Artificial Intelligence · Cross-listed from cs.RO
FACT: Fidelity-Aware Construction of Articulated Twins
Kuixiang Shao, Chuansen Nie, Yinuo Bai et al. (+2 more)
Visually plausible articulated assets may still fail during contact interactions or exhibit inaccurate motion. We present FACT (Fidelity-Aware Construction of Articulated Twins), an agentic framework that progressively constructs articulated twins to improve geometry, contact, and dynamic fidelity. The
agent drives an evidence–diagnosis–revision loop on a shared editable representation, selecting measurements and model edits using quantitative feedback, while numerical tools execute and validate the updates. It reconstructs editable articulated geometry from images through feature planning, targeted measurements, and diagnostic refinement. On this reference, it repairs collision proxies through task-aware local repartitioning before fidelity-constrained compression. Finally, it constructs response models from passive-response
videos, using simulation residuals to guide model revision and constrained physical parameter fitting. Experiments show that FACT improves
geometric reconstruction over baselines, enables more reliable interaction with simpler collision proxies, and better reproduces held-out physical responses than direct parameter inference.
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All authors Kuixiang Shao, Chuansen Nie, Yinuo Bai, Jiayuan Gu, Jingyi Yu
Artificial Intelligence · Cross-listed from cs.LG
Beyond Compression: Diagnosing How Post-Training Changes Mathematical Reasoning
Hongyang Li, Yiming Zhu, Xiao Li et al. (+3 more)
Post-training is central to mathematical
reasoning in modern large
language models (LLMs), but endpoint pass@1 alone underidentifies what has changed. Gains may reflect newly reachable solutions, cheaper sampling of latent solutions, surface robustness, or memorisation. We compare three post-training paths under a common diagnostic readout: our sufficiently trained off-policy
distillation trajectories, released Qwen3 off-policy-plus-on-policy
distillation endpoints, and a released DeepSeek-Math endpoint trained with Group Relative Policy Optimisation (GRPO). Our probe uses cross-surface pass@K over verbatim
prompts, paraphrases, numerical isomorphisms, and translations, plus consistency, distribution-shape, and verified supervised-fine-tuning (SFT) membership analyses. We find two regimes. On easier AMC problems, large-K ceilings are near saturation, so post-training mainly compresses sample cost. On harder AIME problems, post-training expands the large-K ceiling over the base model: sufficient off-policy
distillation already raises this ceiling, Qwen3 released endpoints raise it further, and DeepSeek-Math GRPO does not dominate sufficient off-policy
distillation at large K. English-dominant
distillation improves non-English
reasoning but preserves
language-tier gaps. A controlled-overfit audit finds limited sensitivity in current SFT-membership probes. Compression is one regime of post-training, not a universal explanation.
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All authors Hongyang Li, Yiming Zhu, Xiao Li, Caesar Wu, Said Mammar, Pascal Bouvry
Artificial Intelligence · Cross-listed from cs.LG
A Comprehensive View of Fairness through Distributional Stability
Gayane Taturyan, Charlotte Laclau and Stephan Clémencon
We view
fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predictions change under perturbations that modify the composition of protected groups. A predictor is fair if it remains stable under such shifts. Under this perspective, several classical notions of
fairness arise as stability with respect to specific perturbations, with the associated unfairness gap given by a Lipschitz constant of a prediction-rate functional. This formulation also yields guarantees that hold uniformly over a range of demographic compositions at test time, without requiring knowledge of the deployment distribution. It leads to a learning procedure based on convex combinations of reweighted predictors, formulated as a second-order cone program, for which we establish generalization bounds. Experiments on standard
benchmarks illustrate the approach.
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Artificial Intelligence · Cross-listed from cs.IT
Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes
Amal Seddas, Vladyslav Shashkov, Maryna Viazovska et al. (+1 more)
Evolutionary program search driven by large
language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and beyond. We apply this approach to the longstanding problem of improving the best-known bounds for binary linear codes. Building on the EvoTune evolutionary framework and the ShinkaEvolve codebase, we introduce LinCodeEvolve, which evolves code-construction programs against an exact minimum-distance evaluator. A strategy loop combines diversity-driven search and expert supervision: when progress plateaus, new strategies are used to redirect the search. LinCodeEvolve discovers seven record-breaking codes, $[172,21,66]$, $[173,20,68]$, $[176,21,68]$, $[181,21,70]$, $[184,21,72]$, $[189,22,72]$ and $[200,21,77]$, six of which have concise quasi-cyclic descriptions. With standard code modification techniques, they improve $22$ entries of the tables. Every code is verified by exhaustive enumeration. These results suggest that LLM-guided search can help find improved codes and complement existing methods in coding theory.
