Machine Learning
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory
Vakul Raghavan, Utkarsh Grover, Mingyang Mao et al. (+2 more)
Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size
memory must retain many associations, connect information across positions, and preserve relevant structure without being overwhelmed by everything it observes. These are distinct requirements: a
memory may store more information without representing the relationships needed for
retrieval, and it may transport those relationships without preserving the ones that matter. Thus, the challenge is not simply extending context length, but constructing a
memory that remains usable as context grows. We study this problem in large-chunk test-time training (TTT) and identify two basic failures: single-step writes accumulate interference, limiting capacity, while self-associative writes cannot form cross-position bindings. We introduce Double-Take (DT), which repairs these failures with multi-step unpinned updates and cross-position mixing, and adds bidirectional change-sensitive writes for selective retention. Capacity alone leaves end-to-end recall at the $0.10$ chance floor; adding binding raises it to $0.92$. Trained only on $32\,\mathrm{kb}$ genomic contexts, DT reaches $0.927$ AUROC at $131\,\mathrm{kb}$ and $0.856$ near $1\,\mathrm{Mb}$, while transferring zero-shot from human to mouse. Together, these results identify capacity, binding, and selectivity as three requirements for usable long-range
memory.
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All authors Vakul Raghavan, Utkarsh Grover, Mingyang Mao, Chengwen Wang, Xiaomin Lin
Sound
AV-FlowEdit: Training-Free Prompt-Driven Audio-Visual Editing with Event-Aware Spectral Trajectory Preservation
Shaohua Pan and Xiao Liu
Joint audio-
video diffusion models generate synchronized content, but editing existing clips requires target changes without disrupting non-target structure or cross-modal correspondence. We present AV-FlowEdit, a training-free and inversion-free framework for
prompt-driven audio-
video editing. Our key insight is that source–target flow differences provide both cross-modal editing directions and preservation cues. Under shared perturbations, they jointly update the
video and audio branches, supporting visual-only, audio-only, and joint edits while retaining pretrained cross-modal interactions. To suppress drift accumulated during sampling, Cross-Modal Event-Aware Spectral Trajectory Preservation combines flow saliency, aligned audio activity, multiscale low-frequency displacement, and
diffusion-stage scheduling. This adaptively preserves unsupported regions while allowing intended audio-visual events. AV-FlowEdit requires no model training, external masks, or optimization-based inversion. Experiments show strong
prompt alignment and temporal consistency, together with competitive audio quality and cross-modal coherence.
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Cryptography and Security
CAPE - Context-Aware Prioritization Engine for Vulnerability Scoring and Management
Rakesh Podder, Viktoria Koscinski and Indrajit Ray
Vulnerability patching is critical for safeguarding an enterprise's software ecosystem from cyber attacks. However, patching requires adequate preparation for potential system downtime and for unforeseen effects of the patched software on a live system. Thus, effective risk mitigation requires prioritization strategies that explicitly account for the operational context of the affected software. While vulnerability prioritization based on Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS) and CISA Known Exploited Vulnerabilities (KEV) catalog, can help, their differing goals and methodologies often lead to inconsistent, context-unaware decisions resulting in mis-allocated remediation efforts. In this work, we present the Context-Aware Prioritization Engine (CAPE), a vulnerability analysis framework that enriches each vulnerability scanner report of vulnerabilities (CVEs) with deployment-specific evidence. This evidence is used to perform vulnerability reachability, centrality, and exploitability analysis, producing a ranked priority score for the effective distribution of remediation effort. We evaluate CAPE on 30 open-source agentic applications comprising 6,138 scanner-reported CVEs. Our evaluation shows that a significant majority of CVEs are not reachable and exploitable which results in significant disparities between CAPE and context-free baselines - the CVSS, EPSS, and KEV. It also shows that CAPE consistently surfaces confirmed-exploitable CVEs at higher priority than all baselines, demonstrating that context-aware analysis provides substantially more accurate remediation guidance than context-free severity scoring alone.
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Hardware Architecture
TPCS: A Cycle-Level Simulation Paradigm with Native Execution Semantics for RTL Modeling and Generation
Chengzhen Meng, Xiaoyu Li, Chen Bai et al. (+2 more)
The growing diversity of domain-specific architectures and microarchitectural designs demands fast and accurate evaluation methods during early design stages. However, existing approaches suffer from a fundamental trade-off: high-level simulators offer speed but lack fidelity, while RTL-based tools provide accuracy at the cost of significant simulation overhead. This gap limits efficient pre-RTL microarchitecture exploration. Therefore, we propose Transaction-Pruned Cycle Simulation (TPCS), a novel modeling and simulation framework. It serves as a unified intermediate abstraction spanning microarchitecture modeling, cycle-accurate simulation, and synthesizable RTL implementation. TPCS organizes combinational logic as transaction-based function invocations and leverages host CPU control flow to naturally prune inactive logic, eliminating the need for explicit event scheduling or signal tracking. This enables cycle-accurate simulation with significantly improved performance. TPCS preserves structural correspondence with RTL and supports automatic generation of synthesizable designs. TPCS achieves 36$\times$-42$\times$ speedups over parallel Verilator on SMP systems and 3.4$\times$-7.2$\times$ over single-threaded Verilator on accelerators, with cycle-level waveform agreement on the evaluated traces. The generated RTL demonstrates comparable timing and area to handcrafted implementations, with relative changes of -4.7% to +2.6% in clock period and -3.3% to +4.0% in standard-
cell area. By bridging the abstraction gap between high-level modeling and RTL design, TPCS enables a unified and agile pre-RTL workflow, allowing efficient microarchitecture exploration with both high performance and implementation fidelity.
