Systems and Control
Adaptive Handling of Deadline Misses via Stochastic MPC
Melanie Gallant, Paolo Pazzaglia, Christoph Mark et al. (+2 more)
Real-time control systems may experience sporadic computational overloads that cause controller executions to overrun their expected deadlines, affecting the timely update of control inputs. Such overruns are classically handled with static decisions, e.g., by either stopping and discarding the late execution at the deadline instant, or continuing it while skipping the next activations. This paper first demonstrates that such static policies can lead to suboptimal performance when the controlled system undergoes varying operating conditions, and thus proposes a control-driven hybrid overrun strategy that optimally adapts the deadline miss handling at runtime. We model the closed loop as a Markov jump linear system, whose modes encode overrun histories, and we formulate a scenario-tree-based
stochastic model predictive control problem that predicts and optimizes over possible sequences of deadline hits and misses. We evaluate our proposal on linear
benchmarks including a double integrator and an aircraft pitch system.
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All authors Melanie Gallant, Paolo Pazzaglia, Christoph Mark, Kevin Schmidt, Martina Maggio
Cryptography and Security
Pin It on Anyone: Certified Targeted Displacement of Multi-Bit LLM Watermarks Without the Key
Paweł Batorski, Przemysław Spurek and Paul Swoboda
Multi-bit watermarking embeds a message, typically an account identifier, into LLM output so that harmful text can be traced to its origin. We show an attack technique that allows to alter the watermarking message. This allows to, for example, exchange the embedded account ID to an arbitrary different one. The attacker mainly needs access to a few accounts for generating LLM output and model logits, but crucially not the secret key used to watermark. The attacker then rewrites the text, keeping its meaning, so that the watermarking message decoder recovers the watermarking message embedded by the attacker. We are, to our knowledge, the first to propose such an attack, which we call a targeted displacement. Theoretically, we present upper and lower bounds on the probability of our attack succeeding. This bound depends on the number of accounts the attacker has access to, the length of the message and some characteristics of the given text. Our attack can succeed whenever we can bound the ambiguity of the generated text well enough. Empirically, we test our attack technique against five watermarking methods and four LLMs. Attack success tracks our predicted bounds, allowing us to alter watermarking messages in almost all cases with high accuracy, given enough accounts. Our code can be found at https://github.com/Batorskq/PinIt.
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Computer Vision
Urbex: Agentic Spatial Grounding for City-Scale 3D Scenes
Yu Wang, Rui Dai, Wei Chen et al. (+5 more)
City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outdoor scenes. We propose Urbex, an agentic
vision-
language framework that instead casts grounding as active spatial search. Urbex represents a city-scale 3D scene as an interactive multi-view environment, allowing a VLM
agent to query landmarks, zoom into local regions, render oblique views, and commit a grounded bounding box. We optimize this tool-use policy with reinforcement learning on CityRefer, using smooth localization rewards and lightweight shaping rewards for efficient exploration. Experiments show that Urbex achieves the best Hit@1 and IoU on CityRefer, supports candidate-free grounding without ground-truth boxes, and is substantially faster than the strongest city-scale ranking baseline. Zero-shot results on STPLS3D-Refer further indicate improved cross-scene robustness.
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All authors Yu Wang, Rui Dai, Wei Chen, Yi Wang, Bo Dang, Kaikui Liu, Xiangxiang Chu, Yansheng Li
Artificial Intelligence
ABot-ER: Unified Embodied Reasoning through Agentic Data-Model Co-Evolution
Qiuyu Ding, Heng-Da Xu, Changda Xia et al. (+14 more)
We introduce ABot Embodied
Reasoning (ABot-ER), a unified embodied
reasoning foundation model that breaks the tension between breadth and depth in embodied
reasoning. Embodied intelligence demands the joint mastery of perception, planning, and execution, yet reconciling generality with specialization across this spectrum remains an open challenge. Within a single unified model, ABot-ER commands specialist-level depth across all three competencies, spanning spatial, scene, and temporal
reasoning in perception; long-horizon action organization in planning; and point grounding and affordance in execution. The Agentic Data-Model Self-
Evolution Engine continuously adapts data strategies to the model's evolving capability state. The Task-Conditioned Adaptive Training Strategy uses task
tokens to activate lightweight low-rank residuals over a shared backbone, separating updates from heterogeneous output formats while retaining shared embodied knowledge. Across a systematic evaluation on twelve
benchmarks covering three core competencies and five capability dimensions, ABot-ER achieves state-of-the-art overall performance across perception, planning, and execution within a unified parametric framework, demonstrating that breadth and depth can coexist in unified embodied
reasoning.
