Artificial Intelligence
When Agent Context Goes Stale: Incoherence in Volatile Agent Context
Yingying Liu, Junzhou Fang and Chenxiong Qian
Modern
agents increasingly ground their
reasoning in observations returned by tools, such as file contents read from a workspace. However, the data sources underlying these observations may later be modified by users, other
agents, or external tools, while the model retains only the stale content in its context window. Existing
agent runtimes provide little support for notifying the model that a previously observed fact has become stale, causing
agents to reuse outdated observations and make incorrect claims about the current workspace state. We propose Concord, a context coherence framework that maintains the consistency between tool observation in
agent context and the mutable sources from which they were derived. Concord links each observation to its source, detects source changes, and uses configurable handling policies to update, annotate, or suppress stale context before reuse. Concord is applicable across different
agent runtimes and external resources, and can be easily extended to new runtime-resource settings. We implement Concord as a general framework, and instantiate a concrete use case to assess its effectiveness. We construct ConcordBench, where previously observed file contents become stale after subsequent edits. Across three evaluated frontier models, Concord produces answers consistent with the restored workspace state in all evaluated cases under these constructed conditions, matching the oracle on recover count for this
benchmark, while using 46.4% fewer
tokens than the strongest non-oracle baseline.
Read the full abstract ▾
Machine Learning
Fast Convergence through Distributed Augmentation for Class-Imbalanced Federated Learning
Arathi Nair M, J. Harshan and Anwitaman Datta
In
federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to augment minority-class samples to achieve local class balance. Existing approaches treat augmentation as a heuristic and do not establish how the amount of augmentation influences the convergence of
federated learning, leading to excessive augmentation and increased training time. To address this limitation, we first establish the relationship between augmentation and the convergence behavior of
federated learning. Leveraging this insight, we propose DAFL, a distributed augmentation framework that determines the minimum augmentation required for each client-class pair by jointly minimizing augmentation and training time while constraining global class imbalance, thereby improving minority-class F1-score. Experimental results demonstrate that DAFL consistently improves minority-class F1-score while substantially reducing training time, particularly under severe global class imbalance and high label proportion imbalance.
Read the full abstract ▾
Computation and Language
When Verifiable Counts Depend on Wording: Auditing Wording Robustness in Instruction Following
Qishi Zhan, Seoyeon Jang, Zihan Dong et al. (+3 more)
Verifiable instruction-following
benchmarks often express each constraint through one fixed template. We test whether scores remain stable when the operational requirement is unchanged but its wording varies. We introduce WISE, a matched evaluation suite and reporting protocol instantiated on exact word count, keyword inclusion exactly once, and an inclusive 8–12 word range. Across 100 matched tasks, up to thirteen models from seven providers, and repeated generations scored over the complete visible output, wording alone produces substantial compliance shifts. In an avoidance-family panel, five avoidance and exclusion forms fall below the positive baseline, while constructional controls also shift compliance substantially: in the nine-model control panel, compliance is 54.9% for the original positive form, 48.2% for a longer positive form, 36.7% when the target appears later, and 33.8% for AVOID1. A strict JSON-structure probe shows wording sensitivity beyond counting, with a different direction of effect. Effect sizes, failure directions, weakest forms, and model rankings vary across realizations. Under the most disruptive exclusion form, the top-ranked model changes and 24.1% of strictly ordered model pairs reverse. Human validation further shows that unanimous agreement on an exact-count interpretation can coexist with substantially different model behavior. WISE supplements conventional scores with mean and worst-form compliance, wording gaps, failure profiles, and ranking stability.
Read the full abstract ▾
All authors Qishi Zhan, Seoyeon Jang, Zihan Dong, Minxuan Hu, Ziheng Chen, Tonghui Qu
Software Engineering
Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models
Jian Gu, Hongyu Zhang, Chunyang Chen et al. (+1 more)
Code
language models must be maintained like the software around them: when a library evolves, a model keeps writing the interface that it saw during training. Repairing the model itself lets one correction reach all downstream uses. Existing repair methods attribute a failure to
neurons, select the highest-ranked ones, and apply a generic update. This pipeline assumes that the attributed
neurons are the ones to patch and that a generic update fits every failure, and neither assumption has been examined. We examine both on executable API
evolution tasks in Python and Rust with three code models and identify two gaps. The targeting gap separates the
neurons that failure attribution targets from the
neurons that can carry a patch: their top sets have a Jaccard overlap of only 0.15 to 0.20. The tailoring gap separates a generic update from a patch built for the failure: the patches that different carrier
neurons need are nearly orthogonal. To address both gaps, we propose ASTRA. It targets
neurons by contrastive semantics, an attribution that scores a
neuron by its contribution to the logit contrast between the target
token and the produced
token. It then tailors the patch by solving one small linear system in closed form, which corrects all failing
tokens of a sample jointly and needs neither an optimizer nor a backward pass. Contrastive semantics selects significantly better carrier
neurons than gradient-based attributions in 3 of 6 settings and comparable ones in the others. On average, ASTRA reaches 66.7 percent Pass@1, against 48.4 percent for the best of AlphaEdit,
STAR and low-rank adaptation, and repairs a sample in 2.7 seconds. It is the best method in all 6 settings and for every type of API change, and this advantage persists under an unseen phrasing of the test
prompt. Its side effects on unrelated code are small on the large models and larger on the small one.
