Computer Vision
IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion
Siyang Liu, Peiyi Zhou, Tianle Jin et al. (+3 more)
Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented fusion control. However, existing text-guided fusion methods often rely on shallow semantic-visual interaction and limited
attention mechanisms, which restrict their ability to robustly handle complex degradations and fully exploit textual guidance. In this paper, we propose an iterative text-guided image fusion framework that incorporates text-conditioned feature interaction across multiple fusion and refinement stages. The proposed method integrates deepest-level Cross-
Attention, multi-scale Cross-Gate Fusion, and stage-specific text-conditioned modulation, allowing the global text
embedding to condition hierarchical feature fusion and residual refinement. By repeatedly injecting the pooled text
embedding across hierarchical decoder and refinement stages, the proposed framework provides degradation-aware global semantic conditioning while preserving complementary information from the visible and infrared modalities. Experiments on several
benchmark datasets show that the proposed method improves several information-preservation and perceptual-quality metrics, while exhibiting metric-dependent trade-offs on some
datasets.
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All authors Siyang Liu, Peiyi Zhou, Tianle Jin, Rongrong Bian, Zheke Jin, Mengze Gao
Machine Learning
Subliminal Learning as Trait-Direction Drift: A Mechanism and Targeted Control under SFT Distillation
Zhixuan Liu, Zhichen Dong, Yuyu Fan et al. (+2 more)
Beyond intended capabilities, model
distillation can transfer hidden traits from a teacher. A teacher biased by a system
prompt can generate semantically clean training data, such as numeric sequences, that still causes a downstream student to inherit the hidden preference, a phenomenon known as subliminal learning. Prior work has identified several parts of this process. How the signal builds up during training and produces behavioral transfer remains unclear, making targeted mitigation difficult. We propose and validate trait-direction drift as a mechanism for subliminal learning: biased generation creates measurable preference gaps in teacher data, and student-recognizable gaps induce trait-aligned updates during supervised fine-tuning that accumulate into behavioral transfer. Guided by this mechanism, we propose probe-space corridor regularization, a targeted defense that constrains drift along a calibrated trait direction during
distillation. The method substantially reduces hidden-trait transfer, preserving task performance: for example, it lowers malicious-response transfer from 29.55% to 6.45% with low main-task accuracy cost, and consistently suppresses animal-preference transfer across the main Qwen setting. The preference-gap, training-trajectory, and intervention evidence links subliminal learning to trait-direction drift and motivates corridor regularization as a targeted control during
distillation.
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All authors Zhixuan Liu, Zhichen Dong, Yuyu Fan, Xiangtian Li, Chao Yang
Machine Learning
Modelpedia: A Catalog of Model Findings for the Meta-Science of AI
Franciszek Bernat, Dawid Płudowski, Michał Jan Włodarczyk et al. (+6 more)
Scientific knowledge about AI models is produced faster than the community can organize it. Every few months a new foundation model reshapes the field and hundreds of papers, blogs, and technical reports document how each behaves or fails. Yet, these findings remain scattered and effectively unretrievable. To address this gap we present Modelpedia, an automated, LLM-assisted framework that extracts findings about models from published papers, links it to the model,
dataset, method, and concept it concerns, and aggregates the result into a searchable public catalog. Applying the prototype to accepted ICLR 2024 and 2025 papers, we extract over a thousand findings and, treating the catalog itself as an object of study, run a meta-analysis of how the community investigates models. Now, we invite the community to explore, contribute to, and build on the open catalog, and to help establish model findings as a shared foundation for the meta-science of AI.
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All authors Franciszek Bernat, Dawid Płudowski, Michał Jan Włodarczyk, Luca Longo, Jianlong Zhou, Andreas Holzinger, Riccardo Guidotti, Wojciech Samek, Przemysław Biecek
Robotics
Adaptive Depth-Map-Guided Bundle Adjustment for Correspondence-Free Multi-View Point Cloud Registration
Yiran Zhou, Yingyu Wang, Shoudong Huang et al. (+1 more)
Robotic processing of irregular steel scrap requires dense 3-D measurement to replace manual visual assessment in hazardous cutting workcells. The reconstructed map is used to estimate piece dimensions, boundary geometry, feasible preheating and cutting regions, and collision-aware torch paths. The reconstruction errors therefore propagate directly to downstream measurement and planning. Existing multi-view registration methods commonly rely on feature extraction and data association to establish correspondences between views. In workcells with smooth metallic surfaces, repeated structures, occlusions, and partial overlaps, however, wrong correspondences may be established, leading to inaccurate pose estimation and distorted reconstruction. This paper presents an adaptive layered depth-map-guided bundle adjustment framework for correspondence-free multi-view point cloud registration. The scene is represented by a global 2.5-D grid, where each
cell can adaptively maintain multiple depth hypotheses. Raw depth observations are directly projected into the global map to form depth constraints without explicit feature correspondences. At grid