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All authors Amal Seddas, Vladyslav Shashkov, Maryna Viazovska, Emmanuel Abbe
Artificial Intelligence
ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
Xianhui Zhang, Jian Yu, Chengyu Xie et al. (+6 more)
Ensuring the safety of
reasoning large
language models (LLMs) across
languages is essential for their reliable deployment. However, when exposed to jailbreak attacks in non-high-resource
languages, these models may generate unsafe responses even when their
reasoning traces identify safety risks. To address this issue, we propose aligning cross-lingual thoughts and responses (ACTR), a framework that improves
multilingual safety
alignment by strengthening the use of existing safety
reasoning. Specifically, we first present the think gap score (TGS) to compare the normalized contributions of
reasoning traces to
attention outputs during response generation across
languages, and use
reasoning- trace substitution to measure the cross-lingual safety gap. Next, using a corpus of jailbreak queries, we assess
neuron importance through changes in response representations caused by
neuron masking and compare the high-importance
neuron sets obtained with
reasoning enabled and disabled to identify safety think
neurons that support the use of safety
reasoning. Finally, we devise
neuron-selective consistency optimization (NSCO), which uses a frozen judge model to reward agreement between the safety categories of
reasoning traces and responses while updating only the parameters associated with the selected
neurons, requiring no human-annotated responses or preference data. Across two
reasoning models, ACTR achieves lower average attack success rates than the evaluated state-of-the-art methods on AdvBench-X and MultiJail, with safety gains extending to unseen
languages, while preserving or improving average performance on
multilingual knowledge and mathematical
reasoning tasks and limiting false refusals of benign requests. Warning: this paper contains examples with unsafe content.
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All authors Xianhui Zhang, Jian Yu, Chengyu Xie, Chenhang Cui, Shuyi Miao, Pengyang Shao, Yu Zheng, Fei Shen, Tat-Seng Chua
Artificial Intelligence
MatToolBench: Benchmarking Multimodal Agents in Real-World Materials Science Workflows
Mei Wu, Rui Xie, Runyu Zhang et al. (+6 more)
Multimodal GUI
agents have achieved impressive results on general software
benchmarks, yet their ability to operate professional scientific software remains largely unexplored. In materials science, sparse domain-specific web data, specialized interfaces, and tacit workflow conventions create blind spots that general-purpose pretraining cannot readily bridge. We present MatToolBench, the first real-environment
benchmark for evaluating
multimodal GUI
agents on professional materials science software, comprising 204 tasks across 10 tools in three modalities: GUI operation, OriginPro scripting, and code-based database queries, all executed inside a Windows 11 VM. Each task is decomposed into fine-grained sub-criteria by domain experts, enabling interpretable partial-credit scoring; the GUI component of our multi-level evaluation pipeline achieves an average F1 of 0.98. For OriginPro figure-generation tasks, we further conduct a human-LLM agreement study to validate the use of a
multimodal judge for secondary aesthetic assessment. Our experiments show that strong performance on general
benchmarks does not transfer to professional scientific workflows, and that this gap is not a visual-grounding problem alone: failures arise from domain-specific operational knowledge, sparse pretraining coverage of scientific software, weak cross-tool artifact handoff, and critical states exposed only visually. Even the best model reaches only 25% success rate on GUI tasks and 45% on code tasks. MatToolBench therefore serves as a challenging diagnostic
benchmark and real-environment testbed for data-scarce, knowledge-intensive scientific workflows.
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All authors Mei Wu, Rui Xie, Runyu Zhang, Yuqiang Li, Tianfan Fu, Bo Chen, Kai Yu, Xin Chen, Lu Chen
Artificial Intelligence · Cross-listed from cs.LG
Beam Search as Test-Time Self-Distillation via Counterfactual Contexts
Su Ee Tan, Xiaotong Ji, Rasul Tutunov et al. (+2 more)
Self-
Distillation Fine-Tuning (SDFT) enables a
language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mutual information, which guides on-policy learning without external supervision. However, SDFT operates at training time: it requires gradient updates and access to expert demonstrations, making it inapplicable at inference. We propose test-time self-
distillation, a decoding-time method that extracts a steering signal from the self-
distillation framework without any parameter updates, reward models, or training data. Our key insight is that counterfactual contexts, i.e. fixed textual templates that hypothetically
prime the model for excellent versus poor
reasoning, can substitute for the demonstration. The log-odds ratio of a candidate answer under these two counterfactual conditions defines a new reward signal. We derive the optimal KL-regularized policy under this reward, which takes the form of a Gibbs reweighting of the base distribution. Crucially, this reweighting is global: it cannot be decomposed into independent per-
token operations without ignoring future trajectory quality. We therefore approximate the target distribution via beam search. Experiments on mathematical
reasoning (MATH500), code generation (HumanEval), and graduate-level science QA (GPQA) across multiple model scales show that test-time self-
distillation improves over standard sampling, low temperature, beam search and power sampling baselines on average, demonstrating that the self-
distillation principle can be operationalized at inference time.
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All authors Su Ee Tan, Xiaotong Ji, Rasul Tutunov, Haitham Bou-Ammar, Matthieu Zimmer
Artificial Intelligence · Cross-listed from cs.CL
Selecting The Most Informative Tokens in Natural Language Autoencoders
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez et al. (+4 more)
Natural
language autoencoders translate a
language model's internal activations into readable explanations. Explaining every
token position is costly. Which positions should an auditor inspect to understand a potential threat? We study this question across $4.7$ million explanations on
prompt injection and concealment. We compare signals from model computation with a ranker trained only on chat structure. Chat structure usually selects more relevant explanations than the computational signals, without requiring a model forward pass for position selection. On three of four
datasets, explaining just $5\%$ of positions retains nearly all of the success rate from explaining every position, where success means obtaining an explanation about the threat. The benefit varies with the audit task. We also show that pretrained verbalizers recover words that models have learned to conceal through fine-tuning, without additional verbalizer training. These results identify where auditors can concentrate explanation generation and show that useful explanations can extend beyond the model a verbalizer was trained to describe.