Read the full abstract ▾
All authors Jian Gu, Hongyu Zhang, Chunyang Chen, Aldeida Aleti
Computation and Language
Inductive Claims Extraction at Scale
Sandrine Chausson and Björn Ross
A large part of political discourse on social media is built and expressed at a level of claims: i.e. declarative, typically single-clause statements, which convey a particular interpretation of reality and can range from factual to evaluative. Moreover, rather than occurring randomly, claims coalesce, recur in patterns, and come to be associated with different world views. When paired with structural computational tools such as Social Network Analysis, claims can be a powerful unit of analysis to study political phenomena such as echo chambers or polarisation. In this paper, we present a pipeline that uses a large
language model (LLM) to inductively extract and catalogue claims from large social media corpora, and apply it to two different Twitter
datasets: one relating to the 2020 US presidential election and the other to the 2022 FIFA World Cup. We comprehensively evaluate the approach by measuring the pipeline's recall and precision against manually annotated samples, run ablation studies isolating the contribution of its various components, and perform a qualitative error analysis. We discuss the value of the approach in the context of Computational Social Science research, and illustrate its capabilities by presenting the claims catalogue obtained from each
dataset.
Read the full abstract ▾
Computer Vision
Answer with Evidence: Consistency-Aware Grounded Visual Question Answering for Roadside Traffic Scenes
Runwei Guan, Rongsheng Hu, Shangshu Chen et al. (+10 more)
Roadside traffic
reasoning requires every free-form textual claim to be backed by visual evidence. Existing grounded
multimodal large
language models (MLLMs) frequently exhibit say-point mismatch, in which the textual answer contradicts the bounding boxes the model localizes. Evaluation metrics that score answers and boxes separately leave this failure unpenalized. We trace the mismatch to the conventional answer-then-ground factorization, which commits to a numerical claim before any object is enumerated. To measure it, we build RoadSceneVQA-G, a
benchmark of 34.7K question-answer pairs in which every free-form answer is linked to the set of boxes that witnesses it, and we propose the Answer-Grounding Consistency (AGC) evaluation suite. To address it, we introduce Enumerate-then-Answer (EtA), which reverses the generation order so that answer-evidence agreement becomes a property of the output structure, and Enumeration-Consistent Policy Optimization (ECPO), a reinforcement learning stage that uses the union of multiple rollouts as a recall teacher without ground-truth boxes. EtA raises say-point consistency from 26.6% to 93.7% and grounding F1 from 52.2% to 73.0%, and ECPO further increases F1 to 75.6% without per-box supervision. On gRefCOCO, the same framework outperforms the strongest compared method, indicating that it transfers beyond traffic scenes. The project is available at https://github.com/GuanRunwei/RoadSceneVQA-G.
Read the full abstract ▾
All authors Runwei Guan, Rongsheng Hu, Shangshu Chen, Ningwei Ouyang, Shaofeng Liang, Heyi Lin, Jinjing Zhu, Yang Shi, Dongming Wu, Jianfeng Dong, Daizong Liu, Henghui Ding, Hui Xiong
Computer Vision
When and What to Prune? Stage-Aware Visual Token Pruning for Efficient VLA
Tianjun Shi, Haotian Xiong, Ziyu Gong et al. (+2 more)
Visual
token pruning is an effective way to accelerate
vision-
language models and is especially useful for
vision-
language-action (VLA) inference, where many visual
tokens must be processed before predicting
robot actions. Existing
pruning methods usually estimate which
tokens can be pruned based on
attention scores or feature diversity, retaining
tokens that are either highly attended or visually different from others. However, most of them use fixed
pruning schedules, such as
pruning once at a preset layer or
pruning at uniformly spaced layers. Such schedules can be risky for VLA models, because the model may not know which visual regions matter for the action in early layers.
Tokens that look unimportant at first may become useful after the model combines visual observations with the
language instruction. In this work, we propose SAPrune, a training-free visual
token pruning framework for efficient VLA inference. Instead of
pruning at fixed layers, SAPrune uses a small calibration set to observe how action-to-visual
attention changes across layers, and chooses
pruning layers only after the
attention pattern becomes more reliable. At each selected layer, SAPrune applies a dual-path
pruning rule: one path protects strongly attended visual
tokens from
pruning, while the other prevents useful surrounding context from being discarded. Experiments on LIBERO, SIMPLER, and real-world robotic tasks show that SAPrune prunes 87.5% of visual
tokens and achieves up to 1.718x inference speedup while maintaining competitive task success rates.
Read the full abstract ▾
All authors Tianjun Shi, Haotian Xiong, Ziyu Gong, Qi Lu, Li Li
Multiagent Systems
Synergizing Drone Delivery Order Pooling and Road Network Monitoring through Monitoring-Task Orderization
Yulong Hu, Meng Xu, Sen Li et al. (+1 more)
This paper investigates the real-time dispatch of a shared drone fleet for on-demand food delivery and urban road network monitoring. We consider a courier-drone collaborative setting in which couriers transport orders to launchpads and drones complete the final delivery leg to kiosks. Drones may consolidate multiple origin-destination orders within one flight and make monitoring-aware route adjustments to collect real-time traffic information subject to delivery-time constraints. This yields a joint decision problem coupling dynamic order-to-drone matching, multi-order pooling, routing, and time-varying monitoring under fleet-level competition and uncertainty. We propose monitoring-task orderization, which periodically converts road-network nodes with high congestion and stale information into virtual monitoring orders. Pooling these virtual tasks with food-delivery orders creates a unified heterogeneous task set and transforms the coupled matching-and-routing problem into an order-level decision process. Building on this abstraction, we formulate a decentralized graph-interdependent Multi-
Agent Markov Decision Process and develop Graph Multi-
Agent Q-Learning (Graph-MAQL), which captures localized inter-
agent dependencies through bipartite match coordination graphs.
