Artificial Intelligence
Work2Skill: Capturing Human GUI Experience for Reusable Agent Skill Induction
Yuqi Huang, Hui Li, Yifei Cao et al. (+9 more)
Computer-use
agents can perform increasingly complex GUI tasks, yet unfamiliar software and workflows requiring human domain knowledge remain challenging. Human demonstrations convey procedural knowledge through the same GUI interaction space, but reuse requires interpreting actions in context and separating workflow rules from task-specific details. We thus introduce Work2Skill, a framework that captures human GUI experience for reusable
agent skill induction. WorkRecorder provides the framework's infrastructure for collecting this experience, aligning screen recordings, mouse and keyboard events, and available accessibility-tree information. Using WorkRecorder, we construct WorkDemo-350, the first synchronized
multimodal dataset of human desktop GUI demonstrations for reusable
agent skill induction and downstream evaluation. Its 350 demonstrations are screened for completeness and task success. We then propose an agentic skill-induction method that inspects evidence on demand to produce reusable skills capturing workflow rules, step dependencies, and completion checks. When the same four execution models are equipped with skills induced by each method under shared execution settings, Work2Skill achieves the highest scores and success rates. Mean score improvements over the corresponding No Skill baseline are 4.67–10.54 points on MyPCBench Bounded Action and 3.11–7.50 points on a 286-task subset of OSWorld-Verified. Our code and data are available at https://github.com/china-qijizhifeng/Work2Skill.
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All authors Yuqi Huang, Hui Li, Yifei Cao, Zikun Cai, Tianci Xie, Yangfan Gao, Pinxue Guo, Chaoyun Zhang, Bingxuan Li, Rui Zheng, Hang Yan, Ning Liao
Computer Vision
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs
Qiji Zhou, Junyi Hu, Peiyu Wang et al. (+1 more)
Visual interventions provide a training-free way to reduce
hallucinations in
multimodal large
language models. However, an intervention may have little leverage at some layers and may damage a correct answer even when it changes the output. We investigate where visual interventions retain leverage and when they improve predictions. First, we apply the same visual-residual edit at successive decoder layers, matching its RMS strength across depth. Across six checkpoints from three model families, we observe a persistent drop in intervention effect and define its operational onset for this edit as the Evidence Anchoring Boundary (EAB). With boundaries fixed on calibration queries, held-out effects on disjoint evaluation queries before EAB are $13.78\times$–$26.10\times$ larger than those after it. Comparison with intermediate readouts shows that the edit can lose leverage before answer preferences stabilize. Second, we compare counterfactual subtraction across presence, attribute, and relation questions. Subtraction can correct a misleading preference, leave a decision unchanged, or remove a preference supporting the correct answer. Based on these observations, we develop calibrated selective routing, which preserves the original prediction unless calibration supports an intervention for the question type. It improves full-set attribute accuracy by $1.56$–$1.81$ percentage points across three checkpoints, while universal subtraction is neutral or harmful.
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All authors Qiji Zhou, Junyi Hu, Peiyu Wang, Yue Zhang
Computer Vision
Zihao Pan, Mengting Chen, Cheng Shi et al. (+6 more)
Visual representations are largely developed for understanding, yet what makes them useful for generation remains unclear. We study this question in pixel
diffusion transformers and uncover two key insights. First, better representation quality does not necessarily translate into greater generative utility, as externally aligned semantics can be progressively lost during pixel synthesis. Second, architectural separation with specialized pixel decoders mainly redistributes semantic and spatial modeling, without necessarily yielding representations better suited to generation. These insights suggest that a useful generative representation must be both properly formed and actively exploited. Motivated by these insights, we introduce Representation Guidance (RG), which directly forms and exploits internal representations while retaining a plain
Transformer architecture. A low-dimensional bottleneck forces generation-relevant information into a compact representation, which is then explicitly used to guide decoder generation by modifying the flow-matching target according to its induced prediction offset. RG learns clearer semantic and spatial structure while scaling efficiently in both parameters and computation. On ImageNet 256x256, RG-L/16 with only 459M parameters outperforms substantially larger pixel
diffusion transformers, while RG-XL/16 achieves an FID of 1.55. RG further reaches an FID of 1.74 at 512x512 and extends to text-to-image generation, achieving 0.89 on GenEval and 87.1 on DPG-Bench.
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All authors Zihao Pan, Mengting Chen, Cheng Shi, Taihang Hu, Zhengrong Yue, Zikang Wang, Yuan Wang, Wei Li, Sibei Yang
Artificial Intelligence
Beyond Semantic IDs: Finite-Beam Reachability and Continuous-Query Generation in Generative Recommendation
Yu Sun, Renkai Xiang, Zuodong Xiang et al. (+6 more)
Generative recommendation (GR) of social media platforms commonly retrieves items by autoregressively decoding discrete Semantic IDs (SIDs) with finite-width beam search. This work examines how this output interface constrains whether held-out targets enter finite returned lists, while distinguishing catalog-wide scoreability from empirical top-K support. We introduce two continuous-query paradigms: CQG-Single generates one
retrieval query in a single pass, and CQG-AR autoregressively generates continuous residual updates, retaining step-wise decoding while removing the SID item-
token vocabulary, code lookup, and discrete beam expansion. These designs thereby eliminate the SID-specific reachability constraint induced by finite-beam decoding and avoid the technical challenge of aligning a discrete SID vocabulary with the pretrained backbone's vector space. Both methods make every encoded item directly scoreable through dense or approximate-nearest-neighbor
retrieval. Under a unified protocol with valid-prefix constrained decoding, CQG-Single exceeds the TIGER-style baseline in all seven runs while using 55.3% as many trainable parameters. CQG-AR remains comparable on ML-1M and is numerically stronger on Beauty and Sports
datasets. A controlled depth ablation further shows that additional continuous refinement does not monotonically improve shallow accuracy. These findings motivate decoupling output representation, autoregressive modeling, and
retrieval budget when evaluating generative recommendation systems.