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All authors Qiuyu Ding, Heng-Da Xu, Changda Xia, Jiajun Shen, Zihao Guan, Zhengchen Yuan, Jianfang Zhang, Di Yang, Jiayi Tian, Yuting Xu, Fei Wang, Shiao Liu, Wei Zhang, Xiaolong Wu, Wenbin Tang, Zedong Chu, Mu Xu
Artificial Intelligence
Choosing LLMs for Strategic Agent Interactions
Arastun Mammadli, Herbert Woisetschläger, Jawad Fayaz et al. (+1 more)
Game-theoretic
benchmarks claim that large
language model (LLM) strategic decision-making varies across game types, interaction structures, and capability dimensions. However, they only evaluate strategic performance across individual fixed models. In parallel, contemporary LLM routers pick models under quality-cost trade-offs, but with no notion of a strategic structure. They do not account for an opponent that might be unknown, and that can adapt over time and shift the feedback signal. To address this, we extend the game-theoretic evaluations and define strategic selection as the meta game layered on top of the base game. In each round, a strategic selector fields an LLM from a portfolio of models to navigate a game payoff
matrix that is latent and learned online. We find that the evaluation signal is game-specific, and that extracting a reliable signal requires a much higher number of seed runs $N$ compared to traditional benchmarking setups. Evaluating across 5 games and 10 LLMs, we show a model's strategic competence varies significantly depending on the game setup and the opponent faced. Building on this, we implement a suite of selector methods and find that approaches that rely on query
embeddings or LLM self-reflection do not consistently capture the underlying strategic signal. A lightweight multi-armed bandit
algorithm performs more reliably across games, though it still falls short of the best fixed model. Finally, we show that simple non-stationary and
adversarial bandit variants fail to outperform a stationary bandit baseline, pointing to a gap in how current methods handle a shifting opponent in strategic settings.
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All authors Arastun Mammadli, Herbert Woisetschläger, Jawad Fayaz, Shiqiang Wang
Robotics
Let the Mind Conceive, Let the Hand Achieve: Following Multimodal Instructions for Long-Horizon Robotic Manipulation
Youpeng Wen, Gang Wang, Tianyu Huang et al. (+5 more)
A
robot following a multi-step instruction needs to act on the right objects in the right order, even as the scene changes. It must also maintain awareness of task progress and determine when to advance. To address these challenges, we introduce Orchestra, an
agent harness that separates task planning and execution management from action generation. Orchestra decomposes a complete
multimodal instruction into ordered subtasks, tracks completed steps, and updates the locations of relevant objects and destinations during execution. It maintains a shared context containing the active subtask, spatial grounding, and recent observations. An action policy uses this context to generate actions, while a separately trained progress evaluator estimates when to advance to the next subtask. To evaluate whether
robots trained on atomic instructions and short trajectories can execute longer subtask sequences, we introduce a
benchmark named RMBench-Compose. Orchestra achieves a mean success rate of $68.22\%$ across nine RMBench tasks, outperforming Mem-0 by $26.22$ percentage points. On RMBench-Compose, it achieves an Average Success Length (ASL) of $7.35$ subtasks, $2.46$ times that of HarnessVLA. Across four real-world multi-step tasks, it improves mean ASL by $25.0\%$ over Orchestra using the base $\pi_{0.5}$ policy. These results show that Orchestra helps
robots carry out longer sequences of subtasks while preserving the requested subtask order and acting on the specified objects. Our code and project page are available at https://youpengwen.github.io/Orchestra.
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All authors Youpeng Wen, Gang Wang, Tianyu Huang, Weiwei Zhang, Zhijian Huang, Wei Chen, Xieyuanli Chen, Yunhui Liu
Data Structures and Algorithms
Almost Optimal Relative Error Streaming Quantiles with Full Mergeability
Tomáš Domes and Pavel Veselý
Quantile summaries provide a scalable way to estimate the distribution of individual attributes in large
datasets that are often distributed across multiple machines or generated by sensor networks. The state-of-the-art summaries with relative error guarantees of $\pm \varepsilon\cdot \mathrm{rank}$, offering increasingly higher accuracy towards the distribution's tails, are ReqSketch (Cormode et al., JACM 2023) and the recent streaming
algorithm by Gribelyuk et al. (SODA 2025). For inputs of size $N$ and probability $\delta$ of exceeding the error bound, ReqSketch achieves space of $O(\varepsilon^{-1}\cdot \log^{1.5}(\varepsilon N)\cdot \sqrt{\log \delta^{-1}})$
memory words, and offers full mergeability, allowing distributed or parallel processing of
datasets. In contrast, the latter
algorithm exceeds the lower bound of $\Omega(\varepsilon^{-1}\cdot \log(\varepsilon N)\cdot \log\log\delta^{-1})$ from (Cormode & Veselý, PODS 2020) only by a factor of $O(\log\varepsilon^{-1}\cdot (\log \varepsilon^{-1} + \log\log N)\cdot\log^2\log\delta^{-1})$, but it lacks the mergeability property, is substantially more involved than ReqSketch, and has unclear practical performance. Both of these data structures consist of relative compactors from ReqSketch that subsample the stream in a structured way and work well in the streaming setting but are difficult to analyze under mergeability. We design a new relative-error
quantile summary that achieves full mergeability and also improves the state-of-the-art streaming space bound to $$O(\varepsilon^{-1}\cdot \log(\varepsilon N) \cdot\log \log \delta^{-1} \cdot (\log \varepsilon^{-1} + \log \log(\varepsilon N) + \log \log \delta^{-1})),$$ which is substantially closer to the lower bound. At the same time, our data structure is simpler than the
algorithm from (Gribelyuk et al., SODA 2025) and therefore more suitable for implementation.
