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18 papers in the last four weeks, up 100% on the four weeks before. 0.2% of all new papers.

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Latest papers 101

Jun 12, 2026cs.AI

Democratizing and accelerating AI-driven pathology research through agentic intelligence

Computational pathology has advanced rapidly with the emergence of foundation models, yet widespread adoption remains limited by substantial technical complexity and programming requirements. Here we present PathLab, an autonomous agentic framework that translates natural-language research objectives into executable and validated computational pathology workflows through the structured composition of domain-specific skills and tools. By organizing workflow generation around reusable methodological modules, including data preprocessing, model development, evaluation and interpretation, PathLab enables studies to be specified at the level of scientific intent rather than implementation details. We evaluated PathLab across 12 public datasets spanning four representative task families: region-of-interest classification, whole-slide image classification, segmentation and survival prediction. Across all task categories, PathLab achieved non-inferior performance relative to expert implementations, while consistently enforcing semantic validity of user prompts and proactively rejecting incompatible workflow specifications prior to execution. In controlled user studies, PathLab substantially reduced the time required to generate executable analytical pipelines and enabled domain experts without programming experience to independently design, execute and evaluate computational pathology studies. Together, these results establish PathLab as a reliable interface between biomedical intent and computational execution, enabling computational pathology studies to be designed at the level of scientific questions rather than programming expertise. By lowering technical barriers to advanced AI methodologies, PathLab provides a foundation for the broader democratization of computational pathology.

Jiabo Ma, Cheng Jin, Yihui Wang +19

Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China · Guangdong Province Key Laboratory of Molecular Tumor Pathology, Guangzhou, China +9

Computational Pathology Agentic Workflows Ai-Based Biomedical Intelligence Path+1

Jun 11, 2026cs.CV

CaricHarmony: Contrastive Diffusion Paths for Identity-Preserving Caricature Synthesis

Sketch-based caricature synthesis suffers from a fundamental failure mode: when identity and shape conditions are combined in diffusion models, they create destructive interference that causes inevitable collapse toward either bland portraits or unrecognizable distortions. We identify the root cause as \emph{condition signal contamination} -- competing probability distributions in the denoising trajectory that make balanced generation impossible. We present CaricHarmony, the first training-free method that explicitly resolves this contamination through parallel uncontaminated diffusion paths. During inference, we maintain three paths:

\mathcal{P}^{\mathrm{i}}

(pure identity),

\mathcal{P}^{\mathrm{s}}

(pure shape), and

\mathcal{P}^{\mathrm{i+s}}

(harmonized output). Novel energy functions operating on cross-attention features provide gradient guidance that steers

\mathcal{P}^{\mathrm{i+s}}

toward optimal balance:

\mathcal{E}_{\mathrm{shape}}

ensures sketch fidelity through layout and semantic alignment, while

\mathcal{E}_{\mathrm{id}}

employs token-level correspondence matching robust to extreme distortions. Unlike DemoCaricature requiring 70 seconds per-identity fine-tuning or CaricatureBooth constrained to Bezier curves, CaricHarmony accepts any sketch format and generates in under 16 seconds. Experiments demonstrate state-of-the-art performance: 0.8615 shape CLIP score (vs. 0.8450) under comparable identity consistency score, with 7.81 overall user preference score (vs. 6.06). Our method fundamentally reconceptualizes the ID-shape conflict as conditioning signal contamination for diffusion models, enabling unprecedented creative control while preserving recognition.

Dongyu Wang, Dar-Yen Chen, Yi-Zhe Song

SketchX, CVSSP, University of Surrey

Sketches Diffusion Models Diffusion Dynamics Explanation Consistency Score Harmonization Path

Jun 8, 2026cs.LG

Self-Consistent Generative Paths via Admissible Random Variational Transport

Modern generative models often define an entire probability path from a simple prior to the data law, rather than only an endpoint map. Diffusion models follow stochastic denoising paths, flow matching learns transport fields, consistency and distillation methods compress paths into one or a few steps, adversarial models match terminal distributions, and VAEs generate through latent kernels. Existing unifying views mainly describe how such paths are constructed. We study a complementary question: when is a generated probability path self-consistent? We define a self-consistent generative path as a random fixed point of admissible local variational transport corrections. In this framework, a local correction is specified by a random variational transport operator combining a divergence or geometry term, an energy term, and a structural constraint. The framework contains random regularized optimal-transport proximal steps as a structured instance, while also allowing non-OT divergences, latent kernels, adversarial constraints, causal discrete kernels, and terminal one-step maps. The theory yields a random fixed-point path residual (R-FPR), which measures the gap between the actual generated path and an admissible local correction. We prove well-posedness, random fixed-point existence and attraction, non-contractive existence, residual-to-generation error bounds, empirical residual concentration, proxy perturbation bounds, continuous-time limits, and operator-level generalization with model-specific corollaries. The resulting theory turns endpoint matching into path self-consistency testing and provides a residual-control principle for diagnosing failures, regularizing training, and guiding adaptive sampling across diffusion, flow, one-step, VAE, GAN/WGAN, and autoregressive generators.

Lei Luo, Yingzhen Zhang, Jian Yang

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Diffusion Models Path

Jun 6, 2026cs.AI

A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology

Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to transform clinical workflows, the intersection of AI and evidence-based medicine remains under-explored, with primitive attempts restricted to text-only general medicine. In this work, we present PathPocket, a multimodal AI agentic co-pilot designed specifically for evidence grounded pathology. We construct the most comprehensive pathology evidence corpus to date, encompassing approximately 110,472 public and authorized documents structured across a rigorous hierarchy of evidence from clinical guideline to expert opinion. From this meticulously graded foundation, we build a large-scale multimodal pathology hypergraph containing over 4.55 million entities and 7.10 million relations. Serving as a robust knowledge engine, this hypergraph provides traceable evidence for a collaborative multi-agent reasoning framework integrating input understanding, evidence retrieval, filtering, and diagnosis generation. This enables PathPocket to seamlessly resolve a wide spectrum of clinical tasks, ranging from text-only queries to complex multimodal diagnostics involving region-of-interest (ROI) and gigapixel whole-slide images (WSIs). We rigorously evaluate the system on a multidimensional benchmark of over 200,000 real-world cases, where it significantly outperforms existing state-of-the-arts. Crucially, extensive user studies demonstrate that PathPocket substantially improves the diagnostic accuracy and confidence of pathologists. By directly grounding pathology interpretations in verifiable literature, PathPocket offers a practical and scalable solution for the future of evidence grounded computational pathology.