Agent-task value estimates are then used as edge weights in a dynamic heterogeneous bipartite matching program for globally feasible execution. Experiments using real-world data reveal strong operational synergy between delivery and monitoring. Monitoring-task orderization improves monitoring performance by 25.1% with less than a 1% reduction in delivery performance, while Graph-MAQL improves the aggregate objective by up to 20.8%, reduces deadline violations by over 40%, and transfers zero-shot to higher demand intensity without retraining.
Read the full abstract ▾
All authors Yulong Hu, Meng Xu, Sen Li, Nikolas Geroliminis
Machine Learning
A Unified Scaling Law for Time Series Foundation Models
Xilin Dai, Yiding Liu, Zewei Dong et al. (+2 more)
We develop a Unified Scaling Law and a Unified Theory of Time Series Learning to understand how model capacity and historical information support
forecasting. Across different lookback lengths and forecast horizons, we analyze 18,768 experimental
cells from 21 checkpoints on 23
dataset-frequency tasks spanning six domains. Our empirical methodology integrates local resource relations into a parsimonious, fitted five-parameter law: capacity gains increase with history, context gains diminish toward saturation, and horizon effects enter as a common shift. Fitted without Toto 2.0, the law predicts its horizon-averaged capacity-scaling curves with mean absolute percentage errors of 1.09% and 1.50% at input lengths 2048 and 4096. To understand how history supports prediction, our learning theory uses Gaussian regression to analyze rule identification and predictive capability. We hypothesize that full-shot models learn by accumulating information in weights, while frozen time series foundation models (TSFMs) use history by extracting information through activations. Matched-history comparisons establish the predictive value of additional history. Controlled parameter exchanges and activation interventions provide evidence that history-derived rule information can be retained, reused across queries, and used to recover a contribution to long-context prediction. Together, these findings inform capacity scaling, context allocation, and the development of models that retain and apply historical rules. Code and main results are available at https://github.com/Fifthky/UniScale.
Read the full abstract ▾
All authors Xilin Dai, Yiding Liu, Zewei Dong, Jiang-Ming Yang, Qiang Xu
Data Structures and Algorithms
Multilevel Dynamic Thinning for Matroid Secretary
Dennis Joyce
In the matroid secretary problem, weighted elements arrive in random order, and an online
algorithm must irrevocably accept elements forming a high-weight independent set. Dynamic Thinning is a recent, conceptually simple $3.1462$-competitive
algorithm for the matroid secretary problem that maintains a random reference set. Given this reference set, the elements in its maximum-weight independent subset have been accepted independently with a time-dependent probability. We extend this approach by replacing the single reference set with a finite hierarchy of nested reference sets. For every fixed $\eps>0$, the resulting
algorithm is $(e+\eps)$-probability-competitive for arbitrary matroids, with $O_\eps(n^2)$ queries in the worst case.
Read the full abstract ▾
Cryptography and Security
Who Is Your Agent Serving? Provider-Side Indirect Prompt Injection in Proactive Agents
Rui Wang, Chao Wang, Xinchen Wang et al. (+3 more)
Proactive personal
agents increasingly decide what to recommend, how to personalize advice, and what follow-up assistance to offer, creating a new user-decision attack surface for provider-side indirect
prompt injection. We show that an external provider need not access private user context, compromise the
agent, or gain additional permissions: by controlling only content associated with its own target, it can redirect an otherwise benign
agent to advance that target, recruit legitimately available user context to justify it, and proactively reduce the friction of adoption. We characterize this failure mode through Target Control, Private Binding, and Prospective Support, which respectively steer what the
agent advances, how it connects the target to the user, and what target-specific assistance it offers next. Across three proactive-
agent environments and six simulated user models, the full attack increases target authorization in all tested environment-user-model combinations, with a macro gain of up to 77.4 percentage points. Controlled replay shows that correct user-target binding is more consequential than additional proposal detail alone, while a multi-turn extension reveals that provider objectives can remain influential even without final authorization by reshaping how the
agent responds to user constraints and resistance. These findings expose a broader trust boundary: capabilities designed to serve the user can be redirected toward objectives originating outside the user-
agent relationship.
Read the full abstract ▾
All authors Rui Wang, Chao Wang, Xinchen Wang, Yufeng Zheng, Binbin Liu, Yaofei Wang
Machine Learning
Loopy: Low-Bit Quantization Framework for Looped Language Models
Zeyu Li, Yipu Zhang, Jintao Cheng et al. (+2 more)
Looped
language models provide a parameter-efficient way to scale iterative test-time computation by repeatedly executing a shared recurrent core. Post-training quantization (PTQ) can reduce the
memory footprint and inference cost of looped
language models, but errors introduced by a quantized shared core affect subsequent cores. Among PTQ methods, channel scaling and orthogonal rotations preserve the floating-point computation while producing representations with different quantization quality. We find that quantization configuration candidate rankings can change with recurrent depth, motivating configuration selection at the target deployment depth. However, evaluating every candidate over the full calibration set at this depth is costly. We therefore propose Loopy, a PTQ framework that formulates shared-core quantization through a recurrent-depth-aware objective, selecting shared low-bit representations by their final prediction loss at the target deployment depth. Channel scaling and orthogonal rotations parameterize the candidate representations. To approximately solve this selection problem efficiently, Loopy progressively allocates calibration windows to promising candidates while preserving complete target-depth execution, using only forward evaluations. Across eight settings, Loopy achieves the state-of-the-art results among different baselines. On Ouro-1.4B under W4A4, Loopy reduces LAMBADA perplexity by 36.5% relative to SpinQuant. Our code is available at https://github.com/Shameless0817/Loopy-review.git.
