Machine Learning
WorldGraph: Graph-Native World Modeling
Zezhong Ding, Yipeng Li and Xike Xie
World models infer latent states of an environment to capture its underlying dynamics and predict future
evolution. Many real-world environments, however, are inherently relational and observed as evolving graphs, where entities, relations, and their properties change over time. Prior graph-related world models use graph structures to organize internal states or support task-specific
reasoning, rather than treating an evolving graph itself as the modeled world. We instead study graph world modeling (GWM), where graph
evolution itself constitutes the world dynamics. We formulate graph world modeling over observed graph
evolution, latent graph states, and heterogeneous graph-transition predictions. Based on this formulation, we construct GWM-Zero, a
benchmark covering node-, edge-, and graph-level transitions over eight temporal graph
datasets. We propose WorldGraph, which combines a state-aware graph
transformer for multi-granularity structural and transition-conditioned
evolution modeling with transition-aware GRPO using dynamic grouping and structure-aware verifiable rewards. Extensive experiments on GWM-Zero show that WorldGraph consistently outperforms representative graph representation, temporal graph learning, graph pretraining, and graph world-model baselines across all three transition granularities.
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Computation and Language
Toward a Graded Measure of Belief Stability in Large Language Models
Samantha Dies, Branden Fitelson and Tina Eliassi-Rad
Large
language models (LLMs) increasingly mediate how people access and reason with information, yet factual reliability is usually evaluated one judgment at a time. We introduce graded belief stability, a relational measure of how well a belief persists within an LLM's broader belief system. Unlike individual belief probability, it asks whether support for a claim persists when that claim is considered alongside the model's other epistemic commitments. We operationalize this idea with a Direct Conditional
estimator that uses internal model representations to estimate conditional belief probabilities. Across 12 LLMs and three domains, lower-stability beliefs exhibit greater mean behavioral movement under conversational challenge in 83.3% of model-domain settings after matching on individual belief probability. Graded belief stability therefore extends reliability assessment beyond how strongly an LLM supports a claim to how robustly that belief is supported within its broader system of beliefs.
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Artificial Intelligence
TableSeek: Structure-Preserving Agentic Evidence Seeking over Heterogeneous Table Corpora
Jiaming Tian, Liyao Li, Wentao Ye et al. (+5 more)
Open-domain table
retrieval seeks tables that contain sufficient evidence for answering a question or verifying a claim. Yet semantic relevance is often misleading: topically similar tables may lack the required facts, while answer-bearing evidence is often confined to a few
cells whose meaning depends on surrounding schema and table context. Heterogeneous schemas, value formats, and serializations further weaken one-shot matching. We present TableSeek, a structure-preserving agentic search framework for heterogeneous table corpora. Instead of ranking tables once, an LLM
agent iteratively follows sparse clues, inspects schema-preserving previews, identifies schema- and value-level mismatches, and refines its investigation. TableSeek uses
cells and schemas as evidence anchors while retaining complete tables as evidence units, enabling fine-grained localization without losing the context required for interpretation and answerability checking. Without relying on retriever training or a precomputed semantic index, TableSeek produces transparent evidence-seeking trajectories and achieves competitive end-to-end performance against strong
retrieval-and-reranking pipelines on heterogeneous table
benchmarks. These results suggest that active, structure-preserving evidence seeking is a promising paradigm for open-domain table
retrieval.
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All authors Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao
Machine Learning
Transfer Calibrated Prediction Powered Inference
Aditya T. Vadlamani, Jae Ho Chang, Srinivasan Parthasarathy et al. (+1 more)
Prediction-powered inference (PPI) and its power-tuned extension (PPI++) improve confidence intervals by combining a small gold-standard labeled sample with a large AI model's predictions. Its efficiency gain relies on low residual variance, which may not hold if the predictor is pre-trained on a different source domain. We propose Transfer Calibrated Prediction-Powered Inference (TC-PPI), adapting the source-domain predictor to the target domain using gold-standard samples through cross-fitting. This approach supports various adaptation methods, such as sparse linear calibration, LoRA, and fine-tuning. Our jointly tuned cross-fit
estimator, Joint-TC-Cross-PPI++, maintains unbiasedness and is simultaneously at least as efficient as classical inference, PPI, and PPI++, thereby protecting against negative transfer. We provide high-dimensional MSE bounds for calibration and show empirical improvements over baseline methods across various real-world applications.
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All authors Aditya T. Vadlamani, Jae Ho Chang, Srinivasan Parthasarathy, Subhadeep Paul
Machine Learning
SPINET: Sheaf Protein Inverse Folding Network
Jens Lundsgaard, Colin Mikulski, Zhixuan Yan et al. (+1 more)
Proteins change shape as they function, yet most inverse folding models predict amino acid sequences from a single, fixed backbone. A central challenge in
protein engineering is to design
proteins that undergo specific motions, which requires accounting for how their structures change over time. This motivates inverse
protein folding conditioned on
protein motion. We introduce SPINET, which predicts sequences from molecular dynamics trajectories. It uses cellular sheaves to represent residue interactions within each frame and recurrent units to integrate information across frames, then predicts all amino acids in a single pass. We evaluate SPINET on mdCATH and ATLAS, where it outperforms all evaluated static and ensemble baselines in sequence recovery. On mdCATH, it achieves 56.7% top-1 recovery, compared with 44.5% for the strongest static baseline and 40.7% for the strongest ensemble baseline. We also evaluate whether the predicted sequences are compatible with conformations sampled along the target trajectory. On mdCATH, they achieve a median TM-score of 0.760, and structural recovery favors target conformations over unrelated decoys for 99.5% of test domains.
