong transfer across
datasets, from subsets to full corpora, and from smaller to larger models.
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Artificial Intelligence · Cross-listed from cs.CV
GeoLatent: Geometry-Guided Latent Structuring with Routed Optimization for 3D Reasoning
Yakun Zhu, Yi Bin, Yujuan Ding et al. (+5 more)
Despite progress in
vision-
language models, 3D spatial
reasoning from 2D images remains challenging. Text-based methods describe intermediate geometry with discrete
tokens, limiting fidelity for continuous spatial relations. Continuous latents offer richer representations, but a single latent type does not explicitly separate the cues needed across spatial tasks. Decomposed spatial latents address this by representing position, direction, and global geometry separately under
geometric supervision. Yet the geometry representation can still collapse toward one dominant direction, and unrestricted
attention can leave the latents underused during answer learning. We introduce GeoLatent, combining Common–Residual Geometry
Alignment (CR-GEO) with routed optimization to structure the geometry states while promoting latent-mediated answer learning. CR-GEO separates shared from residual teacher geometry; routed optimization jointly trains geometry and
language, temporarily directs visual answer learning through the latents, and restores full
attention with geometry supervision. In controlled comparisons, CR-GEO raises geometry effective rank from 1.00 to 3.87, while blocking latent readout at the bottleneck lowers direction accuracy from 89.1% to 25.8% on 128 fixed questions. After recovery, the differentiated geometry representation and latent-mediated visual route remain available alongside direct image access. GeoLatent achieves 73.0% on SPAR-Bench and 72.1% on SPBench, outperforming previously reported methods on both.
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All authors Yakun Zhu, Yi Bin, Yujuan Ding, Zheng Wang, Pengpeng Zeng, Duo Peng, Jingkuan Song, Heng Tao Shen
Artificial Intelligence · Cross-listed from cs.RO
HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution
Kyochul Jang, Seohyeon Park, Ohchul Kwon et al. (+9 more)
As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable tool and coordinating manipulation and, when needed, locomotion to complete the task. Existing
benchmarks do not jointly evaluate these capabilities on a humanoid. We introduce HumanoidToolBench, an 18-task
benchmark spanning three scenarios, three execution levels, and two tool-set modes, together with ToolBook, a
dataset of 3.1k demonstrations collected in simulation and on a real Unitree G1. Evaluation of seven policies in simulation and three on the real
robot reveals substantial gaps between selecting a suitable tool and completing the task. Focused GR00T N1.7 probes show reduced selection accuracy on unseen tools and continued task execution under unrelated instructions. Code and data are available at https://snu-pi.github.io/HumanoidToolBench/.
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All authors Kyochul Jang, Seohyeon Park, Ohchul Kwon, Sangjun Park, Junhyeok Choi, Seungyeop Yi, Chaeyun Kim, Sangkyu Lee, Idan Szpektor, Avi Caciularu, Jongmin Park, Youngjae Yu
Artificial Intelligence
Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints
Abid Mohamed Nadhir, Ahmad Al Hanbali and Beggas Mounir
Privacy-preserving machine learning presents significant deployment challenges on the cloud for intelligent systems with confidential data. Fully Homomorphic Encryption (FHE) offers a compelling solution for secure computation, preserving data confidentiality of cloud computations. However, applying FHE to reinforcement learning (RL) requires replacing non-linear operations with
polynomial approximations, which diverge catastrophically due to a unique recursive error phenomenon known as the Bellman drift. This article introduces the Homomorphic Advantage Operator (HAO), a stabilization framework designed to prevent
polynomial approximation divergence in FHE-based deep RL. HAO adapts the zero-mean centering projection from advantage-based value estimation directly to temporal-difference (TD) targets. This linear projection annihilates the uniform state-value baseline that drives the Bellman drift, maintaining per-state action rankings while requiring zero additional non-linear multiplicative depth and avoiding expensive ciphertext bootstrapping. The proposed HAO framework was evaluated using a three-tier experimental methodology, including a tabular Markov Decision Process (MDP), an encrypted CartPole environment using real CKKS cryptographic operations, and a 20-node logistics routing
benchmark with dense continuous features. The results demonstrate that the proposed HAO strictly bounds network pre-activations within the safe
polynomial approximation domain. The proposed HAO RL
agents achieved 0% boundary breaches across all random seeds used, whereas regularization alone (L2 weight decay and gradient clipping) breached the bound on 3 of 5 seeds and the unstabilized baseline did so in 83.8% of episodes. Finally, HAO
agents improve optimal policy accuracy by 18.0 percentage points in tabular domains and remain stable when DP-SGD-style Gaussian noise is added to the clipped gradients.