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All authors Chengzhen Meng, Xiaoyu Li, Chen Bai, Zhenyu Zhang, Hongjun Dai
Computer Vision
Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment
Xu Zhang, Haohan Wang, Zheng Zhang et al. (+9 more)
The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate (CTR). Recent methods generate appealing ad images from product images by constructing CTR-based reward signals and refining
prompt generation via Reinforcement Learning. Such paradigms equally penalize all attributes within an underperforming image, even though some are actually beneficial to clicks. In this work, we propose Evolving Creatives with Clicks (ECC), which reformulates creative optimization as fine-grained editing rather than generation from scratch. ECC selectively refines attributes that hurt CTR while preserving those that contribute positively. Specifically, faced with a rich attribute space across massive images, ECC first identifies 28 attributes significantly correlated with CTR via ANOVA and Random Forest regression. Based on attribute contrasts between images with disparate CTRs, we instruction-tune a
prompt model to generate editing suggestions, then refine it with real click preferences since reference-derived suggestions are not uniformly beneficial. Furthermore, we introduce Consistent Preference Optimization (CPO) as ECC's preference optimization strategy, which enforces consistency across semantically equivalent shuffled suggestions to mitigate DPO overfitting and improve preference generalization. Extensive experiments demonstrate that ECC significantly improves the CTR of ad images, outperforming state-of-the-art models by a substantial margin. The complete data pipeline, including attribute extraction and analysis, the resulting
dataset, and source code will be released upon acceptance.
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All authors Xu Zhang, Haohan Wang, Zheng Zhang, Yingjie Xia, Wei Feng, Honghe Zhu, Jingjing Lv, Changping Peng, Xuheng Qin, Zehua Zhang, Zheng Luo, Qicheng Li
Software Engineering
TestSuiteBench: Coding Agents Struggle to Generate Strong Test Suites from Specifications
Kazuki Egashira, Niels Mündler-Sasahara, Mark Niklas Müller et al. (+1 more)
Coding
agents are increasingly used to create large software projects. While code-generation
benchmarks have scaled accordingly to full applications, test-generation
benchmarks remain limited to individual functions or bug fixes. In particular, generating complete test suites for complex software from scratch remains unexplored. To address this gap, we introduce TestSuiteBench, a
benchmark of 165 open-source applications, each with a structured and detailed specification of intended behavior, a reference implementation, a reference test suite, and on average 74 mutants. Each mutant is generated from a single requirement of the specification and refined to isolate that behavior: 73% are killed by exactly one reference test and, on average, by only 0.2% of the remaining tests. We find that even frontier coding
agents struggle to generate strong test suites given our specifications, achieving mutation scores below 60%, despite their suites being largely valid and 88% of their tests passing on the reference implementation. We observe that generated test suites often lack relevant input combinations and are smaller than reference test suites. Meanwhile, implementations generated for the same specifications pass 82% of the reference tests. These results show
agents can build software they cannot verify, a gap that directly limits fully autonomous software development.
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All authors Kazuki Egashira, Niels Mündler-Sasahara, Mark Niklas Müller, Martin Vechev
Machine Learning
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits
Jiahang Sun, Zhiwei Shang, Zhipiao Liu et al. (+4 more)
Large
Language Model (LLM)
agents have demonstrated remarkable capabilities in tackling complex tasks through agentic workflows. However, manually designing these workflows is labor-intensive and lacks flexibility to adapt to diverse queries. While automated workflow optimization is a promising direction, existing methods often incur expensive API costs during training-phase feedback collection. Furthermore, they fail to adaptively reuse high-quality workflows, which creates a bottleneck for both performance and cost-effectiveness. To address these limitations, we propose CB-Orchestrator, an adaptive framework that decouples workflow generation from selection. In the first stage, we construct a diverse pool of candidate workflows via evolutionary search. In the second stage, we formulate workflow selection as a Contextual Bandit problem, which enables sample-efficient learning by balancing exploration and exploitation, thereby significantly reducing the feedback required for training. During inference, the model adaptively selects the optimal workflow tailored to each query. Evaluations across five diverse
benchmarks demonstrate that CB-Orchestrator consistently outperforms all baselines. Notably, on the MATH
dataset, CB-Orchestrator achieves better performance than strong recent baselines while reducing training-phase API
token overhead by no less than 40.55% and total end-to-end training (including workflow pool generation)
token overhead by no less than 29.73%.
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All authors Jiahang Sun, Zhiwei Shang, Zhipiao Liu, Hongwei Yang, Guoqing Xie, Shuang Qiu, Zhongxiang Dai
Distributed Computing
Capturing Electrical Windows Through Reconfigurable Optical Networks: Transfer-aware Scheduling of Multi-Region AI Data Centers
Xi Tong, Zijian Li, Xiaoyi Fan et al. (+3 more)
Artificial-intelligence data centers (AIDCs) can provide spatiotemporal load flexibility by migrating training workloads in response to regional electricity prices and renewable-energy (RE) availability. However, transfer-blind scheduling may overestimate migration benefits by overlooking transfer time or prevent training from resuming within renewable-rich or low-price windows when considering transfer time. This paper proposes a transfer-aware compute-electricity-network scheduling framework that coordinates training placement, checkpoint transmission, and optical
circuit switching (OCS) to improve local RE utilization and reduce grid-purchase cost. A compact compute-electricity master problem generates candidate schedules using estimated transfer delays. The framework then schedules optical
circuits, verifies checkpoint delivery under shared-link capacity constraints, and revises infeasible schedules using updated delay estimates. It integrates day-ahead planning, event-triggered intraday updates, and real-time dispatch. In a five-AIDC
benchmark with equal completed work and network resources, the proposed method reduces the real-time grid purchase cost by 12.50% and increases the local RE utilization by 2.724 percentage points relative to static optical operation. Sensitivity and ablation analyses indicate that OCS is particularly beneficial when renewable-rich or low-price windows shift across sites and network bottlenecks hinder timely checkpoint delivery.