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All authors Jens Lundsgaard, Colin Mikulski, Zhixuan Yan, Dhananjay Bhaskar
Computation and Language
Quantitative Measurement of Language Distance among Closely Related Indo-European Languages Using Pretrained Language Models: A Case Study on the North Germanic Branch
Yiping Bai
Among closely related North Germanic
languages, the quantification of
language distance has traditionally relied on qualitative methods, lacking a unified multi-dimensional computational framework.
Multilingual pretrained models based on the
Transformer architecture can map texts from different
languages into a shared vector space, enabling quantitative measurement of
language distance. This paper focuses on the three North Germanic
languages—Danish, Norwegian (Bokmål), and Swedish—and proposes a three-metric quantitative framework based on pretrained
language models: (1)~sentence-level semantic distance, computed as cosine similarity between LaBSE and mBERT encodings of parallel sentences; (2)~orthographic fragmentation rate, measuring subword tokenization efficiency when cross-applying monolingual BERT vocabularies to parallel texts; (3)~MLM predictability, comparing prediction confidence and
entropy in masked
language modeling using mBERT across
languages. Using 150 trilingual parallel sentence triplets from the Tatoeba corpus as controlled samples, we obtain consistent distance rankings on two independent models: LaBSE: da–no $0.012 < $ no–sv $0.016 < $ da–sv $0.020$; mBERT: da–no $0.016 < $ no–sv $0.045 \approx $ da–sv $0.046$. This ranking is consistent with the historical linguistic conclusion that “400 years of Danish rule over Norway (1380–1814) led to highly cognate written
languages.” The three metrics—semantic, orthographic, and predictability—converge on the same conclusion, providing a reproducible computational framework for the quantitative study of distance among closely related
languages, extensible in principle to more branches of the Indo-European
language family, pending validation on additional
language groups.
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Artificial Intelligence
Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization
Xuanqi Zhang, Ruinan Jin, Running Yang et al. (+4 more)
Self-evolving
agents can continually improve their behavior, while tools define the executable action space through which they interact with the environment. However, exposing the full tool library to model introduces substantial irrelevant context and can impair tool-use decisions. We study tool-space self-
evolution, where each recurring task type maintains a persistent tool space which is constructed from accumulated output experience. We identify three limitations of existing methods: (1) output-unaware selection: they rely primarily on tool descriptions or model priors rather than observed tool outputs; (2) statelessness across request: they select tools independently for each request without consolidating prior output experience into persistent task-specific state; (3) inference cost: they repeatedly search, rank, or reason over candidate tools for subsequent requests of the same task. We address these limitations through output-aware tool scoring, persistent task-specific tool spaces, amortized tool selection, and reusable configurations across models. We introduce LOTS (Likelihood-Only Tool Scoring), which evolves an
agent's tool space from accumulated output experience while keeping model parameters fixed. After each request, LOTS holds the model's generated answer and estimates each tool's contribution by measuring how much the answer likelihood changes when its observed output is removed. These contributions are aggregated within each recurring task to rank tools and update its persistent space. Across three
benchmarks, LOTS improves task performance while substantially reducing tool context. More importantly, sequential experiments demonstrate that task-specific spaces persist and continue to improve over time, while cross-model experiments show that learned configurations transfer across different models.
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All authors Xuanqi Zhang, Ruinan Jin, Running Yang, Yuxuan Zhang, Minghui Chen, Wenlong Deng, Xiaoxiao Li
Computer Vision
Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos
Wonseok Roh, Seung Hyun Lee, Won Jeong Ryoo et al. (+5 more)
Current methods for
egocentric view action recognition often face challenges in perceiving dynamic hand movements relying solely on geometrical or physical information. In this work, we effectively address this problem by gaining insights into the correlation between functional hand configurations and objects, which improves the detailed interpretation of real-world scenarios. To this end, we introduce a practical taxonomy of hand types based on the functioning perspective and utilize it for per-frame hand type labeling on existing
datasets. We also propose a novel hand action recognition framework considering semantic details of the hand type as prior. This approach boosts the network's understanding of the continuous hand interaction throughout the action sequence. Our whole pipeline consists of three main modules: (1) Feature Extraction, (2)
Egocentric Knowledge Module, which estimates 3D hand pose, object category, and hand type leveraging short-term cues, and (2)
Egocentric Action Module, which aggregates per-frame knowledge, including text
embeddings of hand type, over a longer time. In our extensive experiments with large-scale
benchmarks, FPHA and H2O, our model outperforms current state-of-the-art methods, demonstrating its superior performance.
