The quickest transshipment problem is a problem defined in a dynamic network with multiple sources and multiple sinks. The goal of this problem is to send all the supplies from sources to sinks as quickly as possible. It is known that this problem can be solved in polynomial time. In particular, Schlöter, Skutella, and Tran proposed a strongly polynomial-time discrete Newton method for this problem. In this paper, we propose a simple polynomial-time discrete Newton method for the quickest transshipment problem.
Semantic granularity, which refers to the levels at which visual representations organize image content into meaningful entities, is fundamental to image understanding. However, whether pretrained visual representations inherently encode semantic structures at multiple granularities remains poorly understood. This paper presents a representation-driven approach for discovering semantically meaningful image regions at different granularities through clustering of pretrained visual representations without semantic supervision. Specifically, we concatenate selected feature maps extracted from a pretrained visual model and apply a singular value decomposition (SVD)-based projection to derive pixel-wise principal representation map tensor (PRMs). The resulting PRMs captures latent semantic structures embedded in pretrained visual representations, making them accessible to subsequent semantic clustering. We then introduce the semantic continuity rate (SCR), based on the silhouette principle, to quantify the clustering behavior of PRMs at different candidate granularities. Furthermore, we analyze the signal-to-noise ratio (SNR) of SCR profiles to assess the PRMs suitability for discovering semantic-like meaningful regions at multiple granularities. Our results indicate that PRMs exhibiting a higher proportion of relatively high SNR values over an initial range of cluster numbers tend to yield more semantically meaningful regions across multiple granularities. We evaluate the proposed approach on the COCO-2017 and VOC-2012 validation datasets using Hungarian-matched intersection over union (HIoU). The results demonstrate competitive segmentation performance among the compared methods, supporting the feasibility of discovering useful semantic granularities through clustering PRMs without semantic supervision.
Silin Chen, Andy Liu, Kangjian Di, Yichu Xu, Han-Jia Ye, Wenhan Luo, Ningmu Zou
Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, where models are trained on normal samples without requiring labeled anomalies. However, even normal images may contain sensitive product designs, manufacturing information, or institution-specific population characteristics, making centralized data collection difficult in practical deployments. Federated UAD provides a promising decentralized solution, but it remains challenging because clients use only normal training samples, their feature distributions remain highly heterogeneous within the same task, and communication should be efficient while limiting the exposure of sensitive client information. To address these challenges, we propose FedDyMem, an efficient federated learning method with dynamic memory and memory-reduce for unsupervised image anomaly detection. Instead of exchanging full model parameters, FedDyMem facilitates knowledge sharing between the clients and server through reduced dynamic memory banks. In the local clients, a memory generator and a metric loss are employed to improve the consistency of the feature distribution for normal samples, leveraging the local model to update the memory bank dynamically. For efficient communication and data privacy, a memory-reduce method based on weighted averages is proposed to significantly decrease the scale of memory banks. This representation reduces communication overhead and empirically lowers reconstruction fidelity under inversion attacks. On the server, global memory is constructed and distributed to individual clients through k-means aggregation. Experiments on six federated UAD tasks constructed from eleven public industrial and medical datasets show that FedDyMem consistently improves anomaly detection and localization while reducing communication cost.
Junchen Ding, Jialiang Dong, Yichen Zhu, Yi Liu, Gelei Deng, Willy Susilo, Siqi Ma, Yuekang Li
The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the unchecked proliferation of "AI slop." These artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports, impose a cognitive burden on human triage pipelines that mirrors a denial-of-service attack. This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage. We formalize a taxonomy of AI slop grounded in a structured literature review and dissect its root cause: the gap between the causal deductive reasoning of security experts and the autoregressive probabilistic generation of current LLMs. We operationalize this gap through a measurable proxy, the Deductive Coverage Score, and show that chain-of-thought prompting and tool-using agents narrow but do not close it. We review mitigation strategies and argue that passive detection and watermarking target provenance rather than correctness, facing fundamental entropy constraints. We instead advocate for active neuro-symbolic verification, mapping each pipeline component to prior systems with documented limits on security inputs. Finally, we specify two evaluation instruments, CVE-Bench and Slop-Score, including dataset construction, metric formulas, and anti-gaming provisions. By shifting evaluation from linguistic fluency to mathematical verifiability, this survey provides a roadmap for securing emerging AI-driven triage systems.
