Computer Vision
Do Emotion Concepts Generalize Across Sources, Modalities, and Architectures in Vision-Language Models?
Bohao Xing, Xin Liu, Kaishen Yuan et al. (+5 more)
Recent studies suggest that large
language models encode
emotion concepts as structured internal representations, but most existing work focuses on text and a single architecture. Therefore, we ask, do
emotion concepts generalize across sources, modalities, and architectures in
vision–
language models (VLMs)? To address this, we construct CMES (Cross-Modal
Emotion Stimuli), a multi-source collection of
emotion-conditioned stories, real facial expressions, synthetic portraits, and synthetic
emotion-evoking scenes. For each stimulus source, we extract a separate set of six Ekman
emotion vectors from each of three VLMs. We report four main findings as follows: 1) Image-derived
emotion vectors form a low-dimensional geometry similar to that of text-derived vectors. Valence is relatively stable across sources, while arousal varies more. 2) Text- and image-derived
emotion vectors have modest cosine similarity but still show held-out cross-modal correspondence. Text-derived vectors can also steer image interpretation. 3) Cross-architecture correspondence remains even when native cosine is near zero. Transformations estimated from generic ImageNet activations recover both correspondence and causal transfer without using the six
emotion vectors or their labels. 4) After aligning representations across architectures, we construct a shared
emotion subspace that preserves affective geometry and selective steering effects. The corresponding consensus
emotion vectors also generalize to a held-out fourth architecture at two model sizes. These results suggest that
emotion representations can share relational structure and causal effects across sources, modalities, and architectures, even when individual vector directions differ.
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All authors Bohao Xing, Xin Liu, Kaishen Yuan, Deng Li, Rong Gao, Guoying Zhao, Xiaolan Fu, Heikki Kälviäinen
Machine Learning
Predictive Dual Smoothing for Column Generation
Senne Berden, Noah Schutte, Andrea Lodi et al. (+1 more)
Solving large-scale linear programs efficiently is an important challenge in many optimization settings. A key technique is column generation, which alternates between solving the master problem over a restricted subset of the variables, and using a pricing subproblem to identify new variables to add. The pricing subproblem is guided by the dual solution of the current restricted master problem, but oscillations in these dual solutions can substantially slow convergence. Dual stabilization methods address this issue. Dual smoothing is a common stabilization method, which guides the pricing subproblem using a combination of the current dual solution and duals from previous iterations. However, while past dual solutions can stabilize the dual trajectory, they do not necessarily guide pricing towards useful new variables. We therefore introduce predictive dual smoothing, which instead combines the current dual solution with a learned prediction of future duals to steer pricing towards variables that are more useful in subsequent iterations. The predictor is trained offline using supervision extracted from standard column generation trajectories and is used only to modify the pricing subproblem's objective function, while exact reduced-cost checks and fallback pricing with the unsmoothed duals preserve correctness. Experiments on cutting stock and generalized assignment problems show that predictive dual smoothing substantially reduces generated columns and wall-clock time relative to standard column generation and existing classical and learned stabilization methods. These gains extend to out-of-distribution instance sizes, and predictive smoothing provides further improvements when combined with strong classical stabilization.
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All authors Senne Berden, Noah Schutte, Andrea Lodi, Tias Guns
Computation and Language
Beyond Token Alignment: Event Completion for Cross-Tokenizer On-Policy Distillation
Jiacheng Liu, Jingwei Song, Qituan Zhang et al. (+2 more)
On-policy
distillation (OPD) transfers knowledge between
language models through teacher supervision on student-generated trajectories. With different tokenizers, a single teacher
token may require multiple student
tokens to generate, creating intermediate states where the event is entered but not yet completed. Existing cross-tokenizer methods align
tokens or text spans to construct comparable prediction targets. We study a complementary problem after partial generation: once the student produces a prefix of a teacher
token, multiple next
tokens may complete the same remaining bytes, but the teacher only specifies the required completion rather than how probability should be divided among these valid continuations. We introduce Event-Set Completion
Distillation (ESCD), which complements cross-tokenizer probability
alignment with completion-set supervision. ESCD aggregates prefix-related teacher events and supervises the total probability of byte-compatible one-step student completions, avoiding tokenizer-dependent probability splits among individual
tokens. The method reuses student trajectories and predictions, requiring neither additional rollouts nor changes to the student vocabulary. Experiments demonstrate consistent gains in mathematics, code, and scientific
reasoning across model families and tokenizers, extending to large-scale MoE
distillation from a 1T teacher to a 35B student. Local analyses show that retaining completion sets better matches the reference supervision, while one-step completion covers over 99% of observed compatible teacher mass after partial event entry in the studied tokenizer pairs. These findings support event entry and event completion as complementary supervision targets for cross-tokenizer knowledge transfer. Code will be released on GitHub.
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All authors Jiacheng Liu, Jingwei Song, Qituan Zhang, Siheng Chen, Linfeng Zhang
Artificial Intelligence
SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing
Vasilis Perifanis, Nikolaos Pavlidis and Symeon Symeonidis
Large
language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly from query
embeddings, model representations, preference data, or clusters of similar examples. Such approaches can be effective, yet the representation used for routing rarely states what a query actually requires. We introduce SeLMRoute, a routing framework that separates the extraction of candidate-independent semantic evidence from the learning of candidate performance and the application of deployment objectives. A decision model first evaluates a set of interpretable questions about the query, such as its
reasoning requirements and use of external knowledge, with each judgment retained as a probability distribution. The resulting probabilistic semantic state is used by a lightweight supervised router to estimate candidate model performance. Routing objectives are applied after performance estimation, which allows the same semantic state to support performance-oriented and cost-aware decisions. On the LLMRouterBench (15
datasets, 20 candidate models, 11,481 queries), SeLMRoute achieves an average accuracy of $72.08\% \pm 0.45$, while grouped five-fold out-of-fold evaluation reaches $72.64\%$, compared with $69.23\%$ for the strongest fixed candidate. The representation achieves the highest mean performance among the evaluated semantic, dense, lexical, and domain-level representations. In a separate 13-model performance-cost setting, SeLMRoute improves performance in all five grouped splits, with a mean PerfGain of $2.66\%$. Our code is available at https://github.com/Indigma-Innovations/SeLMRoute.
