Machine Learning
Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models
Jivan Waber, Vanessa Piccolo, Yatin Dandi et al. (+1 more)
We establish a
polynomial sample complexity separation between
symmetry-aware and
symmetry-agnostic feature learning. We study growing-rank multi-index models with high-dimensional Gaussian
covariates in $\mathbb{R}^d$ and $r=\Theta(d^\delta)$ teacher directions forming a cyclic
symmetry orbit, where $0<\delta<1/2$. We compare three ways of exploiting this structure: architectural weight sharing, data augmentation over the full
symmetry group, and learning without access to the
symmetry. In particular, we analyze a
symmetry-tied convolutional network, an untied network, and the same untied network trained with full-group data augmentation, using spherical online SGD with correlation loss. For a class of
polynomial links with information exponent $p\ge3$, we prove matching sample complexity bounds up to logarithmic factors: the tied and augmented learners achieve weak directional recovery in $\widetilde{\Theta}(d^{p-1})$ samples, whereas the
symmetry-agnostic learner requires $\widetilde{\Theta}(rd^{p-1})$. For the pure quadratic Hermite link, the same separation holds for weak recovery of the teacher subspace, with sample complexities $\widetilde{\Theta}(d)$ and $\widetilde{\Theta}(rd)$, respectively. Thus, full-group data augmentation matches the sample efficiency of architectural weight sharing, and both provide a
polynomial advantage over training without
symmetry. For $p\ge3$, the proof reveals a two-stage mechanism: fluctuations at initialization select one direction in the teacher orbit, after which localized growth amplifies its overlap to the weak recovery scale while competing overlaps remain near their initialization scale.
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All authors Jivan Waber, Vanessa Piccolo, Yatin Dandi, Florent Krzakala
Computer Vision
Deformable CT-US Registration via Anatomy-Aware Implicit Neural Representations
Agnieszka Lach, Magdalena Wysocki, Feng Li et al. (+7 more)
Slice-to-volume registration between ultrasound (US) and preoperative computed
tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structures intra-operatively that are discernible in CT. While optical tracking enables initial rigid registration, contact from the probe induces soft tissue deformations that inhibit accurate
alignment. In this work, we introduce a deformable CT-ultrasound registration framework that incorporates anatomical priors derived from CT to improve registration under deformation. Rigid registration is first established using a
robot-assisted optical tracking system, after which a deformable transformation is estimated using a sinusoidal implicit neural representation (SIREN) optimized per frame. Tissue stiffness is approximated from CT-based HU values and used as spatially varying regularization, suppressing deformation in rigid structures such as bone while allowing more flexibility in soft tissue. Two additional constraints capture the physics of probe contact: a contact-zone displacement prior that drives the displacement field to compress tissue below the probe face, and a fan-geometry regularization term based on beam direction and convex transducer field of view. Model parameters are optimized with a normalized gradient field (NGF). The proposed approach improves
alignment over rigid initialisation by 17% and outperforms classical deformable baselines while maintaining near-zero topological folding.
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All authors Agnieszka Lach, Magdalena Wysocki, Feng Li, Mohammad Farid Azampour, Benjamin D. Killeen, Felix Ginzinger, Mathias Braun, Philipp Steininger, Heinz Deutschmann, Nassir Navab
Computer Vision
From the Drosophila Visual Connectome to General-Purpose Computer Vision
Zongyu Li, Akito Yamauchi, Huaizhi Liu et al. (+4 more)
Biological connectomes encode structured solutions to visual computation that may provide reusable inductive biases for artificial
vision. We develop ConnectomeX around FlyVision, a trainable architecture that preserves parallel ON/OFF processing, recurrent computation and population-level graph interaction while scaling model capacity across tasks. FlyVision reached 99.34% accuracy on MNIST with 80,608 parameters and 78.03% on CIFAR-10 with 81,408 parameters. On ImageNet-1K, FlyVision Base and Large reached 60.79% and 66.25% top-1 accuracy with 1.8 and 3.7 million parameters, while a Large local-k7 model with a learned low-frequency branch reached 66.53%, compared with 69.25% for ResNet18 with 11.7 million parameters. On a 22-class skin-disease
benchmark, FlyVision Large achieved 63.78% accuracy and 95.28% macro-AUROC with 2.99 million parameters. In four-class chest radiography, ImageNet-pretrained FlyVision Base and Large reached 92.60% and 92.76% accuracy with 1.33 and 2.97 million parameters, compared with 91.56% for ImageNet-pretrained ResNet18 with 11.18 million. BrainAGE extends FlyVision to volumetric T1-weighted MRI by applying a shared ImageNet-pretrained FlyVision Large encoder to 24 sagittal, coronal and axial slices per scan and combining slice-level age estimates by confidence-modulated Gaussian voting. On 433 held-out scans, three-axis fusion achieved a mean absolute error of 5.98 years and R^2 = 0.868. Across the 224x224 classification tasks, the best FlyVision configuration remained within three percentage points of ResNet18 on ImageNet-1K and skin-disease classification and exceeded it on chest radiography with substantially fewer parameters. These results show that a conserved connectome-informed computation can scale from compact recognition to large-scale natural and biomedical
vision.