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All authors Yu Sun, Renkai Xiang, Zuodong Xiang, Junwei Zhou, Yike Zhang, Jianxin Zhang, Zhihao Zhu, Xiaoyu Cao, Hailu Xu
Machine Learning
Diffusion Precipitation Generation with Medium-range Weather Forecasting
Guowen Li, Yang Liu, Mengxuan Chen et al. (+11 more)
Accurate daily precipitation forecasts are important for weather-sensitive decisions. Although recent Weather Foundation Models (WFMs) have achieved significant progress, they are trained on reanalysis data, which exhibit regional and intensity-dependent biases. Since retraining WFMs on observation-based targets is costly, we study Dynamics-Precipitation Decoupling, a plug-and-play framework that uses a separately trained generator to map non-precipitation forecasts from a frozen weather model to observation-aligned daily precipitation. Its key challenges are to capture heterogeneous rainfall distributions and align daily accumulations with atmospheric signals that evolve across space and time. To address them, we train a
diffusion generator conditional on atmospheric states and satellite-gauge estimates of precipitation. A multi-scale recurrent encoder summarizes atmospheric
evolution, while cross-
attention aligns atmospheric and precipitation features during denoising. Extensive evaluation of various backbones, including Pangu-Weather, GraphCast, and HRES, on global and regional forecasting shows improved daily precipitation accuracy and event detection, with better representation of localized accumulation patterns. Global ensemble evaluation shows lower Brier scores than ENS and training-from-scratch GenCast at all reported lead times, with lower CRPS in most comparisons. Remarkably, a heavy-rainfall case study using 2,288 stations in China further supports improved daily accumulation patterns and event detection against ground observations.
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All authors Guowen Li, Yang Liu, Mengxuan Chen, Jiashun Cheng, Haoyuan Liang, Shilei Cao, Yujie Wang, Juepeng Zheng, Fang Wang, Yerong Feng, Meng Jin, Hong Cheng, Yutong Lu, Haohuan Fu
Artificial Intelligence
SmartSalesBench: A Resistance-Aware Multi-Turn Benchmark for Evaluating LLM Sales Agents
P N Bhargav Teja, Velpula Lokesh Reddy, Madhu Basumata et al. (+2 more)
We present SmartSalesBench, the first multi-turn conversational
benchmark explicitly designed to evaluate large
language model (LLM) sales
agents against psychologically grounded, resistant buyer personas. Existing task-oriented dialogue
benchmarks assume cooperative users and measure task completion; SmartSalesBench inverts this assumption, placing
agents against personas programmed to resist and challenge across conversations of up to twenty turns. The
benchmark includes five
agent profiles, twelve buyer personas, fifteen smartphone products across six price tiers, and four controlled experiments with 3 iterations totaling 13,680 sessions. Both sales-
agent and buyer persona generations are grounded through
retrieval-augmented generation (RAG). We introduce nine evaluation metrics grouped into three tiers: primary outcome metrics (CR, BRS, RIR), secondary quality metrics (SMS, PFS, ETI), and exploratory metrics planned for future reporting (ITS, ARI, SPI). Our evaluation combines automated LLM-as-judge scoring with internal human assessment (n = 50, ąppa} = 0.71, Pearson r = 0.81 with BRS).
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All authors P N Bhargav Teja, Velpula Lokesh Reddy, Madhu Basumata, Susen Sarkar, Rajasekhara Reddy Duvvuru Muni
Robotics
Adaptive Whole-Body MPC with Optimal Posture Planning for Humanoid Robots under Unknown Payloads
Manh Cuong Nguyen, Duc Cuong Vu, Viet Anh Vu et al. (+5 more)
Humanoid
robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty significantly affects whole-body dynamics and may destabilize conventional controllers. We propose an adaptive whole-body control architecture that integrates three components: (i) a centroidal model predictive controller (MPC) that generates dynamically feasible reference motions, (ii) an adaptive fuzzy balance controller that compensates for payload-induced model mismatch by generating corrective torques distributed across the supporting-leg joints, and (iii) a waypoint-based optimal posture planner built on a triple-integrator model that produces smooth sit-to-stand and stand-to-sit trajectories satisfying center-of-mass (CoM) stability constraints, joint limits, and whole-body feasibility requirements. Together, these components enable stable lowering, grasping, lifting, carrying, and placing behaviors under significant payload uncertainty. Extensive simulation and hardware experiments demonstrate that the proposed framework improves balance robustness and reduces tracking degradation during posture transitions and lifting tasks compared to non-adaptive baselines.