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Artificial Intelligence
From Human Narrative to Harmonic Structure: A Human-Centered Investigation of Algorithmic Music Generation through the Chord Wheel Diagram
Josef Pavlíček, Petra Pavlíčková and Irena Štrausová
Contemporary AI-based music generation can produce compositions that satisfy formal requirements of tonality and musical coherence. However, whether musical expression can be described by mathematical properties alone remains a fundamental question. Human composers operate within personal and cultural contexts that influence harmonic decisions and deliberate departures from established patterns. This study investigates six narrative-driven popular songs by Bob Dylan, Johnny Cash, and Ritchie Valens. Original human harmonies are compared with outputs of an explainable computational harmonizer operating on the same melodies without access to the original chord progressions. We examine harmonic vocabulary, functional persistence, repetition, non-diatonic events, and tension-resolution patterns using Chord Wheel Diagrams and BPMN-based representations. Results show that high melody-chord compatibility does not necessarily imply preservation of the original human harmonic decision pattern. Some generated harmonizations retain the economical structure of the reference, while others alter harmonic diversity or suppress distinctive events while remaining compatible with the melody. Rather than quantifying artistic quality, the study introduces narrative-conditioned harmonic structure as a complementary perspective for computational music analysis. The findings suggest that generative systems may benefit from modeling not only harmonic correctness, but also structural identity, context, and human compositional intention.
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Computer Vision
SurgGMF: Fully Causal Gaussian Motion Forecasting for Anticipatory Surgical Scene Rendering
Jingqian Sun and Yichao Tang
Dynamic
surgical scene modeling is essential for robotic perception, simulation, and decision support. Although existing neural rendering methods enable efficient reconstruction and rendering of deformable
surgical scenes, they remain primarily focused on observed-frame reconstruction rather than
forecasting future scene states. To this end, we present SurgGMF, a fully causal Gaussian motion
forecasting framework for anticipatory
surgical scene rendering. Rather than predicting future RGB images directly, SurgGMF forecasts future Gaussian motion states represented by position, scale, and rotation residuals (X/S/R) from historical Gaussian motion fields. To prevent target leakage, we introduce a full-causal-last rendering protocol, where future Gaussian states are rendered without accessing target-frame Gaussian attributes while preserving causal appearance propagation. We evaluate SurgGMF on 12 EndoNeRF and StereoMIS
video slices using neural temporal learners and classical dynamics baselines under a unified
forecasting protocol. Learned Gaussian motion
forecasting consistently outperforms classical dynamics baselines in render space, demonstrating gains beyond hand-crafted state extrapolation. Latency analysis further reveals an accuracy–efficiency trade-off: under the current implementations, TKAN achieves the highest accuracy, whereas GRU and LSTM provide more favorable module-level latency profiles. These results establish SurgGMF as a reproducible framework for causal Gaussian motion
forecasting and advance
surgical Gaussian representations from retrospective reconstruction toward predictive scene modeling.
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Computer Vision
What Visual Generators Need from Teachers: Rethinking Representation Alignment
Yongcong Wang, Hingchin Chen, Mingyu Fan et al. (+6 more)
Representation
alignment speeds up
diffusion transformer training by pulling an intermediate block of the model (student) toward features of a frozen pretrained encoder (teacher). Which teacher layer to align, and for how long, is still set by convention, and each alternative costs a training run. We find that
alignment helps where the student cannot linearly recover the teacher's features, not where it already resembles them. Since a deep teacher layer is largely predictable from the one below, we isolate what each layer adds, its increment, and measure how much of it an unaligned student recovers. The student fills the teacher's hierarchy from the bottom up and stalls near the top, which we call hierarchy filling: even after 400K steps it recovers almost none of the deepest. The recoverability gap is the unrecovered share of an increment, read from one unaligned checkpoint. In short runs that each align one teacher layer at one block, the gap nearly reproduces their ranking by FID improvement, and CKA, a measure of feature similarity, largely reverses it. Representation
Alignment and Recoverability Estimation (RARE) picks the teacher layer with the largest gap before training. During training, it tracks each
token's remaining distance to that layer, the online counterpart of the gap, weights
tokens by it, and phases out the loss once the average distance stops falling. With SiT-B/2 on ImageNet $256\times256$, RARE reaches an FID of 18.02 without guidance and 4.46 with it, ahead of seven
alignment baselines including REPA, iREPA and HASTE. It also trains in 14% fewer GPU-hours than iREPA. Its FID stays below iREPA's across model scales, teachers,
datasets and backbones.