Zhe Xu, Zhengyu Zhang, Zhiyuan Cai +12

Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +7

Computational Pathology Pathology Foundation Models Multimodal Clinical Data Multimodal Agents Copilot Path

Jun 5, 2026cs.RO

Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach

Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge because the problem is nonlinear and nonconvex even in simplest scenarios. While traditional optimal control methods can be used to find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as possibly multiple circular 'no-go' zones. A mission is regarded as a failure if the vehicle enters this restricted zone at any time or does not reach a neighborhood of the destination. The DDPG agent is trained through trial and error in a simulated environment, learning a direct mapping from its current state (position and heading) to a series of feasible actions that guide the agent to safely reach its destination. The reword function has three parts: (a) an attractive field centered at the final destination, (b) some repulsive fields centered at the origins of circular obstacles, and (c) a penalty of control energy consumption (the magnitude of heading change) that indirectly in favor for straight path. The DDPG trains the agent using these incentives to find the largest possible set of starting points wherein a safe path to the destination is guaranteed. This provides critical information for mission planning, showing beforehand whether a task is achievable from a given starting point, assisting pre-mission planning activities. The approach is validated in simulation. A comparison between the DDPG method and a traditional optimal control (pseudo-spectral) method is carried out. The results show that the learning-based agent produces effective paths while being significantly faster, making it a better fit for real-time applications.

Qiang Le, Yaguang Yang, Isaac E. Weintraub

Hampton University, Hampton, VA 23669, USA · Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA

Path Planning Path Offline Reinforcement Learning Threat

Jun 4, 2026cs.RO

3D Underwater Path Planning via Generative Flow Field Surrogates

Autonomous underwater vehicle (AUV) launch and recovery (LAR) into the hull of an advancing host platform requires traversal of a complex, three-dimensional propeller wake whose hydrodynamic structure cannot be characterised by a uniform current model. High-fidelity Reynolds-Averaged Navier-Stokes (RANS) Computational Fluid Dynamics (CFD) simulations resolve this structure with sufficient accuracy for path planning, but their computational cost renders them impractical for onboard use. We address this gap by integrating two conditional generative adversarial network (cGAN) architectures -- a regularised PatchGAN and a 2D3DGAN with self-attention -- as drop-in replacements for RANS CFD data within a three-dimensional, energy-weighted A* path planning framework. Both generators are driven by a hierarchical pipeline that synthesises full

128^3

voxel flow field volumes from scalar operating condition inputs alone, with end-to-end inference times of approximately 28-146

μ

s, compared to hours for a single RANS computation. We benchmark all four environmental knowledge levels: uniform current, ground-truth CFD, PatchGAN, and 2D3DGAN~SA across 19,800 independently generated trajectories spanning 550 distinct flow conditions. Full CFD wake knowledge reduces energy expenditure by 5.7-12.5% and high-velocity wake-core encounters by up to 77.8% relative to uniform-current planning, with both benefits scaling with operating severity. The cGAN surrogates recover approximately 45-60% of the CFD energy benefit and high-velocity cell avoidance benefit while operating at inference speeds compatible with edge device use. These results provide the first systematic quantification of the downstream path planning value of cGAN-predicted hydrodynamic fields in a three-dimensional maritime robotics application.

Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth +1

Flinders University · PrioriAnalytica

Autonomous Underwater Vehicles Queryable Sector Flow Fields Computational Fluid Dynamic Path Planning Path

May 30, 2026cs.CV

DarkVesselNet: Multi-Modal Remote Sensing and Trajectory Reasoning for Dark Vessel Detection

Dark vessel detection requires fusing what vessels report through AIS with what satellites observe through radar and optical sensors. DarkVesselNet is a multi-modal remote sensing stack that combines Sentinel-1 SAR, Sentinel-2 optical imagery, geospatial foundation model backbones, AIS trajectory reasoning, TGARD-style gap detection, and a Pi-DPM-inspired anomaly head. The repository exposes the system as a tested Python package and a public Hugging Face Space. The paper presents the sensor stack, backbone abstraction, fusion path, anomaly head, and current validation. The evidence currently available is software-grounded: tests for SAR speckle filtering, optical band ratios, Haversine distance, TGARD gap emission, sensor coregistration, backbone token shapes, and differentiable anomaly scoring.

Arun Sharma

University of Minnesota, Twin Cities

Ship Detection Remote Sensing Synthetic Aperture Radar Automatic Identification System Path

May 27, 2026cs.RO

Accelerating Robot Path Planning via Connectivity-Preserving Region Proposal Network

Mobile robot path planning methods are often constrained by vast search spaces, resulting in latency in samplingbased algorithms. Learning-based approaches frequently suffer from local region fragmentation and global topological inconsistency. To tackle the problem, we present the Connectivity- Preserving Region Proposal Network (CP-RPN), a segmentationguided model designed to predict compact and topologically connected candidate regions, significantly compressing the search space. Specifically, we design a segmentation model that leverages a Deformable Attention Transformer (DAT) to capture long-range dependencies for global connectivity, with a Deconvolutional decoder to preserve fine-grained spatial details. To guarantee the connectivity of the predicted mask, we design a composite loss function that combines Cross-Entropy loss for pixelwise supervision, a Connectivity-Aware loss to enhance local coherence, and a Topological Continuity loss based on persistent homology to enforce global connectivity. Building on these highconnectivity corridor-like regions, the Voronoi diagram is used to plan the path, backed by a local A* fallback mechanism to ensure robustness. Experimental results demonstrate that CPRPN reduces the candidate region size by over 60.13% compared to the MPT baseline and achieves deterministic low-latency planning (avg. 0.11s) with a 99.60% success rate, outperforming traditional sampling-based algorithms in stability.