Akira Yoshiyama, Akmal Ashirmatov, Jonas Hübotter, Ido Hakimi, Andreas Krause
Test-time scaling methods spend additional compute at deployment to improve a large language model's performance on a hard target problem. Most methods rely on a verifier that scores candidate solutions for correctness, using it to select among or refine the model's outputs. In many deployment regimes, however, a strong verifier on the target problem is unavailable. We introduce Test-Time Self-Play (TTSP), an approach to test-time scaling using asymmetric self-play without reliance on a verifier. On a difficulty-calibrated subset of LiveCodeBench v6, TTSP is the best-performing method at every verifier strength we evaluate, and the gap to the strongest baseline widens as the verifier weakens; in the no-verifier limit, TTSP yields a 35\% absolute improvement in mean@8. The gains hold across a range of verifier strengths, except when the base policy has near-zero coverage of the correct answer.
Kyrylo Kolesnichenko, Irvin Steve Cardenas, Jong-Hoon Kim
We present SONIC-K1, to our knowledge the first open-weights whole-body motion tracker for the Booster K1 humanoid, obtained by adapting the SONIC framework to this smaller robot. The policy is trained from scratch on a single shared GPU with a small fraction of the compute reported for SONIC. In MuJoCo simulation, it tracks nearly all held-out motion clips and performs well on external benchmarks under SONIC's success criterion. The first release ran on the real K1 but shook its legs on steps and turns, and simulation showed a sensitivity to actuation delay. We therefore post-trained it with the robot's deployment torque limits, randomized actuation delay and gain randomization. The post-trained v1.0 stays robust under real-robot torque limits and large actuation delays, where the first release often failed, and in our runs on the real K1 it walks and turns without the earlier leg shaking. These results show broad motion-tracking capability on a modest training budget. Code, policy weights and evaluation tools are publicly available.
Zikun Nie, Suyuan Zhao, Yizhen Luo, Siqi Fan, Zaiqing Nie
Protein optimization remains a longstanding goal in life sciences. Existing machine learning-assisted directed evolution (MLDE) methods primarily rely on sequence-only features, overlooking the critical spatial constraints and co-evolutionary interactions encoded in protein structures. However, directly integrating structural information remains challenging due to the scarcity of reliable mutant structures. To address these issues, we propose StructEvo, a novel structure-aware reinforcement learning framework for protein directed evolution. StructEvo employs a delta-structure fusion encoder to approximate mutant structure features via feature differences, enabling dynamic incorporation of spatial knowledge. The vast mutation space is then decomposed into manageable subspaces through a structure-aligned hierarchical action network, while a geometric constraint further stabilizes delta feature learning. Our approach outperforms prior state-of-the-art methods by 9.2% and 16.3% on two challenging optimization benchmarks, and further identifies an experimentally validated epistasis pattern in GFP, highlighting the importance of structural guidance for effective protein directed evolution.
Kinetic equations in the high-field regime describe particle transport dominated by electric-field acceleration and collisional relaxation. These equations are widely used in simulations of plasmas and semiconductor devices. Typical models include the Vlasov--Poisson--Fokker--Planck (VPFP) and Boltzmann--Poisson systems. Their high dimensionality and multiscale nature pose two of the major challenges to accurate and efficient numerical solution. To address these challenges, we propose an asymptotic-preserving tensor neural network (APTNN) method. Specifically, to reduce high-dimensional computational costs and memory requirements, we adopt the canonical polyadic (CP) decomposition structure of TNNs to approximate their solutions. We show that the equation residuals retain this decomposition form, allowing high-dimensional loss integrals to be evaluated through combinations of one-dimensional integrals. The resulting computational and storage costs are reduced from exponential to polynomial growth with dimension. To improve the prediction accuracy in the high-field limit, we enhance the baseline AP loss by incorporating the first moment equation. The added loss term directly constrains the limiting flux closure to reduce its approximation error in the high-field regime. The enhanced AP loss can still be evaluated dimension-wise. Numerical results show that APTNN improves prediction accuracy in the high-field regime with significantly reduced computational costs and memory usage. For a 3D3V VPFP problem with multiple random inputs, training takes less than 30 minutes on a single GPU.