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All authors Wonseok Roh, Seung Hyun Lee, Won Jeong Ryoo, Jakyung Lee, Gyeongrok Oh, Sooyeon Hwang, Hyung-gun Chi, Sangpil Kim
Computer Vision
Antonio Jun, Haoshui Yu, Zhengyi Lu et al. (+2 more)
Vision-
Language Models (VLMs) are far more reliable at recognizing what appears in a
video than at
reasoning about its spatial and temporal properties, such as metric distances, object dimensions, and consistent object identities across frames. We present
Geometric Code, a perception-to-geometry pipeline that computes explicit spatial structure from
video and supplies it to VLMs as context to augment
reasoning. A perception layer segments and classifies objects and recovers depth, camera pose, and intrinsics from
monocular RGB
video. A deterministic
geometric engine then back-projects, merges, and cleans these outputs into a spatial code, including per-object positions, dimensions, counts, inter-object distances, appearance order, and room geometry. The code is serialized into VLMs'
prompts, either alongside the
video or replacing it entirely. Specifically, there is no component trained or fine-tuned in our approach. On VSI-Bench, augmenting 2B and 4B open models with the spatial code improves average accuracy by +4.1 points over the frames-only baseline, with the largest gains on numeric estimation tasks such as absolute distance (+24.1 points). The results suggest that explicitly computed geometry, delivered through the
language channel, recovers spatial competence that small VLMs cannot extract from pixels alone.
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All authors Antonio Jun, Haoshui Yu, Zhengyi Lu, Huirong Fu, Yao Qiang
Sound · Cross-listed from eess.AS
SPEAR-Gen: Generation-Aware Pre-training for Unified Speech Representations
Xiaoyu Yang, Arthur Hinsvark, Antonios Alexos et al. (+3 more)
Speech understanding and generation place different demands on
speech representations, and existing models are typically optimised towards one capability or the other. To reduce this gap, we introduce SPEAR-Gen, a
speech representation model that learns a single representation for both capabilities. Task-aligned feature aggregation consolidates complementary linguistic and paralinguistic information across a frozen encoder into discrete targets for masked prediction, while a coarse-to-fine objective combines log-Mel reconstruction with residual flow matching to preserve spectral structure and fine-grained acoustic variation. Experiments on SUPERB and
speech resynthesis show that SPEAR-Gen maintains strong understanding performance while substantially improving resynthesis quality and speaker preservation. These results demonstrate that a single
speech representation can effectively support both understanding and generation.
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All authors Xiaoyu Yang, Arthur Hinsvark, Antonios Alexos, Osama Hanna, Philip C. Woodland, Yiting Lu
Machine Learning
ExpertoRhythm: Morphology-Aware Learning for Waveform Reconstruction and Cuffless Blood Pressure Estimation from Single-Channel PPG
Amir Arjomand, Kenneth B. Kent and Georgiy Krylov
Continuous cuffless blood pressure (BP) monitoring from photoplethysmography (PPG) has strong potential for wearable health and telemonitoring, but accurate estimation remains difficult because PPG-to-BP mapping must preserve subtle waveform morphology and pressure-range-dependent dynamics. We introduce ExpertoRhythm, an
attention-enhanced 1D U-Net that reconstructs the arterial blood pressure (ABP) waveform from a single-channel PPG signal and derives systolic and diastolic BP directly from the reconstructed waveform. The central contribution is a composite morphology-aware learning objective that integrates range-weighted SmoothL1 reconstruction with a window-range regularizer to emphasize high-dynamic BP segments and reduce amplitude under/over-shoot. On the UCI cuff-less BP
dataset with 942 subjects, ExpertoRhythm achieves 2.46/1.46 mmHg MAE for systolic/diastolic BP (SBP/DBP), while obtaining a 30.4% average relative error reduction over pure MSE across waveform reconstruction and BP estimation metrics. Clinical-style evaluation further demonstrates low bias and strong agreement across the BP range, including high-pressure windows up to 200 mmHg, satisfying AAMI criteria and achieving BHS Grade A. These results suggest that morphology-aware waveform reconstruction from a single PPG channel can provide an accurate and practical pathway toward continuous cuffless BP monitoring in wearable and remote-care settings.
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Robotics
Beyond Retrieval Relevance: Scene-Grounded Risk Entailment for Vision-Language Driving
Jiaxin Liu, Ruilin Yu, Liang Peng et al. (+8 more)
Retrieval-augmented generation (RAG) gives
vision–
language driving systems access to external safety knowledge, yet a retrieved risk rule may be relevant without applying to the current scene. A
vision–
language model (VLM) receiving such knowledge must ground objects, bind entities across time, and verify relations before deciding how to act, leaving the support for risk conclusions implicit. We address this relevance–applicability gap with a Driving-Risk Knowledge Graph (DRKG) and Semantic Web Rule
Language (SWRL)
reasoning stage before VLM decision-making. Structured perception instantiates scene facts, from which SWRL rules derive events and directed risk relations when their antecedents are jointly satisfied. Recognized events, bound risk relations, and semantic descriptions of activated rules form compact evidence that conditions the VLM and
diffusion planner. In matched comparisons on nuReasoning, our method improved the nuReasoning planning score (NPS) by 1.30 points and the non-at-fault collision score (NC) by 2.76 points over the relevance
retrieval-based baseline. These gains indicate that scene-applicable risk evidence improves safety-weighted planning relative to semantically retrieved risk knowledge.