cells where multiple surfaces produce conflicting depths, a softmax-based layer assignment links each observation to compatible depth hypotheses. The resulting nonlinear least-squares formulation jointly refines sensor poses and the layered depth map, with correspondences implicitly induced by the depth-map representation and projection model. Experiments on self-collected industrial
datasets show that the proposed method achieves consistently competitive reconstruction accuracy while maintaining robustness and low computational cost in challenging industrial scenarios. We release the open-source code implementation at: https://github.com/YiranZhou-Robotics/ADM-BA.git
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All authors Yiran Zhou, Yingyu Wang, Shoudong Huang, Liang Zhao
Software Engineering
Fine-Tuning Large Language Models to Classify Pull Request-Issue Alignments: Going Beyond Prompting
Mustafa Yasir Altunhan, Hüseyin Özgür Kamal{\i} and Eray Tüzün
Context: Accurate
alignment between pull requests (PRs) and corresponding issues is crucial for efficient software development and maintaining code quality, as misalignments can reduce traceability, hinder defect localization, and decrease maintainability. Objective: This study aims to improve automated PR-issue
alignment classification by leveraging fine-tuned large
language models (LLMs) across multiple
alignment categories, and conducts interpretability analysis to investigate the effects of PR-issue fields on the predictions of fine-tuned LLMs. Method: Our methodology consists of
dataset preparation, LLM fine-tuning, and interpretability analysis. We first extended an existing
dataset and applied data augmentation to address class imbalance. GPT-4o was then fine-tuned via instruction tuning, and open-source LLMs including CodeLlama-7B, CodeQwen1.5-7B, StableCode-3B, CodeGemma-7B, and Deepseek-Coder-6.7B were fine-tuned using classification-specific heads. Interpretability analysis using Shapley Additive Explanations (SHAP) was conducted to examine the influence of PR-issue fields on predictions for the best-performing open-source LLM. Results: Fine-tuned LLMs outperformed baseline models, achieving average improvements of 6.15% in accuracy and F1-micro, 14.69% in F1-macro, and 6.15% in recall. CodeLlama-7B emerged as the best-performing fine-tuned LLM overall, while interpretability analysis revealed that code diffs together with issue body and PR body contents exert the greatest influence on predictions. Conclusions: Fine-tuning substantially enhances PR-issue
alignment classification, improving both accuracy and efficiency. Interpretability analysis provides actionable insights into the
dataset features driving
alignment decisions, deepening understanding of how LLMs reason over software artifacts.
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Hardware Architecture
Hardware Acceleration of Block-Diffusion LLM for Edge Devices
Wei-Hsing Huang, Kiseok Lee, Ming-Yen Lee et al. (+7 more)
Single-stream (batch-one) edge inference cannot amortize weight traffic across requests. Full-
attention diffusion LLMs recompute the entire sequence at every step; native block
diffusion makes completed blocks immutable and exactly cacheable, yet refinement still streams prefix KV and FFN weights. We co-design WIFiV-LPDDR, a wide-I/O LPDDR system for precision-tagged reads, BRQ-KV for a canonical low-rank-plus-INT8-residual prefix with query-dependent per-entry precision, and DAT-FFN for drift-mapped canonical replacement, adjacent-stage-corrected low-bit delta, or cached-state carry while keeping live activations unquantized. Both map to an input-stationary mixed-precision systolic array. For the evaluated 1.5B/7B models on modeled Jetson-class platforms, the full stack provides arithmetic-mean energy-reduction factors of 3.79x/3.96x and arithmetic-mean latency speedups of 2.88x/4.44x at the reported DAT-FFN settings; every corresponding compressed model-
benchmark score drops by less than one absolute percentage point from its baseline.
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All authors Wei-Hsing Huang, Kiseok Lee, Ming-Yen Lee, Weiyu Sun, Cheng-Jhih Shih, Gayatri Tanksali, Arpit Khandelwal, Pin-Jun Chen, Yingyan Celine Lin, Shimeng Yu
Distributed Computing
Update for Decisions, Not Freshness: Goal-Oriented Status Updating and Selective Offloading at the Network Edge
Jianpeng Qi, Qiyang Zhang, Chao Liu et al. (+5 more)
In an edge–cloud collaborative edge-computing environment, an edge node (EN) must decide whether each user task should be executed locally, forwarded to a remote service (or cloud) node (SN), or rejected. The EN observes its local state directly but receives the SN state only through an intermittently refreshed cache. Status updating and task control therefore form an asynchronous closed loop under partial observability. Freshness-driven schemes, including those based on Age of Information (AoI), do not directly value an update by its effect on subsequent task decisions. We propose CoSMO (Co-design of Semantic-state Management and Offloading), a cooperative event-driven reinforcement learning (RL) framework that coordinates semantic status management and selective offloading through realized task utility. CoSMO learns a compact representation of the heterogeneous SN service state. At the SN, a recurrent semi-Markov double deep Q-network (Double DQN)
agent jointly selects send/no-send and the next decision interval. At the EN, a task-terminal off-policy value-learning
agent makes hierarchical gate–route decisions from local observations and stale remote semantics. The
agents maintain separate observations and value targets but share the same realized task-utility stream, without centralized execution. Across the evaluated workload families, CoSMO's reported relative improvement in on-time completion rate over the best-performing competing method averages 18.6%–21.2%. For capacity-aware decision accuracy across the three strict-overload points, the corresponding reported gains average 17.6%–$17.9%.