Requested-value control is stricter than continuous steering: given $c$ on a human-rating scale, the generated image should attain that value from only limited ratings. We present ScoreDial, which fits a differentiable regressor $R$ to 100-479 human-rated images; once $R$ is fixed, a requested value $c$ is defined on its output scale, and learned target conditioning is paired with error-adaptive guidance that repeatedly reduces $|R(\mathbf{x})-c|$ during sampling. Across tire slip resistance, interior visual complexity, food desire-to-eat, and soap translucency, using one set of sampler settings, ScoreDial reaches requested values and achieves $R$-MAE at most 0.013 over a range of width 0.8 and Spearman $ρ=0.980$, the maximum attainable under our protocol. Because $R$ also drives sampling, we evaluate agreement with $R$ separately from matched sweeps, an independent auditor fitted to the same ratings on a backbone no deployed regressor uses, and blinded human judgments. MPGD-flow also reaches the requested values under $R$, but ScoreDial has larger auditor displacement on every attribute, the largest among all compared methods, and a stronger MPGD-flow setting gives up target agreement without closing this gap. On every attribute, removing target conditioning leaves low $R$-MAE but more than halves auditor displacement, separating precise score correction from attribute movement. Blinded rating studies with 15 raters per attribute show mean human ratings track $c$ on tire and soap, with linear $R^2=0.665$ and $R^2=0.786$, and in forced-choice comparisons of matched sweeps, raters chose ScoreDial's as the clearer progression in at least 84% of judgments against every baseline. Code: https://github.com/Kamijo-Gen/ScoreDial.
Alexander Valverde, Danny Xie-Li, Zhichao Wang, Marcos V. Conde
Engineering drawings specify mechanical parts through orthographic projections. Recognizing which part a drawing depicts underlies part search, design reuse, and digitization, yet no public benchmark measures whether vision models can do it. We introduce Blueprint2CAD, a benchmark for drawing-to-render correspondence: given an orthographic line drawing and a shaded render, usually from another viewpoint, decide whether they show the same part. Its data layer, BlueCAD-30K, applies an open drafting pipeline to 29,925 Zero-to-CAD models, adding view-aligned renders, clean and dimensioned drawings from six viewpoints, and surface-type metadata. Frozen encoders retrieve the matching part from a 10,000-model gallery with up to 65% Recall@1 when viewpoints are known but 43% when they are not, and the four encoders we test lie within six points of each other: viewpoint, not backbone, is the bottleneck. Six zero-shot vision-language models reach only 0.52-0.67 verification accuracy, with model-specific Yes/No biases that chain-of-thought shifts but does not remove; a quick LoRA fine-tuning on in-domain pairs improves verification accuracy by a few points but sharpens these biases and leaves the viewpoint failure intact.
Speech communication plays a crucial role in the Internet of Things and autonomous networks. However, traditional communication systems suffer from insufficient semantic extraction and limited noise suppression capability. To address these issues, we propose a Speech enhancement-based Deep Semantic Autoencoder (S-DSA) communication system for wireless communications. It consists of a dual-branch semantic encoder, a channel encoder, a channel decoder, and two semantic decoders, enabling joint source-channel coding for robust transmission. A key novelty lies in an adaptive semantic noise decoupling mechanism that disentangles target speech semantics from noise representations through an interaction module, thus maintaining semantic consistency under low SNR conditions. Furthermore, a joint training strategy optimizes speech reconstruction and noise estimation, while a lightweight testing strategy reduces complexity by disabling the noise-decoding path. The proposed S-DSA enables more effective separation between speech and noise, and yields higher robustness as well as better reconstruction quality. These findings highlight the potential of the proposed S-DSA as a practical solution for next-generation intelligent wireless communications.
Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual inconsistency. Existing video inpainting models operate solely on pixels, while audio editing models, especially for the sound removal task, are typically driven by text and therefore rely on limited single-modal control, which is less effective than multimodal guidance that provides stronger semantic grounding and temporal synchronization cues. In this paper, we present Text-Visual Guided Sound Removal (TV-AudioRemover), a target sound removal framework that leverages the visually edited video together with a natural-language instruction to suppress the sound associated with the removed visual object from the original audio mixture. To acquire high-quality training data, we devise a pipeline to construct a million-scale dataset of single-object audio-visual aligned samples, from which we synthesize mixture-target pairs customized for model training. To effectively leverage visual context and follow instruction intent, we augment the model architecture with task tokens, generalizable instruction modeling, and modality-specific global guidance. We further adopt multi-task training to strengthen task-role comprehension, and employ a hard-mixture curriculum that leverages semantically similar acoustic mixtures during fine-tuning to enhance fine-grained source discrimination. To support evaluation, we present AV-Remove-Bench, a comprehensive audio-visual object removal benchmark, along with dedicated objective metrics and an MLLM-based evaluation protocol. Experiments demonstrate that our method achieves state-of-the-art performance on both subjective and objective metrics. Project page: https://yjx-research.github.io/TV-AudioRemover/.