Read the full abstract ▾
All authors Zeyu Li, Yipu Zhang, Jintao Cheng, Xin Li, Wei Zhnag
Sound
Task-Aware Joint Pruning and Distillation for Efficient Audio Deepfake Detection
Miao He, Peng Cheng, Zhongjie Ba et al. (+4 more)
Advances in
speech synthesis have made deepfake
speeches increasingly convincing, posing growing threats to security. While self-supervised learning (SSL) based detectors achieve state-of-the-art performance, their computational demands (typically 300M+ parameters) prevent deployment on resource-constrained devices. Existing compression methods, designed mainly for content-centric tasks, struggle to maintain competitive performance when directly adapted to deepfake detection. We propose a Task-Aware Joint
Pruning and
Distillation framework that combines cross-domain knowledge
distillation with movement-guided structured
pruning to transfer forgery-discriminative knowledge and preserve critical structures under aggressive compression. Our framework reduces the model to 31.9M parameters with 6.3$\times$ FLOPs reduction, with an average performance drop of only 1.30% across multiple
datasets compared to the uncompressed baseline, demonstrating strong potential for on-device deployment.
Read the full abstract ▾
All authors Miao He, Peng Cheng, Zhongjie Ba, Qing Wen, Li Lu, Xin Yang, Kui Ren
Logic in Computer Science
A Logic for Minor-Free Graph Classes: Model Checking, Dependence, and Combinatorial Reconfiguration
Nikolas Mählmann, Patrice {Ossona de Mendez}, Nicole Schirrmacher et al. (+4 more)
We introduce sub-connectivity logic, denoted by $\textsf{FOscon}$, an extension of first-order logic for graphs with a specified set $Z$ of admissible edges. Its additional atom $\textsf{scon}(x,y;\bar z)$ asserts that $x$ and $y$ are connected by a path using only edges of $Z$ and avoiding the vertices in $\bar z$. Our main structural result states that, for every weakly sparse graph class $\mathscr C$, the class of all expansions of graphs in $\mathscr C$ by rooted spanning forest orders has bounded twin-width if and only if $\mathscr C$ excludes a fixed minor. When $\mathscr C$ excludes a fixed minor, we can also compute an additional linear order that preserves bounded twin-width in
polynomial time. We translate $\textsf{FOscon}$ into first-order logic over an expansion by a depth-first spanning forest order of the admissible-edge graph. Combining this translation with our structural result, we obtain a model checking
algorithm for $\textsf{FOscon}$ with running time $f(|\phi|,h(G))\cdot |G|^c$, where $f$ is computable, $c$ is an absolute constant, and $h(G)$ is the Hadwiger number of $G$. The translation and model-checking
algorithm extend to binary relational structures, with the Hadwiger number measured on the Gaifman graph. For a graph class $\mathscr C$ let $\mathscr C_Z:=\{(G,Z):G\in\mathscr C,\ Z\subseteq E(G)\}$. We show that for every weakly sparse graph class $\mathscr C$, the class $\mathscr C_Z$ is monadically dependent for $\textsf{FOscon}$ if and only if $\mathscr C$ excludes a fixed minor. For monotone classes that admit efficient minor encodings, a corresponding hardness result makes this frontier computationally tight. We give applications to combinatorial reconfiguration and solution discovery problems, and study a guarded extension of $\textsf{FOscon}$ motivated by database queries.
Read the full abstract ▾
All authors Nikolas Mählmann, Patrice {Ossona de Mendez}, Nicole Schirrmacher, Sebastian Siebertz, Giannos Stamoulis, {Dimitrios M.} Thilikos, Alexandre Vigny
Information Theory
Limits of Weight-Bounded Redundant Checks for Peeling Decoding of Random Regular LDPC Codes
Jianshen Zhu
We study peeling decoding on the binary erasure channel when redundant parity checks may be added without restriction in number, but each check is subject to a row-weight constraint. For random simple binary regular low-density parity-check (LDPC) codes, we consider the parity-check
matrix formed by all nonzero dual codewords up to a prescribed weight. We show that, throughout an explicit logarithmic row-weight range, this complete family of redundant checks has the same asymptotic normalized-residual threshold as peeling with the original LDPC
matrix. In the same range, with probability tending to one over the random code, it still has a linear-size stopping set that is uniquely decodable. Consequently, every parity-check representation whose peeling decoder succeeds on every uniquely decodable erasure pattern must contain checks above this logarithmic scale, and the supports of those heavier checks must collectively contain a linear number of coordinates. Thus row weight remains an asymptotic limitation even when the number and selection of redundant checks are unrestricted.