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Computation and Language
Lightweight but Linguistically Fair? Quantisation Robustness across Bavarian, German, and English
Jophin John, Michael Hoffmann, Piroska Lendvai et al. (+1 more)
Post-training quantisation reduces the
memory cost of large
language models, but it remains unclear whether compression preserves
language skills of regional varieties as reliably as their standard-
language counterparts. We study this question for Bavarian, Standard German, and English using BAVGROUND, a multiple-choice
benchmark, where 206 source questions are aligned across all three
languages. Across 14 open-weight 7B–10B instruction models, we compare BF16 with 8-bit GPTQ and two 4-bit settings, GPTQ and AWQ. Eight-bit GPTQ remains close to BF16 and largely preserves the relative behaviour of the three
languages, while 4-bit quantisation was found to be much less stable. The two 4-bit methods reduce average accuracy by about 1.4–1.5 percentage points and change 11.9–16.0% of BF16 decisions, with changes concentrated on examples where the BF16 model is already uncertain. This instability does not translate into a consistent Bavarian disadvantage. Although Bavarian, a low-resource dialect, has lower baseline accuracy than German and English, the
language most affected at 4 bits varies across models and quantisation methods. Calibration further shapes these cross-
language effects: the relative Bavarian–German effect changes with calibration
language, while changing the calibration source has little average effect on GPTQ-4 but can affect AWQ-4 much more strongly. Free-form evaluation on five models further shows that 4-bit compression can degrade generated responses even in settings where multiple-choice accuracy changes only modestly, while 8-bit GPTQ remains comparatively stable. Overall, our results show that quantisation robustness for regional
language varieties depends on the model, quantisation method, calibration data, and evaluation setting, and cannot be measured by aggregate multilingual accuracy alone.
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All authors Jophin John, Michael Hoffmann, Piroska Lendvai, Florent Dufour
Computer Vision
CityCode: Building Agent-Operable 3D Cities from Sparse Aerial Observations
Jiahui Sun, Ran Bi, Yifei Liu et al. (+8 more)
Urban spatial intelligence calls for 3D cities that
agents can measure, interpret, and modify across successive tasks. Maintaining such cities as persistent, code-based states requires lightweight acquisition and support for consistent state updates without repeated dense reconstruction. We therefore formulate a new setting for grounded urban world construction: generating large-scale, instance-level parameterized 3D cities from one low-altitude aerial RGB image per region and known camera parameters, while recovering metrically scaled buildings and explicit road geometry and connectivity. We introduce CityCode, an
agent-operable representation that unifies metric geometry, urban
topology, visual semantics, and 3D assets through persistent entity identities, with dependency-aware versioned execution for validated updates and historical restoration. We further develop Air2Code to construct this representation by combining visual structure extraction with adapted metric depth estimation. To address occlusion and incomplete coverage, recovered structure and observed appearance guide generative completion and subsequent structure-aware 3D generation for buildings and backgrounds. Experiments on CitySample-Aerial and UrbanScene3D demonstrate substantially more accurate metric structure recovery and superior visual fidelity to the target cities compared with the evaluated baselines. Across 50 task chains of 50 steps each, CityCode supports continued querying, editing, planning, and historical restoration over evolving city states. Together, CityCode and Air2Code turn sparse aerial observations into persistent,
agent-operable urban worlds for long-horizon urban spatial intelligence.
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All authors Jiahui Sun, Ran Bi, Yifei Liu, Xiaolong Zhou, Yishuo Xu, Yuanqi Li, Congcong Zhu, Lei Yang, Yanwen Guo, Zhihang Zhong, Hongjie Zhang
Logic in Computer Science · Cross-listed from math.CO
Prescribed and simultaneous failures of log-concavity in trees with perfect matchings
Heshun Fan
We construct trees with perfect matchings and independence sequences that fail log-concavity near the independence number $\alpha$. For every integer $k\geq1$, one family fails at $\alpha-k$ and has a witness of order $O(k^2\log(k+2))$. For every integer $t\geq2$, a single tree has simultaneous failures at $\alpha-1,\alpha-3,\ldots,\alpha-(2t-1)$, with a witness of order $O(t^3)$. All these trees satisfy $\alpha=N/2$, where $N$ is the number of vertices, and contradict
Conjecture 4.2 of Ramos and Sun. A sufficient condition uses a fixed tree and the numbers of attached leaves. For fixed $k$ or $t$, explicit bounds give
geometric decay of $a_j^2/(a_{j-1}a_{j+1})$ at these indices as the trees grow; $a_j$ counts independent $j$-sets. The proofs count independent sets by empty matching pairs. The Lean proof assistant checks formal proofs of the parameterized results and their quantitative bounds; exact 90- and 180-vertex examples illustrate the constructions.
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Data Structures and Algorithms
A Deterministic Near-Linear Time Approximation Algorithm for Restricted Shortest Paths in Directed Graphs
Vikrant Ashvinkumar
In the restricted shortest paths problem, we are given an $n$-vertex $m$-edge directed graph $G$ whose edges are assigned two non-negative weights (or resources) called lengths and costs, a source vertex $s$, and a cost threshold $C$. The goal is to find, for each target vertex $t$, the length of the shortest $(s,t)$-path whose total cost is at most $C$. Ashvinkumar, Bernstein, and Karczmarz (SODA 2025) gave the first randomized bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate
algorithm on directed graphs running in sub-$mn$ time, answering a problem posed by Bernstein (SODA 2012). We substantially improve this on two fronts, supplying a deterministic bicriteria $(1+\varepsilon,1+\varepsilon)$-approximate
algorithm running in near-linear time. Our approach straightforwardly generalizes to more than two resources. For $k$ resources, a deterministic $(1+\varepsilon_1,\ldots,1+\varepsilon_k)$-approximate
algorithm running in near-linear time is possible, with $\varepsilon_1=\ldots=\varepsilon_k=\varepsilon$.