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All authors Xi Tong, Zijian Li, Xiaoyi Fan, Cong Zhang, Ershun Du, Qin Wang
Computation and Language
KV-Repurpose: A Testbed for Evaluating Non-Prefix KV-Cache Reuse
Taiga Ikeda, Daisuke Miyashita and Jun Deguchi
KV-cache reuse in large
language model serving is commonly limited to exact
prompt prefixes, preventing reuse when the same retrieved document appears after different system
prompts, conversation histories, or dynamic metadata. We present KV-Repurpose, an open-source, vLLM-based system for developing and evaluating segment-wise KV-cache reuse beyond exact prefixes. The system combines cache-aware segment scheduling,
prompt construction, capacity management, workload generation, and runtime monitoring. Experiments on long-context
benchmarks show that naive segment reuse largely preserves output quality for single-document question answering and summarization, while exposing clear limitations in cross-document
reasoning. Under a Zipfian RAG-style workload in which 20% of requests share a fixed prefix, KV-Repurpose achieves a substantially higher
token-level cache hit rate than prefix caching at the same 30 GB cache capacity, 63.9% versus 3.3%, resulting in lower TTFT. Even with 1 TB of cache capacity, prefix caching reaches only a 17.9% hit rate in this workload. Source code is available under the Apache-2.0 license at https://github.com/taiga333/eval-kv-repurpose.
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Computation and Language
CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation
Chandravardhan Singh Raghaw, Shahid Shafi Dar, Arnav Nishith Sharma et al. (+2 more)
Radiology reports are essential for diagnostic
reasoning and patient care, yet their manual preparation is time-consuming and cognitively demanding. Automatic radiology report generation (RRG) offers a scalable alternative, but existing models often produce lengthy, unstructured narratives that overlook diagnostic cues and multi-view information. We present Clinical-Knowledge Guided Disease-aware Visual-Textual
Alignment for QA-Style Radiology Report Generation (CliQ-RRG), a unified two-stage framework for interpretable and clinically grounded reporting. In Stage 1, CliQ-RRG employs a Disease-aware Visual-Textual
Alignment module that aligns image and text representations using predicted disease
embeddings, reinforced by a Prior-Guided
Attention Module (PrAM) to capture multi-view dependencies across current and prior scans. In Stage 2, GraphRAG-guided
retrieval augments intermediate textual representations with graph-grounded clinical evidence, and a large
language model restructures them into concise, interpretable question-answer (QA) pairs with diagnostic summaries. Experiments on two public chest X-ray
benchmarks demonstrate that CliQ-RRG consistently outperforms prior methods across both lexical and clinical metrics, generating accurate and clinically coherent QA-style radiology reports. Code is available at: https://github.com/chandravardhan-singh-raghaw/CliQ-RRG.
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All authors Chandravardhan Singh Raghaw, Shahid Shafi Dar, Arnav Nishith Sharma, Shrey Modi, Nagendra Kumar
Artificial Intelligence
Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents
Ziang Ye, Wentao Shi, Lei Chen et al. (+7 more)
Large
language model
agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap
agents in dead-end trajectories.
Agents must therefore be able to proactively recover from such states and turn their exploratory experience into guidance for subsequent decisions. We introduce ReTRAIL, a framework that enables
agents to rewind the environment to earlier states while preserving valid progress and carrying forward knowledge learned from abandoned exploration. The environment state moves backward while the
agent's knowledge continues to move forward. ReTRAIL trains a shared policy with two complementary signals: State-Anchored GRPO compares alternative continuations from revisited states to improve execution, while a branch-advancement reward guides reflection through the progress of subsequent exploration. Experiments across four interactive
benchmark MineSweeper, Sokoban, ALFWorld and ScienceWorld show that simply adding a rewind primitive provides limited benefit and can even degrade performance. In contrast, ReTRAIL enables
agents to rewind from unproductive states and use insights from abandoned branches to guide subsequent exploration. These results demonstrate the value of coupling environment rewinding with experience reuse within an ongoing task for long-horizon task solving. The code is available at https://github.com/whi497/ReTRAIL.