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All authors Zongyu Li, Akito Yamauchi, Huaizhi Liu, Vishwanatha Rao, Jia Guo, for the Frontotemporal Lobar Degeneration Neuroimaging Initiative, for the Alzheimer's Disease Neuroimaging Initiative
Computer Vision
Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses
Tianyi She, Jiawei Liu, Weifeng Liu et al. (+3 more)
Recent advances in LipSync generation technology have led to the creation of highly realistic
videos, posing severe societal risks. However, existing defense strategies struggle against LipSync forgeries, as advanced LipSync generation methods not only achieve better lip synchronization but also eliminate visual artifacts. An important reason is that they overlook an inherent biological coupling between lip movements and head poses in natural
speech videos. In this paper, we propose LipDA, a novel framework for joint LipSync Detection and Attribution, which takes advantage of the inconsistency between head and lip. For detection, the framework learns to quantify this discrepancy by contrasting lip and pose features from authentic versus forged
videos. For attribution, our method is designed to capture the unique temporal dynamics and audio-visual synchronization patterns that act as the fingerprint of models, enabling source tracing. We conduct extensive experiments on two challenging LipSync
datasets as well as our own proposed large-scale and multi-generator
dataset. LipDA achieves over 97% AUC in detection and 97.5% accuracy in model attribution, significantly outperforming existing methods. Code and the proposed LipSync-A
dataset are available at https://github.com/AnsonShe/LipDA.
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All authors Tianyi She, Jiawei Liu, Weifeng Liu, Hanqing Zhao, Weiming Zhang, Kejiang Chen
Systems and Control
From 2D system to 1D system via restriction to a vertical strip: applications to time-controllability and zero-time-controllability
Aishwarya Bharti and Debasattam Pal
Motivated by the role of finite-time restriction in one-dimensional (1D) systems, we investigate the restriction of two-dimensional (2D) systems defined over $\mathbb{N}^2$ to a vertical strip. We give a constructive proof that such a restriction induces a 1D system. A Gröbner basis-based
algorithm is then developed to compute this restricted 1D system from a given 2D system. The framework is then applied to develop an
algorithm for testing the time-controllability and zero-time-controllability of scalar autonomous 2D systems; these properties are known to be equivalent to the existence of a deadbeat controller. Finally, we show that, for time-controllable or zero-time-controllable scalar autonomous 2D systems, the induced 1D system generates all the admissible trajectories of the original 2D system, thereby reducing the complexity of trajectory generation.
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Computer Vision
Image Bitstream Fine-grained Understanding for Privacy-Friendly AIoT
Zhen Yu, Wenyang Liu, Kejun Wu et al. (+3 more)
Image Bitstream Fine-grained Understanding (IBFU) aims to directly perform fine-grained classification and semantic description generation from encoded image byte sequences. In contrast to conventional pixel-domain visual understanding, IBFU conducts semantic analysis without fully decoding images into the pixel domain. Since pixel-level visual content is not explicitly reconstructed during inference, this paradigm reduces visual exposure within the processing pipeline and suits
privacy-friendly Artificial Intelligence of Things (AIoT) applications. In this paper, we propose Bitstream Fine-grained Generator (BFG), a novel foundation model tailored for IBFU. BFG consists of two main components: a Bitstream Semantic Encoder (BSeE) and a Fine-grained Semantic Generator (FSeG). BSeE directly models semantic representations from encoded image bitstreams without explicit pixel reconstruction, while FSeG transforms the extracted bitstream semantics into detailed natural-
language descriptions through autoregressive generation. To train BFG and comprehensively evaluate IBFU in practical AIoT scenarios, where image bitstreams may suffer corruption during transmission and storage, we construct a large-scale Corrupted-bitstream Fine-grained Understanding
dataset (CFU-D), containing both intact bitstreams and corrupted variants across multiple corruption types and severity levels. Experiments show that BFG maintains stable fine-grained caption generation under bitstream corruption. For example, the performance only has slight change from 0.6339 to 0.6077 in terms of average CIDEr score on Stanford Dogs Caption
dataset, while
vision-
language models, such as Qwen-VL-Chat, BLIP-2, GLM, Gemini, and GPT suffer severe performance decrease. This paper provides a practical paradigm for
privacy-friendly fine-grained understanding in AIoT.
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All authors Zhen Yu, Wenyang Liu, Kejun Wu, Chengwang Xiao, Renjie Qiao, Chengtao Cai
Computation and Language
Knowing When Not to Answer: Cross-Domain and Multi-Turn Generalization of Latent Underspecification Signals
Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov et al. (+6 more)
Large
language models routinely answer questions that cannot be answered from the information given, and in dialogue they answer before enough has been said. Unanswerability is linearly decodable from hidden states, but it is unclear which of its forms share a representation and whether the signal is useful in dialogue. We contribute a turn-labeled multi-turn
benchmark (423 conversations, 1,661 labeled turn-states) and an evaluation harness with a simulated user who answers clarifying questions, and use them with six
datasets and six open-weight LLMs to test how far probes for unanswerability carry. Probes transfer robustly between
datasets that share a ground of unanswerability: missing information in math (AUROC 0.77-0.97) and in a passage (SQuAD 2.0<->MuSiQue, 0.77-0.90). Probes for epistemic "known-unknowns" transfer poorly to math, but this separation weakens under lexical controls and changes with layer and coordinate system, so it remains unresolved. Single-turn probes fail zero-shot to detect when a conversation becomes answerable; in-structure probes recover it, but no better than a bag-of-words classifier. A gate on the calibrated probe, with no model fine-tuning, fires on underspecified turns far more precisely than chance, and its end-task success comes within 0.08 of a gate given the true labels. Yet across four models it does not reliably beat vanilla generation or prompted consolidation. The remaining gap lies mostly in how models use a clarification, not in detection.