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Multiagent Systems
Strategic Multi-Agent Resource Allocation via Statistical Contracts
Ben Chugg, Michael I. Jordan and Aaditya Ramdas
We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources among competing
agents who possess data or data-generating mechanisms that may be relevant to the principal's goals. Each
agent can pay a participation cost to opt into a statistical contract, in which case they generate data from a private distribution and receive an allocation based on a predetermined (and publicly known) mechanism. Each
agent is either "null" or "non-null" and the principal's goal is to design the contract to limit the allocation to the former while maximizing transactions with the latter. Using e-values, we define a "continuous-closure," a set of allocations which prevents null
agents from profiting in expectation and provides post-hoc risk control for general loss functions under arbitrary dependence. When the loss is the fraction of resources allocated to null
agents, the region admits
polynomial-time linear optimization. We also give explicit, interpretable closed-form approximations, and we analyze participation from a non-null
agent's perspective, giving conditions under which entry into the contract is profitable. Finally, we extend the framework to support online arrival of the
agents, in which case the principal has to make irrevocable on-the-fly decisions, and provide a method which gives time-uniform error-control in this setting.
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Machine Learning · Cross-listed from stat.ML
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning
Caixing Wang
Random features provide a standard approach to scaling kernel methods to large
datasets by approximating the kernel with a finite-dimensional feature map. Existing works of random feature methods primarily focus on well-specified models and light-tailed noise conditions, leaving a gap in understanding their performance under more general conditions, such as misspecified models and heavy-tailed noise. In this paper, we provide a comprehensive and improved analysis of kernel ridge regression with random features (KRR-RF) under general conditions. In the light-tailed noise setting, we improve upon the worst-case uniform bounds of the feature map used in the previous works and lead to the minimax optimal learning rates with $2r+\gamma>\alpha_0$, where $(r,\gamma,\alpha_0)$ denote the regularity of the target function, the effective dimension of the kernel, and the
embedding index of the RKHS, respectively. In the heavy-tailed noise setting, we leverage a recently developed
Hilbert-valued Fuk-Nagaev inequality and extend the same optimal results under finite weak moment conditions. The additional cost to achieve robustness is a mildly stronger explicit sample-size requirement than in the light-tailed case. For both settings, our results recover the conditions random features budgets (up to log factors) required in the previous works. In the most benign case when $\alpha_0=\gamma$, KRR-RF are minimax optimal for all $r \in (0,1]$. To the best of our knowledge, this is the first work to establish optimal learning rates for KRR-RF under weak moment conditions in the misspecified setting. Numerical experiments and a real data analysis validate the derived learning rates and demonstrate their practical relevance in the presence of mis-specification and heavy-tailed noise.
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Computation and Language
Probing the Geometry of Color Representations in Multilingual Language Models
Pablo Loyola, Edison Marrese-Taylor and Andrés Hoyos-Idrobo
Do multilingual
language models learn a universal internal geometry for color, and is it perceptually grounded or primarily linguistic? We decompose color representations into two
geometric axes: (P1)
alignment to perceptual color space, and (P2) cross-lingual
symmetry. Analyzing layerwise
embeddings across models and multilingual
datasets, we find that perceptual grounding is weak:
alignment to CIE-LAB peaks in intermediate layers, then collapses toward output layers. Cross-lingual
symmetry degrades with depth, suggesting later layers encode
language-specific structure. Functionally, we show that
geometric properties predict task performance: models achieve 96.7% accuracy on color classification at layers where P1
alignment peaks, with P1
alignment (r=0.70) significantly correlating with downstream capabilities. Our results reveal a dissociation: text-only LMs learn universal color-
language geometry, not universal color-perception geometry, providing diagnostics for where perceptual structure is preserved in
language models.
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Artificial Intelligence
AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents
Xiangrui Liu, Shuo Tang and Siheng Chen
Training business automation
agents requires executable tasks that connect a natural-
language request to an application state and a verifiable end state. Traditional Rubrics requires Authors to recreate application interfaces, initial records, policy evidence, and outcome checks for each task, entangling business logic with repeated infrastructure work. We introduce AssetForge, an asset-augmented task-construction pipeline that pairs a reusable construction-asset library with task-specific Rubrics. The assets provide application interfaces, world-state components, policy/evidence carriers, and native assertion definitions; the Rubric directs their instantiation for a particular business task. Executable checks and an additional model-based review validate each candidate, return targeted feedback, and support bounded repair before acceptance. On AutomationBench v1.0.6 public-600, fine-tuning Qwen3.6-35B-A3B on 6,000 AssetForge examples yields 41.17% on the official strict pass@1 metric, 4.4 times the 9.33% base score (+31.83 points). With the same backbone and GLM-5.3 supervision, AssetForge reaches 41.17% versus 30.33% for traditional Rubrics, a 10.84-point gain (35.7% relative) at the same accepted-example scale (6,000 versus 6,000 examples). Both DeepSeek- and GLM-supervised AssetForge models improve AppWorld completion over the base model on Test-Normal and Test-Challenge. These results establish the advantage of AssetForge's complete construction pipeline on AutomationBench public: the coupled asset-Rubric interface directs Authors toward business logic, while executable checks and model-based review validate their tasks.