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All authors Jiaxin Liu, Ruilin Yu, Liang Peng, Jingkai Wang, Chengxiang Zhao, Zhenxin Zhu, Bing Wang, Guang Chen, Hangjun Ye, Hong Wang, Jun Li
Computer Vision
CAST: Reconstruction-Coupled Acceleration of Interactive World Models
Leyang Chen, Junyi Wu, Fanqing Kong et al. (+2 more)
Interactive world models must respond quickly to controls while preserving scene consistency. Existing acceleration methods can miss heterogeneous control responses and spatial transport when recovering skipped features. We observe that interaction-induced feature changes correlate with approximation error, while low-frequency interpolation errors are phase-sensitive and show more predictable phase progression. These findings motivate CAST, a reconstruction-coupled inference framework. CAST selects anchors by interaction sensitivity and cross-layer coverage, reconstructs skipped residuals with frequency- and confidence-aware Phase-Aware Reconstruction (PAR), and coordinates historical KV routing according to downstream reconstruction responsibility. On
Matrix-Game 3.0 and HY-World 1.5, CAST achieves 2.15x and 3.48x speedups, respectively, while maintaining visual quality close to Native (Figure 1). It also attains the highest VBench scores among compared methods and leads non-native baselines on seven and six of thirteen WorldMark dimensions, demonstrating a balance of generation speed, visual quality, and interactive responsiveness under real-time control. Code is available at https://github.com/lokiniuniu/CAST.
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All authors Leyang Chen, Junyi Wu, Fanqing Kong, Shaoqiu Zhang, Yulun Zhang
Computer Vision
Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detection
Zhiqiang Qin, He Xie, Junfei Yi et al. (+5 more)
Existing 3D industrial anomaly detection mainly targets local
geometric deviations. In contrast, many industrial anomalies violate object-level design or assembly rules, which we define as 3D logical anomalies. To address these challenges, we introduce the Industrial Logical Anomaly Detection
Dataset (ILGAD), the first scalable
benchmark dedicated to logical anomalies in industrial point clouds. ILGAD contains 2,774 samples from 15 categories with point-level annotations and covers existence, specification, pose, and assembly-state errors. To detect such 3D logical anomalies, we propose a consistency
reasoning framework that assesses whether local geometry, structure coverage, and spatial relations conform to the normal design. The framework detects
geometric changes, unsupported expected structures, and abnormal local arrangements. Experiments on ILGAD, Anomaly-ShapeNet, and IEC3D demonstrate superior object-level detection and point-level localization, showing that the framework effectively detects logical anomalies and generalizes to conventional
geometric defects.
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All authors Zhiqiang Qin, He Xie, Junfei Yi, Yang Yang, Hao Wang, Yunkang Cao, Hui Zhang, Yaonan Wang
Computer Vision
Analytical and Convolutional Neural Network-Based Motion-Vector Propagation for Efficient Video Object Detection
Ashiyana Abdul Majeed, Mahmoud Meribout and Neethu Joseph
Continuous
video analytics requires accurate localization at low latency within embedded power budgets. This paper presents a hardware-software design methodology that reuses codec motion vectors (MVs) between detector invocations. Two alternative models support translation and scale changes: analytical motion-vector propagation (Analytical-MV) and learned propagation using a convolutional neural network (CNN) (CNN-MV). The learned model uses convolutional operations and independent object updates suited to parallel execution on an edge graphics processing unit (GPU). Analytical-MV combines a harmonic-mean precision-recall score (F1) of 0.909 with a mean end-to-end latency of 9.03 ms and an energy consumption of 0.177 J per frame, yielding the lowest latency and energy among the evaluated configurations. Relative to detection on every frame, it reduces mean latency by 25.9% and energy per frame by 36.4%. CNN-MV offers a different trade-off: its fastest configuration raises recall from 0.871 for Analytical-MV to 0.890 and lowers mean power from 19.64 to 17.32 W, while achieving a latency of 18.42 ms and an energy consumption of 0.319 J per frame. It is therefore useful when recall or operating power is more important than minimum latency and energy. Execution on a deep learning accelerator (DLA) further reduces time-averaged GPU utilization relative to GPU execution. Host-processing optimization substantially improves both latency and energy, demonstrating the value of jointly designing temporal models and their execution pipelines.