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All authors Yongcong Wang, Hingchin Chen, Mingyu Fan, Shuo Jiang, Teer Zhang, Yucong Sun, Zijia Wang, Yiming Lu, Chengchao Shen
Information Theory · Cross-listed from stat.ML
Information-Theoretic Analysis of Next-Token Prediction under Markovian Data
Masoud Kavian, Abdellatif Zaidi and Milad Sefidgaran
We develop an information-theoretic framework for generalization in next-
token prediction under temporally dependent data. We consider independent trajectories generated by finite-
memory Markov processes and distinguish algorithmic dependence, quantified by mutual information, from temporal dependence, characterized by mixing. For cross-
entropy loss, we derive an expected generalization bound using the Donsker–Varadhan variational representation and a McDiarmid-type concentration inequality for Markov chains. A refinement captures the joint effect of context length and temporal mixing through the mixing properties of the history-state process. We then extend the bound through a rate–distortion formulation, replacing mutual information with the minimum information rate required to represent the learned model within a prescribed distortion in the generalization gap, yielding informative guarantees for deterministic
algorithms over continuous hypothesis spaces. For margin-based prediction, we derive explicit bounds for linear and self-
attention next-
token predictors via noisy low-dimensional compression, revealing the roles of context length, model complexity, sample size, margin, and temporal mixing. Experiments on TinyStories and ETTh2 show that longer contexts can reduce both training and test losses, but typically reduce training loss more, enlarging the generalization gap. A complementary ETTh2 analysis identifies an effective predictive-
memory scale near 24 hours, with no statistically supported improvement beyond this scale, offering a plausible explanation for test-performance saturation at larger contexts.
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Robotics
Action Sequence Transfer via LLMs for Heterogeneous Environments
Choongho Chung, DongHwan Shin and Sung-Hee Lee
We present an action sequence transfer system that adaptively transfers user action sequences across different target spaces. Given an input action sequence from a source space and scene graph representations of both the source and target environments, our system predicts a corresponding action sequence in the target space by adapting to the spatial and object constraints of the new environment. To achieve this, we leverage multi-level representations of user activity to generalize actions at varying levels of abstraction. To demonstrate our system, we collect a new scene graph-based
dataset derived from the Ego4D GoalStep
dataset for evaluation. Results indicate that our system can generate valid action sequences even between spaces with drastically different object configurations.
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Machine Learning
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Chen Shao, Donald Loveland, Tobias Käfer et al. (+1 more)
Graph Neural Networks (GNNs) are effective for learning node and link
embeddings through permutation-equivariant aggregation. However, standard GNNs collapse automorphic nodes, i.e., those with identical structural roles (or orbits) into indistinguishable representations, leading to the node
automorphism problem. This collapse limits their expressive power and degrades link prediction performance. Existing approaches to characterize GNN expressiveness rely primarily on Weisfeiler-Lehman (WL) analyses, but these methods are typically qualitative and often misaligned with empirical results. To address this gap, we begin by introducing a novel quantitative framework to assess GNN expressiveness for link prediction. We first formalize edge-level
automorphism through edge orbits, which capture the set of structural role pairs for nodes that share a link. Then, we introduce the edge
automorphism ratio (EAR), a scalar metric that quantifies a GNN's ability to distinguish links in a given graph. We empirically demonstrate that EAR correlates strongly with performance, validating its practical benefit. Building on this insight, we design EDGE-ORBIT EQUIVARIANT GRAPH NEURAL NETWORK (EO-GNN), a GNN architecture that addresses
automorphism collapse while preserving equivariance and incurring minimal computational overhead. EO-GNN accomplishes this through two core designs combined with WL-based node hashes: (i)
automorphism-aware dropouts and (ii) subgraph orbit-biased aggregation. Empirical evaluations on synthetic and real graphs show improvements of up to 42.36% and 28.44%, respectively, in predicting links in scenarios with high
automorphism.
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All authors Chen Shao, Donald Loveland, Tobias Käfer, Danai Koutra
Information Theory
Robust Resource Management for SAGIN using DNN-Driven Channel Uncertainty Learning
Haonan Zhang, Weihua Wu, Qi Zhang et al. (+3 more)
This paper focuses on the joint robust beamforming and resource allocation for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise adjustment of beamforming and resource allocation, posing a major challenge to meeting heterogeneous users' strict quality of service (QoS) requirements. To address this challenge, we first formulate a chance-constrained optimization problem to minimize the total transmit power while satisfying QoS requirements under a predefined outage probability. By leveraging semidefinite relaxation (SDR), the objective function is transformed into a linear function of the traces of the beamforming
matrices. Then, we propose a deep neural network (DNN)-driven channel uncertainty learning to dynamically learn and model the uncertain CSI as an asymmetric uncertainty set. Under the constructed CSI uncertainty set, a robust counterpart method based on pre-trained network parameters is developed. It characterizes the uncertainty set as a finite union of convex sets, thereby providing a tractable approximation for the original chance constraints. Finally, we design an adaptive iterative
algorithm to jointly optimize the resource allocation and beamforming vectors. Simulation results show that our proposed DNN-driven method outperforms traditional robust and non-robust methods, achieving a superior energy efficiency and robust reliability in SAGIN.