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Artificial Intelligence
PyPottery: an AI-powered end-to-end suite for pottery processing and publication
Lorenzo Cardarelli
The study of ceramic materials constitutes a cornerstone of archaeological research, yet the post-production workflow for pottery documentation remains labor-intensive and creates significant publication bottlenecks. This paper presents PyPottery, an open-source, AI-powered suite designed to semi-automate the complete ceramic documentation pipeline. The suite comprises four integrated modules: PyPotteryScan for automated image extraction and handwriting recognition; PyPotteryInk for automatic inking of pencil drawings; PyPotteryTrace for semantically-aware vectorization; and PyPotteryLayout for automated layout generation. Evaluated on 50 hand-drawn sheets containing 240 pottery drawings from the Terramara di Montale (Italy), the framework achieved substantial time savings confirmed by usability study participants, who reported a median perceived speedup of 40$\times$ over traditional workflows (range: 17.5$\times$–120$\times$). These results highlight the potential of AI-assisted tools in archaeological documentation, while the paper addresses the strategic redistribution of cognitive labor toward augmentation rather than automation.
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Artificial Intelligence
Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval
Arman Behnam and Binghui Wang
Memory-augmented large
language models must decide which
memories to retain, and recent systems do so by estimating each
memory's effect on task performance. However, these estimates rely entirely on retrieved
memories. When a
memory is never retrieved, store-level interventions produce identical outcomes, leaving its utility unidentified. This is a
retrieval-level positivity violation, invisible to diagnostics that examine only
memory operations. We introduce Causal
Memory Policy (CMP), a causal framework that restores identification by intervening on
retrieval itself, reserving a fixed number of context slots for
memories sampled with known propensities. CMP estimates
memory utility by self-normalized inverse propensity weighting under a balanced assignment design. We prove the causal factorization of
memory utility through
retrieval, the unbiasedness and exact variance of the
estimator, and the optimal decision rule under irreversible operations. Empirically, identification fails for 54% of required
memories on LongMemEval and 67% on LoCoMo, and the failure persists in a deployed
memory system. CMP improves discrimination between required and non-required
memories from 0.54 to 0.66 AUC. Finally, we show that identified
memory utility alone is insufficient for retention decisions: per-query utility reaches 0.78 AUC on the query for which it is estimated, yet no aggregation available to a retention policy predicts a
memory's value on unseen queries. Code is available at: https://anonymous.4open.science/r/cmp-release-D0C3/.
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Artificial Intelligence
External Observers May See More Clearly: Cross-Model Span-Level Hallucination Detection in Large Language Models via Hidden State Probing
Kingshuk Gupta and Davide Buscaldi
As Large
Language Models (LLMs) increasingly serve as foundational
reasoning engines, their tendency to hallucinate remains a critical vulnerability. While recent internal state probes offer a promising alternative to slow external
retrieval systems, they largely reduce
hallucination detection to a
token-wise binary classification task, failing to capture the structured, sequential boundaries of semantic drift. Here, we introduce an internal hidden state framework for fine-grained, span-level
hallucination detection. By inspecting layer-wise activation patterns, we attempt to detect the exact
hallucination onset and continuation
tokens in an LLM generation. Our experiments show that this approach successfully isolates
hallucination onsets, achieving substantial improvements in Precision-Recall AUC over random baselines despite extreme class imbalance. Ultimately, we propose a novel cross-model detection framework in which one model observes the internal representations elicited by another model's generation. We find that an external observer can match or exceed a generator's self-detection of its own
hallucination onsets, including when the observer is the smaller model, suggesting that self-detection is not the ceiling for onset localisation.