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Cryptography and Security
Long-Term Operational Planning Using Scenario-Based System Load Forecasting
Akash Debnath, Shiuli Subhra Ghosh, Jaime De La Ree et al. (+2 more)
The rapid expansion of hyperscale data centers is significantly increasing electricity demand in Northern Virginia. Dominion Energy, the region's primary electric utility, must reinforce its transmission network to support this growth. These projects require planned outages that must be evaluated months in advance to support construction planning and outage coordination while meeting NERC and PJM N-1 reliability requirements. Long-term outage studies are commonly performed day by day using monthly or seasonal peak-load assumptions. Although this approach simplifies analysis, it can be overly conservative because it does not capture granular load variability. Consequently, short-duration outages that may be feasible under realistic loading conditions are often postponed or denied, delaying critical transmission expansion and grid modernization projects. This paper evaluates the operational value of incorporating realistic multigranular load forecasts into long-term, contingency-based outage assessments and compares the results with conventional peak-based methods. Daily, weekly, and monthly forecasts are developed using statistical and machine-learning models, including SARIMA, Prophet, Gradient Boosting, and Random Forest, followed by bottom-up temporal reconciliation to maintain consistency across forecast horizons. Results show that granular load forecasts reduce unnecessary conservatism and improve outage accommodation, particularly for short-duration requests, without changing existing reliability criteria. The proposed approach can strengthen long-term outage coordination and support timely transmission reinforcement and grid modernization.
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All authors Akash Debnath, Shiuli Subhra Ghosh, Jaime De La Ree, Alex Freeman, Kavin D Jones
Artificial Intelligence
Same Tasks, Different Apps: Why Mobile GUI Agents Fail to Generalize?
Tien Tran, Namho Koh, Daiki E. Matsunaga et al. (+2 more)
Mobile GUI
agents deployed in real settings must work across different applications that support the same functionality. Most existing
benchmarks test each task in only one app, so a high score can mean the
agent understands the task, or only that it knows that particular app. We introduce AnyAppBench, a category-controlled live Android
benchmark that evaluates cross-application generalization while keeping the user goal fixed. It spans 10 functional categories, 100 task templates, and 520 task–application pairs over 52 applications.
Agents run from raw instructions and with app-independent sub-goals, and a VLM judge labels every failed run under a fixed failure taxonomy whose reliability is measured by human annotation. We find that, across 13
agents, success on the original application does not transfer reliably to new applications with the same goal. Furthermore, providing high-level sub-goal decomposition produces only small, category-dependent changes that do not close the gap, and the mix of failure types changes with the target interface. Based on those insights, we believe the AnyAppBench
benchmark provides an important stepping stone toward robust real-world deployment of mobile GUI
agents. Our code, data and the leaderboard can be found at the project website https://anyappbench.github.io/.
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All authors Tien Tran, Namho Koh, Daiki E. Matsunaga, Ayush Jain, Kee Eung Kim
Computation and Language
Word Similarity Datasets for Indian Languages: Annotation and Baseline Systems
Syed S. Akhtar, Arihant Gupta, Avijit Vajpayee et al. (+2 more)
With the advent of word representations, word similarity tasks are becoming increasing popular as an evaluation metric for the quality of the representations. In this paper, we present manually annotated monolingual word similarity
datasets of six Indian
languages - Urdu, Telugu, Marathi, Punjabi, Tamil and Gujarati. These
languages are most spoken Indian
languages worldwide after Hindi and Bengali. For the construction of these
datasets, our approach relies on translation and re-annotation of word similarity
datasets of English. We also present baseline scores for word representation models using state-of-the-art techniques for Urdu, Telugu and Marathi by evaluating them on newly created word similarity
datasets.
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All authors Syed S. Akhtar, Arihant Gupta, Avijit Vajpayee, Arjit Srivastava, M. Shrivastava
Artificial Intelligence
You Can't Have It Both Ways: Concept Entanglement Limits Diffusion Model Unlearning
Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram et al. (+1 more)
Concept unlearning in text-to-image
diffusion models aims to suppress a target concept (e.g., horse) while preserving related but distinct content (e.g., donkey), yet existing methods either leak under indirect
prompts or visibly degrade other concepts. We show that these failure modes stem from the geometry of concept representations rather than from any particular
algorithm. Formalizing concepts as activation-space regions, we prove that the overlap between a target and other concepts lower-bounds the damage any robust erasure must inflict on them, with the trade-off scaling linearly in the degree of overlap. Across thirteen unlearning methods, including methods designed to preserve non-target concepts, no method achieves both strong erasure and strong neighbor preservation: STEREO nearly eliminates indirect leakage but cuts neighbor generation by more than 75%, while sparse inference-time methods preserve neighbors but leak. Damage increases with our overlap measure, monotonically so for STEREO; the $\kappa$-scaling reproduces on SDXL, and neighbor-selective damage recurs on FLUX. Perfect unlearning is the wrong target for entangled concepts; methods should be evaluated on the Pareto frontier our
theorem establishes.
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All authors Yian Wang, Ali Ebrahimpour-Boroojeny, Hari Sundaram, Varun Chandrasekaran
Artificial Intelligence
Waggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms
Mingxi Zou, Wei Zhu, Zhuo Wang et al. (+4 more)
As LLM
agents increasingly collaborate on complex tasks, how to organize their interactions becomes a central design question. Existing multi-
agent systems typically learn or adapt explicit roles, hierarchies, routing policies, or communication
topologies. We shift the learning target to a reusable local law that can be shared across interchangeable
agents and adapt coordination as populations or interaction conditions change, without redefining a global organization. We introduce Waggle, a shared anonymous policy over bounded local views that jointly selects task actions, semantic communication, and local commitment updates. Repeated execution of the same law allows coordination to form, persist, and reorganize online without explicit roles or global
topology. To learn this law across interchangeable
agents and evolving coordination, we develop Swarm-Consistent
Distillation (SCD), combining anonymous-orbit consistency with rollout-grounded prediction of the next local coordination field, with no added inference-time components. Across diverse coordination settings, the same learned law remains effective as populations and interaction budgets change, retains over 96% of substrate-specific oracle quality, and transfers without retraining; SCD further improves reorganization after counterevidence. Together, these results show that LLM-
agent organization can emerge and adapt through repeated execution of a learned local law.