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All authors Jianpeng Qi, Qiyang Zhang, Chao Liu, Jing Sun, Yimei Liu, Yanwei Yu, Yingjie Wang, Wei Ni
Computation and Language
StateSwap: Probing Support-Elimination Hidden States in Multiple-Choice Questions
Chao Gao, Haijiang Liu, Qiyuan Li et al. (+3 more)
Large
language models often answer the same multiple-choice question inconsistently when it is posed under support-oriented and elimination-oriented framings. We investigate whether these discrepancies arise from different internal representations induced by the two framings. We introduce a dual-framing protocol with minimally varied
prompts that use either support- or elimination-oriented framing while keeping the evaluation target fixed. To probe the internal computation, we append an untrained special
token, [STATE], and treat its residual-stream activation as an intervention interface. Across both models, the two framings induce separable [STATE] activations concentrated in intermediate layers. Swapping these activations between paired
prompts systematically changes predictions and improves cross-framing agreement, providing intervention-based evidence that the activations are behaviorally relevant. Beyond instance-level substitution, mean-difference steering directions derived from the dual-framing contrast exhibit more bounded layer-wise responses than matched contrastive activation addition directions under the evaluated protocol.
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All authors Chao Gao, Haijiang Liu, Qiyuan Li, Caicai Guo, Frank van Harmelen, Jinguang Gu
Cryptography and Security
JENGA: Exploiting Counter-Based RowHammer Countermeasures to Break Real-Time Predictability
Valentin Abgrall, Marcello Traiola, Ruben Salvador et al. (+3 more)
Safety-critical real-time systems must satisfy multiple dependability requirements, notably time predictability and security. In such systems, tasks must complete within bounded and known execution times, typically characterised through Worst-Case Execution Time (WCET) analysis. At the same time, DRAM-based platforms are increasingly sensitive to the RowHammer read-disturbance security vulnerability, which has motivated the development of numerous hardware and software countermeasures in both academia and industry. However, the impact of these defences is generally evaluated in terms of average-case performance, a metric that is insufficient for safetycritical real-time systems, where worst-case behaviour is the primary concern. In this paper, we study the impact of RowHammer countermeasures based on hardware counters on the timing behaviour of real-time systems. We use a Per-Row-Activation-Counter (PRAC) countermeasure as a case study, standardised for recent DDR5
memories, and show that it can introduce significant timing variations. Based on this observation, we introduce JENGA, an attack in which an attacker-controlled task manipulates the internal state of the RowHammer countermeasure mechanism to increase the execution time of a victim real-time task beyond its expected WCET. We implement JENGA in a gem5 and Ramulator 2.0 simulation environment and evaluate its impact on TACLeBench workloads. We show that such an attack can delay tasks up to 200% of their WCET, making the initial timesafety assumptions unsafe. To address this issue, we derive a safe analytical bound that accounts for mitigation-induced delays in WCET analysis for DRAM systems protected by hardware countermeasures, such as PRAC-N.
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All authors Valentin Abgrall, Marcello Traiola, Ruben Salvador, Maria Méndez Real, Alessandro Palumbo, Angeliki Kritikakou
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Text-guided flow matching enables sample-efficient crystal structure generation
Wentao Li
Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials design. Text provides a compact way to combine composition,
symmetry, prototype and property cues, yet it has not been clear whether such information can steer flow-based
crystal generation. Here we introduce TFMat, a text-conditioned flow-matching framework that uses structured materials
language as a semantic prior for a CrystalFlow generator. Across Perov-5, Carbon-24 and MP-20
crystal structure prediction
benchmarks, TFMat improves one-candidate match rates over CrystalFlow and reaches a 92.04% MP-20 match rate with 20 candidates; in de novo generation, it improves element-count and density distribution
alignment while retaining coarse property consistency in composition-selected outputs. These results position structured text as an inspectable control layer for translating human-readable materials intent into candidate
crystals for downstream simulation and validation.
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Networking
Lacan: Making Accountability in Anonymous Networks Real
Naoya Takada, Yutaro Yoshinaka, Kentaro Kita et al. (+3 more)
Anonymity and accountability are essential properties for our everyday activity on the Internet. However, they appear contradictory, and their reconciliation remains far from reality. Existing approaches fall short in this regard, as they either rely on an on-path trustee, per-packet authorization, per-packet public-key cryptography, or per-session intervention by a central authority. We propose Lacan, a protocol that reconciles anonymity and accountability within a realistic design. In Lacan, a sender enjoys anonymity provided by on-path relays, as long as she complies with a contract established with the receiver. Upon a contract violation, the verifier, an off-path trustee on behalf of the receiver, links the malicious message to the sender's identity indirectly via the packet, path, and session, thereby reducing public-key operations from per-packet to per-session. This linkage remains robust even against malicious relays and receivers, grounded in our novel chain of successor proofs for accountable path reconstruction, together with traceable signatures, path validation, and key-committing encryption. We analyze the anonymity and accountability, implement the protocol, and evaluate the performance.