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Artificial Intelligence
HydroJEV: A one-second, training-free screen for cyber-attack and fault attribution in water distribution networks
Tianwei Mu, Shengyan Jiang, Mingzhe Yuan et al. (+5 more)
When a SCADA alarm is raised in a water distribution network, operators must decide quickly whether it reflects a cyberattack, a physical fault, a normal transient or a faulty sensor. Supervised classifiers need labelled incidents that utilities rarely have, and frontier large
language models (LLMs) take tens of seconds per decision. We tested whether Jev, a training-free model that returns class probabilities in about one second, can serve as the first tier of this triage. On a four-class cause-attribution
benchmark built on the C-Town network in EPANET, Jev was compared with a hand-written rule tree, a supervised classifier and seven cloud LLMs on identical evidence in four sealed, pre-registered rounds. With only a label-free prior correction, Jev matched the rule tree (macro-F1 0.62-0.64 against 0.56-0.61 in distribution) and exceeded the supervised classifier by 0.36-0.42 on event subtypes absent from its labels, in all four rounds, and it outperformed the classifier whenever fewer than about four labelled events per class were available. Jev also decided 20-40 times faster than frontier LLMs. Accepting only benign Jev verdicts confirmed by the rule tree spared an LLM reviewer 35-38% of windows on fresh sealed sets without loss of macro-F1. Transferred unchanged to two further networks, this gated cascade stayed within the non-inferiority margin of its reviewer on all four sets. A fast, training-free screen can therefore take over about a third of the review load in SCADA anomaly triage while preserving the accuracy of deliberate review.
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All authors Tianwei Mu, Shengyan Jiang, Mingzhe Yuan, Qing Luo, Min Xiao, Wenhong Wang, Jun Li, Manhong Huang
Artificial Intelligence · Cross-listed from cs.LG
Distributionally Robust Schrödinger Bridge
Jinhwan Sul, Panagiotis Theodoropoulos, Vincent Pacelli et al. (+2 more)
Schrödinger bridge (SB) learns
stochastic transport between prescribed initial and target distributions. When the initial distribution shifts at test time, the learned dynamics can fail to recover the target distribution. We introduce the Distributionally Robust Schrödinger Bridge (DRSB), which learns a single controller that accounts for uncertainty in the initial distribution. The DRSB objective consists of control energy and a KL penalty between the resulting terminal distribution and the target distribution. DRSB seeks a single controller that minimizes the worst-case value of this objective as the initial distribution varies within an ambiguity set around the nominal distribution. We derive an exact variational formulation of this objective and connect its fixed-terminal-cost subproblem to
stochastic optimal control and distributionally robust optimization. This formulation motivates an alternating
algorithm that updates the
adversarial initial distribution, estimates the terminal log-density ratio, and trains the controller. We develop Wasserstein and Sinkhorn variants using
stochastic control optimality conditions to approximate the gradients required for
adversarial updates. Experiments on two-dimensional transport tasks and image-to-image translation show improved robustness to input perturbations relative to standard SB, with a tradeoff in nominal performance. On Gaussian mixture transport, Sinkhorn DRSB also achieves lower mean sliced Wasserstein distance than fixed-level noise augmentation at both tested unseen noise levels.
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All authors Jinhwan Sul, Panagiotis Theodoropoulos, Vincent Pacelli, Jaemoo Choi, Evangelos Theodorou
Artificial Intelligence · Cross-listed from cs.LG
CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning
Yafei Zhang, Songshuo Lu, Sicong Liao et al. (+2 more)
Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large
language model (LLM) post-training, with substantial gains in mathematical
reasoning and code generation. In practical systems, however, policy updates and differences between rollout and training engines can make sampled responses off-policy. Sequence-level masking addresses this mismatch by deciding whether an entire response should contribute to optimization. A common masking rule uses the length-normalized
geometric mean of sampled
token probability ratios. Its signed log-ratios can cancel across positions, concealing substantial bidirectional policy drift. We propose Cancellation-Aware Response Masking (CARM), a sequence-level mask that takes the absolute value of each
token log-ratio before averaging, preventing opposing probability changes from canceling. We prove that accepted responses satisfy a joint bound on the fraction of sampled-
token ratios outside a prescribed band and their mean log-distance beyond its boundaries. Experiments on mathematical
reasoning and code generation show that CARM improves mean@16 averaged over AIME 2024/2025/2026 and BeyondAIME by up to $3.13$ percentage points over
geometric-mean masking, and increases average pass@1 across four code
benchmarks by $2.88$ points over the strongest evaluated baseline. These findings support CARM as a theoretically grounded and effective method for response-level off-policy control in LLM reinforcement learning.