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All authors Ziang Ye, Wentao Shi, Lei Chen, Zhengzhou Cai, Yu Wang, Zhiyuan Yao, Zihan Wang, Xierui Song, Fuli Feng, Yixin Cao
Computation and Language
CoBe: A Benchmark For Conversational Counterfactual Text Editing
Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar et al. (+6 more)
Counterfactual text editing is the ability to revise a given text in light of a hypothetical change while preserving facts not causally affected by that change. When asked what would have happened if a doctor had not recommended surgery, the model output should retain the patient's medical history and modify only the consequences of the decision. This requires causal understanding: a model must determine, without being explicitly instructed to, what events are affected by the hypothetical change and what should remain fixed. This skill is central to human
reasoning, especially in safety-critical domains like healthcare or law. By contrast, the task of associational text generation merely generates text statistically associated with the altered condition. In the medical example, an associational response might make the patient's symptoms less severe because milder cases correlate with no surgery. Such responses produce a plausible scenario, but not the counterfactual version of the given one. Prior counterfactual-text
datasets do not clearly separate these modes of
reasoning. We introduce the Counterfactual
Benchmark (CoBe), a testbed for models' zero-shot ability to counterfactually edit a given textual scenario when prompted to do so in a conversationally natural way. CoBe contains 2000 questions spanning healthcare, science and engineering, business, and everyday scenarios. We test a suite of frontier and smaller models, with frontier systems achieving an average score of $58.05\%$. Our findings indicate that models frequently default to associational generation rather than preserving the causal structure of the original scenario. Moreover, performance does not scale consistently with either model size or output
token length, suggesting that the failure may reflect model training objectives rather than insufficient model capacity.
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All authors Arvind Raghavan, Inwoo Hwang, Shreyas Havaldar, Kai-Zhan Lee, Aurghya Maiti, Yushu Pan, Jeffrey Wu, Mingxuan Li, Elias Bareinboim
Computer Vision
A4-MR: Abnormality-aware Anatomical Alignment for MR
Noel D'souza, Zhijian Yang, Nishank Singla et al. (+14 more)
Recent advances in large-scale foundation models have enabled strong generalization across a wide range of clinical and research tasks in
magnetic resonance imaging (MRI). However, existing
vision-
language MRI models remain limited in anatomy-specific localization and pathology-aware representation, particularly in real-world settings. We introduce A4-MR, a 3D
vision-
language foundation model trained on approximately 50,000 MRI studies comprising 491,000 series, roughly half of which are paired with radiology reports. A4-MR is designed to: (1) learn anatomy localization through large-scale pretraining with noisy labels; (2) align image and text representations at the anatomical level; and (3) learn distinct representations of healthy and abnormal anatomy. Across a diverse set of downstream tasks, A4-MR outperforms existing foundation models, including a +1.7 AUROC improvement over MedSigLIP for abnormality detection. Using a frozen encoder, A4-MR also surpasses an end-to-end task-specific MedNeXt model for knee segmentation by +1.4 Dice and performs comparably to the best task-specific model for abdominal organ segmentation on AMOS, within 0.4 Dice. Finally, without anatomical
prompts, A4-MR localizes 3D anatomy comparably to MedSAM2 provided with ground-truth bounding boxes. Together, these results demonstrate that anatomy-level
multimodal alignment and abnormality-aware representation learning can enable a single MRI foundation model to support localization, segmentation, and pathology-related tasks.
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All authors Noel D'souza, Zhijian Yang, Nishank Singla, Pranay Doshi, Krisztian Koos, Fei Tan, Bruno Nunes, Laszlo Rusko, Bernadett Kolozsvari, Eszter Sabo, Emanuele Valeriano, Vikash Gupta, Seyed Iman Zare Estakhraji, Marc Lebel, Farzin Haddadpour, Taha Kass-Hout, Erhan Bas
Sound
LiveSynth: A Streaming Neural Synthesizer for Instrument Cloning and Text-to-Instrument
Kyungsu Kim, Yejin Kim and Kyogu Lee
Recently, generative neural synthesizers have enabled zero-shot instrument cloning from reference recordings and timbre generation from text
prompts. However, existing systems generate complete audio segments offline, precluding their use as playable instruments. We present LiveSynth, a streaming neural synthesizer with flexible timbre control and interactive generation capabilities, including zero-shot instrument cloning and text-to-instrument synthesis. A feedback-free causal
Transformer conditioned on MIDI events and a CLAP timbre
embedding outputs continuous audio latents at 100 Hz, which a causal variational autoencoder decodes into audio. Frame-wise timbre conditioning enables continuous timbre morphing, while MIDI-conditioning dropout enables autonomous continuation from a performed prefix. On the NSynth and Slakh
datasets, LiveSynth achieved timbre similarity and spectral reconstruction quality comparable to offline baselines. On an M3 MacBook Air, LiveSynth required 8.8 ms of computation per 10 ms audio frame (an RTF of 0.88) with 10 ms algorithmic latency, enabling real-time interaction with a performer.
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Computer Vision
UniMergeTrack: Hierarchical Model Merging for General RGB-X Visual Object Tracking
Shiao Wang, Xiao Wang, Yuhe Ding et al. (+5 more)
RGB-X tracking leverages complementary modalities, such as thermal infrared, event, or depth, to improve robustness under challenging conditions. Existing general-purpose RGB-X trackers typically follow two paradigms: independent task-specific training and joint training across modality combinations. However, the former suffers from knowledge isolation and model redundancy, while the latter introduces optimization interference across heterogeneous tasks. In this work, we present UniMergeTrack, a new model merging paradigm that preserves task-specific optimization while enabling effective cross-task knowledge integration. UniMergeTrack first trains modality-specific trackers independently and then performs hierarchical model merging. Specifically, transferable backbones are merged across RGB-X tasks via high-pass-filtered merging, and complemented by lightweight task-specific experts that preserve modality-specific cues. Tracking heads are merged within each task using optimal transport-guided channel
alignment to mitigate hidden channel mismatch. A subsequent distribution
alignment fine-tuning stage further improves compatibility between the unified backbone and task-specific heads. Extensive experiments on the LasHeR, RGBT234, VisEvent, VOT-RGBD22, COESOT, and CDTB
benchmarks demonstrate that UniMergeTrack achieves state-of-the-art or competitive performance across RGB-Thermal, RGB-Event, and RGB-Depth tracking tasks, while exhibiting strong generalization to unseen
datasets. The source code will be released upon acceptance. The source code of this paper will be released on https://github.com/Event-AHU/OpenEvTracking.