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All authors Naoya Takada, Yutaro Yoshinaka, Kentaro Kita, Junji Takemasa, Yuki Koizumi, Toru Hasegawa
Computation and Language
Post-hoc Alignment of LLM-judges to Human Judgment Distribution
Sebastian Steindl, Nikos Voskarides, Alberto Gasparin et al. (+1 more)
The LLM-as-a-judge (LLMaJ) framework offers a cost-effective and reproducible solution for automatic evaluation. However, current evaluation practices typically compare LLMaJ judgments against aggregated ground-truth labels, overlooking the valuable information contained in Human Label Variation (HLV). Inspired by an increasing line of work that proposes to leverage HLV, we systematically study LLMaJ performance on predicting both a single, aggregated ground truth hard-label and unaggregated soft-labels that represent Human Judgment Distributions (HJD). Our results across five diverse
datasets reveal that while LLMs achieve near human-level performance at hard-label prediction on most tasks, they exhibit poor performance when predicting soft-labels. To address this limitation, we propose NAPHA (
eNtropy-Aware Post-Hoc
Alignment), a simple yet effective lightweight post-hoc
alignment method that matches the LLM distribution to the HJD by first assigning an instance to a discrete
entropy class and then routing it to specialized, trained
alignment models. We find that NAPHA consistently improves soft-labels prediction across base LLM models and
datasets, with particularly strong gains on high-
entropy instances where capturing diverse human perspectives is most critical. We also show via oracle experiments that improving
entropy class prediction can substantially enhance NAPHA's practical effectiveness.
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All authors Sebastian Steindl, Nikos Voskarides, Alberto Gasparin, Diego Marcheggiani
Machine Learning
Let Confidence Change, Not the Prediction: Prediction-Preserving Repair for Post-hoc Calibration
Daehwan Kim, Haejun Chung and Ikbeom Jang
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only the net effect of these changes on correctness, not how often predictions change; the Top-1 Prediction Change Rate (TPCR) instead measures this frequency. We propose Calibrator-Output Repair for Top-1 Decision Preservation (CORD), the first post-fit adapter to impose exact prediction preservation by repairing the full calibrated probability vector. From the original and calibrated outputs alone, CORD determines the mass assigned to the original top-1. The calibrated conditional distribution allocates the remaining mass over the other classes, yielding a repaired vector whose own argmax recovers the original prediction. On the calibration split, CORD coordinates the repaired masses to retain the calibrated outputs' mean mass on original predictions whenever attainable. The adapter alters neither the fitted calibrator nor its direct output, fits no additional supervised map, and requires no user- or validation-tuned hyperparameter. Across CIFAR-10/100 and ImageNet-1K, CORD attains zero TPCR by construction and lowers mean ECE, NLL, and Brier relative to the corresponding direct outputs in every
dataset; paired gains persist under distribution shift and across calibration-set sizes. CORD thus removes the preservation constraint from calibrator fitting and assigns exact recovery of the original decision to subsequent output repair. Our code is available at https://github.com/labhai/CORD.
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Performance
DART: Aiming for Tail-Delay Control in Reconfigurable Networks
Hossein Mohammadalizadeh and Holger Karl
Many systems serve different job classes by switching among configurations. Often, reconfiguration takes a
stochastic amount of time that depends on direction and can differ in distribution between configurations. Reaching a target configuration may require crossing intermediate ones. Under a high-percentile delay objective, rarely served or pass-through configurations accumulate the long-waiting jobs that dominate the sojourn-time tail. Controlling that tail couples three decisions: which target to choose, which path to take, and whether and how many jobs to serve at the configurations crossed on the way. We introduce DART, an opportunistic commitment policy that commits to a target configuration to avoid myopic detours while serving a traversed configuration only when its weighted delay or backlog justifies the pause. We show how these decisions shape the weighted sojourn-time tail. Across six stress
topologies, each isolating a different tail-control challenge, DART achieves the lowest P99 sojourn time in every case, up to 23% below the strongest baseline. The advantage holds under heterogeneous, heavy-tailed, and asymmetric reconfiguration times.
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Information Theory
List Size of Random Linear Codes: Optimality and the Limits of Average-Radius List Decoding
Chen Yuan and Ruiqi Zhu
Guruswami, Li, Mosheiff, Resch, Silas and Wootters conjectured that, for every fixed
prime power $q$ and $p\in(0,1-1/q)$, random linear codes of rate $1-h_q(p)-\varepsilon$ have list size $\frac{h_q(p)}{\varepsilon}\cdot(1+o(1))$ as $\varepsilon\to0$, both for list decoding and average-radius list decoding. The
conjecture was known for $q=2$ and remained open for larger alphabets. We prove the
conjecture for list decoding for every
prime power $q$, with the sharper bound $L=\frac{h_q(p)}{\varepsilon}+O_{p,q}(1).$ In contrast, we disprove the
conjecture for average-radius list decoding. We show that the required list-size leading constant is at least $p\left(1+\log_q\frac{(q-1)(1-p)}{p}\right)$, which is strictly larger than $h_q(p)$ whenever $q^p(1-p)>1$. Moreover, for $p=1-\lambda/q$ with fixed $\lambda>1$, the required leading constant can be as large as $\left(\frac{\log\lambda}{e\lambda}+o_q(1)\right)\frac{q}{\log q}.$
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Computation and Language
OUTLETS: Output-Length Prediction from Speculative Decoding Backbones
Weihuang Wen, Yingying Liu, Yichuan Liu et al. (+5 more)
The heavy-tailed distribution of output lengths in Large
Language Model (LLM) serving poses major challenges for resource provisioning and cluster scheduling. Although output-length prediction can mitigate these issues, existing approaches have key drawbacks: external proxy models add substantial latency and often have limited fidelity, whereas internal state-based methods are efficient but rely on shallow probes of current model states. We identify a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length. Building on this insight, we introduce OUTLETS (Output-Length Prediction from Speculative Decoding Backbones), which repurposes the speculative backbone as a trajectory-aware length predictor. When its draft representations are already computed for speculative decoding, OUTLETS adds only a lightweight regression head and achieves lower MAE than the evaluated methods. Under saturated disaggregated serving, OUTLETS predictions enable standard scheduling policies to prioritize shorter requests and distribute requests more evenly across decoding instances, reducing short-request P99 latency by 34.8%.