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All authors Yafei Zhang, Songshuo Lu, Sicong Liao, Zhi Chen, Yaohua Tang
Artificial Intelligence
Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control
Yimeng Liu, Mi Zhang, Younsuk Dong et al. (+1 more)
Large
language model (LLM)
agents increasingly combine
reasoning, tool use, and action, but most evidence comes from episodic tasks with relatively immediate feedback and reset failures. Long-running physical control operates in a different regime: actions alter future states, errors compound across decisions, and an
agent must improve from experience without being allowed to rewrite the physical rules that make execution safe. We study this regime through irrigation, where daily decisions interact with soil-water dynamics over entire growing seasons. We present Mimir, a physics-grounded LLM
agent organized around two repair timescales. At the fast timescale, a structured physical interface and deterministic simulator turn an LLM output into a proposal that we numerically check, revise, and subject to bounded deterministic action selection before execution. At the slow timescale, recurrent failure patterns are consolidated into persistent contextual principles that condition future proposals, while the physical model, evaluator, and execution constraints remain immutable. Under a common retrospective evaluator across multiple sites, crops, and years, Mimir attains the lowest reported aggregate control cost among the evaluated references and uses about 51% less irrigation than the historical schedule replay. The ablation study show higher control cost when forward simulation, verified revision, or persistent context is removed; model-scale and model-family studies show no monotonic gain from increasing LLM size. The resulting lesson show that persistent physical
agents can combine semantic
reasoning with bounded, evidence-driven self-improvement while reserving physical truth and actuator authority for explicit numerical mechanisms.
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All authors Yimeng Liu, Mi Zhang, Younsuk Dong, Zhichao Cao
Artificial Intelligence
Global Coherence: When Every Agent Is Right and the Team Is Still Wrong - A Local-to-Global Semantic Foundation for Multi-Agent Collaboration
Xin Heng
AI
agents can each make locally valid decisions yet jointly produce an invalid result. We call this the global coherence problem: a failure of shared state, not merely of intelligence. Our Observation-Aliasing Impossibility
Theorem gives the exact boundary. A policy can guarantee a valid action exactly when all worlds producing the same observation share an admissible action. If k indistinguishable worlds require pairwise-disjoint actions, the best randomized worst-case success is 1/k; more
reasoning, roles, messages, or samples cannot recover the missing distinction. A stronger model can reason better within its context, but it cannot see beyond it. The smallest sufficient context is NP-hard to find, yet counterexample-guided refinement finds a sufficient one and recertifies it as rules change. We give local-to-global runtime semantics X = (H, C, G, F; D):
topology H records overlapping scopes; category C governs state-changing actions; groupoid G keeps reversible translations; sheaf F tests whether local views glue into one world; and history D keeps only what alters legal futures. Each preserves the needed information as state is split, translated, or changed. Models propose; the harness owns shared state and governs commit. Ten studies test the boundary. On a revision
benchmark, a frontier model scores 40/40 when the deciding event is visible; hidden, every arm is consistent with chance (1/3); one restored fact returns 40/40. On TeamBench, ordinary teams exceed a shared budget in 5/5 runs, a visible live count leaves 4/5, and commit enforcement leaves 0/5. In tau2-bench Telecom, current-state checks score 0.07 after silent reverts; the harness scores 1.00. Refinement keeps a sufficient context through rule changes at 28% of the all-field cost. Solvers that already own the state tie it, as predicted. Local intelligence cannot substitute for missing global state.
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Artificial Intelligence
SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL
Hyeonmin Lee, Zheng Wei, Kyungmin Kwon et al. (+3 more)
While Large
Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making immersive authoring a repetitive and physically fatiguing process. We present SPHERE, an adaptive VR generation framework that transforms isolated synthesis into continuous human-AI co-creation. SPHERE extracts persistent spatial preferences from natural
multimodal interactions (
speech and controller edits). To ensure
geometric resilience against spatial distortions, it abstracts these raw edits into hierarchical constraints modeling both local functional and global topological contexts. Furthermore, a human-in-the-loop reinforcement learning mechanism dynamically updates
retrieval policies based on the user's final edited scenes. A mixed-design user study ($N=42$) and an offline ablation demonstrate that SPHERE significantly reduces corrective edits and physical demand, preventing bias toward shallow object-level traits to yield geometrically resilient, profile-aligned layouts. Ultimately, SPHERE demonstrates how capturing demonstrated spatial logic enables controlled spatial adaptation, establishing a reliable, governed human-AI collaboration framework for immersive authoring. Project page and source code will be available at: https://github.com/hyeonmin11/SPHERE