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All authors Shiao Wang, Xiao Wang, Yuhe Ding, Jiarui Xu, Zikang Yan, Bo Jiang, Yonghong Tian, Jin Tang
Cryptography and Security
Space Over Statistic, Retraining Over Ranking: Auditing Training Labels Under Label-Only Backdoors
Arnab Dey and Mohammad Mahfuzul Islam
Some backdoor attacks never touch an image. FLIP only changes labels: on CIFAR-10, flipping 3% of them makes a ResNet-32 send 99.7% of triggered source-class images to the attacker's class. The usual defenses look for the poison inside a model trained on those same labels. So when they struggle, is the problem the test they run, or the space they run it in? We answer with a controlled swap: we keep the test, the labels and the metric fixed, change only the feature space, and judge each defense by retraining the victim on what it keeps. The space decides. On dog$\to$cat the same nearest-neighbor check scores AUROC 0.546 on the poisoned model's features, 0.967 on frozen DINOv2 features that never saw a label, and 0.995 on the same network trained on clean labels, an oracle control rather than a defense. We reproduce SPECTRE, the defense FLIP's authors recommend, to 90.94 / 0.97 against its published 91.47 / 1.7, then price it: on truck$\to$deer it deletes 20% of every class, while a prototype audit on the frozen features deletes 6% for higher clean accuracy and lower attack success. On three harder pairs that audit leaves about a quarter of the attack standing. A count read off encoder geometry and clean labels, before any attack is run, separates the pair where the audit removes the attack from those where it only partly does, though on a single zero-count pair. Three results warn against the easy story. A better detection score does not mean a safer model: on two of four pairs Confident Learning scores the higher AUROC, and on dog$\to$cat leaves 2.6 times the attack success. The prototype family, our best static performer, is the first an adaptive attacker slips past. And past a point, catching more poisons stops reducing attack success. Check labels in a space that never learned them, and judge a defense by the retrained model, not by its detection score.
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Computer Vision
NOFACE3D: NOise-scheduled Focused Anchoring and Controlled Enhancement for 3D Generation
James Rose, Jingwen Liang, Wenchao Ma et al. (+2 more)
Recent image-to-3D models generate complete, high-quality assets from a single image, with global shape and appearance that are already convincing; what remains is the fine scale, where small, thin, elongated, or detail-rich regions are reconstructed softly and under-resolved. This gap can arise in generators with fixed-resolution spatial grids: the same spatial sampling density is used for a large, smooth, low-
curvature surface and for a thin, high-
curvature structure that carries most of the perceived detail. To this end, we introduce NOFACE3D, a training-free framework that refines these detail-critical regions while preserving the rest of the asset. Given a generated mesh, its input image, and a refinement box, NOFACE3D extracts the selected submesh, scales it up to fill the generator's input volume, and re-encodes it into the full-resolution latent grid. Conditioned on the image crop, it refines the region through flow-based sampling with a spatially varying noise schedule that anchors boundary latents. The refined region is then integrated back into the original asset, preserving the surrounding geometry and appearance. We implement NOFACE3D on TRELLIS.2 and its sparse voxel representation; all of it operates purely at inference time on the frozen pretrained generator. We evaluate NOFACE3D on PAniC-3D, TexVerse and BigDetail, a new
benchmark of 339 assets with annotated detail-critical regions. On all three, it generates sharper geometry and finer texture detail than the baselines. Project page: https://idkcouscous.github.io/NoFACE3D/
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All authors James Rose, Jingwen Liang, Wenchao Ma, Oren Jacob, Haomiao Jiang
Robotics
Failure-Resilient Multi-Robot Coordination with $k$-Connectivity Constrained Submodular Maximization
Yoonjae Hwang, Hojune Kim, Ellen Novoseller et al. (+2 more)
Multi-
robot coordination is essential in applications such as coverage, target tracking, and information gathering, where
robots must jointly select actions to maximize team performance. Submodular maximization provides an effective framework for such coordination, and resilient variants further account for
robot failures or
adversarial removals. However, existing methods typically overlook the impact of
robot loss on the communication network, which may disconnect the surviving team and prevent continued coordination. To address this issue, we introduce Resilient $k$-Connected Submodular Maximization, which jointly considers task performance, resilience, and communication connectivity. The resulting feasible family is not a matroid, preventing the direct application of classical
curvature-greedy guarantees. We overcome this challenge through a Menger-based connectivity reduction, a connectivity-aware greedy
algorithm with a feasibility-preserving fallback, and a new
curvature-based analysis that captures the cost of enforcing connectivity through a single factor $\rho$. Our method achieves a worst-case post-failure objective within a factor of $[\max(1-\nu_f(\mathcal{I}),h(N,\beta))]^2 \rho \frac{1}{\kappa_f}(1-e^{-\kappa_f})$ of the optimal connectivity-respecting resilient assignment, while recovering the classical unconstrained guarantee in the appropriate limits. Experiments on multi-
robot target tracking validate the proposed method and characterize the practical impact of connectivity constraints.