Read the full abstract ▾
Robotics
Safe Ergodic Control for Multi-Robot Systems via Quadratic Programming
Yo Toyomoto, Mohamed Elobaid, Bryce L. Ferguson et al. (+2 more)
Ergodic control drives
robots to spend time in each region in proportion to a spatial distribution of interest, making it well suited for dense spatiotemporal environmental monitoring. Existing safe ergodic controllers rely on offline trajectory optimization or hierarchical architectures, which limit real-time applicability and decouple the ergodicity objective from the safety constraint. This paper presents a quadratic programming (QP)-based controller that treats ergodicity and safety jointly. We first introduce a Gaussian-kernel ergodic metric that, unlike the classical indicator-based metric, is time differentiable. This allows the exponential decay of the metric to be imposed as a time-varying control barrier function (CBF) constraint, relaxed by a slack variable, alongside hard CBF constraints for region containment and inter-
robot collision avoidance. We establish that the resulting QP remains feasible from any safe initial configuration and that, up to the slack term, the ergodic metric decays exponentially. Simulations show improved performance over baseline methods, and experiments with three aerial vehicles validate the work on real hardware.
Read the full abstract ▾
All authors Yo Toyomoto, Mohamed Elobaid, Bryce L. Ferguson, Alberto Quattrini Li, Shinkyu Park
Artificial Intelligence
Learning from imperfect teachers for low-resource acoustic generalization
Shuanglin Li, Ruxiao Qian, Jian Liu et al. (+3 more)
Knowledge
distillation (KD) improves low-resource acoustic learning by enriching one-hot supervision with the softened predictive distribution of a fixed teacher network. However, a teacher trained with limited or imbalanced annotations may produce a biased distribution whose components are not uniformly reliable. Although this distribution can still encode useful knowledge, direct full-distribution matching may also transfer teacher-induced biases, thereby distorting the student's decision boundary and degrading its generalization performance. To address this limitation, we propose Boundary-Anchored Mass-Partitioned
Distillation (BA-MPD), a logit-based
distillation objective composed of Boundary-Anchored Correction (BAC) and Mass-Partitioned
Distillation (MPD). BAC addresses missing ground-truth labels in the set of the teacher's top predictions by swapping the true label for the lowest-ranked entry of the set, thus keeping the mass and uncertainty of the set unchanged. MPD then distills this corrected distribution through separate losses that enforce relational consistency within the set, balance the mass between high- and low-confidence groups, and weight lower-confidence dependencies. Ultimately, BAC and MPD together suppress harmful ranking errors and noisy low-confidence details, while retaining all useful teacher information. Experiments on two acoustic
benchmarks under multiple label budgets show that BA-MPD consistently improves over supervised-learning baselines and vanilla KD while remaining competitive with strong logit-based KD baselines. Cross-budget results further show that BA-MPD remains effective when the teacher and student models use mismatched label budgets, demonstrating its ability to exploit imperfect teachers across supervision gaps. Implementation available at https://github.com/ShuanglinLi/BA-MPD.
Read the full abstract ▾
All authors Shuanglin Li, Ruxiao Qian, Jian Liu, Haijun Lin, Wenwu Wang, Siyang Song
Computer Vision
Hybrid-Basis Feature Forecasting for Diffusion Sampling Acceleration
Kai-Liang Cheng, Yuan-Yuan Cheng, Yu-fan Jin et al. (+1 more)
We propose Hybrid-Basis Feature
Forecasting (HybridFF), a training-free, plug-and-play framework for accelerating
diffusion sampling. To capture local smoothness, long-range trends, and complex non-monotonic variations when modeling feature
evolution, HybridFF first estimates coefficients using moving least squares (MLS) for each of multiple complementary basis families and then combines the corresponding predictors using fusion weights. In addition to the choice of basis functions, the fusion weights also play a critical role. We introduce two strategies to balance quality and speedup. HybridFF (Fixed) prioritizes efficiency with model-specific fusion weights calibrated on a small set and held constant during inference. HybridFF (Adaptive) updates the fusion weights online using branch reliability scores computed from an exponential moving average of full-step prediction errors, improving prediction fidelity and generation quality under aggressive caching while retaining substantial acceleration. Experiments across DiT-XL/2, FLUX.1-dev, SD3.5-Large, and HunyuanVideo demonstrate a favorable speedup–quality trade-off over representative single-basis forecasters and caching baselines.
Read the full abstract ▾
All authors Kai-Liang Cheng, Yuan-Yuan Cheng, Yu-fan Jin, Xiao-Ming Fu
Computer Vision
Yunyi Chen, Chenru Wang, Xinyi Ye et al. (+2 more)
Diffusion-based
dataset distillation (DD) suffers from a fundamental objective mismatch: likelihood-driven
diffusion models prioritize density approximation over the discriminative decision boundaries required for downstream tasks. Beyond semantic mismatch, relying solely on density also leads to
geometric coverage loss, where generated samples collapse into a few high-density modes and fail to cover the
manifold's structural diversity. We propose
Manifold-Guided Policy Optimization (MGPO), which reformulates DD as a multi-objective reinforcement learning problem and achieves Dual-Space
Alignment via a pixel-space discriminative reward and a latent-space
geometric reward guided by a class-wise Minimum Spanning Tree (MST). The discriminative reward enforces class separability, while the MST-based
geometric reward encourages generated latents to cover a sparse
geometric skeleton of each class, jointly addressing both failure modes. We further provide an idealized analysis that motivates the MST-based reward, including a Hausdorff approximation bound and a subsampling bound independent of the
dataset size. The reward-modular design extends to structured tasks such as object detection and
segmentation by substituting the frozen task reward model. Extensive experiments show MGPO consistently outperforms existing methods, including a +8.0% mIoU gain on
segmentation under low-budget settings.