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Data Structures and Algorithms
A Practical Combinatorial Interior Point Method for Minimum-Cost Flow
Emil Schätzle, Simon Meierhans, Rasmus Kyng et al. (+1 more)
In a recent breakthrough, Chen, Kyng, Liu, Peng, Probst Gutenberg, and Sachdeva (arXiv:2203.00671) designed the first almost-linear-time
algorithm for minimum-cost flow. Their approach uses an Interior Point Method (IPM) to achieve complexity $m^{1+o(1)}\log U \log C$. However, the $m^{1+o(1)}$ factor hides intricate recursive data structures, large constants and multiple log factors. In this paper, we give an implementation following the core ideas of their IPM with practical data structures and empirically obtained decision rules. We compare our solver with the cost scaling and network simplex solvers of LEMON on the instance families of the DIMACS Flow Challenge 2.0 core
benchmark. Our implementation scales competitively on five of twelve families and better on two. On grid_wide we observe close to linear scaling: $m^{1.04}$ against $m^{1.48}$ for LEMON. We read these results as first evidence that the almost-linear-time framework can be relevant in practice, discuss our insights gained during the implementation process, and motivate where future work should focus to bridge the theory-practice gap.
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All authors Emil Schätzle, Simon Meierhans, Rasmus Kyng, Maximilian Probst Gutenberg
Cryptography and Security
Jev-Guard: Fast Routing and Auditing Agent Actions over Editable Policy Trees
Wenbin Hu, Huihao Jing, Haochen Shi et al. (+9 more)
Agent harnesses require a low-latency guardrail model together with customizable audit policies. However, deploying an auto-regressive guardrail model remains challenging: such models incur high inference latency and are often poorly aligned with customized policies. To address these problems, we present Jev-Guard, a unified model for safety classification and policy routing. We further construct a policy tree over which Jev-Guard performs routing, spanning diverse domains including content moderation, cybersecurity,
privacy, and legal compliance. Specifically, Jev-Guard first navigates the policy tree to select the applicable audit policy given the caller's audit context, and then performs safety classification conditioned on the retrieved policies. On 7,033 evaluation cases, Jev-Guard achieves 93.72% safety accuracy, a mean latency of 58ms, and 93.10% hierarchical routing accuracy. The full routing-and-classification pipeline averages 240ms. Furthermore, we integrate Jev-Guard into the Hermes harness framework. Across
agent trajectories, its separate permission gate cuts mean review time per task from 4.80s for an SFT-LLM judge to 1.33s, with no policy violations are observed for either guard.
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All authors Wenbin Hu, Huihao Jing, Haochen Shi, Yuxuan Liu, Changxuan Fan, Sirui Zhang, Kawai Chung, Hanyu Yang, Zhenhuan Wang, Ziyi Chen, Haoran Li, Yangqiu Song
Computer Vision
ShoeFit: A New Dataset and Dual-image-stream DiT Framework for Virtual Footwear Try-On
Yuhan Li, Zhiyu Jin, Yifan Tong et al. (+5 more)
Virtual footwear try-on (VFTON), a critical yet underexplored area in virtual try-on (VTON), aims to synthesize faithful try-on results given diverse footwear and model images while maintaining 3D consistency and texture authenticity. Unlike conventional garment-focused VTON methods, VFTON presents unique challenges due to (1) Data Scarcity, which arises from the difficulty of perfectly matching product shoes with models wearing the identical ones, (2) Viewpoint Misalignment, where the target foot pose and source shoe views are always misaligned, leading to incomplete texture information and detail distortion, and (3) Background-induced Color Distortion, where complex material of footwear interacts with environmental lighting, causing unintended color contamination. To address these challenges, we introduce MVShoes, a multi-view shoe try-on
dataset consisting of 7305 well-annotated image triplets, covering diverse footwear categories and challenging try-on scenarios. Furthermore, we propose a dual-stream DiT architecture, ShoeFit, designed to mitigate viewpoint misalignment through Multi-View Conditioning with 3D Rotary Position
Embedding, and alleviate background-induced distortion using the LayeredRefAttention which leverages background features to modulate footwear latents. The proposed framework effectively decouples shoe appearance from environmental interferences while preserving high-quality texture detail through decoupled denoising and conditioning branches. Extensive quantitative and qualitative experiments demonstrate that our method substantially improves rendering fidelity and robustness under challenging real-world product shoes, establishing a new
benchmark in high-fidelity footwear try-on synthesis.
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All authors Yuhan Li, Zhiyu Jin, Yifan Tong, Wenxiang Shang, Benlei Cui, Xuanhong Chen, Hangcheng Zhu, Bingbing Ni
Computer Vision
Omni-RS: Unifying Multi-Task Understanding and Generation in Remote Sensing
Pan Yin. Shuai Yang, Yuanhao Liu, Datao Tang et al. (+6 more)
Practical remote sensing applications often require both understanding and generation capabilities for the same geographic scene. However, most existing methods address either understanding or generation in isolation, while existing unified remote sensing models' generative ability is limited to text-to-image. To bridge this gap, we propose Omni-RS, a unified framework supporting four remote sensing understanding tasks and five generation tasks. Specifically, we introduce the "Scene-Guided Two-Stage Training" strategy, which first adapts a
multimodal large
language model to remote sensing understanding and then uses its hidden states to guide a
diffusion transformer for image generation. To enable joint learning across diverse generation tasks, we construct UniGen, a unified multi-task generation
dataset, by organizing heterogeneous remote sensing data into a unified format. Furthermore, we introduce "Detailed Visual Conditioning", which uses visual conditions encoded by a VAE to guide the generation process, thereby supporting additional generation tasks that require reference images. Extensive experiments demonstrate that Omni-RS performs exceptionally well in understanding tasks and has achieved outstanding results in a variety of generative tasks.