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All authors Manh Cuong Nguyen, Duc Cuong Vu, Viet Anh Vu, Duc Tuan Pham, Toan Nguyen, Minh Hiep Phung, Ngo Anh Vien, Minh Nhat Vu
Operating Systems
PredictIO: Decoupling Workload Prediction from OS-Controlled Storage Prefetching
Jun Lu, Yancan Mao, Zhenya Ma et al. (+3 more)
Storage stalls remain a significant source of application latency even on fast storage devices. Prefetching can hide part of this latency by fetching data before it is demanded, but general-purpose operating-system mechanisms typically infer future accesses from recent file offsets. This approach is ineffective when accesses are determined by workload structure, such as recurring sparse accesses during application launch, logical traversal in database scans, or model-layout metadata in LLM runtimes. Existing workload-aware prefetchers usually embed prediction logic inside individual applications and couple execution to application-level I/O paths, making policies difficult to reuse across workloads and systems. We present PredictIO, a programmable interface that separates workload prediction from prefetch execution. PredictIO delivers kernel-observed accesses and application hints to user-space policies, which express predictions as uniform page-range requests. A kernel-space executor validates, bounds, and filters these requests against cached and in-flight pages before issuing asynchronous prefetch I/O. We implement PredictIO for application launch, SQLite range scans, and LLM model loading. Across these workloads, PredictIO reduces latency by 5.3-26.3%, achieves up to 2.99x speedup for SQLite scans, and reduces LLM loading latency by 8.8-47.9%. We further show that a common prediction policy can be reused across SQLite and LMDB through lightweight adapters, that an application-integrated RocksDB readahead policy can be migrated while preserving its workload selectivity, and that the execution backend determines how much benefit migration provides for different access patterns and storage devices.
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All authors Jun Lu, Yancan Mao, Zhenya Ma, Sheng Yue, Yongjian Fu, Ju Ren
Distributed Computing
Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
Jianhua Li, Yun Xing, Girisha De Silva et al. (+3 more)
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At the same time, modern Apple Silicon-based macOS devices are equipped with powerful GPUs that remain largely idle during normal usage. This paper presents Minions, a dynamic and distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to execute large-scale machine learning inference workloads. Minions enables teams to leverage existing hardware without requiring additional GPU procurement. The system provides flexible GPU inference task execution, fault tolerance, and efficient resource utilization. Our experience shows that idle macOS devices can be transformed into a practical distributed GPU inference platform in environments where centralized GPU resources are constrained.
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All authors Jianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan, Sean Suchter
Computer Vision
MotionCoach: Constructing Multimodal Guidance for Video Professional Motion Editing
Siyi Qian, Hanqing Yang, Weihuang Lin et al. (+4 more)
Video editing models achieve strong performance in modifying appearance, backgrounds, styles, and motions, empowered by large-scale training data. Beyond general scenarios, professional motions are significant in performance, sports, and martial arts. This paper focuses on professional motion editing, which involves fine-grained motion technique and temporal structure. Given professional motion names but lacking detailed descriptions, existing models often fail to generate professional motions, such as incorrectly lifting the front wheel under a Stoppie instruction. To address this challenge, we introduce MotionCoach, a trained
multimodal agent that uses multi-turn tools and performs self-verification, producing a grounded
prompt and a reference clip as reliable and sufficient
multimodal guidance to inject external professional motion knowledge into
video editing models. To supply training data, we build a data pipeline to construct trajectories. We cold-start the
agent on MotionCoach-SFT-10k and apply Group Relative Policy Optimization (GRPO) on MotionCoach-RL-2k with rewards on the motion guidance
prompt, the motion outcome
video, and the format. For evaluation, we introduce VPMEBench, comprising 314 professional motion editing tasks with fine-grained references and annotations. Extensive experiments show that MotionCoach improves the overall score from 55.1 to 65.6 on Seedance 2.0 and from 51.2 to 64.5 on MiniMax-H3. Learning to construct
multimodal guidance enables precise professional motion editing without retraining
video editing models.
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All authors Siyi Qian, Hanqing Yang, Weihuang Lin, Zihao Yang, Yichen Lu, Yuanhong Zheng, Sheng Li
Computer Vision
MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph
Yingjie Zhu, Han Cheng, Xuefeng Bai et al. (+3 more)
We introduce MVGraph, a
benchmark for evaluating large
vision-
language models (LVLMs) on multi-graph
reasoning where the evidence required by a query is distributed across multiple interrelated visual graph views. Although such evidence commonly resides in source-specific graphs, temporal snapshots, or local observations, existing graph
reasoning benchmarks remain confined to a single self-contained structure, assessing only graph perception and
reasoning while leaving the
alignment and composition of evidence across views underexplored. MVGraph comprises 8k instances spanning 8 graph-theoretic tasks, abstracted from two multi-graph scenarios (partial and temporal graph views). Each problem is programmatically constructed and verified to be unsolvable from a single view. Beyond end-to-end evaluation, we decompose multi-graph
reasoning into four complementary capabilities—Perception,
Alignment, Composition, and
Reasoning—and design 29 diagnostic tasks with 64k instances to probe each one, along with golden intermediate representations for evaluating the intermediate process. Across 15 LVLMs, we reveal the difficulty of multi-graph
reasoning and identify cross-view edge
alignment and relation-level composition as prominent bottlenecks through stage-oriented diagnostics. Meanwhile, temporal constraints and increasing view counts expose further capability boundaries. Code and data are available at https://github.com/AAAndy-Zhu/MVGraph.