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All authors Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez, Amon Rapp, Luigi Di Caro, Peter Schneider-Kamp, Lukas Galke Poech
Artificial Intelligence · Cross-listed from cs.LG
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Haoran Xu, Xingzhuo Guo, Yuchen Zhang et al. (+3 more)
Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability.
Diffusion models are well suited to modeling complex precipitation distributions, yet existing approaches often introduce increasingly specialized designs, leaving the capability of a standard
diffusion architecture underexplored. We show that a standard
Diffusion Transformer already provides a simple and scalable foundation for precipitation nowcasting, with domain-specific requirements accommodated naturally within its design space. Based on this principle, we develop NowcastDiT and instantiate this flexibility through two complementary adaptations: a dynamics-aware noise prior for temporally coherent forecasts, and end-to-end reinforcement learning with timestep-aware rewards for meteorological skill. Experiments on SEVIR and MRMS
benchmarks show that NowcastDiT achieves state-of-the-art performance in both perceptual quality and meteorological skill. These results suggest that standard DiT can serve as an effective foundation for precipitation nowcasting.
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All authors Haoran Xu, Xingzhuo Guo, Yuchen Zhang, Jincheng Zhong, Jianmin Wang, Mingsheng Long
Artificial Intelligence
Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
Ziheng Huang, Yicheng Bao, Xueheng Li et al. (+8 more)
Streaming
video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches model response timing or compress history, but an online state formed before future questions are known can omit visual details before later questions reveal their relevance; the retained state alone cannot recover them. We introduce Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming
video reasoning. WTI maintains compact natural-
language memory entries tagged with source-
video time ranges; these entries support direct
reasoning when sufficient and otherwise anchor selective recall of finer visual evidence. For each question, WTI answers when current context and
memory suffice, continues watching when required evidence has not appeared, or recalls a relevant past interval and decides again after incorporating the returned chunks, without replaying the full observed history. To train this behavior, we construct WTI-82K, comprising 82,335 timed questions across 4,812 causally aligned trajectories, and develop Stream-GDPO to optimize complete multi-question streaming rollouts using trajectory-level feedback for response timing, source-
video recall, and
memory updates. WTI achieves state-of-the-art aggregate performance among the compared open-source streaming baselines, reaching 83.3% on StreamingBench and 73.6% weighted overall accuracy on OVO-Bench.
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All authors Ziheng Huang, Yicheng Bao, Xueheng Li, Zhenkun Gao, Bangwei Liu, Kunquan Li, Yuxiang Shen, Bangyan Li, Xuejiao Wang, Changbo Wang, Gaoqi He
Artificial Intelligence
FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
Mengjun Yi, Huaian Gu, Yinghao Ai et al. (+2 more)
Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneity makes a single global adapter insufficient for personalized prediction. Existing personalized methods typically use the same low-rank structure for both shared and private adaptation, overlooking their distinct requirements for aggregation and personalization. We propose FedLAFP, a role-aware framework that couples a compact, globally aggregated LoRA branch with a client-private, full-rank-capable RandLoRA branch. The shared branch provides an efficient interface for transferring common knowledge, whereas the private branch combines fixed random low-rank bases with learned scaling coefficients to provide expressive client-specific adaptation without additional communication. Client- and layer-specific mixing coefficients jointly fuse the two branches, and only the shared LoRA parameters are exchanged. A controlled linear study supports this role assignment: LoRA yields more aligned client updates and lower aggregation error, while RandLoRA more accurately recovers client-specific residuals. Experiments across four visual recognition
benchmarks show that FedLAFP consistently outperforms local-only and
federated LoRA baselines, achieving an average personalized accuracy of $86.93\%$ and exceeding the best baseline average by $1.30$ percentage points.
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All authors Mengjun Yi, Huaian Gu, Yinghao Ai, Furao Shen, Jian Zhao
Artificial Intelligence · Cross-listed from cs.CV
MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos
Jiahao Zhan, Yongrui Ma, Qunliang Xing et al. (+6 more)
Despite rapid progress in
video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. However, most existing
video quality evaluations focus on aesthetic quality or text-
video alignment. To address this gap, we study object-centric motion fidelity assessment, evaluating target objects along object consistency, motion continuity, and physical plausibility. To achieve this, we first introduce VidMotion, a diagnostic
dataset of 6,879
videos with designated moving objects and fine-grained annotations including dimension-wise scores and failure causes. We further propose MotionInsight, a diagnostic evaluator that shifts assessment from implicit RGB-frame observation to explicit motion-space diagnosis. By constructing motion-aware representations, MotionInsight makes subtle motion deficiencies more observable. We also introduce motion-specific rewards during GRPO to transform observed motion into a diagnostic assessment. Experiments demonstrate that MotionInsight provides an effective basis for diagnosing object motion deficiencies, producing human-aligned scores along three dimensions and grounded explanations.