Read the full abstract ▾
All authors Yunyi Chen, Chenru Wang, Xinyi Ye, Zexin Zheng, Chi Zhang
Machine Learning
Kolmogorov-Arnold Networks for Personal Context Recognition on ExtraSensory
Hoang-Thang Ta
Kolmogorov–Arnold Networks (KANs) have attracted increasing
attention in recent years, with applications across a wide range of AI tasks. In this paper, we evaluate several KAN variants on the ExtraSensory
dataset for personal context recognition and compare them with a multilayer perceptron (MLP) and TabM. We conduct the main experiments using five user folds and three random seeds per fold and report the average Macro-F1, Micro-F1, and training time. We also perform shallow ablation studies on grid size, the number of grids, and data normalization to examine their effects on KAN performance. The results show that all evaluated KAN variants significantly outperform MLP in terms of Macro-F1 and Micro-F1 and achieve performance comparable to TabM. However, KAN variants generally require more training time, while TabM provides a more favorable balance between predictive performance and training efficiency. These results suggest that KANs are promising for personal context recognition, while their computational efficiency remains an important challenge. Our source code is publicly available at: https://github.com/hoangthangta/ExtraSensory-KANs.
Read the full abstract ▾
Computer Vision
ArticuTable: Generating Instance-Level Interactive Rigid-Articulated 3D Tabletop Scenes from a Single Image
Kai Lv, Yibo Yin, Lijun Guo et al. (+3 more)
Embodied agents benefit from 3D environments that combine visual fidelity to real-world observations with physical interactivity. Existing single-image tabletop reconstruction methods recover plausible scene geometry but typically represent objects as monolithic rigid bodies, limiting interaction to whole-object rigid motion and precluding executable part-level articulation. Meanwhile, recovering a scene layout consistent with the input view remains challenging because a single observation may admit multiple plausible pose-scale configurations. We present ArticuTable, a single-image 3D tabletop reconstruction framework that recovers both executable part-level articulation and an input-view-consistent scene layout. For object modeling, we introduce generation-robust articulation modeling (GRAM), which combines joint fitting guided by a
multimodal large
language model with semantic state
reasoning to recover reliable joint parameters and valid motion ranges from imperfect monolithic proxy meshes, thereby converting them into executable articulated assets. For scene layout, we introduce progressive semantic-
geometric scene registration (PSGSR), which progressively narrows the pose-scale search space under complementary metric, planar, and input-view constraints and resolves orientation ambiguity through structure-aware semantic correspondences, yielding a scene layout consistent with the input view. We further contribute ArticuTable-100, a curated collection of 100 simulation-ready tabletop scenes. Extensive evaluation, including a user study, demonstrates strong performance across visual fidelity, input-view consistency, articulation quality, physical plausibility, and simulation readiness.
Read the full abstract ▾
All authors Kai Lv, Yibo Yin, Lijun Guo, Heng Fan, Kaihao Zhang, Xingping Dong
Multiagent Systems
templar: agentic induction and evolution of standardized radiology reporting templates from large-scale clinical corpora
Xiaotian Hu, Mingxuan Liu, Zhonghan Wang et al. (+9 more)
Structured radiology reporting mitigates the heterogeneity of free-text reports, yet its benefits depend on high-quality reporting templates. In practice, such templates are conventionally built through labor-intensive expert consensus and therefore vary across institutions and lag behind evolving clinical practice. Large
language models (LLMs) enable automated template induction, but existing approaches remain limited: single-LLM induction is constrained by context length, and the corpus-scale method ASTAR produces a static, closed-corpus template without external grounding or downstream adaptation. To address these limitations, we propose TEMPLAR, a TEMPLate-centric Agentic framework for inducing and evolving standardized Radiology reporting templates from large-scale clinical corpora. TEMPLAR treats the template as a persistent central state maintained alongside two provenance-aware knowledge graphs, namely an anatomical graph that constrains template construction and a diagnostic graph that supports finding-to-diagnosis
reasoning. Three
agents operate on this state. The Induction
Agent derives canonical clinical slots from anatomy-constrained Span-Triple atoms via dual-view similarity clustering; the
Evolution Agent then assembles these slots into a hierarchical template and revises it under consistency constraints, external clinical evidence, and downstream structuring feedback; and the Clinical
Agent applies the evolved template to report structuring, reconstruction, and diagnostic
reasoning. Across four
datasets, TEMPLAR outperforms ASTAR, three medical LLMs, and six general-purpose LLMs in coverage, information fidelity, and diagnostic fidelity, while achieving the highest or tied-highest LLM-rated template quality. Its fidelity advantages over ASTAR persist under cross-
dataset transfer, and cumulative ablations support complementary contributions of its key components.