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All authors Pan Yin. Shuai Yang, Yuanhao Liu, Datao Tang, Jialin Li, Hao Wang, Pei Liu, Kaiyu Li, Cong Zhao, Xiangyong Cao
Robotics
CoSMO: Conformal Semantic Mapping for Open-World Safe Robot Navigation
Shuqi Wang, Yiwei Zheng, Pingrui Zhang et al. (+2 more)
Safe autonomous navigation requires not only
geometric obstacle avoidance but also semantic awareness. Different object categories demand different safety clearances, and the set of relevant categories may expand as the
robot explores novel environments.
Vision-
language models (VLMs) offer rich semantic representations for building maps of unknown scenes, yet their confidence scores are poorly calibrated, making raw predictions unreliable for safety-critical planning. Conformal prediction (CP) can provide coverage guarantees, but existing methods assume a fixed label set and stationary data, limiting their use in open-world settings. We propose Conformal Semantic Mapping for Open-World Safe
Robot Navigation (CoSMO), a semantic safety mapping framework that transforms VLM
embeddings into calibrated coverage sets and safety-aware obstacle dilations over a dynamically expanding hazard vocabulary, even under distribution shift. Experiments on the Matterport3D (MP3D)
dataset show that CoSMO reduces semantic collisions by 61.6% relative to a geometry-only baseline, while maintaining formal coverage guarantees even under delayed expert feedback. Real-world experiments on a quadruped
robot further demonstrate generalization by achieving long-run empirical coverage of 90.73% against the 90% target. Project website: https://yangming911.github.io/cosmo-page/.
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All authors Shuqi Wang, Yiwei Zheng, Pingrui Zhang, Yue Gao, Xiang Yin
Machine Learning
Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness
Runze Shi and Ziye Ma
Over-parameterization is ubiquitous in modern machine learning, yet its effect on robustness remains poorly understood. We study this question in
matrix sensing, a canonical non-convex problem that can also be viewed, under an appropriate parameterization, as training a two-layer quadratic neural network. We develop a rank-aware robustness theory for the Burer-Monteiro factorization when the search rank k may exceed the target rank r_
star, thereby capturing over-parameterization. Our analysis shows that robustness depends jointly on the degree of over-parameterization and the local geometry of the attained second-order stationary point: increasing k generally worsens sensitivity, but stationary points with the same search rank can exhibit substantially different robustness. Motivated by this observation, we propose a trajectory-aware
algorithm that uses spectral balancing and randomized sketching to steer optimization toward less sensitive solutions. In
matrix-sensing experiments, the method reduces paired sensitivity by 18-29 percent for isotropic operators and 87-89 percent for weak-tail operators, without degrading reconstruction accuracy. These results demonstrate that parameter count alone does not determine robustness and identify solution geometry and the optimization trajectory as important levers for improving it.
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Artificial Intelligence
Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration
Yevgeny Rakita, Paul Todd, James R. Neilson et al. (+1 more)
This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize technologically useful materials, especially those that are kinetically stabilized. Often, it is a science that follows years of trial and error, culminating in (published) successful recipes, but is truly driven by far more (unpublished) failures. The entry into the era of AI provides an opportunity to use learning
algorithms to encode rules from multi-dimensional synthesis pathways, regardless of their technological usefulness, and to decode both forward prediction and inverse learning modes. The main incentive is to accelerate material discovery by generating a global knowledge base that can be shared among data
agents and contributed to by them. We will focus on several scenarios in the common synthesis process during solid-state synthesis, primarily because these reactions are kinetic in nature and require closer monitoring and control of the reaction history.
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All authors Yevgeny Rakita, Paul Todd, James R. Neilson, Simon J. L. Billinge
Systems and Control
Infrastructure-Aware Runtime Protection of Co-Deployed AI Services at the Industrial Edge
Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min et al. (+1 more)
In modern production lines, a single edge device simultaneously runs multiple AI services like a torque-control model, a
vision inspector, a predictive maintenance routine. All these services run on the same CPU and same shared network segment. When the shared network segment saturates, the services do not crash. The services just produce outputs that are inaccurate or stale, and no longer reflect the plant's conditions. Our paper targets this class of silent, partial failure scenarios in Industrial Edge Systems. We treat each co-deployed service as an autonomous
agent. We introduce the Runtime Criticality Score (RCS), which each service
agent uses to express at any moment, its intrinsic utility and survival urgency under resource constraints. The score is computed from six normalized dimensions covering physical consequence, inference quality, input freshness, recent availability stress, actuator coupling, and available protective actions on the host. Its dimension weights are adjusted online through a lightweight
Bayesian update driven by the type of disturbance currently observed, so the same service is ranked differently under a network delay than under thermal throttling.
Agents publish their RCS to peers over an open, peer-to-peer discovery channel, and use it to negotiate three things without a central controller: (i) local threshold enforcement on their own host, (ii) migration to a willing peer when the current host can no longer protect them, (iii) reassignment of the coordination role when the incumbent coordinator is itself impaired. Beyond the immediate benefit of catching silent degradation, the framework contributes a concrete instance of decentralized, physically grounded
agent coordination.