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All authors Haonan Zhang, Weihua Wu, Qi Zhang, Runzi Liu, Zewei Jing, Weijia Han
Operating Systems
Dynamic Flow, Static Graph: KV Cache Reuse for Efficient LLM Serving on Mobile NPUs
Zhengxiang Huang, Shengheng Chen, Chaoyue Niu et al. (+6 more)
On-device large
language model (LLM) serving is a cornerstone of local-first personal intelligence, offering users data sovereignty, strong
privacy guarantees, and freedom from cloud API latency and cost. Although KV caching is widely used to reduce latency in long-context inference, existing designs were primarily optimized for cloud GPUs with dynamic execution environments and abundant
memory bandwidth. These architectural assumptions do not hold on mobile NPUs, where computation graphs must be statically compiled and both
memory capacity and I/O bandwidth are severely constrained. In this work, we present a compute-storage co-design for mobile-centric prefix and non-prefix KV reuse. We first propose an intra-graph mechanism that maps selective KV recomputation onto static NPU graphs, reconciling algorithmic dynamicity with NPU staticity. We further develop an inter-graph scheduler to optimize chunk merging and minimize padding with dynamic programming. To address mobile bandwidth limitations, we introduce a hierarchical KV manager featuring a tree-hash-semantic hybrid structure, along with cost-aware prefetching and eviction policies. We also build a two-dimensional pipeline that overlaps KV loading, rerotation, and storage with NPU execution, hiding data-movement latency. Experiments across representative on-device workloads and LLMs show that our design reduces time-to-first-
token (TTFT) by $40-60\%$ compared with no reuse and prefix-only caching.
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All authors Zhengxiang Huang, Shengheng Chen, Chaoyue Niu, Yujie Sun, Zhaode Wang, Zeyu Zhao, Chengfei Lv, Fan Wu, Guihai Chen
Robotics
DexWeave: Learning Dexterous Humanoid Loco-Manipulation from Human Demonstrations
Naichuan Sun, Haotian Shen, Yizhang Zhang et al. (+5 more)
Learning dexterous
humanoid loco-manipulation from human demonstrations requires transferring not only human motion, but also the coordinated interaction structure underlying the demonstrated behavior. This is challenging because embodiment differences distort the coupling among body motion, wrist placement, finger articulation, and object interaction, while kinematically accurate references may still be difficult to realize under
robot dynamics. We present DexWeave, a unified framework that connects interaction-consistent motion retargeting with anatomy-aware whole-body policy learning. DexWeave first employs a two-stage retargeting procedure that initializes body and hand motions with specialized solvers and subsequently performs coupled refinement over the upper-body interaction chain while preserving lower-body support. The resulting references are tracked by an anatomy-aware
Transformer policy that represents anatomical regions as structured
tokens and uses directed masked
attention to model their dependencies, with object information selectively conditioning the upper-body pathway for dexterous interaction. The policy jointly outputs body and dexterous-hand actions and is trained directly with reinforcement learning, without pretrained tracking policies, teacher-student
distillation, or subsequent residual refinement. DexWeave improves retargeting fidelity and interaction consistency while achieving higher manipulation performance and faster policy convergence than MLP baselines. We further deploy the learned policies on a physical Unitree G1
humanoid equipped with Inspire dexterous hands, demonstrating dexterous whole-body loco-manipulation in the real world. See our project page (https://dexweave.github.io) for
videos.
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All authors Naichuan Sun, Haotian Shen, Yizhang Zhang, Luying Feng, Haoze Wang, Yuanbo Xiangli, Yaochu Jin, Peidong Liu
Computer Vision
Geometry as Address: Routing Attention to Visual Memory for Long-Horizon Camera-Controlled Video Generation
Zesong Yang, Weikai Chen, Liyuan Cui et al. (+10 more)
Long-horizon camera-controlled
video generation requires recovering previously observed content from an ever-growing visual history. Existing approaches either search historical context implicitly or reconstruct it into persistent 3D
memory, facing inefficient
memory access or accumulated
geometric errors. Our key insight is that geometry need not explain the scene–it only needs to determine where visual
memory should be read from, while
attention decides what should be recovered. Based on this insight, we introduce GEAR, a Geometry-Enabled
Attention Routing framework that uses geometry as an explicit
token-level address for visual
memory. Rather than fusing historical observations into a persistent global 3D representation, GEAR retains them as frame latents and uses per-frame geometry only to establish
token-level correspondences with target views, thereby avoiding persistent error accumulation from global fusion. Guided by these correspondences,
Geometric Correspondence
Attention (GCA) selectively injects geometrically matched historical features into noisy target patches during denoising. We further introduce an Invisible Octree to accumulate visibility evidence and reject geometrically plausible but occluded correspondences. Extensive experiments demonstrate that GEAR achieves state-of-the-art visual quality, precise camera control, and revisit consistency, enabling minute-long
video generation along challenging trajectories.
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All authors Zesong Yang, Weikai Chen, Liyuan Cui, Lutao Jiang, Runze Zhang, Yingda Yin, Xiaoyang Huang, Kai Yan, Keyang Luo, Wangguandong Zheng, Xin Wang, Hujun Bao, Zhaopeng Cui
Information Theory · Cross-listed from math-ph
Scattering Measures and Information Geometry
Tatsuro Oda
We formulate scattering experiments in terms of parameterized finite measures on outcome spaces and develop the associated information geometry defined by the Fisher metric. The decomposition of a scattering measure into total mass and normalized shape separates Fisher information into contributions from changes in overall scattering strength and redistribution among outcomes. Measurement processes are represented by probability or sub-probability kernels acting on the scattering measure. Such kernels contract Fisher information, placing a broad class of experimental operations within a common information-theoretic framework. This formulation connects scattering experiments to the theory of optimal experimental design through the Fisher geometry of scattering measures. The same finite-measure structure arises naturally in
quantum measurements with incomplete detection, clarifying its place in the hierarchy from
quantum measurement to classical outcome statistics.