Read the full abstract ▾
All authors Xiaotian Hu, Mingxuan Liu, Zhonghan Wang, Xinfeng Zhang, Yiming Huang, Ziang Wang, Kasidit Anmahaepong, Yijin Li, Yifei Chen, Hongjia Yang, Zihan Li, Qiyuan Tian
Cryptography and Security
What a Policy Gate Can and Cannot Know: Measured Boundaries of Cross-Platform Command Adjudication
Qishuai Jing
Gateways that adjudicate an
agent's actions before they execute are only as good as their understanding of the action. We study a policy gate that never parses shell syntax: it consumes a typed, realised action (verb, operands, resolved zones, program-object identity) and decides ALLOW, ASK or DENY. Working on a Linux twin of the Windows
benchmark of our previous study [1], we ask how faithfully it adjudicates, what survives translation, and whether deciding stays affordable as the system is used. A frozen 61-case table scores 61/61 in two rounds with no false allow; a 50-operator mutation campaign kills 46 of 50 mutants (92.0%), with all four surviving mutants classified. An 82-row audit yields 43 re-expressions, 21 carrier differences, 16 study-specific inapplicable rows and two unresolved cases; of 25 rows labelled "no counterpart," four remain unmapped here. Consulted by the executor, 25 escapes become zero with no benign payload blocked. In an exploratory one-gateway snapshot, three unauthenticated endpoint labels produce case-level non-refusal majorities of 81.8-98.0%, but immediate-execution majorities of 4.0-52.5%. Adjudication reads no accumulating state; credential verification does, scanning its whole ledger. We report that cost and the fix we would make.
Read the full abstract ▾
Software Engineering
StateWise: Diagnosing and Repairing Persistent Operational State Before Agent Actions
Yongyuan Peng, Zhou Feng, Tongying Wu et al. (+5 more)
LLM
agents combine
reasoning, tool use, and persistent
memory to support work across tasks by reusing stored operational records as premises for later actions. However, environmental or requirement changes can invalidate these records, while existing action review, provenance tracking, and clarification mechanisms may leave the underlying persistent state uncorrected. Our audit of coding-
agent trajectories identifies candidate failure chains in which invalid records are reused, leading to task failures and unsafe modifications. We propose StateWise, a framework for diagnosing and repairing persistent operational state before action execution. StateWise uses record-level counterfactual replanning to identify decision-critical records, then establishes their current validity through reliability checks, read-only verification of machine-observable facts, and targeted clarification of developer-owned intent. Typed evidence grounding binds evidence to specific records and scopes, enabling persistent corrections with repair lineage. The
agent then replans from the repaired state, followed by an independent state-action check before execution. We evaluate StateWise on 150 executable coding-
agent cases across diverse runtime environments, workspace configurations, and repository settings, complemented by cross-model evaluations. Under corrupted persistent state, StateWise achieves 93.3% overall correctness, compared with 38.7% for the baseline
agent, with no unsafe actions. Component ablations, multi-task experiments, and transfer evaluations further demonstrate effective recovery, persistent corrections, and transferability across repositories and tool interfaces.
Read the full abstract ▾
All authors Yongyuan Peng, Zhou Feng, Tongying Wu, Jiahao Chen, Yuan Su, Chunyi Zhou, Tianyu Du, Shouling Ji
Machine Learning
Pythia: Toward Foundation World Models for Multimodal Time Series
Xilin Dai, Hongzhou Chen, Yifan Hu et al. (+3 more)
Time-series foundation models offer a unified approach to
forecasting across heterogeneous domains. Textual context and auxiliary observations provide complementary information about temporal dynamics, yet reusable
multimodal predictive representations remain underexplored. We introduce Pythia, a foundation world model that learns context-conditioned latent dynamics across
datasets through a joint-
embedding predictive architecture. A stop-gradient numerical reference guides contextual corrections to predicted future states. A separate probabilistic decoder then adapts to the frozen predictive representation and observed history, decoupling world-model pretraining from observation-space
forecasting. On MUSE, Pythia-Tiny's normalized mean absolute scaled error (MASE) and weighted sum
quantile loss (WSQL) are 0.6879 and 0.4269, reducing errors by 6.26% and 5.00% relative to the strongest model evaluated in the published MUSE leaderboard. Through a series of controlled experiments, we investigate how to design a time-series world model through shared pretraining and how joint-
embedding predictive learning can incorporate
multimodal information. The results support separating predictive representation learning from probabilistic readout and show complementary contributions from entity descriptions, events, and
covariates.
Read the full abstract ▾
All authors Xilin Dai, Hongzhou Chen, Yifan Hu, Yiding Liu, Zewei Dong, Jiang-Ming Yang
Computers and Society
The Law of DeepSeek
Jyh-An Lee and Xuan Sun
Amid the intensifying competition in artificial intelligence between the United States and China, the emergence of the DeepSeek-R1 model has sent significant ripples through the technology sector, capital
markets, and policy circles. This Article offers a comprehensive analysis of legal and policy landscape surrounding DeepSeek, drawing upon its key technical features-including reinforcement learning, mixture-experts architecture, multi-head latent
attention mechanism, knowledge
distillation, and open-source approach. Firstly, DeepSeek's success raises crtitical questions about the efficacy of the United States' increasingly robust export control measures on chips and
semiconductors, components essential for training large
language models. Secondly, akin to Chinese technology
giants such as TikTok and Huawei, DeepSeek is confronted with information security scrutiny in the United States and other jurisdictions. Thirdly, despite its domain-specific capabilities and competitive API pricing, DeepSeek has faced criticism for producing outputs laden with political and ideological biases, igniting debates over free
speech and censorship. Further more, this Article delves into intellectual property and contractual concerns stemming from the knowledge
distillation technique employed by DeepSeek. Ultimately, it concludes that geopolitical consideration will continue to exert a profound influence on the legal challenges and prospective solutions related to the DeepSeek models.