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All authors Yoonjae Hwang, Hojune Kim, Ellen Novoseller, Guangyao Shi, Gaurav S. Sukhatme
Distributed Computing
Execution-Feasible Dispatch of LLM Pre-training Loads for Renewable-Dominant AI Data Centers
Xi Tong, Ershun Du, Haiyang Jiang et al. (+2 more)
AI data centers (AIDCs) are emerging as large power-system loads, making their operational flexibility increasingly valuable. Existing studies often treat AIDC demand as continuously adjustable or generically delay-tolerant, which overstating the flexibility of large
language model (LLM) pretraining. This paper proposes an execution-feasible dispatch framework for LLM pre-training in renewable-dominant AIDCs. LLM descriptors are first converted into checkpoint-domain daily workloads, then mapped to feasible 3D-parallel GPU modes with explicit throughput, GPU-capacity,
memory, and IT-power limits. The resulting checkpoint- and mode-constrained load states are embedded into a day-ahead power dispatch model. Case studies on a 2048 GPUs AIDC with two LLaMA pretraining tasks show that checkpoint-aware 3D dispatch reduces operating cost by 20.02% and renewable curtailment from 1.077 to 0.014 MWh relative to fixed continuous training. Related results also show that checkpoint records alone do not create load flexibility,practical grid-interactive flexibility arises from checkpoint waiting combined with execution-feasible 3D-mode compensation.
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All authors Xi Tong, Ershun Du, Haiyang Jiang, Long Chen, Qin Wang
Computer Vision
Redirecting Teacher Supervision: Temporal-Response-Guided Latent Distillation for Video-Language Models
Xinkai Lu, Junli Zha, Haolan Wei et al. (+3 more)
On-policy
distillation (OPD) transfers capabilities through teacher predictions on student-generated sequences, as studied by GKD and MiniLLM for
language models and
Video-OPD for
video tasks. We ask how internal supervision can further support temporal learning. Direct hidden-state
alignment does not explicitly use the student's temporal responses to choose a supervision direction. We propose Temporal-Response Guidance (TRG), which measures representation responses to local temporal perturbations, adds the teacher correction's projection onto their span back to the original correction, and restores its original norm. The projection is not normalized separately, and correction components outside the response space are retained. With an orthogonal tangent projector and a detached target, TRG changes the supervision direction while preserving the cosine-loss value and aligned-representation gradient norm at target construction. On TempCompass,
Video-MME, MVBench, and
Video-MMMU, TRG improves the 4B student's macro accuracy by 2.86 percentage points over the reported output-only OPD baseline.
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All authors Xinkai Lu, Junli Zha, Haolan Wei, Jiahui Wang, Chao Zhou, Xiang Song
Cryptography and Security
Provably Subexponential Algorithms for Bounded-Error LWE with Few Samples
Haoxing Lin and Prashant Nalini Vasudevan
We show that a variant of the Wagner/BKW-style
algorithm for LWE of Kirchner and Fouque [KF15] solves decision and search LWE with secrets of size $n$ over
prime moduli $q=n^{\kappa+o(1)}$, for any fixed $\kappa>0$, with errors bounded by $\exp((\ln n)^{o(1)})$, in time $2^{O(n/\ln\ln n)}$ given $n+O(n/\ln\ln n)$ original samples. Further, if each coordinate of the secret is also bounded by $\exp((\ln n)^{o(1)})$, the same time bound holds with errors bounded by $n^{o(1)}$ and only $O(n/\ln\ln q)$ samples. Earlier, it was known that Kirchner and Fouque's
algorithm could solve LWE with these parameters in subexponential time with a much larger number of samples $m = \Omega(n\log{n})$ [HKM18]. Ducas et al. [DEL25] showed that an adaptation of this
algorithm could solve the corresponding dual, the $\mathrm{SIS}^{\infty}$ problem, with a suitable norm bound $q/\operatorname{polylog}(n)$ and $m=n+\omega(n/\ln\ln n)$ variables in subexponential time. Whereas their analysis involves interpreting the
algorithm in terms of chains of
lattices, our approach is quite elementary and is based on bounding the lower moments of a
Fourier statistic.
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Computation and Language
RAMP: Recurrent Adaptive Mixed Precision for Efficient Prefill in Recurrent-Depth Language Models
Ke Wan and Chen Chen
Recurrent-depth
language models improve inference-time
reasoning by repeatedly applying a shared network to refine internal representations, but the additional recurrent computation also increases inference cost. This overhead is particularly pronounced during long-context prefill, where the full input sequence is repeatedly processed across recurrent depth. Weight-and-activation quantization offers a natural way to accelerate this computation through lower-precision arithmetic, yet repeatedly applying quantized computation introduces numerical perturbations that accumulate along the recurrent trajectory. We systematically analyze how such errors propagate across recurrent depth and show that quantization errors introduced earlier are attenuated by more subsequent recurrent steps and therefore have a smaller effect on the final representation. Matched-budget experiments strongly support this prediction, showing that placing quantized computation earlier in recurrent depth consistently yields the best fidelity. Based on this insight, we introduce RAMP (Recurrent Adaptive Mixed Precision), which performs early recurrent refinement using low-precision weight-and-activation computation and restores high precision for terminal refinement. Across context lengths up to 4K, RAMP preserves near-full-precision prefill quality while achieving up to 1.16x end-to-end first-prefill speedup on an NVIDIA H100, corresponding to a 13.9% latency reduction.