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All authors Hyeonmin Lee, Zheng Wei, Kyungmin Kwon, Jumin Seo, Jiwon Park, Hayoung Oh
Artificial Intelligence · Cross-listed from cs.CV
Task-Adaptive Grounded 3D-Programmers Using 2D VLMs
Arman Raayatsanati, Sombit Dey, Anna-Maria Halacheva et al. (+3 more)
Recent
vision-
language models (VLMs) exhibit remarkable generalization and
reasoning abilities, yet 3D understanding in these models is limited by data scale, training diversity, and
reasoning capacity. Instead of naively extending these models into 3D, we take a different approach: we enable powerful 2D VLMs to operate reliably in 3D by introducing 3D grounding and iterative feedback loops with two novel concepts: Canonical Coordinate Framing (CCF) and Task-Adaptive Feedback (TAF). CCF serves as a unified visual representation that anchors both inputs and outputs to a shared Euclidean coordinate system, solving common challenges in 3D grounding such as axis ambiguity, inconsistent metric scale, and floating references. Complementary to this structured framing of the 3D inputs, TAF closes the
reasoning loop with task-adaptive dynamic feedback that enables 2D VLMs to perform varied open-vocabulary tasks within their native visual context. Building on this foundation, we introduce 3D-Prog, a 3D understanding,
reasoning, and generation framework that jointly employs the capabilities of CCF and TAF together with powerful VLMs. Without requiring any retraining, 3D-Prog performs open-vocabulary 3D understanding, manipulation, and generation across both object-level and scene-level tasks. Our experiments show that the joint use of CCF and TAF transforms 2D VLMs into geometry-aware 3D programmers, achieving consistent, interpretable, and high-quality results across diverse 3D tasks.
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All authors Arman Raayatsanati, Sombit Dey, Anna-Maria Halacheva, Jan-Nico Zaech, Luc Van Gool, Danda Pani Paudel
Artificial Intelligence · Cross-listed from cs.LG
On Language Drift during RLVR Post-Training
Michael Sullivan and Alexander Koller
Recent advances in LLM
reasoning models—driven primarily by the paradigm of post-training via reinforcement learning with verifiable reward (RLVR)—have enabled them to accomplish impressively complex tasks. However, in parallel with their rising capabilities, LLMs have increasingly displayed signs of
language drift in their chains of thought (CoTs): unusual, non-standard, and seemingly nonsensical
language use. Although it is well-documented—and can potentially impair CoT monitorability—the causes of
language drift are thus far poorly understood. In this paper, we identify the conditions under which
language drift occurs: we prove theoretically that RLVR optimization pressure permits unbounded
language drift, while supervised fine-tuning does not. We then show empirically that
language drift specifically arises during RLVR on novel
reasoning tasks—i.e. when the target behavior cannot be drawn out of the base model. Finally, we prove that it is not possible to constrain
language drift without constraining expected reward, suggesting that CoT monitorability cannot be improved without harming performance during RLVR post-training at the frontier.
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Artificial Intelligence
Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering
Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou et al. (+17 more)
The rapid maturation of artificial intelligence (AI) and machine learning (ML) has catalyzed a profound shift in how chemical engineering problems are formulated, analyzed, and solved. Advances in computing, data availability, and learning
algorithms have enabled AI/ML methods to impact applications spanning atomic-scale simulations, materials and catalyst discovery, transport and thermodynamics, separations, process systems engineering, and industrial operations. This article provides a perspective on recent methodological developments and representative applications, emphasizing how AI/ML tools are being integrated with first-principles models to address challenges of predictive accuracy, data scarcity, extrapolation, interpretability, and model lifecycle management. Across domains, a unifying trend is the move away from purely black-box approaches toward hybrid and physics-informed frameworks that explicitly respect conservation laws, thermodynamic consistency, and known structural constraints. These approaches not only improve robustness and reliability, but also enable meaningful human-AI collaboration by providing information at an appropriate level of abstraction for the task and decision context. We conclude that AI and ML are not replacing the core principles of chemical engineering; rather, they are amplifying them. As the field advances toward increasingly autonomous, adaptive, and sustainable systems, the thoughtful integration of AI/ML with first-principles understanding and domain expertise will be essential to realizing their full potential across both research and industrial practice.
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All authors Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou, Akhilesh Jain, Ankur Kumar, Thomas A. Kwan, Fèlix Llovell, Andrew J. Medford, Ilias Mitrai, Joel Paulson, Junyi Qiao, Matthew P. Rivera, Kirti C. Sahu, Lev Sarkisov, Zachary P. Smith, Calvin Tsay, Ching-Mei Wen, Victor M. Zavala, Huacheng Zhang, Dan Zhao