Li Wang, Yi Su, Xiabao Wu, Chiran You, Yongchao Liu, Zhan Qiu, Juelu Zhang, Jiajun Zheng 等 12 位作者
Emerging LLMs increasingly interleave full attention with recurrent linear attention to reduce long-context serving costs. Tree speculative decoding could further accelerate these hybrid models, but recurrent linear-attention layers carry parent-dependent states. Existing engines therefore verify proposal trees sequentially and snapshot the full state per node, causing latency and memory to grow with tree and batch sizes. We present Twig, a kernel--runtime co-design for tree speculative decoding of hybrid LLMs. Twig derives a tree-structured factorization of the linear-attention recurrence and realizes it with a resource-efficient GPU kernel that verifies all nodes in parallel, accelerating linear-attention tree verification by 3.4--7.7$\times$. It losslessly represents speculative state updates as token-level factors and reconstructs only the accepted state, reducing transient state memory by 82--99$\times$ and freeing GPU memory for KV caches. We integrate Twig into SGLang, a widely deployed production LLM serving engine, and further optimize its runtime through batch-wide verification budgeting. Across four models, two GPU platforms, and diverse datasets, Twig achieves up to $4.72\times$ the offline decode throughput of autoregressive decoding and up to $2.03\times$ that of the strongest tree-speculative baseline. Under online agent workloads, Twig reduces TTFT and TPOT by up to $67.6\%$ and $49.9\%$, respectively, over the strongest tree-speculative baseline.
Shunyu Wu, Dan Li, Haozheng Ye, Weibin Feng, Jian Lou, Bo Zhang, Wenjie Feng, Chenjuan Guo 等 9 位作者
Assessing the quality of time series (TS) data is fundamental yet inherently challenging due to the multifaceted nature of quality dimensions. Recently, large language models (LLMs) have emerged as a promising paradigm for TS quality assessment via pairwise comparison and per-dimension evaluation. However, existing approaches rely on manually predefined quality dimensions and purely text-based reasoning, leaving it unknown whether LLMs can identify truly relevant quality dimensions or perform grounded and quantitative quality comparisons. To investigate this, we construct TSQBench, a dedicated benchmark for evaluating LLMs on two progressive capabilities: (i) understanding and identifying relevant quality dimensions, and (ii) performing quality comparison under specific dimensions. Our analysis reveals that current LLMs consistently struggle with both dimension identification and evidence-grounded quality comparison. To address these limitations, we propose TSQAgent, a novel agentic reasoning framework for TS quality rating consisting of three collaborative roles: Perceiver for focused dimension selection, Inspector for dimension-wise quantitative analysis, and Adjudicator that aggregates and refines the final judgment. In particular, we introduce an agentic reasoning strategy that instills the ability to identify and prioritize the most relevant quality dimensions, and further propose an agent workflow equipped with external analytical tools to enable precise quantitative comparisons over selected dimensions. Experiments on both the proposed benchmark and eleven real-world datasets demonstrate that our framework not only substantially improves LLMs' capabilities in quality understanding and quantitative comparison but also effectively translates these improvements into better quality-aware data selection, leading to enhanced downstream performance and data efficiency.
Cardiac physiological monitoring is fundamental to evaluating cardiovascular health and diagnosing related diseases. Non-contact radar sensing can capture chest-wall vibrations with spatial specificity, enabling localized physiological monitoring and cardiac assessment. Progress, however, has been limited by the absence of a reliable, research-grade hardware platform for rigorous validation of radar-based methods. Prior systems typically pair radar, namely Doppler cardiography (DCG), with only electrocardiography (ECG) or other coarse references, providing no direct mechanical ground truth and thus constraining fine-grained evaluation of radar outputs. We present a tri-modality wireless system that integrates ECG, seismocardiography (SCG), and DCG under a unified acquisition and control framework for time-aligned recording and analysis. The tri-modality system combines miniaturized wearable ECG/SCG modules with a high-resolution mmWave radar module, supporting spatially resolved sensing and subsequent feature extraction. To our knowledge, this is the first integrated ECG--SCG--DCG hardware system for comprehensive cardiac monitoring; it enables multi-point thoracic acquisition, achieves precise cross-modal synchronization, and maintains specifications suitable for wearable operation. Experiments on a linear-motion platform show close agreement between DCG- and SCG-derived displacements, confirming accurate displacement measurement. Human-subject studies further demonstrate cross-modal consistency and robust extraction of feature points, establishing ECG as the electrical anchor and SCG as the mechanical ground truth for DCG. Overall, the system provides a rigorous, spatially resolved validation platform that advances non-contact cardiac sensing and supports next-generation radar-based monitoring.