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All authors Jiahao Zhan, Yongrui Ma, Qunliang Xing, Xuanyu Zhang, Jingqi Tong, Junlin Li, Li zhang, Shijie Zhao, Tianfan Xue
Artificial Intelligence · Cross-listed from cs.LG
LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter
Hao-Yuan He, Peng-Fei Liu, Si Shen et al. (+1 more)
Speculative decoding accelerates autoregressive inference by verifying multiple draft
tokens in a single target forward pass. However, as the context grows, existing state-of-the-art drafters become increasingly expensive, eroding the very efficiency advantage they are designed to provide. We argue that this scaling is unnecessary. A standalone
language model must grow with its prefix because it is solely responsible for every
token it produces. A drafter, by contrast, only proposes candidates; the target catches and corrects every error before any
token is committed. The drafter's decoding cost can therefore be made entirely independent of the prefix length. We introduce LongSpark, a block-
diffusion drafter that achieves this by extracting fixed-size, multiscale views from the target's verification pass, thereby eliminating the need for a growing persistent state. Extensive evaluations demonstrate that LongSpark achieves state-of-the-art end-to-end efficiency across multiple model scales and realistic serving conditions. Notably, it delivers the lowest time-per-output-
token on long-context tasks while reducing the drafter's context state by several orders of magnitude.
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All authors Hao-Yuan He, Peng-Fei Liu, Si Shen, Ming Li
Artificial Intelligence
Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition
Yihao Ouyang, Shiwei Li, Haozhao Wang et al. (+5 more)
Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-tuning (FFT). Many LoRA variants improve the initialization or optimization of low-rank factors. At each training step, however, their first-order weight-space directions are constrained by the current parameterization. We characterize the corresponding LoRA-accessible gradient space and show that it coincides with the tangent space induced by the current LoRA parameterization. This characterization yields an orthogonal decomposition of the full weight gradient at the current model parameters. We term the component orthogonal to this space the normal gradient. Based on this decomposition, we propose GDLoRA (Gradient-Decomposed Low-Rank Adaptation). GDLoRA reconstructs the full weight gradient from forward activations and backward signals, extracts its normal component, and directly updates the base weights with this component, while retaining standard AdamW optimization for the LoRA factors. GDLoRA incorporates complementary normal gradients without increasing standard LoRA's optimizer-state
memory budget under matched adapter and optimizer configurations. Experiments on natural
language understanding, mathematical
reasoning, commonsense
reasoning, and image classification show that GDLoRA consistently improves over LoRA and narrows the performance gap to FFT. The code is available at https://anonymous.4open.science/r/GDLoRA.
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All authors Yihao Ouyang, Shiwei Li, Haozhao Wang, Xiandi Luo, Zhuoqi Hu, Jinglun Yu, Yichen Li, Ruixuan Li
Artificial Intelligence
AnyAct: Universal Action for Self-Evolving Agents
Lingrui Xu, Yangqin Jiang, Jiachang Zhang et al. (+2 more)
As large
language models (LLMs) advance, AI
agents are increasingly deployed in open-world environments to tackle complex sequential tasks (e.g., document processing, cross-application collaboration), relying heavily on actions ranging from GUI operations to semantic APIs. However, three core challenges persist: the "scale dilemma" of massive tool ecosystems exceeding LLM context windows, the "non-stationarity" of tool quality due to updates or outages, and the "heterogeneity" of feedback formats (pixels, text, structured data) creating information silos. To address these, we propose AnyAct, a universal action layer that unifies available capabilities into a self-evolving action space, enabling
agents to operate efficiently and reliably in large-scale, dynamic tool ecosystems. AnyAct's core design focuses on two objectives: constructing this action space via hierarchical progressive
retrieval (filtering task-relevant actions) and test-time reliability
evolution (
pruning unreliable actions), and enabling reliability-aware action orchestration through a heterogeneous observation grounding module that unifies multi-modal feedback. Additionally, it defines a hybrid action space (primitive + semantic actions) and optimizes for a balance between task success rate and execution cost. Evaluations on LiveMCPBench and OSMCP (a new
benchmark we developed for multi-granularity action collaboration) demonstrate state-of-the-art performance. AnyAct delivers substantial performance gains over baseline methods across various LLM base models on LiveMCPBench and improvements are particularly notable for models with constrained native capabilities. On OSMCP, it achieves 77.27% overall success with only 50 steps, which is half the steps required by most competitors.
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All authors Lingrui Xu, Yangqin Jiang, Jiachang Zhang, Xubin Ren, Chao Huang
Artificial Intelligence
Language as the Interface: Foundation-Model Contrastive Learning Links Transcriptomes and Electrophysiology
Junbo Shen, Jinying Gao and Bo Lei
Integrating transcriptomic and electrophysiological data is essential for building
multimodal foundation models for neuroscience. Patch-seq provides paired measurements of gene expression and intrinsic electrophysiology from the same
neuron, establishing a basis for training cross-modal models. Here we introduce LangPatch, a foundation-model-based contrastive learning framework that uses paired Patch-seq data to align pretrained GenePT representations with electrophysiological phenotypes through a
language-based interface. Gene descriptions and verbalized electrophysiological profiles are embedded by the same frozen text encoder. A context adapter and projection modules connect the modalities through paired contrastive learning. Across mouse visual, mouse motor, and human cortical cohorts, LangPatch achieves the highest mean transcriptome-to-electrophysiology prediction correlation among the evaluated foundation-model and representation-learning methods. It also improves held-out cross-modal
alignment in the two mouse cohorts (FOSCTTM 0.107/0.135 vs. 0.208/0.222 for JAMIE, an existing cross-modal Patch-seq imputation method). It predicts transcriptomic family, type, cortical layer, and marker-gene expression from electrophysiology, exceeding other baselines on most endpoints. More importantly, the method transfers across
brain areas and species: a model trained on mouse visual cortex predicts electrophysiology in motor cortex with approximately 70% correlation retention and in human cortex with 47% (58% on acute-slice recordings). Together, these results demonstrate
alignment between molecular and functional representations of
neurons, providing a building block for
multimodal foundation models in neuroscience.