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All authors Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov, Danil Fedorov, Kirill Redko, Sergey Chuprin, Aidar Shumbalov, Stanislav Chumakov, Anna Kalyuzhnaya
Information Theory
HintKD: Hint Knowledge Distillation for Bandwidth-Constrained Cloud-Edge Inference
Wanling Luo, Jiayi Zhang, Bokai Xu et al. (+1 more)
Collaborative inference between the edge cloud and user equipment (UE) is a promising paradigm for deploying large deep neural networks (DNNs) in 6G networks. However, existing cloud-edge inference and
distillation schemes often require the real-time transmission of high-dimensional intermediate features or soft probability vectors, which imposes a substantial communication burden on bandwidth-limited wireless links. To address this challenge, we propose HintKD, a bandwidth-constrained
distillation framework for cloud-edge inference. The core idea is to compress the teacher's guidance into compact discrete hints rather than transmit raw features directly. Specifically, a cloud-side teacher is first trained with a maximum conditional mutual information (MCMI) objective to preserve informative intra-class variations. Its latent representation is then mapped to a compact codebook through differentiable vector quantization. During deployment, the cloud transmits only a hint index, and the UE uses a lightweight FiLM-based adapter to modulate student features according to the received codeword. Experiments on the DeepSense 6G beam prediction task show that HintKD preserves competitive accuracy while reducing the inference-side communication payload from O(D_t) for feature transmission or O(C) for logit transmission to O(1) for discrete hint transmission. In addition, experiments on the MNIST
dataset further verify the generality of the proposed
distillation method across different
datasets and tasks. These results show that HintKD achieves a favorable accuracy-bandwidth trade-off by replacing high-dimensional feature or logit transmission with a compact discrete hint index.
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All authors Wanling Luo, Jiayi Zhang, Bokai Xu, Ziheng Liu
Computer Vision
Decoy and disclosure radii of invariant shape descriptors
Tanush Shaska and Lubjana Beshaj
A recognizer that compares rotation-invariant descriptors sees a surface only up to the fiber of the descriptor. We measure this fiber by its radius in the orbit distance from the enrolled surface. A large radius admits decoys, that is, distant shapes that pass the matcher. A small radius discloses the enrolled shape to anyone who captures the stored value. For
star-shaped surfaces truncated to spherical harmonics of degree at most $L$, with $n$ coefficients, a descriptor of generic rank $r$ has generic fibers of dimension $n-3-r$ modulo rotations. The standard pool of band powers, even bispectra, and three invariants of the degree-three band therefore admits decoy families of dimension $5$, $13$, $20$ at $L=4,6,8$. Its rank first reaches $n-3$ at $L=16$, and a mirror decoy remains at every $L$. The odd bispectra remove the mirror decoy generically for $L \geq 4$. Yet at fixed mean radius the same pool determines the enclosed volume exactly, and it does not determine whether a surface meets a clearance requirement. We certify two cases by exact and interval arithmetic. At $L=6$ a decoy matches all $32$ invariants to relative precision $2 \cdot 10^{-18}$ at orbit distance at least $0.87$ times the norm of the enrolled tuple. For the radar shape model of asteroid (101955) Bennu, the pool recovers the modeled volume, misses the handedness, and leaves the keep-out radius uncertain by more than $7 \, \mathrm{m}$.
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Information Retrieval
Seeing the Context: Enhancing Recommender Systems with Image-Derived Contextual Signals
Tal Cordova, Tomer Geva and Moshe Unger
Contextual information, capturing the circumstances of a user-item interaction, is central to recommender systems. Prior work draws context from location, time, or reviews, but not images;
multimodal recommender systems mainly use images to enrich item or user representations, not identify situational context. We propose a new representation of context derived from images, spanning physical, social, and modal categories learned via a
vision-
language model. We introduce ICE-Fuse, a pipeline for evaluating this representation that fuses these categories and integrates them into a context-aware recommender system, using TripAdvisor data and Review-aware Graph Contrastive Learning as the recommendation
algorithm. Image context does not outperform established signals standalone, but improves them combined, indicating complementary information. Semantic analysis shows image- and review-derived context capture distinct aspects of the interaction, positioning images as complementary context.