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Computer Vision
DBCF: Dual-Branch Complementary Fusion of Foundation Models for Generalized Deepfake Detection
Fengming Gu, Mingjie He, Zonghui Guo et al. (+2 more)
As image generation and editing technologies have progressed substantially, facial forgeries pose significant challenges to
privacy and public safety. Due to limited ability to capture forgery cues, existing small-scale forgery detection models often struggle to generalize across various domains and unseen manipulations. To address this limitation, researchers have turned to large-scale foundation models, which can provide richer representations and better generalization. Nevertheless, relying on a single foundation model alone remains insufficient for effective forgery detection. While models like CLIP offer robust global semantic cues, they lack the capacity to capture detailed local facial features. In contrast, DINO excels at capturing local structural features of faces, but provides weaker global semantic context. To fully utilize the synergies among multiple foundation models, we propose a hierarchical multi-granular framework that integrates complementary pretrained representations. Specifically, a Global Context Branch (GCB) based on CLIP captures holistic semantic cues, while a Fine-grained Cue Branch (FCB) built on DINOv3 captures localized structural irregularities. In addition, we design a feature fusion module that enables parameter-efficient adaptation of the frozen foundation backbones by adaptively extracting and integrating complementary features from the two models. By jointly leveraging global context and fine-grained cues, our method learns more comprehensive forgery representations and achieves strong cross-manipulation performance. Extensive experiments on multiple
benchmarks demonstrate the benefit of the proposed design, particularly under cross-
dataset and cross-manipulation settings.
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All authors Fengming Gu, Mingjie He, Zonghui Guo, Jie Zhangb, Shiguang Shan
Robotics
ExcavaTwin: Training-Free Geometry-Guided Semantic Elevation Mapping for Autonomous Excavation
Yu Deng, Lingshan Zeng, Tong Hu et al. (+1 more)
Autonomous excavation requires a spatial representation that jointly captures terrain geometry and task-relevant semantics. Existing excavation mapping is largely elevation-centric, while generic semantic models remain unstable in unstructured outdoor scenes. We present ExcavaTwin, a pure-
vision geometry-guided semantic elevation mapping framework without excavation-specific training. Given multi-view RGB images, the framework: 1) reconstructs scene geometry and semantic observations using frozen
vision models; 2) derives terrain and non-terrain
geometric support; 3) performs geometry-constrained multi-view semantic fusion to suppress implausible predictions and recover incomplete observations; and 4) projects the fused state into a task-oriented semantic elevation map. Experiments on public
datasets and real excavation scenes demonstrate reliable
geometric and semantic perception. In real excavation, the system achieved an average update interval of approximately 1.4 s and a mean elevation error of 12.74cm in dynamically modified regions. Larger errors mainly occur during rapid terrain changes and transient visual disturbances caused by machine motion.
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All authors Yu Deng, Lingshan Zeng, Tong Hu, Rushi Dai
Machine Learning
Quality Determines Direction, Length Shapes Magnitude: Length Control for Open-Ended Reinforcement Learning
Zijun Weng, Zhongan Bi, Xuanang Gao et al. (+5 more)
Reinforcement learning (RL) changes not only what
language models say, but also how much they say, often increasing response length at the cost of
token efficiency. Controlling this length growth is particularly challenging in open-ended RL because (i) response length is entangled with quality, (ii) open-ended tasks lack a natural success boundary for deciding when efficiency should be prioritized, and (iii) dense, graded rewards often yield small within-group quality margins, making quality-induced advantages especially sensitive to reward-level length shaping, which can perturb their magnitudes and even reverse their signs. We therefore adopt an asymmetric principle: quality should determine the direction of reinforcement, while length should only shape its magnitude. We instantiate this principle with Quality-Gated Length Advantage Shaping (QGLAS), which first computes advantages from quality rewards alone, then adds bounded bonuses only to shorter positive-advantage responses, leaving all other advantages unchanged. The bonus strength is further adapted to within-group quality separation, allowing conciseness to matter more when quality-favored responses are similar and less when their quality differences are clear. Across different model families, open-ended
benchmarks, and reward sources, QGLAS consistently achieves a stronger quality–length trade-off than representative baselines. At approximately 30% compression, QGLAS retains 98.4–102.0% of the macro-average quality gains achieved by quality-only RL over the base model, compared with 68.3–75.5% for these baselines at comparable compression.
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All authors Zijun Weng, Zhongan Bi, Xuanang Gao, Xiaohui Hu, Shuangyong Song, Yongxiang Li, Kaidong Yu, Xuanjing Huang
Computation and Language
ReMCTS: Reflection-Enhanced Monte Carlo Tree Search for Code Generation
Huifei Wang, Xinying Huang, Yiheng Sun et al. (+1 more)
Open-weight large
language models (LLMs) can generate function-level programs from natural-
language prompts, but plausible candidates still fail on hidden semantics and repeat mistakes across repair attempts. We present ReMCTS, an execution-grounded,
memory-augmented, LLM-guided MCTS-style search framework. It organizes program candidates as tree states, retains branch-local debugging context, retrieves failure experience across branches, and distinguishes failed checks from unavailable evidence. On HumanEval and MBPP-Sanitized, visible-test ReMCTS improves over direct generation in 8 of 10 model-
dataset pairs under held-out evaluation, whereas proxy-only search is less stable. Controlled tree-search, sampling, repair, and
memory ablations characterize the source and limits of these gains. A 30-task HumanEval-X C++ pilot further demonstrates compatibility with compiler-backed execution, but does not constitute a broad
multilingual evaluation.