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Machine Learning · Cross-listed from eess.SP
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring
Po-Heng Chou, Jen-Yu Chiu, Chun-Yu Yeh et al. (+2 more)
Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic
vacuum cleaners, and home fitness equipment, increasingly rely on vibration sensing for intelligent condition monitoring and predictive maintenance. Ensuring reliable operation in these resource-constrained appliances requires efficient, lightweight artificial intelligence (AI) solutions capable of processing non-stationary signals under real-world operational interference. This paper proposes a lightweight AI-assisted vibration sensing framework that employs continuous wavelet transform (CWT) and
Hilbert-Huang transform (HHT) to convert one-dimensional (1-D) vibration time-series signals into two-dimensional (2-D) time-frequency representations. These representations are subsequently analyzed using lightweight deep learning architectures, specifically MobileNetV3-Small, ConvNeXt-Tiny, and the You Only Look Once version 11 classification model (YOLOv11-cls). Public bearing
benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance Systems (IMS), are utilized as representative rotating components to investigate model generalization. The proposed framework is designed with eventual embedded deployment in consumer electronics in mind. Experimental results demonstrate that CWT combined with YOLOv11-cls achieves high diagnostic accuracy, while MobileNetV3-Small provides an attractive low-latency alternative for computationally constrained edge platforms. Conventional random splitting is also compared with
dataset-specific structured isolation. The results highlight the importance of avoiding data leakage and quantify tradeoffs in classification accuracy, parameter count, floating-point operations (FLOPs), model footprint, preprocessing cost, and central processing unit (CPU) inference latency.
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All authors Po-Heng Chou, Jen-Yu Chiu, Chun-Yu Yeh, Wei-Lung Mao, Walid Saad
Machine Learning
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf
Priya Kasimbeg, Ahmed Khaled, Aahlad Chandrabhatta et al. (+8 more)
Evaluating deep learning optimizer is challenging because confounding factors such as hyperparameter tuning and optimizer-model co-design often obscure true algorithmic performance. We present results of the updated AlgoPerf V1
benchmark, in which we evaluate a new set of training
algorithms on wall-clock time-to-target across nine workloads on fixed hardware. This report focuses on the results of the
benchmark's self-tuning ruleset, which requires submissions to use a single configuration across all workloads. Out of the 13 submissions in PyTorch and JAX, Schedule-Free AdamW ranked first. In addition to the wall-clock scores, we also analyze the trade-offs between step efficiency and wall-clock execution, revealing that
matrix-preconditioned and two-loop methods achieve good sample efficiency but require more aggressive computational optimizations to be competitive in runtime. Finally, our hyperparameter saliency and retention analyses show that the learning rate and momentum are highly sensitive across domains and
algorithms, highlighting the need for methods that are more robust against hyperparameter tuning and can generalize well to new tasks.
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All authors Priya Kasimbeg, Ahmed Khaled, Aahlad Chandrabhatta, Yifan Wang, Andy Liu, Niccolò Ajroldi, David Tweedle, Xingyu Yang, Isaac Patole, Frank Schneider, George E. Dahl
Computer Vision
GeoGR: Spatial-Semantic Token Compression and Group Routing for Efficient 3D VLMs
Yongxin Zheng, Hao Wu, Fang Li et al. (+8 more)
Multi-view representations enable pretrained image and
video vision-
language models to understand 3D scenes, but their long visual
token sequences make inference costly. Redundancy arises both from repeated observations of the same scene regions across views and from processing all visual
tokens at every LLM layer, regardless of their relevance to the current computation. We propose GeoGR, a two-stage framework that jointly compresses scene representations and visual computation. At the projector level, GeoSemZip consolidates cross-view observations into 3D voxel
tokens and combines semantic saliency with spatial coverage to construct a compact scene representation. Unselected voxel features are aggregated into contextual anchors to retain scene information that would otherwise be discarded. At the LLM level, we identify an effective visual computation window and observe that query-conditioned
token relevance remains locally stable but changes over longer layer intervals. Guided by these findings, GroupRoute combines late entry and early exit with recoverable group-wise skipping. Low-relevance
tokens bypass complete LLM layers while retaining their hidden states and can rejoin the active group at subsequent anchor layers selected under a computation budget. For compression-aware post-training, we construct a compact
dataset of 67K samples by retaining 30% of the original training pool. With this protocol, GeoGR retains 95.2% and 96.8% of the dense post-trained models' performance on LLaVA-OneVision-7B and
Video-3D LLM-7B, respectively, while accelerating LLM prefill by $6.8\times$ and $6.1\times$. These results demonstrate the consistent effectiveness of GeoGR across 3D VLMs with and without explicit 3D spatial encoding, highlighting a favorable performance-efficiency trade-off. The code is available at https://github.com/EIT-NLP/GeoGR.
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All authors Yongxin Zheng, Hao Wu, Fang Li, Hu Fu, Peiran Yin, Xudong Wang, Haozhe Hu, Xinghao Chen, Yunpu Ma, Wei Zhang, Xiaoyu Shen
Machine Learning
Target-Constrained Pooling for Trajectory Extrapolation
Ning Wang, Claire Cardie and Sainyam Galhotra
Trajectory extrapolation from a partial target trajectory is underdetermined, but completed related trajectories provide information about the unresolved continuation. We propose target-constrained pooling (TCP), which separates the finite-horizon endpoint from continuation shape and estimates that endpoint from two complementary source signals. Uniform pooling captures final response levels, while matched-increment pooling captures the remaining change after aligning source and target states. TCP selects representative sources, projects both endpoint estimates onto a target-consistent region, converts them into corrections of the free forecast, and controls their influence through strength calibration and degradation-aware weighting. Across 25,245 targets from five domains and both forecasting directions, TCP reduces mean RMSE by 35.8% to 42.1% for all ten target-only forecasters and improves all 100 forecaster,
dataset, and direction comparisons. The two endpoint signals consistently outperform either alone, while representative selection remains within 0.64% to 1.02% of the full donor pool using 12.7x fewer donor inputs and 138.8x fewer matching distance computations.