Agents belonging to different owners can advertise capabilities, bid for protection, and reorganize across an open network.
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All authors Nitya Sathyavageeswaran, Dibyajyoti Nath, Ziran Min, Pavan Athreya Narasimha Murthy
Cryptography and Security
BALLAST: Physics-Aware Moving Target Defense for Industrial Control Systems Using a Digital Twin Safety Oracle
Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo et al. (+2 more)
Industrial Control Systems run on static network configurations, which leaves an attacker unlimited time to map the plant. Moving Target Defense answers this by shuffling addresses, but almost every published scheme never checks whether the reconfiguration will itself disturb the physical process it protects, so the defense can destabilise a control loop or trip an actuator. We present BALLAST, which places a learned Digital Twin between the security policy and the plant as a safety oracle. A physics-informed LSTM surrogate forecasts the physical effect of a candidate network change, a reinforcement learning
agent trades security
entropy against process stability, and a connection-draining handover lets in-flight Modbus transactions complete before the old mapping is withdrawn. An explicit shield rejects any action whose predicted trajectory would leave the safe operating envelope. On the SWaT water treatment testbed, Ballast cut the success rate of an adversary's reconnaissance sweep by 85 percent and recorded no safety violation across 50 runs, a 95 percent upper bound of 5.8 percent on the violation rate, against five violations and a tracking error 19 times larger for RL-MTD, the closest published analogue to our design. Measured against the strongest baseline on the physical metrics, a threshold-triggered scheme that itself records one violation rather than RL-MTD's five, Ballast still improves the tracking error by a factor of 2.5 and removes that remaining violation. The gain costs a small and deliberate concession in raw deterrence. Twin inference meets the 50 ms budget on a workstation core and an embedded GPU, and is projected to meet it on both edge CPUs when the three envelope stages are evaluated in a single batched call; the complete decision-to-enforcement path was measured only on the workstation.
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All authors Oscar Mogollon-Gutierrez, Mohammadhossein Homaei, Alberto Lopez-Trigo, Andres Caro, Mar Avila
Computation and Language
Prompt or Policy? Optimizing Physician Slate Presentations with LLM User Simulators
Andrei Niculae, Adrian Cosma and Emilian Radoi
Choosing the right physician is a key challenge for patients seeking medical care. Physician recommendation systems identify relevant doctors, but patients still find it difficult to compare their profiles to make a decision. We formulate slate presentation optimization: given a patient's medical case and a fixed, ranked list of physicians, we generate short, case-specific summaries that make doctors easier to compare, while remaining grounded in the information in their profiles. To optimize physician presentations without relying on real patient interactions, we develop an offline framework that uses persona-conditioned LLMs to simulate patient choices. We generate patient personas from attributes extracted from each medical case and evaluate presentations for factual accuracy, relevance, naturalness and simulated patient choice. We also use a second LLM judge, unseen during optimization, to assess simulator-specific overfitting. We compare automatic
prompt-level optimization with GEPA, parameter-level optimization with GRPO, and their combination across two model sizes. GRPO substantially outperforms GEPA in satisfying the full set of constraints, and its gains in simulated conversion persist under the unseen judge. GEPA alone brings smaller improvements, but using its optimized
prompt to initialize GRPO speeds up training and achieves the highest simulated conversion. These results show that
prompt-level and parameter-level optimization play complementary roles in improving how physician recommendations are presented.
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Computation and Language
SimInter: Evidence-Grounded User Simulation with Interview-Based Profile Enrichment for Conversational Recommender Systems
Erfan Shahabi, Amirhossein Roshandel, Mohammad Sadegh Sadeghi et al. (+1 more)
Conversational Recommender Systems (CRSs) require reliable user simulators for cost-effective evaluation. LLM-based simulators have made realistic simulation possible but typically condition on profiles derived from explicit ratings, demographics, and static sources capturing surface preferences while omitting implicit behavioral characteristics such as decision-making style, aspect-level preferences, and domain expertise. We propose SimInter, a pipeline that elicits these attributes via synthetic interviews between two LLM
agents, grounded in the user's statistical profile and historical conversational evidence. Before profile enrichment, an audit
agent explicitly flags potential fabrication and stereotyping at the turn level. We instantiate and evaluate SimInter on movie recommendation. We simulate 300 ReDial users and, on a matched subset of 100 users, compare SimInter with a statistics-only baseline: SimInter significantly increases the diversity of expressed preference aspects (filtered aspect
entropy: 1.619 vs. 0.859; paired d = 0.96, Holm-adjusted p < 0.001). This gain persists when the simulator converses with two trained recommenders, KBRD and UniCRS, under two independent evaluation judges (+0.28 to +0.56 nats; all p < 0.001). Human evaluation finds 94.3% of enriched-attribute claims supported or partially supported, while human judgments agree with 95.2% of the auditor's binary filtering decisions on a stratified sample.