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Cryptography and Security
Case-Level Verification in Scanner-LLM Cascades: Overcoming the Alert Aggregation Bottleneck to Expand the FRR-TPR Trade-off Space
Hao Sun, Yibin Yao, Chaohai Xie et al. (+1 more)
Dynamic Application Security Testing (DAST) scanners achieve high recall but also produce a large number of false positives, resulting in substantial manual triage costs. Large
Language Models (LLMs), when used for independent detection, achieve extremely high recall (95.4%-100%) but also exhibit prohibitively high false positive rates (49.6%-85.0%), precluding their use as standalone replacements for scanners. A natural solution is a two-stage cascade consisting of scanner detection followed by LLM verification. However, a verification-granularity issue that has long been overlooked in practice creates a structural bottleneck: alert aggregation binds multiple true and false cases into a shared decision unit, such that removing a false positive inevitably eliminates true positives aggregated within the same alert group. This creates a trade-off bottleneck between the False-positive Reduction Rate (FRR) and the True-positive Rate (TPR). We formalize this bottleneck by showing that the alert-level false-positive set is a subset of the case-level false-positive set, and introduce a Case-Level, per-case verification strategy that shifts the decision granularity from the alert level to the instance level, independently replaying HTTP requests and making an independent determination for each detected case. Evaluation on the dual testbeds of Damn Vulnerable Web Application (DVWA) and WebGoat shows that the empirically best Alert-Level operating point achieves FRR=42.86% (TPR=51.7%). The Case-Level Baseline achieves FRR=47.6%, an improvement of 4.7 percentage points (+4.7 pp), while the Case-Focused Evidence Verification
Prompt (CEV-
Prompt) increases TPR from 55.2% to 62.1% at the same FRR.
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All authors Hao Sun, Yibin Yao, Chaohai Xie, Yuqun Lin
Software Engineering
Learning from Failures: A Failure-Driven Prompt Refinement for LLM-Based Vulnerability Analysis
Mandana Ghadamian and David Mohaisen
Large
Language Models have emerged as promising tools for software vulnerability analysis, but their effectiveness depends heavily on
prompt design. Existing research primarily compares prompting strategies using aggregate performance metrics, providing limited insight into why models fail or how
prompts can be improved systematically. We propose Failure-Driven
Prompt Refinement (FDPR), a methodology that analyzes recurring model failures to guide evidence-based
prompt refinement. Using the Damn Vulnerable Java Application (DVJA), we identify recurring failure modes, including false positives, false negatives, unsupported
reasoning, and CWE misclassification, and translate them into targeted
prompt refinements. We then evaluate the resulting
prompt on the Juliet Test Suite and perform cross-model validation to assess generalizability. The results show that failure-driven refinement improves the reliability of LLM-based vulnerability analysis while yielding reusable
prompt design principles. More broadly, this work demonstrates that recurring model failures provide a principled foundation for
prompt engineering, enabling the systematic development of more reliable LLM-based vulnerability analysis systems.
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Systems and Control
Data-driven design of deadbeat observers for higher-order LTI systems
Aishwarya Bharti and Debasattam Pal
In this letter, we derive a necessary and sufficient condition for the existence of a deadbeat observer (DBO) for systems described by higher-order difference equations based on finite historical data. Furthermore, we develop an
algorithm to construct a consistent DBO and to parametrise all DBOs directly from the historical data. Finally, we apply the proposed approach to the problem of state estimation in the presence of unknown inputs.
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Machine Learning
SSR: Sparse Segment Reduction for Ternary GEMM Acceleration
Adeline Pittet, Shien Zhu, Valérie Verdan et al. (+1 more)
Large
Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitigate these demands through weight quantization via ternary values, achieving significant compression often with 50-90% sparsity. However, existing approaches have limitations: methods optimized for ternary weights, such as BitNet, redundant segment reduction (RSR), and its improved version RSR++, do not exploit sparsity structures, while conventional sparse formats neglect ternary characteristics, foregoing dual optimization opportunities. In this paper, we introduce Sparse Segment Reduction (SSR), a ternary
matrix multiplication method designed to accelerate the inference of ternary LLMs and general Ternary Weight Networks (TWNs). SSR has a dedicated optimized ternary data format and an
algorithm that systematically exploits sparsity patterns through computation trees that scale with the sparsity. SSR provides theoretical gains with asymptotically faster inference than RSR++ for sparsity above 50%, while practical evaluations reveal performance improvements across all sparsity levels. Evaluation results show that SSR achieves 2.1-11.3x speedup over RSR++ on ternary GEMM with 45-95% sparsity. Furthermore, SSR achieves 3.5-6.3x end-to-end speedup and 4.9% of
memory saving over RSR++ on the Llama-3 1B model inference.
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All authors Adeline Pittet, Shien Zhu, Valérie Verdan, Gustavo Alonso
Machine Learning
VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents
Jiaju Chen, Min Yang, Jinghua Piao et al. (+4 more)
Multi-turn LLM
agents often receive sparse task feedback across several interactions, while generating each response
token by
token. This creates two related credit-assignment questions: which responses helped achieve the outcome, and which generation decisions mattered within each response? Existing methods typically focus on only one level: turn-level methods evaluate complete responses but do not distinguish the decisions within them;
token-level methods can propagate feedback across turns but do not explicitly model credit for each response. These complementary limitations motivate learning credit at both levels and coordinating it in a single policy update. We introduce VETTA, a credit assignment method that jointly learns turn- and
token-level values through separate heads on a shared lightweight critic. VETTA computes advantages along both temporal sequences and combines each turn advantage with a within-response-centered
token residual for PPO updates. Furthermore, to reduce value-learning cost, the critic retains only early
Transformer blocks from the pretrained checkpoint used to initialize the actor. On two challenging
agent benchmarks, ALFWorld and WebShop, VETTA improves success rates over PPO by 37.5% and 22.3%, respectively, with Qwen2.5-1.5B-Instruct and achieves success rates of 95.5% and 76.0%, respectively, with Qwen2.5-7B-Instruct. Critic-depth comparisons further show strong task performance with substantially lower critic-side computation. These results suggest that a compact shared critic can coordinate turn- and
token-level credit to improve
agent performance while keeping value estimation efficient. Code is available at https://github.com/Jiaju-Chen/VETTA-official.