Zhanzheng Ma, Cancan Zhao, Shuai Zhang +1

School of Management, Hefei University of Technology, Hefei 230009, China

Robot Planning Path Topology Robot Systems Semantic Segmentation

May 25, 2026eess.IV

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evaluated across clinical workflows or prospectively validated in real-world settings. We introduce PulmoFoundation, a multi-center, prospectively validated, randomized controlled trial (RCT)-evaluated foundation model for comprehensive lung pathology assessment across pre-operative, intra-operative, and post-operative care. Built upon Virchow2 via subspecialty-specific pretraining using ~40,000 diagnostic H&E-stained whole-slide images (WSIs), PulmoFoundation was systematically evaluated on ~26,000 WSIs across 32 clinically relevant tasks. In addition to accurately predicting molecular markers and patient survival, our model achieves clinical-grade performance in core diagnostic tasks across biopsy, frozen section, and surgical resection slides. In a registered prospective study of 1,357 patients across 11 diagnostic tasks, our model achieved an average AUC of 92.3%. Using pre-specified triage thresholds, PulmoFoundation could reduce additional second-review burden for 68.8% of biopsies and 83.0% of frozen sections, and defer 44.5% of IHC stain orders, with PPVs of 1.0, 0.991, and 0.966. Beyond prospective validation, we conducted a crossover RCT with eight pathologists, in which AI assistance improved diagnostic accuracy across 4,928 case-reader pairs (91.7% w/ AI vs. 83.8% w/o AI). AI assistance also reduced median diagnostic time by 19.6%, increased diagnostic confidence by 8.7%, and improved inter-rater agreement from moderate (kappa = 0.56) to substantial (kappa = 0.76). Together, these evaluations support PulmoFoundation as a clinically validated decision-support system for lung pathology.

Zhengrui Guo, Zhengyu Zhang, Jiabo Ma +23

Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China · Department of Pathology, Nanfang Hospital, Southern Medical University, Guangzhou, China · Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China +15

Pathology Foundation Models Lung-Nodule Artificial Intelligence Tumor Wireless Foundation Models Path Circuit Depth

May 25, 2026cs.RO

Path Following Control System of Line-of-Sight Guidance for Robotic Dolphin with Multi-Link Mechanism in Underwater Simulator

Biomimetic autonomous underwater vehicle (BAUV) with multi-link mechanism is widely used in aquatic life observation and environmental surveys due to its low power consumption and high maneuverability. An environmental survey requires a path following system that automatically follows specific points. However, the path following system of BAUV is limited, and its evaluation with multi-link mechanism robots has not yet been clarified. The path following system in BAUV requires prior simulation because the model differs depending on the type of biomimetics. In this study, we propose a path following system for BAUVs with a multi-link mechanism and evaluation in underwater simulation. In this result, it was possible to design a path following system suitable for BAUV, determine parameters using a simulator, and evaluate control methods.

Takumi Asada, Takao Oki, Hideo Furuhashi +3

Utsunomiya University, 7-1-2 Yoto, Utsunomiya, Tochigi, 321-8585, Japan · Aichi Institute of Technology, 1247 Yachigusa, Yakusa, Toyota, Aichi, Japan

Underwater Robots Underwater Underactuated Dynamics Multi-Uav Kinematic Chain Coral+1

May 21, 2026cs.AI

PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating long-form Chain-of-Thought (CoT) trajectories during inference. Meanwhile, these trajectories often contain explicit reflection markers such as wait'', but'', and ``alternatively'', signaling hesitation, revision, and the consideration of alternative explorations, respectively. Recent studies on test-time control leverage such markers as lightweight handles for steering reasoning, typically treating them as a single coarse-grained category rather than distinguishing their distinct functional roles. In this paper, we conduct type-wise suppression and fixed-prefix intervention, revealing that reflection markers differ not only in their functional roles but also in when they exert the greatest influence. Specifically, different marker classes affect accuracy and generation length in distinct ways, and marker choices are most consequential before the model settles into a stable reasoning trajectory. Motivated by these findings, we introduce PathCal, a novel training-free decoding controller that calibrates reasoning paths by distinguishing marker types and intervening only at locally uncertain states. At each decoding step, PathCal utilizes the distribution over reflection-markers to estimate local competition between maintaining the current reasoning trajectory and initiating a competing branch, and softly rebalances marker logits when competing-branch evidence becomes excessive. Experiments across six reasoning benchmarks demonstrate that PathCal achieves a better efficiency--performance trade-off, improving or preserving accuracy while reducing generation length, without relying on external verifiers or additional sampling.

Lingyu Jiang, Zirui Li, Shuo Xing +6

1Tohoku University · 2Texas A&M University · 3Worcester Polytechnic Institute

Reasoning Trajectory Chain-of-Thought Reasoning Path

May 15, 2026cs.CV

VLMs Trace Without Tracking: Diagnosing Failures in Visual Path Following

Vision-language models (VLMs) achieve strong performance on multimodal benchmarks, but may still lack robust control over basic visual operations. We study \textit{line tracing}, where a model must follow a selected visual path through successive local continuations. To isolate this ability, we design controlled tracing tasks that introduce nearby competitors while reducing semantic and topological ambiguity such as crossings and overlaps. Across these tasks, even state-of-the-art VLMs frequently lose the target path and switch to nearby alternatives, especially when those alternatives look locally similar to the target. Behavioral interventions and internal analyses indicate that these failures arise from local competition: nearby similar distractors pull the model away from the true continuation. Standard remedies do not remove this bottleneck: model-size scaling provides only limited gains, reasoning partially compensates through costly substitute strategies, and explicit tracing instructions fail to recover stable path following. Finally, tests on tangled-cable scenes and metro maps with richer visual complexity show that the same path-switching failure persists beyond our controlled settings.

Hyesoo Hong, Minsoo Kim, Wonje Jeung +3

Yonsei University

Vision-Language Foundation Models Multimodal Benchmarks Path Continuation

May 14, 2026cs.MA

Estimated Dynamic Equilibrium Model: Supply and Demand as a Sample Path of a Stochastic Process

We introduce the Estimated Dynamic Equilibrium Model (EDEM), an agent-based framework that treats supply and demand as a coupled stochastic process driven by heterogeneous, noisy agent valuations. The model's primary technical contribution is the identification of a generative mechanism for persistent disequilibrium: when market-clearing prices are sequentially sampled from the upper tail of noisy bid distributions and recycled as inputs for future valuations, expected prices drift upward despite strictly zero-mean estimation errors. We derive this order-statistic bias in closed form for i.i.d. uniform bids and use simulations to show that compounding this bias across epochs yields exponential price growth without requiring assumptions of investor optimism or irrationality. This framework extends Miller's divergence-of-opinion theory to a dynamic setting, recovering Walrasian equilibrium and Miller's static premium as limiting cases. Through controlled experiments and sensitivity analysis on a simulated real-estate neighborhood, we identify six distinct regimes-ranging from band-stability to runaway bubbles-emerging from a single agent ruleset. These results offer a potential explanation for the contradictory findings in the empirical divergence-of-opinion literature and suggest that machine-learning valuation algorithms may inadvertently amplify this inherent statistical bias.