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All authors Mingxi Zou, Wei Zhu, Zhuo Wang, Langzhang Liang, Zhiwen Tang, Yinghui Xu, Zenglin Xu
Artificial Intelligence
Evo2Team: When Do Evolved Skills Transfer? From Selection to Deployment
Renxiang Wang and Jiaming Cui
A skill bank that helps one multi-
agent system may leave another's behavior unchanged. A transferred rule helps only when target
agents act on it successfully. We study this path for routing and communication skills in Count-Frequency and AgentsNet, using teams of 4–32
agents and GPT and Qwen model ladders. Source
evolution meets a joint quality, cost, model-tier, and confirmation goal in 14 of 16 settings. We then evaluate Evo2Team, which selects, adapts, and confirms source skills for the target team, alongside six frozen selectors across 28 transfer directions. Evo2Team's target-side exploration cost is below that of evolving a new target bank in every direction, even when reused reference evaluations are charged once. Twenty of 28 held-out outcomes meet the positive-transfer criterion, including three saved diagnostic tests. Selection alone does not explain these outcomes: KNN and CORAL choose different banks in two AgentsNet directions but produce identical recorded executions. When Evo2Team changes execution, gains can reach many tasks, as in a Count-Frequency direction that improves 28 of 32 tasks over KNN. Seven positive AgentsNet outcomes save 6.1–14.6% in deployment cost while using transferred skills on only three to six of fifteen tasks. In five earlier accepted directions, all 22 task records using transferred skills pass three fixed-graph confirmations, but four fail in recorded executions on new graphs. Graphs and model responses change together in this comparison. These results show that skill transfer must be assessed through the actions
agents take, the tasks those actions reach, and the quality and cost of the final deployment.
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Artificial Intelligence
StateGuard: Analytical-State Management with Validity-Aware Intervention for Long-Horizon Data Agents
Wenle Liao, Zhao Wang, Jingchao Zhang et al. (+3 more)
LLM-based
agents have shown strong capabilities in automated data analysis and are increasingly moving toward long-horizon, multi-stage analytical workflows. However, as the analytical process evolves, constraints, variables, and conclusions remain implicitly embedded in interaction histories, making it difficult for
agents to track which analytical artifacts remain valid over increasingly long horizons and changing dependencies. Consequently, stale artifacts may be silently inherited, propagating errors to downstream stages. To address this challenge, we propose StateGuard, an analytical-state validity management framework for long-horizon data
agents. StateGuard externalizes evolving analytical progress into a state graph containing constraints, versioned variables, intermediate conclusions, and cross-state relations, treating each state as an executable, verifiable, and traceable object rather than textual
memory alone. StateGuard maintains state validity through evidence-grounded verification and hierarchical intervention. To equip StateGuard with these capabilities, we first introduce Manager-Oriented Counterfactual Supervision, which constructs 3K state-centric trajectories through counterfactual runtime synthesis to fine-tune StateGuard for state maintenance, verification, and repair. We then apply Validity-Guided Policy Optimization, using runtime validity evidence to provide fine-grained learning signals for protocol correctness, state grounding, and intervention quality. Experiments on three diverse long-horizon data-analysis
benchmarks show that StateGuard consistently improves data-
agent performance while reducing dependency-induced downstream error propagation, demonstrating the advantages of explicit analytical-state management for reliable long-horizon data analysis.
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All authors Wenle Liao, Zhao Wang, Jingchao Zhang, Jiajie Jin, Yimeng Xu, Zhicheng Dou
Computer Vision
PrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences
Chuanzhi Xu, Langyi Chen, Chengkun Yue et al. (+5 more)
Photographic color editing is inherently personal: the same image can appear too warm, too muted, or already satisfactory to different users. Most lookup table (LUT) and reference-guided methods target a specified appearance rather than model persistent preferences from repeated user choices. To address this gap, we introduce PrefLUT, a reusable and refinable user-preference modeling framework for deployable 3D LUTs, encoding ordered preferred/non-preferred image pairs into a lightweight Reusable User Profile that is reused across queries and refined using additional user preference pairs, without per-user optimization. A Query-Conditioned LUT Predictor combines this profile with each image to predict a LUT latent vector and edit strength. An Identity-Residual LUT Decoder and Edit-Strength Controller then produce an exportable 3D LUT. Experiments on three
datasets demonstrate effective personalized editing and general-purpose enhancement. Each quantized profile requires only 260 bytes, and editing takes 1.365 ms/image on an RTX 5090 GPU. We also introduce the Preference-Conditioning Verification Protocol (PCVP), an evaluation protocol to verify whether personalized image edits depend on user preferences and the query image through controlled changes to user profiles, preference orders, pair correspondences, and query images.