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All authors Weihuang Wen, Yingying Liu, Yichuan Liu, Wenqi Zeng, Li Zhou, Chumin Sun, Jie Sun, Tianshu Yu
Information Retrieval
World Model-Guided Reinforcement Learning via Counterfactual User Engagement Simulation
Ang Li, Xin Xu, Bin Liang et al. (+4 more)
Reinforcement learning for user-centric
agents is limited by the cost, latency, and risk of collecting online feedback, as well as by the lack of counterfactual comparisons under the same user state. In this paper, we propose World Model-Guided Reinforcement Learning via counterfactual user engagement simulation (WMG-RL), a framework in which a frozen user simulator provides reward supervision before real user exposure. Motivated by
language world models, we instantiate the simulator as a User Engagement World Model (UEWM), which treats a recommended item as the
agent action and the user's heterogeneous feedback as the environment observation. Rather than learning one fixed environment transition, UEWM learns to infer user-specific dynamics from engagement history and apply them to candidate items. In WMG-RL, a downstream policy proposes multiple candidate items for the same history; UEWM predicts the corresponding engagement feedback in parallel; and the simulated feedback is converted into dense rewards for policy optimization. Experiments show that UEWM provides reliable and transferable reward signals across domains, and that WMG-RL enables a compact 1.7B student policy to match or surpass much larger LLMs on downstream recommendation tasks.
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All authors Ang Li, Xin Xu, Bin Liang, Yue Ma, Fubang Zhao, Yangyang Kang, Kam-Fai Wong
Sound
Artificial Rosetta Stone: Constrained Maximum A Posteriori (MAP) Reconstruction of Symbolic Raga Sequences via Order-k Markov Models
Saanvi Raghavendran and Abhishek Bhattacharjee
Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial information, while a raga encodes constraints limiting allowable completions. This paper formalizes a mathematical framework for this, proposing the Artificial Rosetta Stone (ARS). We separate three claims often conflated: a symbolic sequence can be reconstructed probabilistically; a sequence can be consistent with an explicit grammar; and a historical performance can be authenticated. We only support the first two. We model a raga via a finite alphabet and constraint system, using an order-k Markov model for melodic probabilities. A symmetric Dirichlet prior yields a tractable posterior. We pose missing-note reconstruction as a constrained MAP problem. For fixed-length sequences and finite-order constraints, optimization admits an exact dynamic-programming solution with worst-case time complexity $O(TN^{k+1})$. We derive the parameter count $N^k(N - 1)$, prove a concentration bound under explicit mixing assumptions, and analyze estimation error propagation. A reproducible synthetic experiment uses six raga-inspired alphabets, orders $k \in \{1, 2, 3\}$, and masking rates up to 50%. This is a proof of concept, not historical reconstruction. A real-audio feasibility pilot evaluates 30 usable sequences from 42 Yaman clips via automated pitch extraction,
segmentation, and quantization. Lacking documented provenance and relying on automated transcription, this is not expert-validated archival reconstruction. Claims are tied to stated conditions, not universal properties of Hindustani music. Code: https://github.com/mathacker23/ArtificialRosettaStone.
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Artificial Intelligence
Space Generative AI with Solar Energy Harvesting
Jierui Zhang, Jianhao Huang, Zhanwei Wang et al. (+1 more)
Satellites are emerging as promising platforms to extend generative artificial intelligence (AI) services to remote areas lacking terrestrial infrastructure. However, deploying space generative AI is fundamentally constrained by the limited, time-varying onboard energy supplied by
solar energy harvesting (EH). This paper presents a framework for
solar-powered space generative AI in which a satellite receives a user
prompt, executes a
diffusion-based image-generation model, and downlinks the compressed result within a strict time window. We identify the fundamental computation–communication (C$^2$) trade-offs governed by the shared harvested-energy budgets. Specifically, increasing the number of generation steps improves intrinsic image quality but depletes energy and time available for downlink transmission, whereas prioritizing communication guarantees reliable delivery but sacrifices semantic quality. To balance these trade-offs and maximize end-to-end (E2E) generative performance, we exploit the predictable
solar-EH dynamics induced by deterministic orbital motion and develop a joint C$^2$ resource-optimization framework using a tractable two-step approach. First, we characterize the maximum downlink throughput for a fixed generation depth under continuous
solar EH. This establishes a separation principle that decouples waiting-time selection from optimal transmit-power control. Next, we formulate a joint C$^2$ utility-maximization problem and derive a closed-form, low-complexity step-selection policy in the dominant constant-power regime. Extensive experiments under realistic orbital dynamics demonstrate that the proposed policy dynamically balances generation quality and transmission reliability. This yields significant E2E performance gains over static computation- and communication-centric baselines across diverse
solar-EH states.