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All authors Huifei Wang, Xinying Huang, Yiheng Sun, Yifan Yuan
Computer Vision
From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning
Fan Zhang, Yi Zhang and Ling Wang
Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SRuCT) provides high-resolution references but is not applicable for in vivo imaging. The two domains differ by a 32x resolution gap, are only coarsely paired, and exhibit severe physical differences including partial volume effects, noise, and artifacts. Existing super-resolution networks and pretrained-prior methods (GLEAN/StyleGAN2, Stable SR/LDM) underperform because they target pixel generation—diverse details and SSIM/PSNR—and do not model these physical differences. Pixel generation for a 32x resolution gap is intrinsically ill-posed. We propose a paradigm shift from pixel generation to topological inference: deterministically predicting invariant microstructures from macro-scale low-resolution inputs, evaluated by morphological parameters. The core of our 2D morphology learning lies in training on 2D slices while evaluating on 3D morphological parameters, ensuring that the learned representations capture true three-dimensional trabecular
topology rather than 2D pixel statistics. We realize this paradigm via structural dual super-resolution, coupling forward physical degradation (micro-to-macro) with inverse structural inference (macro-to-micro) through structural duality constraints. The method is an end-to-end, few-shot, compact structural dual network (SDN), comprising a bidirectional modeling network, a multi-scale structural consistency discriminator, and four structural duality constraints. On the test set, SDN achieves morphological parameters largely consistent with SRuCT across six metrics, with SSIM reaching 0.8. Trained on 3.2 um SSRF data, the model generalizes well to 3.25 um isotropic BSRF data from an independent source, validating cross-source generalization and confirming trustworthy structural inference rather than pixel generation.
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Artificial Intelligence
PDE-JEPA: Predictive Representation Learning of Latent Dynamics Modeling for Parametric PDEs
Zhentao Tan, Jianrong Zhang, Ruijie Quan et al. (+1 more)
Physical trajectories contain more than snapshots of a system: they also reveal how its states evolve under governing conditions. However, representation learning for parametric partial differential equations (PDEs) has largely relied on reconstruction-based objectives that emphasize recovering observed physical fields. In this paper, we investigate predictive representation pretraining as an alternative to reconstruction-based learning. We find that predictive representations preserve rich physical information, yet this advantage alone does not ensure accurate field
evolution. Based on these observations, we introduce PDE-JEPA for parametric PDE dynamics. Specifically, we first train an encoder using a masked-latent prediction to capture the underlying regularities of PDE dynamics. To explicitly adapt the pretrained representation toward a more dynamics-aligned state space, we then introduce a geometry projector that aligns latent trajectory geometry with the
evolution geometry of physical fields. Finally, building on this geometry-aligned latent space, we further develop a physics-structured latent predictor that decomposes the dynamics into parameter-independent
evolution and parameter-dependent response components. Extensive experiments on nine widely used PDE
benchmarks demonstrate that our framework outperforms existing state-of-the-art methods by an average of 33.4% in-distribution, while achieving an average improvement of 51.4% when extrapolating to unseen governing parameters. The project page is available \href{https://tanpig-x.github.io/PDE-JEPA/}{here}.
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All authors Zhentao Tan, Jianrong Zhang, Ruijie Quan, Yi Yang
Computers and Society
Loyal Agents: Training LLM Agents to Protect Principal Interests Under Strategic Information Asymmetry
Zimeng Huang, Shilei Chen, Jiatong Zhao et al. (+2 more)
As LLMs increasingly act as delegated
agents, they are expected to protect principals' interests when interacting with external parties. Standard
alignment objectives, such as helpfulness, harmlessness, and honesty, do not specify how
agents should protect principals' strategic interests under delegation. We formalize
Agent Loyalty as an information-control property requiring
agents to prevent Exploitable Information Leakage (EIL) and resist Manipulative Information Uptake (MIU). We introduce LoyalAgent-Bench, comprising 10,298 samples across 42 subscenarios and six domains, and an online GRPO framework that trains against a LLM opponent to generate mechanism-specific reward signals. Experiments show that loyalty is not guaranteed by general capability or existing
alignment, with measurable EIL and MIU gaps under zero-shot evaluation, while our trained 8B models reduce per-response leakage in single-turn exchanges by 31-44pp and improve task utility, evidence faithfulness, and decision accuracy by up to 11pp, 49pp, and 29pp, respectively. For the trained Qwen3-4B model, no degradation is observed on out-of-distribution
benchmarks in math and narrative
reasoning.
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All authors Zimeng Huang, Shilei Chen, Jiatong Zhao, Wenxin Xu, Tonghan Wang
Artificial Intelligence
RSI-Router: Evolving Subtask-Level LLM Routing and Skills for Cost-Efficient Agents
Hao Li, Hangfan Zhang, Zhiyao Cui et al. (+5 more)
Practical deployment of large
language model (LLM)
agents requires strong task performance at affordable inference cost. For long-horizon agentic tasks, this performance-cost trade-off can be improved through within-task large-small model collaboration, as smaller models can handle some stages even when they cannot solve the full task. In this paper, we introduce RSI-router, a routing framework that constructs subtask-level model assignments and model-specific skills through recursive self-improvement over accumulated experience. Each iteration consists of four stages: Subtask Mining derives subtask definitions and identification rules from training trajectories; Routing Strategy
Evolution proposes and evaluates diverse model assignments; Model-Specific Skill
Evolution compares routed and large-model-only trajectories to diagnose failures and develop reusable execution skills; and Pareto-Optimal Router Selection updates the Pareto population using historical and newly generated routers while retaining dominated routers as experience for subsequent
evolution. Routing between DeepSeek-V4.1-Flash and Qwen3.5-9B, RSI-router consistently surpasses the DeepSeek-only baseline at roughly half the inference cost (48.3%) across five agentic
benchmarks. In particular, on ALFWorld, ScienceWorld, and WebShop, it cuts inference cost by 74.7-82.2% while simultaneously improving performance; on Terminal-Bench 2.0, it achieves a 16.7% relative performance gain at 18.0% lower cost. Moreover, RSI-router establishes a stronger performance–cost Pareto frontier than 9 routing methods.