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All authors Chuanzhi Xu, Langyi Chen, Chengkun Yue, Xuanhua Yin, Boyu Wei, Qingwen Zeng, Zihan Deng, Weidong Cai
Artificial Intelligence
From Attack Success to Attack Severity: Counterfactual Memory Attacks on LLM Agents
Mingxi Zou, Langzhang Liang, Zhuo Wang et al. (+3 more)
As LLM
agents increasingly rely on persistent
memory for long-horizon and personalized behavior, they can retain and reuse information across interactions, but this also creates a lasting channel through which malicious
memory writes can influence future behavior. Persistent-
memory attacks are typically evaluated by whether they succeed, yet successful attacks can leave persistent states with substantially different downstream consequences. We study this severity as a distinct attack-design objective and formalize it with counterfactual
memory regret (CMR), the paired increase in expected downstream loss relative to clean
memory. We introduce MemHarm, which predeclares a finite class of sparse, grounded semantic edits, evaluates candidates through the normal
agent memory interface using offline paired-loss feedback, and certifies resolved selections within that class. Compared with attack-success optimization, CMR-guided selection produces substantially larger downstream loss while retaining most of the success-rate gain. Across two
agent benchmarks and diverse
memory designs, MemHarm attains the highest CMR point estimates among the evaluated general attacks on identical support. Factor-removal interventions link this harm to the selected semantic factor, and native-
agent deployments verify the write-to-fresh-process attack path.
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All authors Mingxi Zou, Langzhang Liang, Zhuo Wang, Yiyang Zhao, Lizhen Qu, Zenglin Xu
Machine Learning
Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction
Burc Gokden
This monograph develops a unified account of training and inference in Power Law Decoder Representation
language models (PLDR-LLMs). Exact finite work identities decompose changes in the absolute energy of the row-centered learned map into parameter contributions, signed interactions, and numerical observation defects. Positive affine blocking retains restarts at the row-constant face, while the augmented AdamW state supplies the complete dynamical description. Predictive renormalization acts on the complete conditional training law for a single pass over distinct corpus target blocks, retaining optimizer
memory, remaining data, schedule, and numerical policy. Autonomous reductions require closure; approximate reductions carry successor and emission errors. Finite-population covariance, matched physical clocks,
matrix fluxes, and signed temporal energy connect row dynamics to model-wide observations. Absolute row collapse, relative row concentration, operator stabilization, and predictive accuracy are distinguished. Experiments reveal observer and optimizer dependence, reject the tested autonomous row-state candidates, and support finite conditional prediction and state-specific operator reduction. Independent single-pass families exhibit moving finite fluctuation regions without establishing a thermodynamic critical class. Conditional
symmetry, head limits, covariance flows, and readout error budgets specify assumptions needed to transfer scaling laws to inference. The theory separates exact identities, conditional dynamical claims, and finite empirical findings, with proofs, selected formal checks, and compact numerical evidence.
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Information Theory
RIS-Assisted Multiuser NOMA Networks With Imperfect CSI Under Transceiver Hardware Impairments
Qian Zhang, Maoyuan Wang, Xuejun Cheng et al. (+3 more)
The incorporation of non-orthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) provides a new solution to improve spectral efficiency. Unfortunately, the channel estimation error of RIS is noteworthy due to the absent active
radio frequency link. Meanwhile, the transceivers at base stations (BS) and users also exist hardware impairments (HWI). The imperfect channel state information (CSI) and HWI will degrade the communication performance such as outage probability or data rate. To alleviate such performance deterioration, we propose a robust design scheme to minimize the BS transmit power when the target rate of users is satisfied in RIS-assisted single-beam and multi-beam NOMA networks with transceiver HWI and imperfect CSI. Further, the perfect successive interference cancellation (SIC) condition in single-beam and multi-beam NOMA networks is investigated, and the optimization problem is proposed based on perfect SIC conditions. To tackle the non-convex problem, we provide an effective
algorithm combining Bernstein-type inequality and semi-definite relaxation. Numerical results demonstrate that the proposed scheme outperforms non-robust RIS-assisted NOMA scheme that ignores HWI and imperfect CSI. Moreover, the multi-beam NOMA network obtains higher beam gain compared to the single-beam NOMA network, and thus there is a superior performance in multi-beam NOMA networks.