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All authors Yingjie Zhu, Han Cheng, Xuefeng Bai, Youcheng Pan, Yang Xiang, Kehai Chen
Machine Learning
DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback
He Sun, Junyuan Mao, Wenhao Jiang et al. (+2 more)
Matching repair after utility changes can reuse valid observations, but new evaluations must respect matching capacity and may return out of order. We study announced invalidations with bounded, possibly reward-dependent reporting delays and pending evaluations that do not occupy future matching capacity. Residualload repair (RLR) combines sound outcome certificates with batched matching schedules for unequal sample deficits. Its complete dyadic quota schedule admits a sharp issue-round bound with coefficient cb < 1.607 relative to the terminal residual endpoint load, together with separate delay and evidence-lifetime bounds. RLR-P monitors complete issue-order prefixes without changing the sampling plan before acceptance. With a deterministic pure oracle, a single-repair coupling from the same drained initial state guarantees no increase in certification time, issued observations, or time to a reusable checkpoint. We also derive an information-load lower bound. On fixed priority-
star markets with unknown Bernoulli gaps, PrioritySearch attains the history-adjusted first-order constant for expected certification time as {\delta} {\sim} 0, under hypothesis-independent linear history scaling and sublogarithmic reporting delays. Paired simulations and measured-feedback replay quantify the trade-offs: in the replay, RLR-P has lower mean capped update-certification time than a monitored width-based baseline, at higher oracle CPU cost.
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All authors He Sun, Junyuan Mao, Wenhao Jiang, Mingjun Xiao, Shinan Liu
Computer Vision
Video Codec-Guided Training-Free Token Compression for Video Large Language Models
Xihua Sheng, Ruijie Liu and Chang Wen Chen
Video large
language models (VLLMs) rely on long visual
token sequences to understand complex temporal content, which makes visual
token compression important for efficient inference. Existing training-free compression methods mainly reduce the number of
tokens sent to the
language model, but the compression process itself can still require many visual feature comparisons, especially for intra-frame
token protection and inter-frame temporal
token merging. This paper proposes a
video codec-guided training-free
token compression method for VLLMs that uses coding information already available in compressed
video as a low-cost search prior. The method aligns reference-frame dependencies, motion vectors, and residuals with the visual
token grid. Residual information forms compact candidate pools for
token protection, while motion information is propagated along reference-frame chains to construct localized candidates for temporal
token merging between sparsely sampled frames. Local visual feature similarity comparison is then used within these candidates to make the final compression decisions, without modifying the base VLLM. Experiments on three representative VLLMs and four
video-understanding
benchmarks show that the proposed method achieves performance comparable to the state-of-the-art training-free baseline FlashVID, and in some settings surpasses it, while substantially reducing the feature comparisons required by
token protection and temporal matching. These results demonstrate that codec information can effectively improve the accuracy-complexity tradeoff of
video token compression.
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Sound
Emo-BPO: Emotion Bidirectional Preference Optimization for Diffusion-based Emotional TTS
Jiacheng Shi, Hongfei Du, Xinyuan Song et al. (+3 more)
Diffusion-based TTS has demonstrated strong capability in emotional
speech generation. However, existing emotion
alignment methods primarily reinforce target emotional expressions while overlooking modeling of competing emotional modes, limiting fine-grained controllability. This issue becomes more pronounced under classifier-free guidance (CFG), which relies on contrasting conditional trajectories to shape generation. We propose Emotion Bidirectional Preference Optimization (Emo-BPO), a simple effective framework that jointly learns emotion-aligned and emotion-contrastive score functions from reordered same-text emotional pairs. Our method requires neither auxiliary reward models nor additional annotations, and can be seamlessly integrated into existing
diffusion-based TTS systems to improve emotional controllability and expressiveness.
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All authors Jiacheng Shi, Hongfei Du, Xinyuan Song, Y. Alicia Hong, Yanfu Zhang, Ye Gao
Robotics
JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents
Gangyi Li, LuYao Wang, Jingfu Sun et al. (+7 more)
Embodied
agents must translate
multimodal observations and user requests into executable actions while adapting to product constraints, changing goals, and execution feedback. We present JoyInside-VLM-
Agent, a ReAct-style
multimodal agent developed through product-conditioned trajectory construction and staged policy learning for robotic platforms and smart hardware. A closed-loop sandbox generates
multimodal interaction trajectories covering goal revisions and failure recovery, which are checked against execution outcomes and semantic criteria. Supervised fine-tuning and multi-teacher on-policy
distillation precede reinforcement learning with DPER (Dual-Source Privileged On-Policy Self-
Distillation with
Token-Level Evidence Routing). DPER routes
token-level evidence between a frozen privileged teacher and a privileged student sharing the rollout policy's parameters, modulating reward-derived
token advantages while preserving their signs. Training rewards combine generative process evaluation, multi-judge rankings, and task-queue consistency. JoyInside-VLM-
Agent achieves a BFCL v4 overall score of 77.44, exceeding the evaluated baselines; results on the other public
benchmarks are mixed. On JoyInside-Devices and JoyInside-Robotics, the evaluated system, which includes domain-specific skill refinement, leads the baselines on all reported metrics. Two real-world quadruped case studies illustrate visually grounded search, replanning after execution failure, and explicit failure reporting.