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All authors Hao Li, Hangfan Zhang, Zhiyao Cui, Chunjiang Mu, Yiqun Zhang, Bo Zhang, Danyang Jia, Shuyue Hu
Machine Learning
Learning Propagation Geometry from Message-Passing Feedback
Yingxu Wang, Kunyu Zhang, Xinwang Liu et al. (+4 more)
Learning local geometry enables graph neural networks (GNNs) to adapt how they compare and integrate neighborhood information. However, estimating geometry from aggregated representations can overlook variation among individual messages and dependencies across feature dimensions. We propose GeoF, a recurrent framework that jointly evolves node features and propagation geometry through message-passing feedback. Each node maintains a local symmetric positive-definite geometry, initialized from a structure-aware prototype atlas and parameterized in block log-triangular coordinates. At each step, the geometry determines neighborhood weights, while triangular frame transport maps transformed source messages into the target node's local coordinates before aggregation. Weighted second-order statistics of residuals between aligned messages and the transformed target state capture directional variation and within-block dependencies, yielding a
geometric update target. A shared controller learns complementary corrections through task supervision. A bounded log-triangular update combines these corrections, the target, and the previous
geometric state while preserving positive definiteness. The geometry governs subsequent propagation, closing the feedback loop. With parameters shared across recurrent steps, task-specific readouts support node classification, link prediction, and graph classification. Experiments on
benchmark datasets show that GeoF consistently outperforms state-of-the-art GNN baselines.
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All authors Yingxu Wang, Kunyu Zhang, Xinwang Liu, Mengzhu Wang, Siyang Gao, Chang Tang, Nan Yin
Artificial Intelligence
FromPitch2Board: Benchmarking LLM Agents in Long-Horizon Football Management
Peiyu Zang
Long-horizon
agent benchmarks typically report how far an
agent progresses, but do not identify whether its performance comes from the foundation model, scaffold, responsibility scope, match-control granularity, or horizon. We introduce FromPitch2Board, a deterministic football-management
benchmark that studies five configurable factors through controlled comparisons on a single simulator, using paired seeds and a frozen calibration. We evaluate four foundation models and four
agent scaffolds. In the Model Track, Coach points Z-scores span 0.19, while Manager points Z-scores span 0.68, with GPT-5.6 showing a sharp rise in passivity under responsibility expansion. Its responsibility ladder rises from 46.1 to 58.1 points with recruitment, then falls to 46.8 under full management, localizing the regression to the final responsibility boundary. Across that boundary, its skipped-decision rate rises from 1.1% to 57.9%. Within the Flash-Pro pair crossed across every scaffold, scaffold choice changes Manager points Z-scores by up to 0.48 relative to the fixed stateless scaffold. The 3Y cohort shows a directional reversal in mean ranking between years one and three, while a selected Claude Code+Pro configuration peaks in year three and remains below that peak, showing that responsibility scope and horizon expose behavior changes that a single headline score conceals.
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Cryptography and Security
StallGrid: Measuring Internet-exposed Engagement in Protocol-Native OT Tarpits
Arthur Cordeiro, Casper Andersen and Emmanouil Vasilomanolakis
Operational technology systems face exposure to scanning and protocol-specific attack tools, where compromise risks disrupting physical processes rather than just data. Traditional OT defenses rely on blocking and filtering under strict patch constraints, while tarpitting delays scanners through sustained protocol-level interaction. Modbus TCP and IEC-104 carry no native authentication or integrity protection. Application-layer tarpitting, which stalls scanners inside a legitimate protocol exchange, has been studied for IT and IoT protocols, but not for OT/ICS, whose session semantics (state machines, exception codes) create stalling opportunities IT/IoT lack, and whose patch-constrained environments need exactly this kind of alternative defense. We present StallGrid, to our knowledge the first application-layer tarpits for OT/ICS, stalling scanners via Modbus Exception Codes 0x05/0x06 and prolonged residence in IEC-104's connected state machine. Five variants (three Modbus TCP, two IEC-104) ran simultaneously for 24 days online, logging 6,709 sessions, 2,039 cumulative per-tarpit unique IPs, and over 2,000 hours of accumulated connection engagement. GreyNoise enrichment attributes 87–97% of stall time to malicious-tagged IPs, just 22–30% of connecting addresses; protocol-level behavior further correlates with malicious classification, a fingerprinting signal beyond raw stall time. Shorter induced delay (1.5s) yielded more total engagement than longer delay (3s), observed across both protocols. These results position protocol-native tarpitting as a practical, low-cost complementary defense for OT/ICS environments where patching remains infeasible.
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Robotics
SOR-Nav: Search or Relocate? Context-Gated Exploration and Cross-Region Relocation for Object Navigation
Yuan Ji, Zirui Li, Yuxin Cai et al. (+3 more)
Object
navigation requires an
embodied agent to find an object in an unseen environment under partial observability and a limited motion budget. Existing methods primarily optimize where the
robot should go next by ranking candidate destinations. In contrast to these methods, we present SOR-Nav, a hierarchical
navigation system that explicitly arbitrates between continuing to explore the current context and abandoning it for a more promising reachable region. First, an autonomous semantic exploration system is built that accumulates persistent 3D object clusters and organizes reachable frontiers into a cluster decision graph to provide an efficient search abstraction. Then, SOR-Nav uses a context-gated LLM-driven object-search supervisor to evaluate the suitability of the current search context and decide whether to continue exploration or perform cross-region relocation to another reachable frontier cluster. Across the complete, unfiltered validation sets of HM3D-v1, HM3D-v2, and MP3D, SOR-Nav achieves the strongest reported Success Rate (SR) and Success weighted by Path Length (SPL) on all three
benchmarks. On MP3D in particular, it more than doubles the previous best SPL from 18.1% to 38.5% while increasing SR from 50.7% to 61.8%. Nested HM3D-v2 ablations validate the proposed decision structure, while a continuous three-target physical deployment demonstrates persistent ObjectNav operation in real-world scenarios.