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All authors Jiaju Chen, Min Yang, Jinghua Piao, Xiaochong Lan, Xu Xia, Xiangnan He, Yong Li
Computer Vision
GeoPID: Decomposing and Steering Visual Information in Vision-Language Models
Seulgi Kim, Zhixiong Zhang, Xinwei Zhang et al. (+2 more)
While recent
vision-
language models (VLMs) have shown outstanding performance across diverse applications, they tend to under-use visual information and over-rely on textual context. In this work, we propose GeoPID, a training-free framework that analyzes
multimodal information within VLMs from a
geometric perspective. GeoPID decomposes information into Redundant, Modality-Unique, and Synergistic components through the
geometric relationships between visual and textual representation subspaces. Through an extensive analysis across 22 VLMs and 14
benchmarks, we confirm that correct predictions exhibit stronger
vision-unique components when questions strongly require visual grounding. Building on this
geometric analysis, we introduce a targeted intervention technique that selectively amplifies visual representations along the
vision-unique subspace during inference. As a result, visual grounding capabilities were enhanced without any additional model parameter updates, achieving an average relative accuracy gain of 7.63%.
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All authors Seulgi Kim, Zhixiong Zhang, Xinwei Zhang, Jie Ling, Ronn Shaw
Machine Learning
Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems
Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, François R J Cornet et al. (+2 more)
Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of
language and
vision models to generalize across downstream tasks. While deep learning has driven considerable progress in modeling atomistic systems in recent years, self-supervised pretraining in this domain has not yet achieved comparable downstream generalization. To address this, we introduce Atom-JEPA, a self-supervised pretraining framework that learns latent representations from unlabeled 3D structures through complementary atom-level and substructure-level objectives inspired by joint-
embedding predictive architectures. We pretrain Atom-JEPA on large-scale molecular and crystalline
datasets and evaluate its transfer performance by fine-tuning on a diverse set of downstream property prediction tasks. Atom-JEPA achieves state-of-the-art performance on molecular ADMET and
quantum-chemical property prediction tasks, and is highly competitive in predicting the physical properties of crystalline materials. These results demonstrate the potential of latent-space predictive pretraining to support broad downstream generalization from structural data alone. Code and pretrained model checkpoints are publicly available at https://github.com/khelverskovp/atom-jepa
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All authors Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, François R J Cornet, Mikkel Jordahn, Mikkel N. Schmidt
Computer Vision
UP-MOPD: Update Projection in Multi-Teacher On-Policy Distillation
Taojie Zhu, Jing Jin, Yuan Xia et al. (+8 more)
On-policy
distillation from multiple teachers combines expertise from different domains in a single student, but conflicting gradients can hinder this integration. Gradient corrections directly constrain parameter updates under plain SGD. With optimizers such as AdamW, however, momentum, adaptive scaling, and weight decay can turn a corrected gradient into an update that increases a domain loss to first order. To address this gap, we propose Update Projection for Multi-Teacher On-Policy
Distillation (UP-MOPD). UP-MOPD lets the original mixed gradient update the optimizer state and generate a candidate displacement, then projects only violating candidates before they are committed to the parameters. The projection gives the unique feasible update closest to the candidate in Euclidean distance. In experiments combining medical and general domains, UP-MOPD improves IFEval-loose accuracy late in training by 2.96 points over vanilla M-OPD. It achieves an average score of 60.03 across eight metrics, compared with 59.00 for gradient projection and 59.15 for update rejection. On a public
benchmark covering mathematics, code, and instruction following, it achieves the best average across six tasks (32.67), leads on LiveCodeBench v5, and ties for the best IFEval result.These results support projecting optimizer updates to reduce interference between domains.
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All authors Taojie Zhu, Jing Jin, Yuan Xia, Chenyang Ding, Qunshan He, Wanke Xia, Tao Sun, Yan Chen, Jian Wang, Jinjie Gu, Tao Feng
Human-Computer Interaction
Living Dashboards: Automatically Self-Updating Visualization Dashboards
Mingyu An, Heyon Jeon, Sungbok Shin et al. (+4 more)
Visualization dashboards are widely used interactive tools, but a disconnect exists between the dynamic data they display and their static structure. End-users cannot modify the dashboard to answer new questions. We introduce Living Dashboards, whose views are born, wither, revive, and die in response to how they are used. Rather than requiring manual reconfiguration, a living dashboard observes interaction and natural-
language queries to autonomously wither neglected views and revive those used again. More consequential decisions, such as adding or retiring views, are deferred to the user. We formalize the concept as a four-dimensional design space and implement it in Living Dashboard, a web-based prototype. We evaluate it in an exploratory between-subjects study (N = 12) against an AI-supported baseline on analytical tasks. Living Dashboard participants answered more tasks correctly, reported lower workload, and rated the system higher on usability, though the two conditions differed in more than adaptive behavior alone.