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All authors Gangyi Li, LuYao Wang, Jingfu Sun, Jingyi Yuan, Hang Liu, Hao Zong, Yujia Hu, Tingting Gao, Jianyong Wang, Xu Yang
Artificial Intelligence
MathVis: Can AI Reason through Drawing? A Benchmark for Mathematical Figure Generation
Jingjing Wei, Hao Zhao, Xinke Li et al. (+3 more)
This paper introduces MathVis, a
benchmark designed to evaluate LLMs' ability to visualize frontier mathematical problems and perform diagram-assisted
reasoning. The
benchmark consists of 512 tasks, each comprising a mathematical statement, a reference diagram, executable TikZ or SVG code, a Gold Intermediate Representation (IR), atomic rubrics, and error codes. Through rigorous evaluation, GPT-6-Astra, Claude Fable 5, and Gemini 3.1 Pro Preview achieve pass@1 scores of 31.25%, 26.56%, and 22.85%, respectively. We found that although the tasks require visualization, LLMs produce outputs dominated by formal
languages, symbols, and graphics programs, rather than explicit diagram-mediated
reasoning. Furthermore, while the solutions generated by LLMs may contain all the necessary elements, they are often visually confusing to humans. Ultimately, this
benchmark aims to visualize mathematical problems and promote research on enabling LLMs to simplify their
reasoning via visual diagrams.
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All authors Jingjing Wei, Hao Zhao, Xinke Li, Linxi Yu, Xiaosong Gao, Shanshan Xiao
Data Structures and Algorithms
Subset Sum and Integer Programming Feasibility Test in Near-Optimal Time
Lin Chen, Yuchen Mao and Guochuan Zhang
We give an $O_{d,\varepsilon}(n+N^{d+\varepsilon})$-time
algorithm for multidimensional subset sum on an explicitly listed multiset of $n$ vectors in $[-N,N]^d$, for every fixed $d$ and $\varepsilon>0$. This directly implies an $O_{d,\varepsilon}(n+N^{d+\varepsilon})$-time
algorithm for feasibility test of integer programming. Prior to our work, the best-known
algorithm has a running time of $O_{d}(n+N^{O(d^2)})$ [Eisenbrand, Weismantel'19]. Specifically, if $d=1$, our
algorithm runs in $O_{\varepsilon}(n+N^{1+\varepsilon})$-time for the standard subset sum problem, which is near-linear in the largest input integer. Prior to our work, subset sum can be solved in time near-linear in the target $t$ [Bringmann'17], or near-linear in the mixed parameter $\sqrt{Nt}$ [Chen, Lian, Mao, Zhang'24]. It is a fundamental open problem whether the
algorithm can be made near-linear in $N$, which is settled affirmatively in this paper up to an arbitrary small $N^{\varepsilon}$ term.
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Cryptography and Security
WaveMark: Robust Wavelet-Domain Image Watermarking for Diffusion Models
Jie Cao, Qi Li, Chaklam Cheong et al. (+4 more)
Image watermarking enables provenance tracing for photorealistic images generated by
diffusion models. However, state-of-the-art (SOTA) removal attacks can suppress embedded signals with little perceptual degradation. This vulnerability limits the effectiveness of watermarks for intellectual property protection and misuse attribution. We propose WaveMark, a novel wavelet-domain watermarking method that embeds messages in a
diffusion model's initial noise. To support robust recovery from perturbed carriers, WaveMark distributes keyed replicas of message bits across spatial positions, wavelet orientations, and scales. Each carrier groups coefficients from four latent channels and encodes a randomized bit through magnitude projection, providing an explicit decoding margin. We evaluate WaveMark with SD2.1 on SDP and MS-COCO Captions under 14 attacks. WaveMark achieves 100.0% bit accuracy (BA) without attacks, while maintaining image quality comparable to the evaluated baselines. Under SOTA MarkNull and MarkNull-A, it retains over 88% BA, outperforming all baselines.
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All authors Jie Cao, Qi Li, Chaklam Cheong, Yutong Jin, Enze Yu, Zelin Zhang, Jianbing Ni
Cryptography and Security
AgentValve: Sybil-Resilient Payment Authorization for Permissionless AI Markets
Jianting Zhang, Zhongtang Luo and Aniket Kate
AI
agents increasingly select and pay service providers without human review. In emerging permissionless
markets, however, adversaries can create inexpensive identities and fabricate feedback to inflate provider reputation, making reputation-based payment authorization vulnerable to Sybil attacks and exposing clients to unbounded financial loss. This paper presents AgentValve, a protocol for Sybil-resilient payment authorization in permissionless AI
markets. AgentValve builds on a trust network that represents reputation as finite capacity in a directed graph and authorizes payments through trust paths with sufficient available capacity. A smart contract atomically reserves the capacity supporting each admitted payment, bounding honest
agents' payment exposure by the capacity they extend into the
adversarial region, and thus creating identities or interactions entirely within that region cannot increase this bound. To limit capacity depletion by malicious senders, AgentValve combines payment-backed capacity creation with bounded route length, preventing attackers from depleting honest capacity without incurring corresponding payment costs. AgentValve further decouples off-chain route discovery from on-chain verification and introduces a witness-commit mechanism that reduces route verification to linear work while requiring only constant persistent state per admitted payment. We implement a AgentValve prototype and evaluate it using six months of Ethereum data and synthetic workloads. Experimental results show that under attacks with up to 10,000 synthetic Sybil identities and with chosen attackers, the payment exposure and capacity depletion remain bounded. Regarding practicality, the witness-commit mechanism lowers admission cost by up to 40.8% compared with baselines, and admitting common payments costs approximately \$0.04-\$0.21.