Mikhail L. Arbuzov, Sisong Bei, Alexey Shvets

Independent Researcher · Palo Alto Networks

Equilibrium Stochastic Processes Exponents Path

May 14, 2026cs.LG

Lagrangian Flow Matching: A Least-Action Framework for Principled Path Design

Flow matching trains a neural velocity field by regression against a target velocity associated with a prescribed probability path connecting a simple initial distribution to the data distribution. A central design choice is the path itself. Existing constructions, including rectified and optimal-transport-based paths, transport samples along straight lines between coupled endpoints and thus cover only a narrow class of dynamics. We observe that this corresponds to the simplest case of the least-action principle in classical mechanics, in which the kinetic Lagrangian yields free-particle straight-line trajectories. Building on this observation, we propose Lagrangian flow matching, a physics-based framework in which the probability path and velocity field are determined by minimizing the action of a general Lagrangian subject to the continuity equation and the prescribed endpoints. We show that this dynamic problem admits an equivalent static optimal transport (OT) formulation, yielding a family of simulation-free training objectives that recover OT-based flow matching as the kinetic special case and the trigonometric variance-preserving diffusion path as the harmonic-oscillator case. More general Lagrangians give rise to new probability paths and velocity fields, and numerical experiments show that they induce meaningful changes in the learned dynamics while remaining competitive with existing conditional flow matching models.

Shukai Du, Junzhe Zhang, Yiming Li

Department of Mathematics, Syracuse University · Department of EECS, Syracuse University

Conditional Flow Matching Velocity Field Path Diffusion Models

May 13, 2026cs.MA

Privacy Preserving Multi Agent Path Finding

In the multi-agent path finding (MAPF) problem, a group of agents search in a graph for a path for each agent where no two paths collide. While in all applications of MAPF the agents must not collide with each other, in some of them the agents may not wish to share their paths due to privacy constraints. In this work, we formulate two types of privacy constraints for MAPF and propose algorithms that preserve them. The first type of privacy we consider is planning-level privacy, which means that during planning, the agents cannot identify exactly the planned location of the other agents. We propose a general framework for obtaining planning-level privacy, which works by adding mock agents to the planning process. The second type of privacy we consider is execution-level privacy, which is relevant when agents have limited sensing capabilities. Execution-level privacy is preserved if none of the agents is allowed to sense the location of the other agents during execution. We show how to adapt two popular MAPF algorithms, namely PIBT and LaCAM, such that they preserve execution-level privacy. Lastly, we propose a post-processing technique that allows the agents to reduce the sum of costs of the returned solution without losing any privacy. We also implemented our algorithms and evaluated them empirically, showing that the proposed post-processing technique indeed improved cost significantly.

Rotem Lev Lehman, Roni Stern, Guy Shani

Ben Gurion University of the Negev · Be’er Sheva, Israel

Multi-Agent Path Finding Path Planning Privacy Path

May 13, 2026cs.RO

LMPath: Language-Mediated Priors and Path Generation for Aerial Exploration

Traditional autonomous UAV search missions rely on geometric coverage patterns that ignore the semantic context of the target, leading to significant time waste in large-scale environments. In this paper we present LMPath, a pipeline for generating language-mediated exploration priors for Unmanned Aerial Vehicle (UAV) search missions that leverages semantics. Given a basic geofence and an object of interest prompt, LMPath uses generative language models to determine what regions of the environment should contain that object and a foundation vision model ran over satellite imagery to segment sub-regions that form the exploration prior. This prior can then be used to generate UAV paths with various objectives, such as minimizing the expected time to locate the object of interest, maximizing the probability that the object is found given a limited travel distance, or narrowing down the search space to sub-regions that are most likely to contain the object. To demonstrate it's capabilities, we used LMPath to generate various UAV paths and ran them using a real UAV over large-scale environments. We also ran simulations to demonstrate how paths generated using LMPath outperform traditional path planning approaches for search missions.

Jonathan A. Diller, Fernando Cladera, Camillo J. Taylor +1

GRASP Laboratory, University of Pennsylvania

Path Planning Unmanned Aerial Vehicle Detection Path Exploration Textual Priors Long-Run Coverage Guarantees

May 13, 2026cs.LG

Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein

We propose min Generalized Sliced Gromov--Wasserstein (min-GSGW), a sliced formulation for the Gromov--Wasserstein (GW) problem using expressive generalized slicers. The key idea is to learn coupled nonlinear slicers that assign compatible push-forward values to both input measures, so that monotone coupling in the projected domain lifts to a transport plan evaluated against the GW objective in the original spaces. The resulting plan induces a GW objective value, and min-GSGW minimizes this cost directly in the original spaces. We further show that min-GSGW is rigid-motion invariant, a crucial property for geometric matching and shape analysis tasks. Our contributions are threefold: 1) we introduce generalized slicers into the sliced GW framework, 2) we construct a slicing-based efficient GW transport plan; and 3) we develop an amortized variant that replaces per-instance optimization with a learned slicer for unseen input pairs. We perform experiments on animal mesh matching, horse mesh interpolation, and ShapeNet part transfer. Results show that min-GSGW produces meaningful geometric correspondences and GW objective values at substantially lower computational cost than existing GW solvers.