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All authors Qian Zhang, Maoyuan Wang, Xuejun Cheng, Yufei Zhao, Guanghui Luo, Shiyao Guo
Information Retrieval
STITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generation
Haodong Yang, Mengzhu Chen and Jia Cai
Multi-hop
retrieval-augmented generation requires a retriever to connect evidence distributed across documents while preserving a concise, faithful generation context. Existing indexes leave two complementary gaps: chunk-based RAG can break cross-passage evidence chains, whereas an unlabeled pairwise projection without generating-topic provenance cannot jointly preserve topic-level co-participation and per-occurrence entity descriptions. We propose STITCH-RAG, a hypergraph-based framework with three coupled components. First, a semi-merged topic hypergraph encodes multi-entity co-participation as topic-summary hyperedges while retaining per-chunk entity states linked by canonical-name equivalence. Second, spatio-temporal influence bridging propagation (STIBP) combines topic-space propagation with deterministic chunk-index linkage across name-equivalent states under frequency-adaptive decay. Third, continuous STIBP scores replace binary entity-match seeds in localized Personalized PageRank (PPR). We characterize the condition under which this prior assigns more PPR mass to ground-truth evidence than a binary prior. Under the reported protocol, STITCH-RAG attains the highest reported Contain-Acc and LLM-Acc point estimates among the compared methods on HotpotQA and 2WikiMultiHopQA, and higher Recall@8 than the methods included in the standardized
retrieval comparison. Results on the mixed-domain
benchmark remain auxiliary preference-based evidence because only LLM-judged accuracy is available.
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Software Engineering
JET: Judge-Guided Evolution at Test Time for Agent Programs
Yao Long Teng, Jiayi Cai and Bo An
An
agent's executable program governs how it uses tools, processes observations, and responds to failures. Evolving this program at test time can help adaptation, but deciding which changes to retain is difficult when true rewards are unavailable. Execution traces provide evidence of
agent behavior, yet interpreting that evidence requires a judge that remains useful as tasks and candidate programs change. We introduce Judge-Guided
Evolution at Test Time (JET), which evolves an executable judge on labeled source trajectories, then freezes and transfers it to guide target-side program
evolution. The judge supplies scores and diagnostic feedback without target evaluator access or model-weight updates. On unseen WebShop tasks, JET achieves approximately 13% higher mean reward than fixed-rubric guidance when
evolution begins from an unevolved program (cold start) and 4% higher when it begins from one already optimized on source tasks (warm start), with a 36% relative improvement in cold-start exact success. An exact-judge control on PushT, where the judge reconstructs the scoring rule from observations, shows that without judge error, program search becomes the bottleneck. Analyses identify useful reward-prediction logic in the evolved code and show that better final selection alone cannot explain the gains. These results support executable judge transfer for program adaptation under evaluator-preserving task shifts.
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Computation and Language
Understanding Clinical Cognitive Dialogues Using Large Language Models
Vishalakshi Arumugam, Dan Schumacher, Veronica Rammouz et al. (+4 more)
In-person cognitive assessment is both a test and an interaction. Clinicians explain tasks, repair misunderstandings, and adapt to patient responses, while patients may hesitate, seek clarification, or disengage. Yet clinical dialogue resources rarely label the interaction structure needed to study these behaviors at scale. We present an de-identified corpus of 33 cognitive assessment conversations with 8,250 utterances annotated for three speaker roles and 56 dialogue acts. We use this corpus to
benchmark large
language models on fine-grained dialogue-act classification and next-patient-utterance generation. We also test whether out-of-domain instruction data and explanation-augmented training transfer to this clinical setting. Instruction tuning produces the strongest patient-utterance reference matching and improves classification accuracy.
Reasoning-aware fine-tuning produces the strongest classification results among the LLaMA-3.1-8B variants. However, even the best models struggle to separate closely related dialogue acts, showing that broad conversational intent is easier to recognize than fine-grained communicative function. The corpus and
benchmark make interaction structure measurable in cognitive assessments and support follow-up work on conversational markers, clinician education, and carefully validated simulated patients. This work does not make diagnostic claims. Instead, it provides the data and evaluation framework needed to study these applications.
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All authors Vishalakshi Arumugam, Dan Schumacher, Veronica Rammouz, Erfan Nourbakhsh, Enrique Gonzalez Guerrero, Jeremy Davis, Anthony Rios
Computer Vision
SpatialSkill: Self-Evolving Skills for Cross-View Spatial Reasoning
Ruifan Zuo, Guocheng Hu, Wanshui Gan et al. (+3 more)
Cross-view spatial
reasoning requires a model to align different viewpoints into a coherent spatial representation, yet this ability remains challenging for
vision-
language models despite being natural to humans. Existing methods typically improve spatial
reasoning by updating model weights, which keeps the acquired knowledge implicit and tied to a specific backbone. We propose SpatialSkill, a weight-update-free framework that enables a frozen
vision-
language model to accumulate explicit natural-
language reasoning skills from offline trajectories. Unlike symbolic tasks, perceptual skills cannot be reliably verified simply by executing them: a plausible spatial rule may lack visual support or require transformations that the frozen model cannot perform. SpatialSkill therefore admits candidate skills only after visual-grounding and executability checks, constrains manual
evolution to prevent harmful regressions, and routes skills by spatial-
reasoning category to reduce negative transfer. On CityCube, across four frozen executors, SpatialSkill yields consistent gains, and a 9B executor equipped with SpatialSkill surpasses the strongest closed-source reference in our evaluation. The skills are stored in a versioned natural-
language manual, making the
reasoning strategies explicit and auditable without modifying model parameters. Code at https://github.com/vindahi/SpatialSkill.
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All authors Ruifan Zuo, Guocheng Hu, Wanshui Gan, Junyi Wang, Xiang Lei, Tian Gan