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All authors Jierui Zhang, Jianhao Huang, Zhanwei Wang, Kaibin Huang
Robotics
Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al. (+6 more)
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions often make fixed parametrizations suboptimal and motivate context-dependent online adaptation. Learning such policies is difficult because behavior depends implicitly on numerical MPC solutions, producing nonlinear, potentially nonsmooth, long-horizon dependencies on policy parameters. This creates a bias-variance tradeoff: Reinforcement Learning (RL) optimizes realized closed-loop return from environment samples but is sample-inefficient, whereas Gradient-Based Policy Learning (GB-PL) uses low-variance solver gradients from differentiable MPC to optimize surrogate losses on predicted trajectories but can be biased under model mismatch. We propose Solver-Gradient Guided Reinforcement Learning (SG-RL), a solver-sensitivity augmentation for RL-based online MPC cost-weight adaptation. SG-RL keeps sampled closed-loop return as the objective and uses bounded solver-derived gradients as auxiliary guidance to improve stability and sample efficiency. We instantiate SG-RL in Proximal Policy Optimization (PPO) with four modular
algorithms that inject solver-gradient guidance into actor-update scaling, policy loss, advantage estimation, and value-function learning. On two full-scale autonomous racing platforms with intentional model mismatch, SG-RL reaches PPO's best closed-loop return with up to 70.6% fewer samples, outperforms GB-PL baselines by at least 54% in closed-loop return, and generalizes zero-shot to unseen environments.
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All authors Baha Zarrouki, Arslan Thobani, Jasper Hoffmann, Mattia Piccinini, Rudolf Reiter, Felix Jahncke, Sébastien Gros, Davide Scaramuzza, Johannes Betz
Computer Vision · Cross-listed from eess.IV
Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment
Mohamad Jouni, Aurélien Godet and Mauro Dalla Mura
Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine
vision, but at lower spatial resolution than standard RGB cameras. RGB-guided hyperspectral super-resolution (HSR) addresses this limitation by transferring spatial detail from a high-resolution RGB guide to a low-resolution hyperspectral image (HSI). These dual-camera systems are typically in a horizontal rig geometry, requiring cross-camera image
alignment due to different fields of view. However, residual misregistration can inject spurious high-frequency details. Existing learned unaligned-fusion methods are usually trained for a fixed spectral support and spatial scale factors and can be computationally demanding, limiting their flexibility across sensors. We propose a lightweight and interpretable RGB-guided HSR framework combining cross-modal flow
alignment with model-based Gram-Schmidt orthogonalization fusion. The method first warps the RGB guide onto the HSI grid, then estimates an energy-based confidence weight map by measuring local
alignment reliability. This map is then used both in a weighted least-squares spectral regression and in a gated fusion between the super-resolved estimate and an HSI-preserving estimate. Unlike existing learned methods, the proposed framework has a low computational footprint and supports VIS-NIR spectral supports and scale factors without retraining. Experiments on the Real
benchmark show that the proposed method improves reconstruction accuracy over learned fusion baselines while remaining substantially faster. On a 34-frame sequence acquired with our real RGB-HSI dual-camera setup, a reduced-resolution quantitative evaluation validates the method under genuine cross-sensor radiometric, noise, and
geometric differences, while native-resolution qualitative results demonstrate deployment on the full 51-band VIS-NIR acquisition.
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Computer Vision
Dyn-3D: Unveiling and Resolving Ego-Motion Ambiguity in Vision-Language Models
Jiayu Ding, Zhuodong Liu, Lei Zhang et al. (+6 more)
As
Vision-
Language Models (VLMs) tackle dynamic 3D spatial
reasoning, ego-motion perception becomes essential to resolve
monocular scale ambiguity. However, current models often overfit to smooth trajectory priors rather than genuinely understanding physical motion. Consequently, their spatial
reasoning degrades severely under large displacements, a phenomenon we term Kinematic Collapse. This failure stems from spurious visual-motion correlations in natural
videos and a lack of explicit physical supervision. To evaluate this, we introduce Dyn-3D, a
benchmark using counterfactual 3D rendering to rigorously decouple visual changes from true kinematic properties. Furthermore, we propose the TempoVista framework, featuring the Kinematic-GSPO
algorithm. By
embedding metric physical ground truth into policy optimization, TempoVista explicitly grounds visual representations in 3D space. Experiments demonstrate that our approach significantly improves both motion estimation and robust spatial
reasoning by utilizing camera dynamics as an effective
geometric calibration signal.
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All authors Jiayu Ding, Zhuodong Liu, Lei Zhang, Manyu Xiong, Hongbo Jin, Haoran Tang, Hongbo Zhang, Changen Zhu, Wenbo Xing
Artificial Intelligence
ARISE-RL: Agentic Rubric-Grounded Iterative Self-Evolution with Reinforcement Learning
Fanrui Zhang, Ruixue Ding, Qiang Zhang et al. (+13 more)
Training open-ended
agents via reinforcement learning (RL) is hindered by the lack of verifiable gold answers and scalable rubrics. Moreover, even near the model's capability boundary, long-horizon open-ended agentic tasks often yield brittle and unstable rewards, resulting in weak or noisy rollout contrast that obscures fine-grained optimization signals for group-based policy learning. To address these challenges, we propose ARISE-RL, a novel full-cycle self-
evolution framework that couples a task/rubric Generator and a
reasoning Solver through rubric-mediated co-
evolution. The Generator grounds tool-related rubric criteria in real tool observations and is rewarded for producing valid, intermediate-difficulty tasks aligned with the Solver's evolving capability boundary. The Solver, in turn, learns from fine-grained rubric satisfaction signals through multi-step
reasoning and tool use. We further introduce Reward-Gated Self-
Evolution Distillation (RG-SED), which selectively distills a
memory-augmented variant of the same policy back into itself only when the
memory yields empirical reward improvement, thereby reducing distribution mismatch and avoiding blind imitation of noisy guidance. Finally, to support rigorous evaluation, we present ECR-Bench, an expert-calibrated rubric
benchmark suite covering single-tool deep research and multi-tool travel planning. Extensive experiments demonstrate that ARISE-RL consistently achieves robust and stable overall state-of-the-art performance across all evaluated
benchmarks.