Ashkan Shahbazi, Xinran Liu, Ping He +1

Department of Computer Science, College of Connected Computing, Vanderbilt University · Department of Electrical and Computer Engineering, Vanderbilt University

Gromov--Wasserstein Geodesic Geometric Machine Learning Slices Path

May 13, 2026cs.RO

Multi-Depth Uniform Coverage Path Planning for Unmanned Surface Vehicle Surveying

This paper introduces a novel automatic coverage path planning algorithm for bathymetry surveying with unmanned surface vehicles. The detection range of the mapping sensor employed - a multibeam echo sounder - is heavily influenced by local seafloor depths. Hence, a path designed to uniformly cover the sea surface does not guarantee uniform coverage of the seafloor. Yet this is currently the typical process for bathymetric surveys, with the simplistic boustrophedon scheme along manually selected waypoints at constant depths being the most widespread planner used. The proposed scheme incorporates coarse prior depth information to pre-process the target region and adaptively guide path generation and sensing range configuration. By explicitly accounting for depth variations, the proposed algorithm designs a coverage path with optimised spacing between survey passes that adjusts the sensing beam aperture to achieve more consistent seafloor coverage. The proposed method is shown to offer significant improvements in both synthetic and real-world scenarios. Validations in challenging synthetic terrains achieves coverage ratios beyond 99%, a marked improvement when compared with traditional boustrophedon paths revealing a maximum 75% coverage. The same trend appears in realistic simulations using real bathymetric data from a coastal harbour, with coverage reaching over 92%, and significantly surpassing boustrophedon sweeps with coverage rates below 65%. Beyond improved performance, the scheme also brings a fully automated design, suitable for autonomous marine vehicles, thus offering practical utilities for real-world applications.

Maider Larrazabal, Tong Yang, Izaro Goienetxea +1

AZTI Foundation, Bizkaia, Spain. · Dept. of Comp. Sc. and Artificial Intelligence, University of the Basque Country (UPV/EHU), Donostia-San Sebasti´an, Spain. · Dept. of Comp. Sc. and Technology, Tsinghua University, P.R. China. +1

Geometric Bathymetric Data Forward-Looking Sonar Path Planning Path

May 11, 2026cs.RO

Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields

Safe autonomous Uncrewed Aerial Vehicle (UAV) navigation in urban environments requires real-time path planning that avoids obstacles. MaxConvNet is a potential-field planner that leverages properties of Maxwell's equations to generate a path to the goal without local minima. We extend the 2D MaxConvNet magnetic field planner to 3D, using a convolutional autoencoder to predict obstacle-aware potential fields from LiDAR-derived 101^3 voxel grids. Evaluation across 100 randomized closed-loop trials in two distinct Cosys-AirSim urban environments, a dense night-time cityscape and a suburban district shows a 100% path planning success rate on both maps without retraining. In offline path planning, 3DMaxConvNet produces path lengths comparable to A* on unseen maps while reducing runtime from 0.155--0.17s to 0.087--0.089s, or about 1.7--1.95 times faster than A*. Against RRT*(3k), 3DMaxConvNet achieves similar path quality while reducing planning runtime from 17.2--17.5s to about 0.09s, which is roughly 193--201 times faster than RRT*(3k).

Haechan Mark Bong, Giovanni Beltrame

Department of Computer Engineering and Software Engineering, Polytechnique Montréal, QC., Canada. · MILA, QC., Canada.

Unmanned Aerial Vehicle Trajectories Path Planning Unmanned Aerial Vehicles Obstacle Avoidance Planning Path+1

May 11, 2026cs.AI

PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering

Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the retrieval-augmented generation paradigm to ground Large Language Models~(LLMs) with structured knowledge from KGs. However, training effective models to retrieve question-relevant evidence from KGs typically requires high-quality intermediate supervision signals, such as question-relevant paths or subgraphs, which are time- and resource-intensive to obtain. We propose PathISE, a novel framework for learning high-quality intermediate supervision from answer-level labels. PathISE introduces a lightweight transformer-based estimator that estimates the informativeness of relation paths to construct pseudo path-level supervision. This supervision is then distilled into an LLM path generator, whose generated paths are grounded in the KG to provide compact evidence for inductive answer reasoning. ExtensiveISE experiments on three KGQA benchmarks show that PathISE achieves competitive or state-of-the-art KGQA performance, and provides reusable supervision signals that can enhance existing KGQA models, without relying on costly LLM-refined supervision signals. Our source code is available at https://anonymous.4open.science/r/PathISE-2F87.

Shengxiang Gao, Chao Lei, Jey Han Lau +1

The University of Melbourne

Knowledge Graphs Question Process-Level Supervision Path Induction

May 11, 2026cs.RO

A cell-decomposition based path planner for 3D navigation in constrained workspaces

This paper proposes a cell decomposition algorithm for binary occupancy grids that ensures mutual complete visibility from each cell to at least one adjacent cell. This decomposition establishes a simplified framework for verifying path feasibility that can be easily embedded in optimization problems. To illustrate its utility, we formulate both second-order cone programs (SOCP) and their mixed-integer variant (MISOCP) within the proposed framework. Furthermore, we propose the KSP-SOCP method, which combines Yen's k-shortest path algorithm with the SOCP, achieving improved solutions compared to a standard SOCP approach while avoiding the computational burden of MISOCP. The cell decomposition algorithm, KSP-SOCP, and MISOCP approaches were evaluated in 9 city-like workspaces. The decomposition efficiently partitioned each map, enabling both optimization methods to compute feasible paths. The proposed KSP-SOCP achieved time performance comparable to the MISOCP while requiring less memory, making it highly suitable for large-scale problems.

João P. L. Morais, Luciano C. A. Pimenta, Marcelo A. Santos +1

Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil · Department of Management, Information and Production Engineering, University of Bergamo, Dalmine, BG, Italy · Department of Electronic Engineering, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

Path Planning Shortest Paths Combinatorial Optimization Workspaces Decomposition Path

May 10, 2026cs.LG

Let the Target Select for Itself: Data Selection via Target-Aligned Paths

Targeted data selection seeks training examples from a candidate pool that improve downstream task performance. While trajectory-based selectors effectively guide this process, existing approaches typically construct reference states by warming up on the candidate pool itself, thereby inheriting pool-dependent distributional biases. To address this coupling, we introduce Target-Aligned Candidate Selection (TACS), which constructs a short, capacity-constrained reference path using a compact target-validation proxy. Candidates are ranked by their normalized loss reduction along this trajectory, requiring only forward passes and allowing the path to be reused across distinct pools for a fixed model-target pair. Empirically, TACS achieves competitive downstream performance across controlled logistic, vision, and NLP benchmarks, while slashing selection compute by up to 69% and candidate storage to less than 10 MB in large-scale instruction tuning.