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Artificial Intelligence
Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization
Guoqing Zhang, Rafik Hadfi and Takayuki Ito
Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization. While classical queueing theory, specifically open Baskett–Chandy–Muntz–Palacios (BCMP) networks, provides an interpretable mathematical
topology for healthcare operations, analytical models rely on stationary assumptions and fixed routing
matrices that degrade under state-dependent real-world dynamics. Conversely, centralized reinforcement learning approaches struggle to accommodate the decentralized structure of hospital governance, where individual clinical departments function with localized observations, heterogeneous resources, and divergent operational objectives. In this paper, we present a Multi-
Agent Systems (MAS) framework titled Physics-Informed Multi-
Agent Coordination, which embeds empirically calibrated BCMP queueing
topologies as physical priors within a decentralized multi-
agent reinforcement learning architecture. Formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) under coupled resource constraints, our method enables autonomous departmental
agents to cooperatively negotiate patient routing and dynamic service scaling. To mitigate environmental non-stationarity without inducing excessive communication overhead,
agents exchange localized action fingerprints along network edges and optimize a spatially decomposed reward structure. Empirical evaluations driven by real-world MIMIC-IV patient trajectories indicate that this cooperative multi-
agent approach substantially reduces cumulative system delay compared to static Markovian approximations, heuristic dispatching, and independent multi-
agent baselines, while maintaining clinical safety constraints.
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Artificial Intelligence · Cross-listed from cs.CV
Embedded Bi-Temporal Building Damage Assessment for On-Board Data Reduction
Thomas Goudemant, Benjamin Francesconi, Marjorie Bellizzi et al. (+1 more)
Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire relevant imagery shortly after an event, but exploitation is limited by uplink and downlink capacity and by ground-processing latency. We address this with a bi-temporal building damage assessment pipeline built on a siamese detector derived from YOLOX, designed to compress information at both ends of the ground/space link. On the ground, pre-disaster reference images are encoded into a compact latent space – compressed by up to a factor of 64 – and uplinked to the satellite. On board, this reference is compared with a fresh post-disaster acquisition so that the downlink carries only actionable object-level products, bounding boxes and damage classes, instead of full scenes. This cuts the data exchanged in both directions, while on xBD the strongly compressed reference still preserves most of the detection performance. Because on-board acquisitions suffer from residual pre/post co-registration errors, we introduce a latent-space shift estimation and correction module that regresses the global offset from the coarse feature level and realigns the post-disaster features before fusion. It substantially improves robustness to de-registration – especially under large shifts, where fusion-only variants collapse – while also raising nominal accuracy and remaining compatible with the strongest compression. We finally port the pipeline to two embedded targets, a Xilinx Versal VCK190 and an NVIDIA Jetson AGX Orin, and report hardware performance (latency, throughput, power efficiency). The core detector and its compression port cleanly to both, but the operators needed for long-range robustness survive only on the Jetson GPU, whereas the Versal DPU does not.
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All authors Thomas Goudemant, Benjamin Francesconi, Marjorie Bellizzi, Adrien Dorise
Artificial Intelligence
CADOC: Cache-Aware Dynamic Object Context for Long-Horizon Agents
Junjie Yao, Zhangchen Zhou and Zhi-Qin John Xu
For a long-horizon
agent, context is the bottleneck: the history is resent with every request, the window caps task length, and
reasoning degrades as the history grows. Replacing structured objects with compact
retrieval Cards shortens the
prompt and keeps the exact originals retrievable, but editing the history can break prefix-cache reuse, and prior recoverable methods time their edits by forecasts of future reuse or by preset intervals. We propose CADOC (Cache-Aware Dynamic Object Context), an online
algorithm that replaces structured objects with compact Cards while preserving exact, on-demand
retrieval of their original contents. CADOC schedules replacements in batches by balancing accumulated waiting cost against shared cache-reconstruction cost. Its scheduling rule follows from an economic order quantity trade-off, recovers the optimal integer batch under stationary assumptions. Across evaluation, CADOC consistently achieves the lowest aggregate input cost among the compared configurations, which reduces input cost by approximately 40% on average while maintaining task performance close to full context. CADOC thus provides a cost-derived approach to compressible context management, demonstrating that efficient compression depends not only on shortening
prompts but also on scheduling edits to preserve cache reuse.