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All authors Mingyu An, Heyon Jeon, Sungbok Shin, Jinwook Seo, Eduard Gröller, Niklas Elmqvist, Vaishali Dhanoa
Emerging Technologies
Infrastructure-Native Computing with Electric Power Grids
Yubo Song, Subham Sahoo and Freja Basse
Computing is conventionally implemented by hardware engineered for information processing. Here we investigate infrastructure-native computing: the use of a physical system built for another primary function as a fixed computational operator. In time-domain simulations of an IEEE 14-bus electrical network, Kirchhoff's current law and Ohm's law relate voltage-reference perturbations applied at distributed controllable nodes interfaced by power electronics converters to current responses through a
topology-dependent transformation. A trained digital encoder and decoder exploit this transformation for image classification, reaching 91.5% accuracy on MNIST and 82.25% on Fashion-MNIST. The modeled operator is represented by 933 surrogate parameters, compared with 12,340 task-trained parameters for an accuracy-matched fully connected core transformation. Current superposition further supports concurrent spatial sharing of the operator and sequential temporal reuse, with per-stream accuracies above 85% and 93%, respectively, in surrogate-model evaluations. Evaluations on CIFAR-10 and repeated 10-class tasks sampled from a Butterflies-and-Moths
dataset show that the incremental utility of the physical operator depends on the representation supplied by upstream digital feature extraction. These results provide a simulation-based proof of concept for infrastructure-native computing with electrical networks and identify
topology, accessible control channels, and input representation as determinants of its computational utility.
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Computation and Language
Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answering
Yang Hong, Yajun Yang, Xin Wang et al. (+2 more)
Large
language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from
hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge source for reliable
reasoning. However, existing LLM-guided graph
reasoning methods typically rely on hop-wise greedy or beam-style
pruning during evidence
retrieval. Such local decision processes are inherently myopic: evidence that appears weak near the source may become crucial only after deeper graph context is explored, causing answer-critical branches to be discarded prematurely and making the
reasoning chain difficult to recover. To address this limitation, we propose Foresight-over-Graph (FoG), a foresight-aware evidence
retrieval framework for knowledge base question answering (KBQA). FoG iteratively constructs a question-relevant evidence subgraph and uses far-to-near feedback to guide path exploration, and maintains a compact
memory subgraph to support continued exploration. Extensive experiments on widely used KBQA
benchmarks demonstrate that FoG achieves state-of-the-art performance, with a particularly large improvement of 16.58% in Hit on CWQ, while also reducing LLM calls and
token usage. Our code is available at https://github.com/yhong7/FoG .
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All authors Yang Hong, Yajun Yang, Xin Wang, Liping Jing, Qinghua Hu
Information Theory
Multi-Agent Reinforcement Learning for Movable Antenna-aided Cell-Free Massive MIMO Systems
Bokai Xu, Jiayi Zhang, Shuaifei Chen et al. (+4 more)
The inherent non-convex minimum-separation constraints introduced by movable
antennas present a formidable challenge to the joint optimization of
antenna positions and transmission strategies, rendering conventional methods computationally infeasible, particularly in large-scale
cell-free massive multiple-input multiple-output (
MIMO). In this work, we propose the graph-based learning individual intrinsic reward heterogeneous-
agent proximal policy optimization (GLIIR-HAPPO)
algorithm, a novel heterogeneous multi-
agent reinforcement learning (MARL) framework that fundamentally overcomes this impasse by systematically decomposing the original coupled optimization into coordinated subproblems. To ensure tractability, we embed the non-convex
geometric constraints into a penalty-augmented reward structure and develop a specialized
geometric solver that enables the positioning
agents to efficiently navigate the high-dimensional action space. Specifically, we propose an architecture featuring a dynamic-interaction graph critic for adaptive cross-role coordination, together with role-conditioned
federated distillation that synchronizes same-role policies through compact actor-output statistics. Beyond architectural design, we establish a rigorous theoretical analysis that derives monotonic performance improvement bounds and establishes convergence guarantees for the proposed bi-level optimization. Numerical simulations demonstrate that our framework yields significant sum-rate improvements over state-of-the-art MARL schemes. Moreover, the performance of our advanced architecture closely approaches its fully centralized counterpart, while drastically reducing communication overhead.
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All authors Bokai Xu, Jiayi Zhang, Shuaifei Chen, Ziheng Liu, Huahua Xiao, Derrick Wing Kwan Ng, Bo Ai
Machine Learning
Decision-Focused Learning in MDPs: An Occupancy Measure Approach
Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh et al. (+2 more)
In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solving a linear system over all state-action pairs, limiting its scalability. We address this by reformulating the MDP as an occupancy measure-based linear program (LP), whose feasible region is induced by predicted dynamics, and we derive a closed-form gradient by identifying the active constraints in the feasible polyhedron via the pivoting
algorithm. This occupancy measure-based LP layer raises two challenges: (1) LP's solution gradient is discontinuous when active constraints change, and (2) the LP backward cost still scales with the state size, which is costly for large or continuous state spaces. We address the challenges with an augmented Lagrangian surrogate and smooth the boundary jumps by random row sketching of the constraints, and a learnable soft state-aggregation layer and its function-approximation generalization that scales the LP to large finite and continuous-state MDPs. Across multiple tasks, our methods reach lower regret than KKT-based DFL and two-stage baselines with significantly lower computation cost. The source code for all experiments is available at https://github.com/A-Eshragh/State_Aggregation_Project.