Huitao Yang, Hengzhi He, Tung Sum Thomas Kwok +1

University of California, Los Angeles

Alignment-Aware Selection Adaptive Sampling Reweighting Distributional Alignment Target Domain Instruction Tuning+2

May 8, 2026cs.AI

Online Goal Recognition using Path Signature and Dynamic Time Warping

Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses. However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages. This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that efficiently captures key semantic features of trajectories, enabling more meaningful comparisons between them. Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.

Douglas Tesch, Nathan Gavenski, Leonardo Amado +2

Pontif´ıcia Universidade Cat´olica do Rio Grande do Sul · King’s College London · University of Aberdeen

Multi-Object Tracking Pathways Path State Space Models

May 8, 2026cs.AI

Repeated Deceptive Path Planning against Learnable Observer

We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work assumes static, non-learning observers, real-world adversaries-such as in critical goods transportation or military operations-can adapt by learning from historical trajectories. To address this gap, we introduce Repeated Deceptive Path Planning (RDPP), a new formulation that explicitly models learnable observers. We show that existing DPP methods fail under this setting, as they cannot adapt to evolving adversarial predictions. While incorporating observer previous predictions into updates enables some adaptation, such incremental updates cause accumulative lag that degrades deception. To this end, we propose Deceptive Meta Planning (DeMP), a two-level optimization framework that combines episode-level adaptation, which enables short-term policy adjustment to counter updated observer, and meta-level updates, which leverage cross-episode feedback to capture how observers update their models and accelerate adaptation in future episodes. In this way, DeMP mitigates the accumulation of adaptation lag, enabling sustained deception against a learning observer. Experiments across environments demonstrate that DeMP significantly outperforms existing approaches in RDPP while maintaining competitive path cost. Our results highlight the importance of modeling repeated interactions with learnable adversaries, providing new insights into deception and privacy in multi-agent systems.

Shiyue Cao, Pei Xu, Likun Yang +6

School of Artificial Intelligence, University of Chinese Academy of Sciences · National Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institution of Automation, Chinese Academy of Sciences

Partially Observable Markov Decision Process Path Planning Deception Planning Path

May 7, 2026cs.AI

Knowledge-Graph Paths as Intermediate Supervision for Self-Evolving Search Agents

Self-evolving search agents reduce reliance on human-written training questions by generating and solving their own search tasks. We build on Search Self-Play (SSP), a representative Proposer and Solver framework in which questions are generated and answered via multi-step search and reasoning. In practice, however, SSP faces two bottlenecks: the Proposer constructs questions from isolated answer entities without relational context, yielding many invalid or unverifiable questions in early self-play training, while the Solver receives only a binary outcome reward that discards useful signal from partially on-track search trajectories. We address both bottlenecks by reusing knowledge-graph paths as construction-derived intermediate supervision for both question construction and reward shaping. First, we ground question construction in LLM-guided knowledge-graph subgraphs, providing relational context for the Proposer. Second, we observe that constructing and solving a multi-hop question can involve overlapping intermediate entities: the factual bridges used to formulate the question may provide approximate waypoints for answering it. Exploiting this overlap, we introduce Waypoint Coverage Reward (WCR), which grants graded partial credit to incorrect Solver trajectories according to their coverage of entities on the construction path, while preserving full reward for correct answers. Across seven QA benchmarks and nine model configurations, our approach improves the average score over standard SSP in all configurations, including notable gains on multi-hop QA tasks. These results suggest that knowledge-graph paths can be reused as lightweight intermediate supervision, providing both relational guidance and process feedback without additional task-specific human annotations or manually labeled process steps.

Huyu Wu, Jun Liu, Xiaochi Wei +3

Xiaohongshu Inc., Beijing, China

Self-Evolving Agents Search Agents Knowledge Graphs Self-Play Process-Level Supervision Path+1

May 5, 2026cs.DC

CP $^2$ N $^2$ : A Control Plane for Heterogeneous Physical Neural Networks

Physical neural networks (PNNs) exploit diverse physical substrates---spanning molecular, wetware, and solid-state devices---to perform embodied neural computation close to physical processes. However, their operational heterogeneity in signal modalities, timing regimes, reset requirements, calibration lifecycles, and observability prevents their integration as interchangeable execution endpoints in edge, fog, and cloud workflows. We present CP

^2

N

^2

, a substrate-aware control-plane architecture that bridges software-level orchestration and physical neural computing. CP

^2

N

^2

introduces a three-plane model that decouples high-level orchestration, twin-state synchronisation, and substrate-specific execution under versioned Physical Neural Resource Contracts. It extends Model Context Protocol (MCP)-style discovery and invocation with explicit lifecycle management, atomic leases, telemetry freshness verification, and fail-closed safety boundaries. We evaluate a reference prototype across contrasting behavioural twins, a same-host multi-process deployment under fault injection, and the Cortical Labs SDK Simulator. In a 945-request concurrency campaign, CP

^2

N

^2

enforces typed contention and fail-closed recovery with sub-millisecond local orchestration overhead. Furthermore, in a 160-decision Agent-to-PNN campaign with a hosted LLM planner, independent enforcement ensured that 100% of executed actions remained strictly safe, verified, and reconciled despite planner-level inconsistencies. CP

^2

N

^2

thus provides an enforceable systems abstraction for integrating scarce, stateful physical AI substrates into agentic computing environments.

Stefan Fischer, Maliheh Hariri, Sebastian Otte

University of Luebeck, Institute of Telematics · University of Luebeck, Institute of Robotics and Cognitive Systems

Edge Devices Neural Network Path

Apr 28, 2026cs.RO

Optimal UGV-UAV Cooperative Partitioning and Inspection of Shortest Paths

We study cooperative shortest path planning for an unmanned ground vehicle (UGV) assisted by an unmanned aerial vehicle (UAV) scout in environments with unknown road blockages that are only discovered when a robot reaches the damaged point. This formulation generalizes the original Canadian Traveller Problem (CTP), which assumes a single ground vehicle and that the traversability status of all incident edges is revealed upon arrival at a vertex. We first analyze the case where the start and the goal are connected by

k

disjoint paths, and prove that the worst-case competitive ratio

ρ

for a single UGV is

2k-1

. With UAV assistance, and under the simplifying assumption of negligible initial transit and deadheading UAV costs, the ratio

ρ

improves to

2(k-1)\frac{v_G}{v_G + v_A} + 1

, where

v_G

and

v_A

denote the UGV and UAV speed, respectively. To address general graphs and non-negligible UAV initial transit and deadheading costs, we present an optimal candidate-path partitioning algorithm that assigns path prefix inspection to the UGV and path suffix inspection to the UAV, and prove the optimality of the UAV inspection strategy on general graphs. We evaluate our algorithm by performing experiments on road networks from the world's 50 most populous cities with randomized blockage locations, and show that the algorithm reduces UGV travel time, with larger improvements as the UAV speed increases.