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Artificial Intelligence · Cross-listed from cs.CR
OPFL: Optimistic Verification of Federated Learning via Empirical Boundary
Hongxu Su, Jianzhu Yao, Xuechao Wang et al. (+1 more)
Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into local training makes it difficult to verify whether clients follow the prescribed training procedure or submit malicious updates, such as model poisoning. A natural approach is to replay client training for verification. However,
privacy-preserving replay produces numerical results that cannot be directly matched with local client execution because the two run in different environments. We present OPFL, an optimistic verification framework for
privacy-preserving
federated learning. To protect data
privacy, OPFL performs replay inside secure multi-party computation (MPC). Although gradients computed on MPC and local GPUs are not bitwise identical, we observe that their absolute differences are stable and bounded. OPFL therefore calibrates an empirical boundary offline and uses it to distinguish benign numerical deviations from malicious manipulation. To reduce the cost of expensive MPC replay, OPFL adopts optimistic verification by post
auditing only sampled training steps. Experiments on LeNet, BERT, and Qwen show that the boundary generalizes across
datasets, input lengths, and GPUs, while achieving $0$% ASR against model poisoning and PGD-based attacks. On a LeNet workload, at $p=0.01$, OPFL is approximately $98.6\times$ faster than full MPC-based FL and $625.5\times$ faster than ZK-based approach.
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All authors Hongxu Su, Jianzhu Yao, Xuechao Wang, Pramod Viswanath
Artificial Intelligence · Cross-listed from cs.CV
Visual Parallel Search: Learning to Search High-Resolution Images with Parallel Tile Inspection and Adaptive Zoom
Xijia Tao, Yihua Teng, Xinyu Fu et al. (+5 more)
High-resolution visual question answering often fails because a
multimodal model does not acquire the small, spatially localized evidence needed to answer a question. Sequential zooming can recover detail, but it asks the main model to choose a region before obtaining a reliable overview. We introduce VPS, a visual parallel-search framework in which a main
agent first invokes grid_search to inspect image tiles in parallel with question-conditioned sub-
agents, and then adaptively invokes zoom_in on a precise or merged region. The same interface supports both training-free inference and post-training of the main and sub-
agents. Across five
benchmark splits and three model sizes, VPS improves mean accuracy over dedicated zoom-only search in 14 of 15 same-model comparisons, with gains up to 8.0 points and especially strong improvements for smaller main models. ZoomBench retains an approximately 3.2-point gain at every tested size. We further develop a supervision pipeline with hint-free verification and a paired role-specific GRPO surrogate for learning the controller and tile-reader roles. SFT improves observed accuracy on all five
benchmark splits, including a 4.17-point gain on HR-Bench 4K. Role-specific RL further reshapes search behavior: main-only RL reduces mean tool use from 2.65 to 2.11 with similar pass@1 in an internal four-response evaluation, while external accuracy changes are mixed. Joint training reveals an asymmetry between local evidence reading and global search control. Together, these results support VPS as an effective inference-time scaffold and a trainable decomposition for visual evidence acquisition.
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All authors Xijia Tao, Yihua Teng, Xinyu Fu, Cheng Gong, Ziru Liu, Xudong Xie, Rui Liu, Lingpeng Kong
Artificial Intelligence · Cross-listed from cs.CV
Parameterized Stripe Attention for Efficient Video Generation
Xingyu Jia, Baole Ai, Ang Wang et al. (+2 more)
Diffusion Transformers (DiTs) enable high-quality
video generation but suffer from substantial inference latency, primarily attributable to the computationally expensive full spatio-temporal
attention. While sparse
attention methods offer potential solutions, existing approaches face an inherent flexibility–efficiency dilemma: predefined masks lack the flexibility to capture diverse
attention patterns, while runtime-determined masks introduce overheads and sacrifice hardware efficiency. We identify the lack of a unified structural characterization of DiT
attention as a key limitation of existing methods, and establish that
video DiT
attention exhibits periodic diagonal stripe structures along both temporal and spatial dimensions. To formally encode these structured patterns within a single efficient kernel, we present PSA, a parameterized stripe
attention that formalizes the observed stripe regularity, unifying diverse
attention patterns for efficient mask generation. This unified representation enables a single hardware-efficient CUDA kernel to process all sparse patterns, achieving FlashAttention-3-level Model FLOPs Utilization. To determine optimal sparsity configurations, we propose a training-free offline search
algorithm that automatically maximizes sparsity under a specified error tolerance for each
attention head. Experiments on HunyuanVideo and Wan~2.1 demonstrate that PSA achieves 1.57$\times$ and 1.37$\times$ end-to-end speedups over FlashAttention-3 baselines, with acceptable visual quality degradation.
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All authors Xingyu Jia, Baole Ai, Ang Wang, Kang Zhao, Yong Li
Artificial Intelligence · Cross-listed from cs.SE
Cross-Organizational SysML Model Integration: A Survey of Challenges and AI-Supported Tasks
Zirui Li, Torsten Brix and Stephan Husung
Cross-organizational collaboration is widely regarded as a key promise of SysML-based Model-Based Systems Engineering (MBSE), yet practitioners still face persistent challenges when exchanging and integrating system models. In parallel, Large
Language Models (LLMs) raise expectations for AI-assisted model understanding and integration, while reliability and required human oversight continue to pose challenges. This paper reports the results of an online questionnaire survey with 29 MBSE stakeholders involved in cross-organizational collaboration. Respondents rated eight predefined integration challenge categories and six AI-supported task types on five-point Likert scales. The results indicate that stakeholders perceive model integration as a multi-dimensional
alignment problem across semantics, behavior, traceability, and exchange interoperability. These perceptions vary by organizational role and frequency of integration involvement. AI is rated highly useful for analysis tasks such as semantic structure analysis and inconsistency detection, and respondents predominantly prefer human-in-the-loop use with mandatory verification. These findings motivate AI support that enhances, rather than replaces, engineering responsibility in SysML-based integration.