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Systems and Control
Virtual Transmission Lines from Data Center Workload Migration: Coordinated Multi-Year Transmission Expansion Planning
Mengxin Zhu, Qian Kai, Yadong Liu et al. (+2 more)
The rapid growth of electricity demand from AI data centers is creating a dual mismatch in power systems: a temporal mismatch between fast-rising demand and slow transmission expansion, and a spatial mismatch between concentrated data-center loads and geographically dispersed renewable resources. These mismatches can exacerbate transmission congestion, resulting in unserved data-center load and renewable-energy curtailment. This study models geographic workload migration as a virtual transmission line (VTL) that changes nodal data-center demand without transferring physical electricity. A mixed-integer linear programming model is then developed to co-optimize multi-year candidate-line construction and workload migration, subject to construction delays, renewable-energy curtailment, unserved data-center load, workload-balance requirements, communication-bandwidth constraints, and link-latency limits. The model is evaluated over an eight-year planning horizon on a modified IEEE 14-bus system. The results show that coordinating VTL with physical transmission expansion reduces total system cost, unserved data-center load, and renewable-energy curtailment while alleviating transmission congestion. VTL-aware planning also requires one fewer candidate line than build-only planning. However, VTL alone cannot meet system demand and does not eliminate the need for physical transmission reinforcement. Sensitivity analysis indicates that VTL provides the greatest value when transfer capacity is tight, construction costs are high, load growth is rapid, and data-center flexibility is greater. Additional validation on the IEEE 118-bus system over a five-year planning horizon shows that coordinated planning retains its cost and reliability benefits while reducing physical expansion requirements.
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All authors Mengxin Zhu, Qian Kai, Yadong Liu, Yingjie Yan, Haoyu Zhao
Information Theory
Beyond Hypercontractivity: Stronger Communication Converses via Rényi Contraction
Ryo Hommura Moore and Amedeo Roberto Esposito
We study the relationship between strong data-processing inequalities for Rényi divergences and hypercontractivity. For Rényi divergences of order greater than one, we characterize the contraction of Rényi divergences through a property of the dual Markov operator that we name power contraction. Using the contraction coefficient, we derive an upper bound on rectangle probabilities in terms of their marginals that is at least as strong as the corresponding bound obtained via hypercontractivity. Combining this result with inversion of the binary Rényi divergence, we derive communication lower bounds for one-way communication-assisted agreement
distillation. Examples involving binary symmetric and rare-event Z-channels illustrate the improvements over standard hypercontractive converses.
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Numerical Analysis
Linear and entropy-stable scheme for the dynamic van der Waals model of non-isothermal liquid-vapor flows
Yunzhuo Guo, Zhonghua Qiao and Xianmin Xu
In this paper, we propose a linear and decoupled fully discrete scheme that preserves the
entropy production property for the dynamic van der Waals model. This model provides a thermodynamically consistent diffuse-interface framework for describing non-isothermal phase transitions in single-component
liquid-vapor systems, containing the coupled conservation laws of mass, momentum and energy. We first introduce an equivalent reformulation of the governing equations that avoids the use of chain-rule identities, which generally fail under discrete differentiation. Further, a component-wise convex-splitting method is employed to calculate the discrete chemical potential and temperature. This treatment provides a discrete
entropy-stable inequality without a time step restriction. Based on the explicitly treated velocity, the scheme allows the three conservation equations to be solved linearly and sequentially in a decoupled manner. Since the momentum equation does not directly affect the
entropy-stability analysis, we use a classical linear semi-implicit scheme to solve it. Numerical experiments verify the expected numerical accuracy. Simulations of droplet evaporation and two- and three-dimensional Leidenfrost effects further demonstrate the proposed method can capture complex non-isothermal phase-transition dynamics while numerically reproducing the discrete
entropy production behavior.
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Machine Learning
Low-Rank Kolmogorov-Arnold Generator Flows for Interpretable Operator Learning
Minjong Cheon
Neural operators that learn physical laws from data should generalize beyond their training distribution and expose the learned law in closed form. The Kolmogorov-Arnold neural operator achieves both for position-dependent dynamics by learning a dense pseudo-differential symbol over all positions and frequencies, at a cost that grows with the square of the resolution and with closed forms that must be read out by hand. This dense symbol is unnecessary: a finite-order variable-coefficient differential operator has a symbol whose separation rank is bounded by the number of its coefficient functions, independently of the resolution, and the symbols that this network learns on its own
benchmarks have rank two. We introduce the Kolmogorov-Arnold flow (KAFlow), which learns the generator of a physical operator as a low-rank symbol built from univariate Kolmogorov-Arnold functions, fixes the gauge of these functions so that every term has a unique place, integrates the dynamics with exact exponential integrators, and distills its closed form automatically. On the synthetic
benchmarks of the Kolmogorov-Arnold neural operator, KAFlow lowers the out-of-distribution loss by 3.4 to 11 times and recovers every coefficient to within 10^-4 without symbolic templates, reaching the precision floor of the data. On long-horizon
quantum dynamics, it lowers the state infidelity by up to 6 orders of magnitude, and by 30 times when trained only on position and momentum measurements. KAFlow also learns a variable-order fractional operator 15 times more accurately than existing operator networks and discovers nonlocal, fractional and non-
polynomial dispersion relations on which sparse regression fails.
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