Ninh Nguyen, Srinivas Akella

Multi-Uav Path Planning Shortest Paths Road Networks Unmanned Aerial Vehicle Detection Path+2

Apr 27, 2026cs.LO

Logic of Fuzzy Paths

We introduce a new family of temporal logics intended for specifications in motion planning (MP). It builds upon the signal temporal logic (STL), which is a linear-time logic over real-valued signals that possess quantitative semantics and thus became popular in the areas of cyber-physical systems, robotics, and specifically robot MP. However, in contrast to STL, the proposed logic works with paths as first-class citizens, separating the concerns of geometry and of logic. This in turn leads to simpler and more understandable formulae, and a more refined notion of satisfaction being able to reflect also preferences over behaviours. Technically, the logic is built on fuzzy, time-varying signal constraints. As a consequence of this expressivity, it is (i) more usable for human-given specifications in MP and (ii) more amenable to learning specifications from demonstrations than other logics. The former is important for the traditional style of verification in robot MP; the latter is becoming recognized as crucial for mining data-given tasks and controller synthesis in human-aware MP. We expose the advantages of our proposed logic on examples and show the versatility and flexibility of the framework on a number of scenarios. Finally, we give a learning algorithm with a prototype implementation and discuss the possibilities of model checking and monitoring.

Kush Grover, Pratham Gupta, Jan Křetínský

Fondazione Bruno Kessler · Indian Institute of Science · Masaryk University

Signal Temporal Logic Logic Motion Planning Pathways Path Robust Control+1

Apr 27, 2026cs.LG

PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction

Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene-expression representations on mutation, copy number variation, pathway, and clinical context, and uses dual-level attention to capture both intra-pathway driver signals and inter-pathway clinical relevance. We evaluated PathMoG on 5,650 patients across 10 TCGA cancer types and observed consistent improvements over representative survival baselines. The framework further provides gene-level, pathway-level, and patient-level interpretability, supporting biologically grounded and clinically relevant risk stratification.

Di Wang, Chupei Tang, Junxiao Kong +3

1Cuiying Honors College, Lanzhou University, China · School of Mathematics and Statistics, Lanzhou University, China · School of Mathematics and Statistics, Lanzhou University, China and Shanghai Innovation Institute +1

Multi-Omic Integration Multidisciplinary Tumor Boards Graph Neural Networks Path

Apr 22, 2026cs.LG

The Path Not Taken: Duality in Reasoning about Program Execution

Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of program execution rather than relying on surface-level patterns. Existing benchmarks primarily focus on predicting program properties tied to specific inputs (e.g., code coverage, program outputs). As a result, they provide a narrow view of dynamic code reasoning and are prone to data contamination. We argue that understanding program execution requires evaluating its inherent duality through two complementary reasoning tasks: (i) predicting a program's observed behavior for a given input, and (ii) inferring how the input must be mutated toward a specific behavioral objective. Both tasks jointly probe a model's causal understanding of execution flow. We instantiate this duality in DexBench, a benchmark comprising 445 paired instances, and evaluate 13 LLMs. Our results demonstrate that dual-path reasoning provides a robust and discriminative proxy for dynamic code understanding.

Eshgin Hasanov, Md Mahadi Hassan Sibat, Santu Karmaker +1

Department of Computer Science, University of Central Florida

Large Language Model Benchmarks Duality Path

Apr 20, 2026cs.MA

Multi-UAV Path Following using Vector-Field Guidance

This paper presents a decentralized, collision-free framework for path following guidance of multiple uncrewed aerial vehicles (UAVs), while maintaining uniform spacing along a reference path. A vector field-based guidance law is employed to drive each UAV toward the reference path. A rotational repulsion mechanism, utilizing relative distance and bearing between UAVs, is proposed to avoid collisions during convergence to the path, and an inter-UAV spacing error-based velocity control law is presented to achieve uniform separation along the path. Analytical guarantees are established for collision avoidance and convergence of the inter-UAV spacing errors to zero, ensuring uniform separation along the path. Numerical simulations demonstrate the efficacy of the proposed method.

Gautam Kumar, Amit Shivam, Ashwini Ratnoo

Department of Aerospace Engineering, Indian Institute of Science Bangalore, India 560012 · Department of Electrical and Computer Engineering, University of Porto, Porto, Portugal

Multi-Uav Unmanned Aerial Vehicles Collision Avoidance Vector Fields Repulsive Distribution Guidance+1

Apr 16, 2026cs.RO

Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios

Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical methods are often compared on small ad hoc examples or on rectangular grids. This paper presents a reproducible benchmark of deterministic single-vehicle coverage path planning heuristics on irregular hexagonal graphs derived from synthetic but maritime-motivated areas of interest. The benchmark contains 10,000 Hamiltonian-feasible instances spanning compact, elongated, and irregular morphologies, 17 heuristics from seven families, and a common evaluation protocol covering Hamiltonian success, complete-coverage success, revisits, path length, heading changes, and CPU latency. Across the released dataset, heuristics with explicit shortest-path reconnection solve the relaxed coverage task reliably but almost never produce zero-revisit tours. Exact Depth-First Search confirms that every released instance is Hamiltonian-feasible. The strongest classical Hamiltonian baseline is a Warnsdorff variant that uses an index-based tie-break together with a terminal-inclusive residual-degree policy, reaching 79.0% Hamiltonian success. The dominant design choice is not tie-breaking alone, but how the residual degree is defined when the endpoint is reserved until the final move. This shows that underreported implementation details can materially affect performance on sparse geometric graphs with bottlenecks. The benchmark is intended as a controlled testbed for heuristic analysis rather than as a claim of operational optimality at fleet scale.