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Artificial Intelligence
ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction
Alberto Bernardi, Luca Costabello and Christophe Gueret
Knowledge Graph
Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missing links. However, the quality of the learned representations varies a lot across different areas of the graph. If previous research has loosely linked the problem to relation types or degree bias, we show that it is more widespread and it correlates with the degree imbalance of the entities in test triples. In particular, the prediction of a target entity that has a degree much smaller than the degree of the anchor entity is extremely problematic. This is critical in recommender systems and other use cases, where these triples represent important corner cases. To address this issue, we propose an inference-time latent search optimization method capable of significantly improving model predictions on the most imbalanced triples. Built on top of a pre-trained model, it explores the
embedding space at evaluation time, blending known and out-of-band information to mitigate the degree imbalance bias. We show the value of our approach on imbalanced triples from common
benchmark datasets, where we outperform conventional methods, opening the door to the successful adoption of Knowledge Graph
Embedding models on these critical corner cases.
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Artificial Intelligence · Cross-listed from cs.CV
Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation
Xingtong Ge, Yutong Wang, Lunjie Zhu et al. (+7 more)
Few-step streaming audio–
video generation requires both causal modeling and step
distillation, yet standard training recipes face two context-related challenges. Teacher forcing pairs clean history with a noisy target, but supervises predictive contextual representations only indirectly through velocity prediction. Meanwhile, directly reusing bidirectional score models in causal Distribution Matching
Distillation (DMD) creates a mismatch between generation and scoring contexts. We address these challenges with Salt++, a two-stage post-training framework comprising Causal Self-Flow (CSF) and context-aligned autoregressive DMD. CSF exploits contextual information asymmetry by varying the history while keeping the noisy target fixed: a noise-mixed-history student aligns its intermediate representations with those of a clean-history exponential-moving-average teacher. This self-supervised signal encourages the student to extract semantic information and improves cross-modal
alignment. Context-aligned AR DMD shares the causal mask and prefix across generator sampling, fake-score training, and real-score evaluation to match generated and reference distributions under a block-conditional KL objective. With calibrated teacher guidance, it performs clean-prefix few-step
distillation and then adapts to generated histories without switching objectives or requiring separate consistency
distillation. At 480p, Salt++ improves visual and motion quality by 57% and 45% over OmniForcing on JavisBench under the same 4-step causal setting. A separate scale-wise post-training stage extends Salt++ to 4-step $1664\times960$ generation, outperforming bidirectional LTX-2 on six of seven reported metrics. Project page: https://xingtongge.github.io/Saltpp
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All authors Xingtong Ge, Yutong Wang, Lunjie Zhu, Haitao Lin, Fangyu Lin, Yushi Huang, Xin Zhang, Yi Zhang, Yu Liu, Jun Zhang
Artificial Intelligence
CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
Mengjun Yi, Langxing Yang, Suhan Guo et al. (+2 more)
Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental mismatches under heterogeneous client data: a structural aggregation mismatch caused by independently averaging LoRA factors, and a statistical collaboration mismatch caused by enforcing a single global adapter across divergent clients. To address these issues, we propose CF-LoRA, a clustered
federated LoRA fine-tuning framework that combines decoupled factor aggregation with adaptation-aware client clustering. CF-LoRA first learns a globally shared $A$ factor while retaining personalized $B_i$ factors, then identifies clients with similar adaptation patterns based on the cosine similarity of their learned $B_i$ factors, and finally performs intra-cluster $B$-factor aggregation with a frozen $A$ factor. By decoupling LoRA factor aggregation, CF-LoRA preserves the low-rank structure and mitigates the structural aggregation mismatch, while adaptation-aware clustering promotes collaboration among clients with similar adaptation patterns and reduces negative transfer caused by statistical heterogeneity. Experiments on four
language tasks and four
vision datasets with RoBERTa and ViT show that CF-LoRA achieves the highest average accuracy in both modalities while communicating only one LoRA factor per optimization round.
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All authors Mengjun Yi, Langxing Yang, Suhan Guo, Furao Shen, Jian Zhao
Artificial Intelligence · Cross-listed from cs.LG
Abductive World Modeling via Causal Representation Learning
Ziqi Liu, Songhan Yang, Linfan Zhou et al. (+4 more)
The central challenge of world modeling is to learn representations that capture how the world evolves. However, existing world models predominantly represent future states without explicitly capturing the latent causes underlying their
evolution, limiting their ability to reason about why and how the world changes. To address this limitation, we propose Abductive World Modeling (AWM), a framework that learns structured causal representations by abductively inferring latent causes from predicted futures. Specifically, we realize AWM through the Hierarchical Abductive State Pyramid (HASP), which organizes the inferred world state into three complementary components - Entity, Dynamic, and Relation - capturing what exists, how it changes, and how entities interact, respectively. By jointly
reasoning over the current observation and its predicted future, HASP abductively infers these latent factors and integrates them into a structured state representation for downstream
reasoning. To the best of our knowledge, AWM is the first framework to introduce abductive state inference into latent-space world modeling for learning structured representations of world dynamics. Experiments across physical prediction, causal
reasoning, and action understanding demonstrate the effectiveness of our approach. Compared with V-JEPA, a state-of-the-art latent-space world model, AWM improves physical prediction AUROC by 10.7%, causal
reasoning accuracy by 16.8%, and action Top-1 accuracy by 68.0%.