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All authors Fanrui Zhang, Ruixue Ding, Qiang Zhang, Xi Chen, Boli Chen, Shihang Wang, Qiuchen Wang, Hongmin Zhan, Jinxin Bian, Li xingchao, Peijin Zheng, Hao cheng, Pengjun Xie, Kaipeng Zhang, Jiawei Liu, Zheng-Jun Zha
Artificial Intelligence
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
Chengqi Yang, Yiran Qiao, Feng Liu et al. (+7 more)
User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus on single-app or app-level behaviors, overlooking the inherently cross-source and multi-granular nature of user activities on mobile devices. At the device level, user intent emerges from complex interactions among heterogeneous behavior sources and hierarchical action structures, posing challenges that cannot be addressed by conventional app-centric modeling. To tackle this issue, we propose CM-PTM, a novel Cross Multi-source Behavior Pre-Training Model tailored for mobile game user representation learning on device-level behavioral logs. CM-PTM employs hierarchical cascaded mask-then-predict proxy tasks that first infer the source of the next behavior and then progressively refine predictions at the app-action level. This design enables unified modeling of cross-source dependencies and fine-grained behavioral dynamics within a single pre-training paradigm. Extensive experiments on large-scale real-world mobile
datasets demonstrate that CM-PTM effectively captures users' endogenous interests and consistently delivers significant performance gains on downstream mobile game recommendation tasks.
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All authors Chengqi Yang, Yiran Qiao, Feng Liu, Xingyu Lou, Zijun Zhou, Xiaoyun Mo, Changwang Zhang, Jiayuan Xu, Jun Wang, Xiang Ao
Artificial Intelligence
Leonardo Ranaldi, Sherrie Shen, Jushi Kai et al. (+1 more)
Despite the growing use of LLM-powered
agents to solve multi-step tasks in complex environments, existing
benchmarks rarely test state preservation, performance across
languages, and application to realistic, grounded scenarios. To address these concerns, we present WorldBench: a comprehensive,
multilingual benchmark of genuine, persona-grounded everyday workflows, where
agents can act in a sandbox via structured actions. WorldBench comprises 1,600 tasks across seven
languages and eight cultures, filtered and refined through feedback from human annotators with
language- and culture-specific expertise. For evaluation, we extend metrics from previous works and introduce Constrained Task Success (CTS), which combines natural
language instructions and testbeds to score task completion, minimal modification, and other complementary metrics through deterministic and LLM-as-a-Judge evaluations. Our experiments show that frontier models reach only 49.2% CTS, with all models demonstrating large gaps between correctness and environment preservation. We thereby show that current
agents remain brittle in
multilingual, agentic scenarios, especially for long-horizon tasks and under state-preservation constraints
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All authors Leonardo Ranaldi, Sherrie Shen, Jushi Kai, Alexandra Birch
Machine Learning
SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations
Yiming Luo, Rongqiang Zhao and Jie Liu
Spurious correlations pose a significant challenge to the robustness of modern machine learning. The inherent imbalance in
dataset distributions often leads traditional Empirical Risk Minimization (ERM) models to rely on majority spurious attributes for classification, resulting in poor performance on minority groups. This problem becomes particularly challenging when the spurious attributes are unavailable. Existing group-label-free methods often upsample minority groups or misclassified real training examples; repeating the same instances can reduce effective diversity and encourage overfitting. To mitigate these spurious correlations from a data-centric perspective in the absence of prior knowledge, we introduce Subpopulation-Aware Generative Enhancement (SAGE), a two-stage generative augmentation framework. Using cluster-derived sub-labels and class labels, we fine-tune a conditional generative model and text encoder, generating targeted synthetic data to fill underrepresented regions in the training set and construct a balanced validation set for last-layer reweighting. We experimentally show that SAGE achieves 89.5%, 85.7%, and 79.1% worst-group accuracy on Waterbirds, CelebA, and MetaShift, respectively, outperforming the best group-label-free baselines by up to 7.7 percentage points.
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Cryptography and Security
What Limits Robustness in Deep Image Watermarking: An Analysis of Mechanisms and Their Scaling Across Capacities
Marta Bistroń and Zbigniew Piotrowski
Robustness remains the principal open problem in deep image watermarking, and what limits it becomes sharper as payload grows. This paper asks whether capacity is itself the limit or only makes other limits visible, and answers in two parts. The first organizes the distortions a watermark must survive and the strategies developed to resist them, ordering each by the axis that governs it: payload capacity for the distortions, differentiability for the strategies. The second identifies and measures three mechanisms that limit robustness in schemes mapping the payload onto a spatial block grid with extraction trained separately from a frozen embedder: desynchronization of the payload grid, the resistance of codec-induced distortion to training, and the narrowing of the usable
embedding-strength window. Payloads from 64 to 16384 bits are measured, well beyond the range those strategies address. Training the extraction stage against a codec proves not merely ineffective but harmful, degrading the reading at the operating points used in training. The limits follow the class of distortion rather than capacity itself, and none is removed by further training on the extraction side, because all three arise before extraction. An evaluation protocol making claims of generalization verifiable is also contributed. The conclusions are properties of a class of designs rather than of one implementation.