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All authors Erfan Shahabi, Amirhossein Roshandel, Mohammad Sadegh Sadeghi, Azadeh Shakery
Robotics
I2O2I-Nav: A Benchmark with a Modular Agent for Long-Horizon Indoor-to-Outdoor-to-Indoor Navigation
Zhengjie Shu, Wanyu Li, Di Xiao et al. (+11 more)
Service and delivery
robots often need to complete long-horizon navigation missions spanning indoor and outdoor environments. Existing embodied navigation
benchmarks primarily evaluate isolated stages or partial cross-domain journeys, providing limited support for assessing how heterogeneous navigation capabilities compose under continuous execution. We introduce I2O2I-Nav, a physics-enabled
benchmark of 20 real-world composite scenes spanning over 115,000 square meters across multiple buildings and floors, with aligned 3DGS rendering and collision geometry. Its three-level evaluation progresses from independent primitives to two-stage compositions and complete four-stage I2O2I missions. Each stage starts from its predecessor's actual endpoint, while stage-conditional metrics track completion and localize failures along the mission. We further propose the MODULAR-
Agent that coordinates frozen navigation policies through completion verification, stage handoffs, and bounded recovery. Evaluation across 1,374 episodes highlights the difficulty of continuous composition. The MODULAR-
Agent achieves 40.5-76% success on individual primitives, but full I2O2I success remains low. Compared with a fixed policy chain using the same underlying policies, it more than doubles success on both Indoor-to-Outdoor (I2O) and Outdoor-to-Indoor (O2I), while improving full-mission success from 1.15% to 4.02%. Stage-wise analysis shows that the MODULAR-
Agent enables more missions to reach the final object-navigation stage, where conditional success remains low, revealing challenges in both long-horizon stage execution and completion decisions.
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All authors Zhengjie Shu, Wanyu Li, Di Xiao, Lingxin Jiang, Lai Jiang, Kaidi Hu, Xincheng Tang, Wenkang Hu, Haoran Wang, Jinchuan Zhang, Chang Chen, Wei Li, Jia Pan, Ruigang Yang
Machine Learning
Bayesian Optimization of CVaR with Unknown Hyperparameters
Valentin Margraf, Jonas Hanselle, Viktor Bengs et al. (+3 more)
We study
Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmental variable. Existing no-regret
algorithms assume that the correct Gaussian process (GP) kernel and its hyperparameters are known. In practice, however, they must be estimated from limited data, often yielding a misspecified surrogate. This is especially damaging for CVaR because misspecification can distort the estimated tail-risk and thus lead to catastrophic decisions. We propose HE-CV-UCB, the first no-regret
algorithm for CVaR
Bayesian optimization with unknown GP hyperparameters. Given a finite candidate set containing a well-specified model, HE-CV-UCB adaptively eliminates those inconsistent with the observed data, actively queries where the survivors disagree, and returns a design that is robust across the surviving candidates. We prove that it attains cumulative regret matching the oracle rate of CV-UCB up to a factor depending only on the candidate set's size. Across synthetic
benchmarks and three real-world applications, HE-CV-UCB achieves competitive performance and frequently outperforms existing CVaR
Bayesian optimization methods.
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All authors Valentin Margraf, Jonas Hanselle, Viktor Bengs, Serafima Lebedeva, Eyke Hüllermeier, Marcel Wever
Computer Vision
AffectWorld: Benchmarking Emotion-Driven Future World Prediction
Changhao Song, Yacong Chen, Hui Gao et al. (+2 more)
The world encompasses both physical environments and human actors. Predicting its
evolution requires understanding both physical laws and how emotions shape human actions. However, existing world model
benchmarks primarily assess physical plausibility and perceptual quality, leaving emotion-driven world
evolution largely untested.We introduce AffectWorld, a
benchmark that evaluates the chain Emotion-Action-World State through three tasks: World-State Consistency, Emotion-Action Consistency, and Causal Consistency. It comprises 1,498 everyday
video samples annotated with plausible emotions, action tendencies, and atomic world-state constraints. A counterfactual subset of 233 samples tests whether changing an
agent's emotion under fixed scene and event conditions yields corresponding changes in actions and consequences. We define eight metrics across the three tasks and an additional counterfactual metric, combining automatic visual assessment with human-calibrated VLM/LLM judgments. Evaluations of five SoTA world models (e.g., Cosmos3) required world-state change scores of 0.100-0.280 and counterfactual emotion sensitivity scores of 0.027-0.070 on a [0, 1] scale. These results reveal substantial gaps in generating the consequences of emotion-driven actions and adapting future predictions to emotional interventions. AffectWorld provides a testbed for advancing world models that capture how emotions shape future physical and social states.
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All authors Changhao Song, Yacong Chen, Hui Gao, Yazhou Zhang, Peng Zhang
Artificial Intelligence
Efficient Safety Guardrails through Latent Dual-Hypothesis Reasoning with On-Demand Trace Reconstruction
Md Asiful Islam, Fahmida Alam and Mihai Surdeanu
Guardrail approaches that rely on
reasoning-based large
language models (LLM) improve safety decisions, but come with substantial inference overhead due to the explicit
reasoning. We introduce LR-Guard (Latent
Reasoning Guardrail), an efficient and explainable guardrail that jointly learns structured latent
reasoning and on-demand
reasoning reconstruction. Our method distills a frozen teacher's analysis of safe and unsafe
prompt interpretations and their comparison into compact latent representations. Joint training optimizes these representations for both safety prediction and reconstruction of the teacher's explicit
reasoning traces, enabling human-readable explanations without requiring intermediate text generation for every decision. Experiments show that LR-Guard outperforms its explicit-
reasoning teacher in most in-domain and out-of-domain settings on safety classification and explanation extraction, while achieving a 19.4x inference speedup at batch size one. Further, reconstructed
reasoning traces from the latent information exhibit high semantic similarity to the teacher's explicit
reasoning.
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