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All authors Ziqi Liu, Songhan Yang, Linfan Zhou, Jiatong Liu, Lijun Peng, Long Wan, Yinqi Bai
Artificial Intelligence
REALHOP: Rethinking Multi-Hop Reasoning Evaluation via Behavioral Auditing
Jiawen Tao, Xiaokun Yuan, Yaoming Li et al. (+4 more)
Complex questions often require multi-hop
reasoning that connects facts distributed across sources or distant regions of a long context through intermediate steps.
Benchmarks commonly evaluate this ability with questions built around predefined
reasoning chains, treating a correct answer as evidence that the intended composition was used. Yet answer correctness alone leaves open whether success depends on the evidence associated with each intended step: models may instead rely on memorized associations, shorter paths, or partial evidence. We examine this dependence using the Behavioral Necessity Rate (BNR), which measures how often targeted evidence removal prevents answer recovery on initially correct instances. Across five existing
benchmarks, panel-mean BNR ranges from 16.6% to 48.9%, exposing a substantial gap between annotated structure and observed dependence. Guided by this diagnosis, we introduce REALHOP, a diagnose-construct-verify framework that rebinds entities, factorizes selected relations, adds complete competing paths, and places evidence at traceable locations. Structural and semantic checks precede freezing; behavioral interventions follow. On 790 paired MuSiQue questions, REALHOP raises panel-mean BNR from 27.4% to 94.4% while retaining high Full accuracy. It also yields high BNR on REALHOP-FRAMES and REALHOP-LONGBENCH. On 216 long-context questions, the matched multiple-choice spread across 16 models grows from 13.9 to 59.2 points and persists under repeated open-ended evaluation. Together, these results show that a conceptually coherent chain and a correct final answer do not by themselves establish multi-hop
reasoning. Verifying that success depends on every intended hop is therefore as fundamental to multi-hop evaluation as measuring answer accuracy itself.
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All authors Jiawen Tao, Xiaokun Yuan, Yaoming Li, Chenxu Liu, Mengzhou Wu, Tong Yang, Maxm Pan
Artificial Intelligence · Cross-listed from cs.CL
SRJudge: Empowering Large Language Models with Selective Reasoning for Fine-Grained Knowledge Concept Tagging
Zhiwei Yang, Jiahua Yang, Huiru Lin et al. (+2 more)
Knowledge concept tagging aims to assign specific concept or topic labels to educational content, which is essential for both educators and learners in traditional and online teaching practices. Recent work has explored large
language models (LLMs) for this task, achieving promising performance. However, LLMs still struggle to select the correct concept from a large-scale candidate set due to the high dimensionality of the decision space. In this paper, we propose a novel three-stage Select-Reason-Judge (SRJudge) framework, which empowers LLMs with selective
reasoning capability for fine-grained knowledge concept tagging. Specifically, the Selector in Stage 1 first narrows the candidate concepts to a top-K shortlist by fine-tuning a small
language model (SLM), e.g., BERT, since the top-$K$ predictions hit the correct concept in most cases, thereby reducing the decision space of correct candidates. Next, the Stage 2 Reasoner employs a lightweight LLM for refined
reasoning over the shortlisted candidates. It further integrates an improved reinforcement learning strategy with a dynamic task-specific reward function and a
pruning mechanism to better align with human
reasoning preferences. Finally, a larger LLM acts as a judger that evaluates the overall rationality of the
reasoning process and its explanations to determine the final output. In addition, we construct two high-quality
datasets for further validation, i.e., the biology
dataset S_Bio and the physics
dataset S_Phy. Experimental results demonstrate that our method consistently outperforms state-of-the-art baselines across
benchmark datasets, verifying its effectiveness and superiority. Resources are available at: https://github.com/Nicozwy/SRJudge.
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All authors Zhiwei Yang, Jiahua Yang, Huiru Lin, Xing Chen, Quanlong Guan
Artificial Intelligence · Cross-listed from cs.LG
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Donguk Kwon, Wooseok Jeong and Dongha Lee
Covariate effects vary across contexts and shift over time, requiring forecasters to assess how to use them for each
forecasting context. As
forecasting proceeds, observations for earlier forecasts become available, providing feedback on past
covariate use for subsequent forecasts. However, when multiple
covariates act together, the forecast error reveals the numerical discrepancy from the observation but not how the
covariates should have been used. We introduce JudgeCast, an experience-based framework for time series
forecasting with
covariates. Following the judgmental adjustment practice, a frozen TSFM provides the base forecast, while a frozen LLM uses the current context and relevant experience to adjust it. Within the adjustment, assessing
covariate effects and determining the numerical adjustment serve distinct roles, so JudgeCast first forms explicit
covariate-wise judgments and then determines the adjustment. After observation, JudgeCast uses the observed residual of the base forecast to reconstruct alternative judgments and evaluates the original and alternatives through their resulting adjustments. The best-performing decision is selected and retained as validated experience for subsequent forecasts. Across diverse real-world
datasets, JudgeCast outperforms strong baselines. Ablations show that explicit
covariate-wise judgment can improve forecast-time adjustment, while residual-guided experience construction yields more reliable
forecasting gains than retaining raw decisions as experience.
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