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Artificial Intelligence
QILP-0: Constructing Observational Declarative Twins of Quantum Circuits
Marina de la Cruz Echeandía, César Luis Alonso, Tony Ribeiro et al. (+1 more)
This paper introduces QXymb, a general framework for constructing observational declarative twins of
quantum circuits, and develops QILP-0, its first complete order-0 specialization. QILP-0 constructs a finite multi-valued propositional logic program from observed
circuit behaviour within a declared observational scope. The pipeline traverses a declared family of
quantum observables incrementally according to a reproducible structural grading and a declared observational reference horizon. Progress is quantified through reference-relative coverage against a fixed target-independent reference. Observable responses are organized through target-independent geometry, while retained latent structure is mapped deterministically back to original observable columns before symbolic processing, preserving observational semantics and provenance. Selected observable profiles are converted into a finite relation through admissible target-independent discretization. The target is used only afterwards to audit twin-admissibility and induce the declarative theory. A theory is certified as an exact observational declarative twin when it completely and correctly reconstructs the resulting finite task-conditioned discrete relation. Logical exactness is therefore separated from numerical, backend, provider, and discretization uncertainty, which is retained as audit metadata. Validation uses two complementary QML settings. Exhaustive Bars & Stripes experiments compare product and grid-CZ
embeddings from 16 to 100
qubits and exercise the native-discrete branch. Low-Depth MNIST analyses all 14,708 digit-0/1 instances before and after a trained variational
quantum transformation and exercises continuous discretization. In every reported relation, the induced QILP-0 theory achieves complete, conflict-free reconstruction with strict accuracy equal to one.
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All authors Marina de la Cruz Echeandía, César Luis Alonso, Tony Ribeiro, Alfonso Ortega de la Puente
Computation and Language
Lagged Coupling: Internal Representations Become Readable Before They Become Causal
Xining Xun
Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as early as step 1,000 at every scale – yet steering along that same reading direction remains null-equivalent in 43 of 48 model-checkpoint
cells. Internal readability systematically outruns causal efficacy, and the lag does not shrink with scale. We call this structure lagged coupling and decompose it into three dissociable tracks: (i) internal readability, saturated (AUROC >= 0.990) from the first checkpoint everywhere; (ii) behavioral readability, which develops gradually and progressively later at larger scales (12B reaches 0.909 only at the final checkpoint); (iii) causal efficacy, almost always null-equivalent, occasionally counterproductive early, with one isolated positive pulse (12B, step 8,000, z = +2.49) our grid cannot resolve. The ordering is dominantly read-before-write (11/11 units, no inversion). Representation headroom along the probe direction grows up to 57x with training and scale while causal write-in stays below 0.11% of headroom – the variable is increasingly written into the representation and increasingly ignored by the readout. Under a fully pre-registered protocol, both single-onset hypotheses resolve INDETERMINATE (scale slope +0.24, 95% CI [-0.60, +0.87]; time vote 3:3) – a disciplined negative explained by the three-track decomposition. A pre-registered OLMo-2 replication preserves the direction at attenuated magnitude. Our results caution against inferring steerability from probe accuracy and establish a developmental bottleneck: representation formation reliably outpaces causal readout consolidation.
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Cryptography and Security
HiveTraceGuard-Pro: A Compact Generative Guardrail for Prompt Injection, Jailbreaks, and Adversarial Obfuscation
Nikita Oblakov, Sabrina Sadiekh and Evgeniy Kokuykin
Production LLMs must handle inputs that attempt to override system instructions, bypass safety policies or elicit harmful responses. A common mitigation is a separate guardrail model. Existing reports, however, provide little evidence on Russian
prompt injection or Russian surface obfuscation. We present HiveTraceGuard-Pro, a 0.6B generative guardrail LoRA-tuned from Qwen3-0.6B. It is trained on Russian and English and uses one binary scoring rule (safe/unsafe) for the final target turn. Its training corpus pairs harmful examples, where a counterpart exists, with benign examples from the same domain and applies eight obfuscation transforms to both labels. In one harness, we compare HiveTraceGuard-Pro with thirty-four other guards on nineteen
benchmark groups, sixteen of which are public. Its aggregate key is 0.7432, behind 0.7641 and 0.7552 for the two higher-scoring guards. Over the sixteen public groups alone, its key is 0.7153 and four of the thirty-four other suite guards score higher. In a fifteen-model comparison, HiveTraceGuard-Pro has the highest clean Russian robustness combined-F1 (0.88) and Russian
prompt-injection recall (0.999). Both results use Russian sets assembled by our team, and at least 27.1% of the
prompt-injection set overlaps the training corpus. Its 14.3 ms median latency is the lowest among those fifteen models in that run. Across the suite, FPR is 0.268 and FNR is 0.156. All reported response results use a legacy standalone-reply serialization rather than the natural assistant-role path of the shipped chat template. We release the merged weights on Hugging Face under Apache-2.0. The corpus, evaluation sets and evaluation code remain internal.
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