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Machine Learning · Cross-listed from stat.ML
From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance
Guanhao Zhou and Xiufan Yu
Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differences involving the smaller sample. This challenge is particularly relevant for complex, non-Euclidean data, such as graphs, whose structural characteristics call for flexible testing procedures. Classifier two-sample tests (C2STs) provide such flexibility, but existing procedures are primarily designed for balanced or comparably sized samples. In this paper, we study C2STs in a highly imbalanced regime, and show that severe sample imbalance may substantially degrade testing performance. Different from conventional imbalanced learning approaches that treat sample imbalance merely as a nuisance for classifier training, we turn it into a source of repeated testing evidence. Building on this insight, we propose an Enhanced Imbalance-Aware Deep Classifier Two-Sample Test (EIAD-C2ST), a classifier-agnostic framework designed for sample imbalance that accommodates diverse data modalities by tailoring classifiers to data structure. For computationally expensive deep classifiers, we further develop a scalable representation-amortized implementation strategy. Theoretically, we establish rigorous guarantees on asymptotic size and characterize conditions for power consistency, allowing the imbalance ratio to diverge. We demonstrate the empirical gains using simulations and an application to blockchain forensics. The results reveal that the majority-sample surplus, when properly leveraged, can be organized into useful statistical evidence, rather than an obstacle, for distributional comparisons with complex data.
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Computer Vision
In-Context Learning Can Help Vision-Language Models Overcome Training Priors
Kun Wang, Xindi Wu, Sanghyuk Chun et al. (+2 more)
Vision-
language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those regularities. It is challenging to disambiguate these prior-induced errors from errors caused by the model missing the underlying visual capabilities. In this work, we use controlled visual in-context learning to encourage the model to focus on the visual evidence, and demonstrate that this alone is frequently sufficient to correct the errors – suggesting that the models in fact do possess the relevant visual capabilities. Specifically, we show that across five
vision-centric tasks, demonstrations yield large gains on prior-conflicting counterfactual examples ($+27.2\%$ for Qwen3-VL 235B and $+15.1\%$ for Qwen3-VL 32B) while leaving accuracy on prior-aligned examples nearly unchanged. These findings generalize across five VLM families with gains from $+9.7\%$ to $+30.3\%$. We analyze visual representations and find that demonstrations indeed redirect
attention toward grounding cues while reducing reliance on cues that support the learned prior. Finally, we find that in-context learning is limited when the underlying visual capability is weak or absent. Together, these results show that current VLMs possess visual grounding capabilities that standard evaluations may overlook, underscoring the need for controlled evaluation design in diagnosing and improving model capability.
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All authors Kun Wang, Xindi Wu, Sanghyuk Chun, Olga Russakovsky, Esin Tureci
Computer Vision
UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning
Xintian Mao, Jiansheng Wang, Huaiyuan Wang et al. (+2 more)
Image deblurring is a fundamental task in computer
vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurring performance of these models drops significantly across scenarios with different cameras and image contents. This motivates the development of a unified motion deblurring framework that can handle diverse blur patterns. To this end, we construct a multi-source
dataset and develop a data
pruning and quality control pipeline. Then, to restore blur images with various motion blur kernels across different scenarios, we propose a unified motion deblurring framework via
Fourier Convolution Learning. Existing deblurring networks usually rely on pixel-level mappings and overlook the convolutional nature of blur. Thus, the network mostly fits the image content itself rather than learning the convolution process, making it difficult to handle multi-source blur images. Instead of relying on spatial mapping, we perform blur-adaptive global filter learning in
Fourier space, which avoids the limited spatial support of kernel estimation. Extensive experiments demonstrate that our unified model effectively handles motion blurs across diverse sources and scenes, achieving competitive performance across both in-domain and OOD
benchmarks. Code has been released at github.com/DeepMed-Lab-ECNU/Single-Image-Deblur.
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All authors Xintian Mao, Jiansheng Wang, Huaiyuan Wang, Qingli Li, Yan Wang
Hardware Architecture
CacheFlex: Direct Software-Managed Access to Higher-Level Cache for Scalable Vector Support
Jingqun Zhang, Maohua Nie, Jiayi Wang et al. (+4 more)
Wider vector/SIMD lanes multiply compute throughput, but cache improvements remain challenging. We identify L1 data cache capacity as the most impactful parameter for performance and efficiency. L1 capacity is constrained by pipeline coupling and latency requirements; private L2 offers an order of magnitude more capacity but is inaccessible from software. This work proposes CacheFlex, an ISA and microarchitecture co-design that enables software-managed, direct access to private L2 SRAM by allowing runtime conversion of parts of L2 into a scratchpad
memory (SPM). Through a lightweight ISA extension, software explicitly controls data placement and lifetime within the L2. CacheFlex integrates into an out-of-order pipeline with cross-domain
memory consistency and OS context-switch support, with merely 2.02% area overhead relative to the L2 in a post-layout 12 nm implementation. We prototype CacheFlex based on the Armv8-A SVE ISA, model it in gem5, and evaluate it using optimized general
matrix multiplication (GEMM) kernels with diverse model-derived shapes, FlashAttention-based fused
attention, and full-layer workloads from LLaMA-3.2-1B and BERT-Base in the prefill phase. As the vector length (VL) scales from VL = 4 (512-bit) to VL = 16 (2048-bit), CacheFlex's kernel-level
geometric-mean speedup and
memory-subsystem energy reduction grow from 1.12x and 26% to 1.44x and 40%, respectively, bridging the widening gap between per-instruction data demand and
memory performance. On full
transformer layers combining CacheFlex with FlashAttention, the design delivers up to 1.78x speedup and 45%
memory-subsystem energy savings (BERT-Base, sequence length T=4096, VL = 16; LLaMA-3.2-1B: 1.64x and 38%). CacheFlex is complementary to hardware prefetching, and its benefit grows as vector width and peak compute throughput increase for compute-intensive AI kernels.