Maria Beatriz Silva, Lucas Edson Silva de Araújo, Sergio F. Chevtchenko, Ermeson Andrade, Danilo Araujo, Saeed Afshar
In the context of Industry 4.0, predictive maintenance is essential to minimize unplanned downtime and ensure operational safety. In machining processes, drill jamming is a critical failure that compromises both workpiece quality and tool integrity. This work proposes an acoustic anomaly detection system using ultrasonic sensors to identify pre-failure patterns in Computer Numerical Control (CNC) machining. We release a public dataset of time- and frequency-domain acoustic features from 17 drills, and benchmark semi-supervised autoencoder-based normality modelling against supervised XGBoost classification using Leave-One-Drill-Out validation. The results demonstrate that the Autoencoder is highly effective for industrial safety, achieving a Recall of 1.00 and a consolidated F1-score of 0.81. Furthermore, the threshold-independent analysis shows that the Autoencoder better separates normal and pre-failure acoustic patterns, achieving a consolidated AUC of 0.8294, compared with 0.7559 for the supervised model under inter-tool covariate shift. In contrast, the supervised XGBoost achieved an average F1-score of 0.55 and failed to detect failures in some of the evaluated drills. The findings highlight that the ultrasonic spectrum provides a reliable window for identifying incipient mechanical instabilities, and the AE model offers a robust safety envelope by anticipating all jamming events across the tested drill bits.
LLM-as-a-Judge pipelines have become the de facto evaluator for agent safety, yet existing benchmarks treat their verdicts as ground-truth proxies without checking whether the verdicts depend on the agent's behavior or merely on how the evaluation policy happens to be worded. We argue that any trustworthy safety judge must satisfy a basic property we call policy invariance, and we operationalize it as three testable principles: rubric-semantics invariance under certified-equivalent rewrites, rubric-threshold invariance under intentional strict-to-lenient shifts, and ambiguity-aware calibration so that verdict instability concentrates on genuinely ambiguous cases. Instantiating these principles as a stress-test protocol with four agent-class judges on trajectories drawn from ASSEBench and R-Judge, we surface a previously unmeasured failure mode: today's judges respond to meaningful normative shifts and to meaningless structural rewrites with comparable strength, and cannot tell the two apart. Content-preserving policy rewrites flip up to 9.1% of verdicts above baseline jitter, and 18-43% of all observed flips occur on unambiguous cases under such rewrites, so safety scores produced by these judges partly conflate what the agent did with how the evaluator was prompted. These findings persist with a frontier-scale judge, a third benchmark, and a second base policy. Beyond the diagnosis, we contribute the Policy Invariance Score and the Judge Card reporting protocol, which expose an order-of-magnitude spread in judge reliability that is invisible to accuracy-only leaderboards. We release the protocol and code so that future agent-safety benchmarks can audit their own evaluators rather than trust them by default: https://github.com/wengshihao/policy-invariance-judge
Jingyang Zhao, Jason William Setiawan, Uman Khalid, Chaoning Zhang, Een-Kee Hong, Hyundong Shin
Sweep coverage requires each point of interest (PoI) to be revisited by some mobile sensor at least once during every sweep period, and it underlies persistent patrolling, environmental monitoring, and infrastructure inspection. Given a set of $N$ PoIs, each associated with a nonnegative weight representing its importance, the weighted budgeted sweep coverage problem (WB-SCP) seeks routing trajectories for a budget of $M\leq N$ identical mobile sensors with a common maximum speed to maximize the total weight of the sweep-covered PoIs. For the unweighted special case in which every PoI has unit weight, the first constant-factor approximation algorithm achieves a ratio of $0.0116-O(\varepsilon)$ for any constant $\varepsilon>0$, and whether the WB-SCP itself admits such an algorithm has remained open. In this paper, we resolve this open question by proposing two constant-factor approximation algorithms for the WB-SCP. The first converts any bifactor $(α,β)$-approximation algorithm into a $β/(2α)$-approximation algorithm, yielding a ratio of $1/16=0.0625$. The second builds on a greedy maximum-coverage framework and improves the ratio to $1-e^{-1/2}-\varepsilon$ for any fixed $\varepsilon>0$. Both algorithms also improve the best-known ratio for the unweighted case, and the second extends to heterogeneous sensors with different speeds while preserving the same guarantee.