Read the full abstract ▾
Game Theory
Settling the Computational Complexity of Max-Min Allocation with Ternary Valuations
Thi Ngoc Anh Vu, Trung Thanh Nguyen, Khaled Elbassioni et al. (+1 more)
We study the problem of computing an allocation of indivisible items that maximizes egalitarian welfare, i.e., the utility of the worst-off
agent, when
agents' item values or marginal values belong to a small set. For additive valuations with values in $\{p,q\}$, where $q>p>0$ and $\gcd(p,q)=1$, we give a
polynomial-time
algorithm when $p=2$ and prove constant-gap hardness when $p\geq3$, already with exactly three high-valued goods per
agent. We also give an $\sqrt{3/2}$-approximation for common positive bi-valued additive valuations. For mixed additive valuations in $\{-p,0,c\}$, where $p\in\{1,2\}$ and $c$ is a positive integer, a reduction to maximum-weight perfect matching resolves the conjectured tractability of $\{-2,0,c\}$-valuations. For submodular valuations with marginals in $\{-2,0,c\}$, where $c$ is odd, we establish an exact unit-gap hardness result and exponential value-query lower bounds, even when all but one
agent are additive. Finally, for $\{-1,0,1\}$-submodular valuations, we prove that no finite multiplicative approximation exists unless $\p=\np$. Together, our results resolve open questions and provide a complete picture of the computational complexity of max-min allocation with ternary valuations.
Read the full abstract ▾
All authors Thi Ngoc Anh Vu, Trung Thanh Nguyen, Khaled Elbassioni, Jörg Rothe
Machine Learning
EMG-GPT: Predictive Pretraining on Residual-Quantized EMG Tokens for Hand Pose Estimation
Ettore Magni, Rolandos Alexandros Potamias, Stefanos Zafeiriou et al. (+1 more)
Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine whether self-supervised pretraining on sEMG can yield transferable representations for continuous hand-pose estimation. We introduce EMG-GPT, a causal
transformer-based model that operates on discrete sEMG representations from a frozen residual vector quantization (RVQ) tokenizer and learns temporal dynamics through depth-autoregressive future-code prediction. The model combines within-frame integration with causal temporal modeling while preserving the geometry of the pretrained codebook. EMG-GPT shows competitive results in both Regression and Tracking tasks, supporting EMG-only pretraining as a viable approach for learning transferable sEMG representations.
Read the full abstract ▾
All authors Ettore Magni, Rolandos Alexandros Potamias, Stefanos Zafeiriou, Konstantinos Barmpas
Computer Vision
PixReenact: Pixel-Conditioned Causal Video Diffusion for Streaming Head-Avatar Reenactment
Gavriel Habib, Dvir Samuel, Or Shimshi et al. (+1 more)
Streaming head-avatar reenactment aims to animate a reference image according to a live driving
video, requiring robust motion transfer, long-term identity stability, and low latency. Existing methods often rely on specialized identity or motion representations, which can discard useful visual information and inherit failure modes from external extractors. In addition, many recent
diffusion-based reenactment methods use offline, clip-based generation, jointly processing and denoising an entire
video clip before producing its output, making continuous low-latency streaming difficult. We introduce PixReenact, a pixel-conditioned streaming reenactment framework built on causal
video diffusion. PixReenact conditions directly on VAE-encoded reference and driving frames, without specialized identity or motion representations. To separate reference identity from driver motion, we train with cross-identity pseudo supervision together with corrective objectives anchored to the original reference and driving inputs. Long self-rollouts reduce autoregressive drift, while state-aware dual-teacher
distillation separately addresses cold-start and steady-state generation. Across three cross-identity
benchmarks and a long-horizon streaming
benchmark, PixReenact demonstrates robust cross-identity reenactment, particularly under challenging conditions such as extreme viewpoints, occlusions, and pronounced facial expressions, while maintaining the reference identity over long streams. A 4-NFE rolling student continuously emits four frames per update with a mean emission latency of 239 ms.
Read the full abstract ▾
All authors Gavriel Habib, Dvir Samuel, Or Shimshi, Rami Ben-Ari
Numerical Analysis
On finite elements for geometrically-exact planar beams with hyperelastic material models
Abhishek Ghosh, Chennakesava Kadapa, Djordje Peric et al. (+1 more)
A geometrically-exact planar beam finite-element framework is developed for the analysis of beams with hyperelastic constitutive models. A conventional three-field beam model is first formulated with full geometrical nonlinearity and a linear elastic material law. The kinematics are then enriched by an additional global field that permits deformation through the in-plane thickness. The out-of-plane direction is incorporated separately through plane-strain and plane-stress constitutive reductions, allowing the cross-sectional deformation assumptions and constitutive behaviour to be formulated independently. Numerical results for the present
benchmarks show that the plane-stress condition gives essentially coincident centreline deformations for the Poisson's ratio values considered. In contrast, the plane-strain condition, which is widely employed in the literature, makes the formulation sensitive to Poisson's ratio coupling, with the beam becoming significantly stiffer at higher Poisson's ratios. Comparisons between Saint Venant-Kirchhoff and Neo-Hookean hyperelastic models further demonstrate close agreement in the global deformation and in-plane thickness response, with small constitutive differences becoming more apparent in the local out-of-plane stretch. The proposed approach offers a simple and consistent framework to integrate hyperelastic material models into planar geometrically-exact beam models.
Read the full abstract ▾
All authors Abhishek Ghosh, Chennakesava Kadapa, Djordje Peric, Mokarram Hossain