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All authors Jingqun Zhang, Maohua Nie, Jiayi Wang, Weihang Li, Rishi Sappidi, Shwet Chitnis, Ang Li
Computation and Language
Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding
Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani et al. (+2 more)
Decoder hidden states from different layers of large
language models (LLMs) often encode complementary information. However, existing speculative decoding methods typically rely on a small set of target-model layers chosen using fixed, position-based heuristics. Selecting an optimal subset of layers is inherently a combinatorial problem, making exhaustive evaluation infeasible even for moderately deep models. In this work, we present a principled framework for multi-layer hidden-state selection for speculative decoding based on two complementary criteria: informativeness, measured through
matrix-
entropy analysis, and diversity, quantified using centered kernel
alignment (CKA). We investigate layer-wise Shannon
entropy, aggregate
entropy under hidden-state concatenation, and pairwise CKA dynamics across depth, revealing distinct
geometric and informational compartments within decoder representations. Motivated by these findings, we propose
entropy and CKA-based layer selection
algorithms that favor hidden-state sets that are both highly informative and minimally redundant. As part of this framework, we introduce
token-
entropy filtering, which removes hidden states associated with low predictive-
entropy tokens, thereby improving the quality of layer-selection statistics. In EAGLE3-style speculative decoding, our selected sets outperform fixed baseline choices, including the default $[2, N//2, N-3]$ rule, while providing a clearer explanation of why some defaults can remain competitive. Our results provide a practical recipe for multi-layer hidden-state selection for speculative decoding, insights into the geometry and information in LLM decoder states, and a foundation for extending similar analysis to related applications.
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All authors Raghavv Goel, Sudhanshu Agrawal, Mukul Gagrani, Risheek Garrepalli, Mingu Lee
Data Structures and Algorithms
Conditional Indexing Lower Bounds and Normalization Boundaries for Elastic Subsequence Matching
Zemin Chao, Zitong Li, Xiangyu Gao et al. (+1 more)
We study static indexes for exact subsequence matching over all equal-length windows of a time series. Under deterministic SETH, for every fixed $\beta\ge1$ and $\delta\in(0,1)$, we exclude $O(N^\beta)$ preprocessing together with $O(N^{1-\delta})$ worst-case query time on domains containing the reduction's polylogarithmic query lengths. The bounded-error counterpart assumes randomized SETH. The results cover specified warping, bottleneck, edit, subsequence, local-
alignment, and complexity-adjusted scores. Their reductions share a data-independent query interface but require different arguments for all-window soundness. We give complete representative proofs for the distinct mechanisms and state the changes needed by related scores. For soft-DTW, integer scaling yields an exact Boolean-predicate reduction in Word-RAM; a model with exact transcendental primitives requires a separate assumption. For independent window-wise Z-normalization, balanced coordinate gadgets and shifted-window moment bounds yield exact lower bounds for squared-cost DTW and discrete Fréchet distance. We give finite-word comparisons and a specified length-dependent rounding rule preserving these gaps. Raw and normalized discrete Fréchet constructions also exclude feasible-solution approximation within any fixed factor below two. Finally, we prove conditional signal-attenuation bounds for normalized anchor constructions and a fast index under the all-window common-mean promise.
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All authors Zemin Chao, Zitong Li, Xiangyu Gao, Hongzhi Wang
Robotics
Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision
Zile Zhong, Yuxin Du, Tao Lin et al. (+4 more)
Vision-
language-action (VLA) models provide a unified framework for visual understanding,
language grounding, and
robot control, yet anticipating how the scene will evolve beyond the current observation can provide additional information for control. Action supervision alone does not explicitly encourage such anticipation, while future-image prediction entangles task-relevant motion with largely static appearance. To address these limitations, we introduce Evo-World, a lightweight VLA model that augments policy learning with future motion prediction through optical-flow supervision, thereby providing a task-relevant predictive signal focused on motion rather than appearance. We further design the training procedure to preserve these predictive representations during policy learning, regulating representation diversity while decoupling representation learning from action optimization. Across simulation
benchmarks, Evo-World demonstrates strong performance, achieving a tier-averaged success rate of 91.1% on Meta-World MT50 , with a 13.0% relative improvement over the non-predictive baseline. On real-world AgileX
robots, Evo-World outperforms the evaluated baselines across single-arm and bimanual manipulation tasks while using only 0.87B parameters and delivering lower GPU
memory usage and higher inference efficiency. These results demonstrate that future motion prediction through optical-flow supervision provides an effective and lightweight approach to enhancing the predictive capabilities of VLA models.