Yajie Bao, Jiaye Fu, Youtian Lin, Shuang Wu, Jiachen Qian, Feihu Zhang, Xun Cao, Yao Yao
High-quality material generation for physically based rendering (PBR) remains challenging because datasets and synthesis pipelines lack standardized representations. Differences in material definitions, rendering conventions, and parameterizations lead to unstable and physically implausible results. We propose MIKU, a unified framework for standardized PBR asset representation and physically consistent material generation across diverse 3D content domains. Its core is a unified processing pipeline that standardizes the workflow from data preprocessing to final texture synthesis, maintaining consistent geometry and texture representations. A normalization scheme harmonizes heterogeneous material parameterizations in a common representation space, enabling robust cross-dataset training and inference. Building on this formulation, we train a novel physically aware diffusion model for PBR texture generation within a two-stage multi-view synthesis framework. First, geometry-aware albedo generation synthesizes multi-view albedo maps under explicit geometric guidance to ensure cross-view appearance consistency. Second, metallic-roughness generation uses the diffusion model to predict physically plausible roughness and metallic maps conditioned on the generated albedo and unified representation. Trained end-to-end on standardized data, the model learns to generate materials that respect category-level material priors and semantic part boundaries. By decoupling representation standardization from appearance synthesis, MIKU substantially improves material generation stability and physical consistency. Extensive experiments demonstrate state-of-the-art texture quality and semantic consistency across object parts, advancing automated material generation for 3D content creation.
Image generation based on text-to-image generation models is a task with practical application scenarios that fine-grained styles cannot be precisely described and controlled in natural language, while the guidance information of stylized reference images is difficult to be directly aligned with the textual conditions of traditional textual guidance generation. This study focuses on how to maximize the generative capability of the pretrained generative model, by obtaining fine-grained stylistic representations from a single given stylistic reference image, and injecting the stylistic representations into the generative body without changing the structural framework of the downstream generative model, so as to achieve fine-grained controlled stylized image generation. In this study, we propose a three-stage training style extraction-based image generation method, which uses a style encoder and a style projection layer to align the style representations with the textual representations to realize fine-grained textual cue-based style guide generation. In addition, this study constructs the Style30k-captions dataset, whose samples contain a triad of images, style labels, and text descriptions, to train the style encoder and style projection layer in this experiment.
Parameter-efficient fine-tuning with LoRA is governed by rank, which controls parameter budget and locally accessible task updates. Existing dynamic-rank methods mainly focus on capacity allocation, leaving unresolved whether the current adaptation space is sufficient for the task. We characterize the first-order information boundary of standard factor gradients, showing that their unobservable subspace coincides with the normal space of the low-rank manifold. Thus, factor gradients alone cannot distinguish under-optimization within the current space from task-relevant directions beyond its first-order reach. This motivates a geometric view of rank evolution, where tangent information optimizes existing degrees of freedom and the normal spectrum quantifies the value of new ones. Under a local quadratic model, we show that leading normal singular directions yield the locally optimal rank expansion, with spectral energy determining its gain. Building on this characterization, we propose GEAR, which turns the tangent--normal decomposition into rank evolution through tangent-space optimization, matrix-free normal-spectrum estimation for rank expansion, and task-induced geometric initialization. Across language and vision benchmarks, GEAR achieves the best average performance in all evaluated settings with substantially fewer trainable parameters. On LLaMA3-8B, it raises the average commonsense score from 86.61 to 88.45 while reducing trainable parameters from 42.0M to 14.4M.
Yiqin Hao, Boyi Zeng, Zitong Wang, He Li, Shixiang Song, Feichen Song, Yifan Liu, Ran Tao 等 10 位作者
Mathematical problem solving and autonomous agents are increasingly important applications of language models, demanding stronger reasoning capabilities. Computational depth is a key resource for such reasoning, but adding model layers increases both training and inference costs, while test-time scaling incurs substantial additional inference computation. We introduce P2N (Previous to Next), which reuses the previous token's deep representations, already available during generation, to increase effective computational depth with negligible decoding overhead. Specifically, P2N adds the previous token's deep hidden state to the current token's shallow representation, allowing subsequent layers to build on previously completed deep computation. This recurrence extends effective computational depth across tokens, while introducing only a single vector addition per decoding step. We train the recurrence using parallel Jacobi updates restricted to a middle-layer core. Experiments with Qwen-style models show that, at 1.5B parameters, P2N improves GSM8K accuracy by over 10 percentage points under both zero-shot and few-shot prompting, with nearly unchanged decoding latency. The benefits also extend to language modeling and average performance on general downstream benchmarks. on general downstream benchmarks.