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All authors Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh, Yuexing Li, Kai Wang
Robotics
From Legs to Wheels: Embodiment-Aware Human Motion Retargeting for Mobile-Base Humanoids
Jiyeon Koo, Eunseom Pyo, Jeonghee Seo et al. (+2 more)
Human
video offers a scalable source of
robot demonstrations, yet most human-to-
humanoid retargeting methods assume a legged
robot with human-like kinematics. This assumption does not hold for mobile-base
humanoids equipped with a wheeled base, vertical lift, and two arms. Human walking must be expressed through base motion, while torso bending may require coordinated lift and arm motion. We address this mismatch with a task-conditioned framework that assigns reconstructed human motion to base, lift, and arm responsibilities before
robot-specific realization. The allocator preserves the human-derived path, stabilizes heading, separates turn and translation when needed, retimes commands to satisfy base limits, and repairs lift and arm trajectories. A deployment adapter then converts the reference to 50 Hz commands using stationary-base detection, deadband and slew-rate filtering, time-consistent playback scaling, and separate linear and angular gains. We evaluate the resulting references with human-derived task-space comparisons, policy-free simulation replay, and a qualitative execution on a physical
robot.
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All authors Jiyeon Koo, Eunseom Pyo, Jeonghee Seo, Taehwa Kim, Andrew Jaeyong Choi
Computer Vision
UniCounting: Instance-Aware Proposal Consolidation for Image-Query-Free Multi-Category Counting
Jinshi Liu, Pan Liu, Lei He et al. (+2 more)
Visual counting is commonly formulated as counting a single specified target, with a model receiving an image-specific exemplar, text query, or target category and returning a single count. We instead study fixed-vocabulary image-query-free multi-category counting. A global vocabulary is fixed for each run, and, given only an RGB image, the model predicts a complete category–count vector without being told which categories appear. We present UniCounting, which casts counting as instance-aware structural inference over an over-complete proposal set. Generic segmenters produce duplicate masks, partial views, and proposals from neighboring instances; semantic scores can name them but cannot determine which denote the same object. Frozen SAM~2.1 generates masks, while frozen DINOv2 and OpenCLIP provide relation and category features. A 3,267-parameter category-shared relation head predicts same-instance affinities from instance-mask-derived supervision. Sparse graph construction, representative selection, labeling, and background-margin admission then convert each admitted component into one count with replayable group evidence. Only the relation head is trained, without count or density-map targets. On COCO clean500, UniCounting obtains lower point-estimate vector $\ell_1$ error and absent-class false mass than calibrated OWLv2-All80, with comparable micro presence F1. Under a matched decoder, the learned relation reduces both errors relative to mask containment, mask IoU, CLIP, and DINO, while revealing a fragmentation–merge trade-off. We also report transfer diagnostics on OmniCount-sub, FSC-147, and CARPK.
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All authors Jinshi Liu, Pan Liu, Lei He, Weichao Luo, Rui Qian
Hardware Architecture
Lachesis: Lifetime-Aware KV Cache Placement for Agent Serving across HBM and High-Bandwidth Flash
Jaehoon Yang, Jeongmin Lee, Haneul Park et al. (+3 more)
Large
language model (LLM) serving is increasingly dominated by agentic workloads, in which
agents and their sub-
agents accumulate context as KV cache across many requests, consuming substantial
memory. High-bandwidth flash (HBF) is a promising solution, providing an order of magnitude greater capacity at HBM-class read bandwidth, but its finite write endurance is the key limiting factor. Our key insight is that KV cache should be placed across HBM and HBF by its lifetime. Placing shorter-lived data in HBM lets HBM absorb more of an
agent run's writes and sends less of them to HBF. As the lifetime of KV cache in agentic serving is dictated by the harness, the program that orchestrates the
agents, we analyze its behavior and identify three axes along which lifetime diverges, temporal, structural, and inter-worker. Guided by these observations, we present Lachesis, a lifetime-aware KV cache placement layer between the
agent harness and the serving engine. At write time, it places each segment in HBM or HBF according to its lifetime, and frees its blocks once the segment is no longer read. In trace-driven simulation, Lachesis extends HBF lifetime by 1.19-3.13x over HBM-first placement, reaching 3.3-12.2 device-years. Even under continuous 24x7 operation at the full load a tight SLO admits, HBF outlasts its five-year warranty on the multi-
agent trace.
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All authors Jaehoon Yang, Jeongmin Lee, Haneul Park, Seung Yul Lee, Nam Sung Kim, Jae W. Lee
Hardware Architecture
ECO: Energy-Oriented Configuration Optimization for Attention FFN Disaggregated LLM Serving
Zou Qingyun, Bin Gao, Zhuobin Huang et al. (+3 more)
Energy-efficient LLM serving requires minimizing serving GPU energy while meeting latency and throughput service-level objectives (SLOs).