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All authors Zile Zhong, Yuxin Du, Tao Lin, Haoxiao Wang, Wei Dai, Binhua Zuo, Bo Zhao
Artificial Intelligence
Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training
Suorong Yang, Jiaxin Wang, Jiaming Su et al. (+4 more)
Large
language models have achieved remarkable advances in complex
reasoning. However, scaling
reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains. In this study, we therefore ask: how far can
reasoning supervision be compressed without sacrificing capability? To this end, we propose an uncertainty-guided progressive contraction framework that concentrates supervision and optimization on the
reasoning trajectories most valuable for learning. Specifically, rather than treating a teacher-generated chain-of-thought (CoT) as a single realized path, we construct uncertainty-profiled
reasoning supervision by building Monte Carlo
reasoning trees around intermediate
reasoning states to expose process-level uncertainty. This uncertainty first contracts the original corpus into a compact teacher-informed pool. A second, learner-aware contraction then performs online routing during SFT, concentrating optimization according to the student's evolving learning state. Together, the two stages progressively contract both supervision and optimization, substantially reducing the student training budget. Experiments across diverse
reasoning benchmarks and LLM families show that our method uses as little as 0.22% of the optimization budget of full-data SFT, over 450x less training exposure, while maintaining or improving
reasoning performance. Notably, our framework generalizes well across heterogeneous student families, black-box teachers, and weak-to-strong transfer. This indicates that it is not only a way to accelerate training but can also enhance
reasoning capability.
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All authors Suorong Yang, Jiaxin Wang, Jiaming Su, Chentao Yue, Furao Shen, Peng Ye, Soujanya Poria
Computation and Language
SEEDS: Preview-Guided Evidence Admission for Search Agents
Jiajin Liu, Chenxi Xu, Dongzhe Fan et al. (+2 more)
Search
agents extend large
reasoning models with multi-round access to external knowledge, yet most existing systems largely conflate retrieving evidence with consuming it. Retrieved documents are commonly incorporated according to fixed or rank-based policies, despite the fact that evidence utility is conditioned on the
agent's current
reasoning state. Consequently, a lower-ranked candidate may be critical for the current step, while a higher-ranked candidate may provide redundant, irrelevant, or misleading information. We formulate this overlooked problem as evidence admission, which asks, at each search step, which retrieved candidates should be allowed to enter the
agent's evolving
reasoning context. To enable such selective control, we introduce SEEDS (Selective Evidence Entry During Search), a preview-guided evidence admission framework for search
agents. SEEDS first exposes lightweight previews of retrieved candidates and then lets the
agent jointly determine which candidates and how many should be fully inspected, conditioned on its current
reasoning state. Unlike fixed or adaptive prefix policies, SEEDS can select arbitrary subsets with flexible cardinality, and it can be instantiated directly with an existing
reasoning agent or further optimized through learning. Across nine knowledge-intensive QA
benchmarks under both local corpus and live web search, SEEDS consistently improves search effectiveness while substantially reducing full-evidence consumption. Experimental analyses show that SEEDS more effectively selects useful candidates beyond the corresponding
retrieval prefix. These results establish evidence admission as a complementary dimension of agentic search, separating what information is retrieved from what information is ultimately allowed to shape
reasoning.
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All authors Jiajin Liu, Chenxi Xu, Dongzhe Fan, Yuanfu Sun, Qiaoyu Tan
Game Theory
Network Topology Shapes AI Intervention Effectiveness on Deception: Truth-Telling vs. Investigative Agents
Zhao Song, Stefan Sarkadi and The Anh Han
Deploying AI
agents to combat dishonest communication is an intuitive response to deception in hybrid human-AI societies. However, the efficacy of such interventions in realistic networked settings remains unclear. We study this problem using an evolutionary game-theoretic model of the Deceiver's Game by extending it to structured networks, where
agents can adopt deception, truth-telling, and investigation strategies. We introduce committed AI
agents (zealots) fixed to either truth-telling or investigative strategies and conduct
agent-based simulations across three network
topologies. Our key findings are: (1) passive committed truth-telling
agents can amplify adaptive deception by creating exploitable honest partners, whereas investigative
agents increase adaptive truth-telling in the oscillatory regime; (2) intervention effects are non-monotonic in deployment density because committed
agents reshape rather than simply suppress the underlying oscillatory dynamics; and (3) response peaks depend on
topology, with sampled scale-free populations showing sharp low-density responses and fully connected populations requiring larger committed fractions. Overall, the results highlight the need to distinguish passive truth-telling from active investigation when using committed
agents to influence networked deception dynamics.
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Artificial Intelligence
Collaborative Learning and Reasoning over Model Slices
Yuheng Wang, Runde Yang, Zhuohan Bao et al. (+9 more)
Large
language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context
reasoning, and test-time computation. Despite these strengths, single-model
reasoning remains vulnerable to error propagation, as later steps depend on the model's earlier outputs, leaving
reasoning blind spots difficult to independently reassess. To address this limitation, we develop collaborative learning, encompassing the construction of heterogeneous collaborators and the learning of effective cross-model collaboration. Specifically, model slicing replaces a large model with smaller, heterogeneous models under a comparable parameter budget, enabling prior
reasoning to be reassessed across distinct model parameterizations. Pyramid supervision combines protocol
distillation with step-level supervision to refine
reasoning and interaction decisions throughout collaboration. We evaluate our approach across four task categories: multi-hop question answering, mathematical
reasoning, code generation, and open-ended constrained generation. The trained heterogeneous models achieve comparable or better task performance than the original large model and established multi-
agent baselines, including reinforcement-learning-based methods, while also achieving higher inference efficiency than the multi-
agent baselines and exhibiting differentiated collaboration patterns across tasks. These findings show that learned collaboration turns model heterogeneity into complementary
reasoning capabilities.
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All authors Yuheng Wang, Runde Yang, Zhuohan Bao, Qiran Zhang, Huatao Li, Yiyang Zheng, Lin Wu, Jingru Fan, Shu Yao, Yihan Li, Zhi Li, Chen Qian