Jingyang Zhao, Jason William Setiawan, Uman Khalid, Chaoning Zhang, Een-Kee Hong, Hyundong Shin
The metric traveling salesman problem (TSP) is a fundamental problem in combinatorial optimization that asks for a minimum-cost Hamiltonian cycle in a complete undirected graph with $N$ vertices, where edge costs satisfy the triangle inequality. The biased TSP (BTSP) is a natural generalization of the metric TSP, in which the vertex set is partitioned into $k$ subsets: edge costs satisfy the triangle inequality within each subset, while edges crossing the subsets may violate the triangle inequality by a certain bias factor. This model captures scenarios in which distances are metric only locally but may be distorted across different regions or types of vertices. In this paper, we study approximation algorithms for the BTSP and its path variant, the biased traveling salesman path problem (BTSPP), which asks for a minimum-cost Hamiltonian path. Previously, constant-factor approximation algorithms were known only for the case $k=2$. Whether the BTSP and the BTSPP admit constant-factor approximation algorithms for any fixed integer $k>2$ has remained an open question. We resolve this question affirmatively. We show that both the BTSP and the BTSPP admit $O(k)$-approximation algorithms with running time $N^{O(2^k k!)}$. Moreover, for the BTSPP with $k=2$, we propose an improved $(α+2+\varepsilon)$-approximation algorithm, where $α<3/2-10^{-34}$ is the best-known approximation ratio for the metric TSP, improving upon the previous ratio of $4$
Yujing Liu, Yixin Liu, Yu Zheng, Lianhua Chi, Alan Wee-Chung Liew, Heng Tao Shen, Shirui Pan
Link prediction (LP) is a widely applied task in graph learning. Conventional LP approaches typically follow a dataset-specific paradigm, requiring independent training for each graph and incurring high computational and maintenance costs in large-scale applications. Motivated by these limitations, recent work explores Universal Link Prediction (ULP), aiming to enable training-free inference on arbitrary unseen graphs. However, existing methods primarily rely on subgraph sampling strategies to construct universal representations, which are unable to capture higher-order information due to the exponential growth of higher-order neighborhoods. As a result, they often under-perform on graphs where long-range signals are essential, such as biological networks. Moreover, their prediction models are typically domain-agnostic and lack the ability to adapt to different graph domains, leading to limited generalization. To address these challenges, we propose an Atlas-based Universal Link Prediction framework (AtlasULP), which introduces the relation atlas to capture structural associations between node pairs from both global and local perspectives, enabling a comprehensive characterization of connectivity signals across diverse graph domains. Building upon this representation, we develop a domain-guided prompting ULP model, which generates domain-aware prompt tokens from contextual structures and performs prompt-guided in-context prediction for adaptive link inference. Extensive experiments demonstrate that AtlasULP consistently outperforms state-of-the-art methods across diverse graph domains.
GreedTok (Lim, Tan, Choo and Lauw, NeurIPS 2025) builds a tokenizer vocabulary greedily for the partition-cover objective: choose $k$ tokens so as to maximize the number of adjacent character pairs merged when the corpus is segmented optimally. The objective is neither submodular nor supermodular, so the classical $1-1/e$ analysis does not apply, and a worst-case guarantee was left open. We prove that GreedTok is a constant-factor approximation: on every corpus and for every budget $k$ it achieves at least $0.2752$ of the optimum, and it is optimal for $k=1$. The bound holds for both the exact greedy loop and the lazy priority-queue loop of the published algorithm and its code. The key ingredient is a shift lemma: if two occurrences of a not-yet-chosen token overlap and both are still usable, they offer exactly the same gain, so GreedTok's left-to-right scoring loses nothing when occurrences collide. A per-occurrence charging argument, with window distributions certified in exact arithmetic or given in closed form, completes the proof. In the other direction, we construct corpora on which GreedTok, with adversarial tie-breaking, achieves at most a fraction of the optimum tending to $(7-\ln 2)/12\approx0.5256$ (below $0.5380$ when every greedy choice is unique). Finally, we show that the objective is NP-hard to approximate within $1-1/31580$, to our knowledge the first inapproximability result for it.