Attention–FFN disaggregation (AFD) enables separate resource allocation and operating controls for
attention and expert computation, but their energy effects remain coupled through the execution pipeline. Realizing its energy-saving potential therefore requires navigating a hierarchical configuration space in which deployment structures constrain admissible controls and shape their end-to-end effects. Finding low-energy configurations that meet SLOs is challenging because physical evaluations are costly and only a small fraction of candidates can be measured. We present Energy-Oriented Configuration Optimization (ECO), which jointly searches deployment structures and their admissible operating controls under a limited measurement budget. ECO constructs a structure-aware energy prior from calibrated stage behavior and pipeline dependencies, then learns residual prediction errors with a Gaussian process. Its cost-aware constrained
Bayesian optimization prioritizes measurements according to expected energy improvement while accounting for SLO feasibility, execution success, and evaluation cost, and returns the lowest-energy measured feasible configuration. Across all 16 scenarios on A6000 and A100 with Qwen and DeepSeek, ECO's frozen configurations, evaluated on disjoint requests, reduce serving energy by 40.5% and increase output
token rate by 20.7% on average relative to baselines while meeting target SLOs. Across the 8 A6000 scenarios, its selected feasible energy averages 33.1% below generic constrained
Bayesian optimization and 25.8% below genetic search.
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All authors Zou Qingyun, Bin Gao, Zhuobin Huang, Weng-Fai Wong, Tulika Mitra, Bingsheng He
Distributed Computing
vTen: Tensor-Centric Verification Framework for Domain-Specific Accelerators
Chanmin Baek, Keehyuk Lee, Mincheol Cha et al. (+3 more)
The semantic gap between tensor-centric software models and signal-level hardware testbenches creates significant productivity bottlenecks in verifying Domain-Specific Accelerators (DSAs). Existing frameworks like Cocotb suffer from prohibitive synchronization overheads due to fine-grained interactions. To address this, we propose vTen, a data-centric framework that strictly decouples verification intent from execution mechanics. By leveraging a declarative DSL and kernel-granular batching, vTen minimizes host-simulator interaction frequency. Evaluation on a production-scale 3D U-Net accelerator demonstrates that vTen achieves a 2x performance improvement in simulation latency and a 60.3% reduction in code complexity compared to Cocotb.
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All authors Chanmin Baek, Keehyuk Lee, Mincheol Cha, Somi Hong, Xuan Truong Nguyen, Hyuk-Jae Lee
Machine Learning
Accelerating the Development of PLGA In Situ Forming Depots Through AI-Driven Multi-Objective Optimization
Pauric Bannigan, Siddarth Chandrasekaran, Brigitte A. G. Lamers et al. (+6 more)
Developing long-acting injectable formulations requires the simultaneous optimization of drug loading, release kinetics, viscosity, injectability, stability and other objectives. To navigate this multidimensional space, Corbion and Intrepid combined Corbion's diverse PURASORB bioresorbable
polymer library with Intrepid Labs' proprietary AI
algorithm (ANDROMEDA 1) to develop in situ forming depots for a therapeutic peptide. Over approximately 15 weeks, 181 unique formulations spanning drug loadings of 6-12% w/w were prepared and characterized through broad design-space mapping and targeted multi-objective optimization. Four lead candidate formulations were identified at 6%, 9%, and 12% w/w drug loading. Each met the predefined viscosity and injectability criteria while providing distinct 30-day in vitro release profiles. The study evaluated
polymers spanning a broad range of molecular weights, including commercially available PURASORB grades and new
polymers under development by Corbion to expand its
polymer toolbox. ANDROMEDA 1 identified that
polymers with intermediate molecular weights provided a favorable balance between sustained release and solution viscosity. Together, these findings demonstrate how integrated
polymer expertise and AI-driven optimization can rapidly identify differentiated formulation candidates, focus the development space, and establish a strong data-driven foundation for further optimization and in vivo evaluation.
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All authors Pauric Bannigan, Siddarth Chandrasekaran, Brigitte A. G. Lamers, Inge Hermsen, Gary Tom, Riley J. Hickman, Bahar Yeniad, Morgan Fox, Christine Allen
Machine Learning
Evolutionary One-Step Generators: Fast and Diverse Sampling for Discrete Design
Marcus Vukojevic, Erik Nielsen, Veronica Lachi et al. (+2 more)
Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity alone does not guarantee a useful candidate library: repeatedly generating the same valid structures leaves few distinct alternatives. Training for both feasibility and diversity is challenging because many relevant criteria can only be evaluated after hard decoding. To address this challenge, we propose EGO (Evolutionary Generators with One-step inference), a framework for training compact generators directly on discrete outputs. The method combines distribution matching with structural constraints and optional diversity or history-dependent rewards, using antithetic low-rank
evolution strategies without requiring criterion-specific differentiable surrogates. Once trained, the generator produces the entire graph in a single neural-network evaluation. On molecular generation
benchmarks, our compact generator achieves over $50\times$ the valid-and-unique yield per estimated dense operation compared to recent one-step flow-map baselines while retaining high chemical validity. In scaffold completion, EGO achieves an observed $44.3\times$ speedup over MoLeR in generation to SMILES and produces approximately $10\times$ as many filter-passing proposals within matched time budgets for generation and screening. Beyond chemistry, EGO produces $1.54\times$ as many distinct held-out elite architectures as relaxed gradient training on NAS-Bench-101. The low generation cost may enable real-time candidate generation across discrete design tasks, supporting interactive exploration of constrained design spaces and rapid construction of candidate sets for downstream evaluation.
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All authors Marcus Vukojevic, Erik Nielsen, Veronica Lachi, Andrea Passerini, Giovanni Iacca