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All authors Yuan Ji, Zirui Li, Yuxin Cai, Shuge Wu, Boon Siew Han, Chen Lv
Software Engineering
When Ambiguity Meets Atypicality: Dual-Perspective Test Input Prioritization for DNNs
Haoran Li, Shihai Wang, Bin Liu et al. (+5 more)
While Deep Neural Networks (DNNs) have achieved remarkable progress in cutting-edge domains, their inherent brittleness has become a growing concern. To ensure the reliability and safety of DNN-enabled software, DNN testing has emerged as an indispensable practice. Within this context, test input prioritization is essential for early fault detection and reducing labeling costs. However, it remains challenging to accurately identify failure-inducing inputs. Although decision ambiguity and distributional atypicality are two widely adopted perspectives for characterizing inter-class competition and intra-class typicality respectively, relying on either perspective in isolation inevitably introduces blind spots. In this paper, we propose DuFP (Dual perspective Feature space Prioritization), a KNN density-based test input prioritization approach for DNNs that jointly incorporates both inter-class and intra-class perspectives. The prioritization framework of DuFP is built upon class-conditional density estimation. Based on the estimation results, prediction correctness is characterized by an ambiguity score and an atypicality score, with the former reflecting decision ambiguity and the latter quantifying distributional atypicality. A hybrid uncertainty score is then constructed by integrating both scores to guide the final prioritization. We evaluate DuFP on prioritization and selection tasks across image and text
datasets under clean, corrupted, and
adversarial scenarios. Experimental results demonstrate that DuFP effectively and efficiently prioritizes fault-inducing inputs and outperforms state-of-the-art approaches.
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All authors Haoran Li, Shihai Wang, Bin Liu, Jialuo Chen, Wenjing Zhu, Yu Liu, Tengfei Shi, Shudi Guo
Robotics
MarsLab: A Martian Rover Simulator for Planetary Rover Autonomous Navigation
Hoyun Kim, Beomsu Kim and Giseop Kim
Future Mars missions will require rover autonomy that can operate across unstructured terrain, changing illumination, atmospheric
dust, and limited communication. Simulation is a practical way to study these conditions before deployment, but existing Mars-relevant resources differ in scope, including mission-oriented simulators, fixed analog
datasets, task-specific environments, and open robotics interfaces. In this context, we present MarsLab, an open-source, ROS2-native Mars rover simulator for autonomy and
navigation algorithm development. MarsLab combines HiRISE-derived and procedural terrain with customizable rock, crater,
solar-illumination, and atmospheric-
dust settings, and runs a Perseverance-class rover model in NVIDIA Isaac Sim. The runtime publishes RGB, depth, RGB-D point clouds,
LiDAR, IMU, wheel odometry, and Ground Truth (GT) pose data through standard ROS2 topics. We demonstrate MarsLab with Simultaneous Localization and Mapping (SLAM)
benchmarks across sensing modalities,
dust levels, scene geometry, and route length, and with Visual Place Recognition (VPR)
benchmarks over repeated Mars Base traversals under illumination and
dust changes. The results illustrate how controlled scene variation and shared GT trajectories can be used to compare trajectory-level estimation and image-level place recognition within the same simulator. Our Project Page: https://kimhoyun-robotair.github.io/MarsLab/.
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Artificial Intelligence
ResonAct: Streaming Metrics for Runtime Diagnosis and Self-Healing in Multi-Agent Systems
Tarun Chintada, Neelamadhav Gantayat, Ishaan Romil et al. (+3 more)
Multi-
agent systems (MAS) are increasingly used to automate enterprise workflows involving multiple specialized
agents, external tools, and long-running task execution. Failures may arise from tool degradation, context propagation errors, coordination breakdowns, or repeated
agent interactions that prevent task completion. While existing observability frameworks provide traces and logs, diagnosis and remediation are largely performed after execution completes, limiting opportunities for recovery during runtime. We present ResonAct, a runtime self-healing framework that enables continuous monitoring, diagnosis, and remediation of multi-
agent systems through streaming operational metrics. ResonAct ingests execution traces,
agent interactions, and tool invocations into a streaming analytics layer that continuously derives task progress, context health, and tool reliability metrics. These metrics serve as runtime control signals for detecting anomalous execution patterns and localizing root causes using a structured failure model. Based on the diagnosed failure, ResonAct dynamically selects remediation policies and performs actions. The framework operates as an external control plane, enabling intervention without modifying application
agents or orchestration logic. We evaluate ResonAct across enterprise workflow scenarios and AppWorld
benchmarks. The results show that the streaming metric-based analysis identifies execution degradations and localizes faults. Furthermore, policy-driven remediation improves task completion rates by up to 10.00 percentage points, with detection precision ranging from 70.59% to 82.91%, recall from 63.09% to 100%, recovery rates from 10.48% to 46.67%, and runtime overhead ranging from $-0.25%$ to 14.12% across the evaluated configurations.
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All authors Tarun Chintada, Neelamadhav Gantayat, Ishaan Romil, Renuka Sindhgatta, Soujanya Soni, Sameep Mehta