Purnata Saha, Rifatul Islam, Qiliang He, John Quarles
Cybersickness is often assessed during VR exposure through discrete overall sickness, which cannot capture specific symptoms or their changes over time. Post-exposure questionnaires such as the Simulator Sickness Questionnaire provide symptom-specific measures but cannot be collected during immersion. We investigate whether separating cybersickness into three symptom-specific dimensions -- nausea, oculomotor discomfort, and disorientation -- during immersion provides more informative continuous measurements. We conducted a between-subjects study with 52 participants in a VR-rollercoaster, comparing a three-slider interface with a single-slider interface. Concurrently, participants also completed an auditory Simon task to measure attention. We additionally collected multimodal set of physiological signals, including GSR, ECG, PPG, eye and head tracking. Results show that each slider correlated most strongly with its corresponding SSQ subscale, whereas the single slider correlated with all SSQ subscales. Using three sliders did not significantly affect usability, workload, or attention. We publicly release the multimodal dataset to support future cybersickness research.
Registering intraoperative Ultrasound (US) bone-surface point clouds to preoperative Computed Tomography (CT) models can support radiation-free orthopedic navigation. Differences in imaging physics produce distinct surface sampling patterns, complicating cross-modal feature matching. Bone strongly reflects ultrasound and casts an acoustic shadow, so US captures only the probe-facing bone surface and leaves most CT points unmatched. This partial coverage makes correspondences ambiguous on near-symmetric bones. We propose CUFormer, a modality-aware registration framework. We introduce a Modality-Aware Transformer (MAT) that encodes input modality through identity embeddings and updates features through modality-specific adapters within a shared geometric matcher. We model imaging visibility and use predicted reference-side visibility to weight coarse and fine correspondences. CUFormer achieves the lowest mean registration errors among evaluated methods on two CT-US datasets, UltraBones100k and UltraBonesHip, and the CT-RGB-D dataset SpineDepth. On UltraBones100k, it reaches mean rotation error 4.53$^\circ$, translation error 3.19\,mm, and Root Mean Squared Error (RMSE) 2.54\,mm, reducing RMSE by 65.8\% relative to the best-performing baseline by this metric. These results demonstrate CUFormer's effectiveness in aligning partial bone surfaces with preoperative CT across anatomical sites.
We study maximin share (MMS) allocations of indivisible goods under nonnegative additive valuations. We give a strongly polynomial-time algorithm that allocates to every agent a bundle worth at least $7/9$ of her MMS, improving the previous $10/13$ guarantee of Heidari et al. (SODA 2026). Our analysis uses a dynamic witness-allocation framework that tracks the remaining goods from each agent's perspective throughout the allocation process. We obtain the computational result by iteratively refining upper bounds on MMS values, using only polynomially many runs of the allocation algorithm. Finally, we formalize and verify our main results in Lean 4.
Hanzhou Liu, Jia Huang, Mi Lu, Srikanth Saripalli, Peng Jiang
We present Stylos, a single-forward 3D Gaussian framework for 3D style transfer that operates on unposed content, from a single image to a multi-view collection, conditioned on a separate reference style image. Stylos synthesizes a stylized 3D Gaussian scene without per-scene optimization or precomputed poses, achieving geometry-aware, view-consistent stylization that generalizes to unseen categories, scenes, and styles. At its core, Stylos adopts a Transformer backbone with two pathways: geometry predictions retain self-attention to preserve geometric fidelity, while style is injected via global cross-attention to enforce visual consistency across views. With the addition of a voxel-based 3D style loss that aligns aggregated scene features to style statistics, Stylos enforces view-consistent stylization while preserving geometry. Experiments across multiple datasets demonstrate that Stylos delivers high-quality zero-shot stylization, highlighting the effectiveness of global style-content coupling, the proposed 3D style loss, and the scalability of our framework from single view to large-scale multi-view settings. Our codes are available at https://github.com/HanzhouLiu/Stylos.
Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine-grained learning behaviors, existing approaches often need to repeatedly process a growing interaction history to reconstruct the learner. This introduces additional context and inference costs and makes the acquired learner-specific simulation capability difficult to reuse across different LLMs. We therefore propose Learner2Skill, which externalizes the simulation capability acquired from historical interactions into a persistent and reusable Simulation Skill. The Skill captures the learner's current learning state and recurring response patterns, evolves as new real interactions arrive, and can be adapted to a new LLM through lightweight executor calibration without reconstructing the learner from scratch. Experiments show that Learner2Skill more faithfully reproduces fine-grained learner behavior while reducing overall token cost, and that the same constructed Skills can be effectively reused across different LLM executors.