Carlos S. Sepúlveda, Gonzalo A. Ruz

Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile · Millennium Nucleus for Social Data Science (SODAS) · Millennium Nucleus in Data Science for Plant Resilience (PhytoLearning)

Path Planning Heuristics Path Planning Feasibility

Apr 16, 2026cs.LG

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning

You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You got brains and so you plan before you act. Luckily, your roboparents equipped you with a generative model to do some Monte-Carlo planning. The world is waiting for you and you have no time to waste. You want your planning to be efficient. Sample-efficient. Indeed, you want to exploit the possible structure of the MDP by exploring only a subset of states reachable by following near-optimal policies. You want guarantees on sample complexity that depend on a measure of the quantity of near-optimal states. You want something, that is an extension of Monte-Carlo sampling (for estimating an expectation) to problems that alternate maximization (over actions) and expectation (over next states). But you do not want to StOP with exponential running time, you want something simple to implement and computationally efficient. You want it all and you want it now. You want TrailBlazer.

Jean-Bastien Grill, Michal Valko, Rémi Munos

SequeL team, INRIA Lille - Nord Europe, France · Google DeepMind, UK∗

Markov Decision Processes Planning Path Robocasa

Apr 16, 2026cs.IR

Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion

Learning Path Recommendation (LPR) is critical for personalized education, yet current methods often fail to account for historical interaction uncertainty (e.g., lucky guesses or accidental slips) and lack adaptability to diverse learning goals. We propose U-GLAD (Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion). To address representation bias, the framework models cognitive states as probability distributions, capturing the learner's underlying true state via a Gaussian LSTM. To ensure highly personalized recommendation, a goal-oriented concept encoder utilizes multi-head attention and objective-specific transformations to dynamically align concept semantics with individual learning goals, generating uniquely tailored embeddings. Unlike traditional discriminative ranking approaches, our model employs a generative diffusion model to predict the latent representation of the next optimal concept. Extensive evaluations on three public datasets demonstrate that U-GLAD significantly outperforms representative baselines. Further analyses confirm its superior capability in perceiving interaction uncertainty and providing stable, goal-driven recommendation paths.

Xiangrui Xiong, Hang Liang, Baiyang Chen +2

Accidenta

Preference Learning Large Language Model Uncertainty Recommendation Path Subgoals

Mar 20, 2026cs.CV

HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction

Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters address complementary aspects of the problem: a Hierarchical Patch Aggregator (HiPA) for multi-image visual encoding, Hierarchical Contrastive Learning (HiCL) for cross-modal alignment via optimal transport, and Slot-based Masked Diagnosis Prediction (Slot-MDP) for structured diagnosis generation. Trained on 749K real-world Chinese pathology cases from three hospitals, HiPath achieves 68.9% strict and 74.7% clinically acceptable accuracy with a 97.3% safety rate, outperforming all baselines under the same frozen backbone. Cross-hospital evaluation confirms generalisation with only a 3.4pp drop in strict accuracy while maintaining 97.1% safety.

Ruicheng Yuan, Zhenxuan Zhang, Anbang Wang +5

College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China · Department of Bioengineering and Imperial-X, Imperial College London, London, UK · Department of Pathology, Xiangtan Maternal and Child Health Hospital, Xiangtan, Hunan, China +1

Pathology Foundation Models Medical Vision-Language Models Vision-Language Alignment Path

Mar 19, 2026cs.RO

REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation

Zero-shot object-goal navigation (ZSON) requires navigating unknown environments to find a target object without task-specific training. Prior hierarchical solutions mainly focus on either scene understanding and representations (belief) or high-level decision-making and planning (policy), yet treat the option, i.e., the subgoal candidate that belief proposes and policy selects, as an interface inherited from adjacent modules rather than a design axis in its own right. In practice, options are predominantly single waypoints scored by destination utility: a lone destination hides the value gathered en route, and a flat list obscures the relationships among candidates. Our insight is that the option space should be a tree of paths. Full paths expose en-route information gain that destination-only scoring systematically neglects; a tree of shared segments enables coarse-to-fine LLM reasoning that dismisses or pursues entire branches before examining individual leaves, compressing the combinatorial path space into an efficient hierarchy. We instantiate this insight in REST (Receding Horizon Explorative Steiner Tree), a training-free framework that (1) builds an explicit open-vocabulary 3D map from online RGB-D streams; (2) grows an agent-centric tree of safe and informative paths as the option space via sampling-based planning; and (3) textualizes each branch into a spatial narrative and selects the next-best path through chain-of-thought LLM reasoning. Across the Gibson, HM3D, and HSSD benchmarks, REST consistently ranks among the top methods in success rate and path efficiency.

Shuqi Xiao, Maani Ghaffari, Chengzhong Xu +1

State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), University of Macau · Department of Naval Architecture and Marine Engineering, and Department of Robotics, University of Michigan

Object Goal Navigation Navigation Path Trees Destination-Only Scoring

Feb 24, 2026cs.RO

Conflict-Based Search for Multi-Agent Path Finding with Elevators

This paper investigates a problem called Multi-Agent Path Finding with Elevators (MAPF-E), which seeks conflict-free paths for multiple agents from their start to goal locations that may locate on different floors, and the agents can use elevators to travel between floors. The existence of elevators complicates the interaction among the agents and introduces new challenges to the planning. On the one hand, elevators can cause many conflicts among the agents due to its relatively long traversal time across floors, especially when many agents need to reach a different floor. On the other hand, the planner has to reason in a larger state space including the states of the elevators, besides the locations of the agents.

Haitong He, Xuemian Wu, Shizhe Zhao +1

Global College, Shanghai Jiao Tong University, China.

Multi-Agent Path Finding Path

Nov 13, 2025cs.LG

FlowPath: Learning Data-Driven Manifolds with Invertible Flows for Robust Irregularly-sampled Time Series Classification

Modeling continuous-time dynamics from sparse and irregularly-sampled time series remains a fundamental challenge. Neural controlled differential equations provide a principled framework for such tasks, yet their performance is highly sensitive to the choice of control path constructed from discrete observations. Existing methods commonly employ fixed interpolation schemes, which impose simplistic geometric assumptions that often misrepresent the underlying data manifold, particularly under high missingness. We propose FlowPath, a novel approach that learns the geometry of the control path via an invertible neural flow. Rather than merely connecting observations, FlowPath constructs a continuous and data-adaptive manifold, guided by invertibility constraints that enforce information-preserving and well-behaved transformations. This inductive bias distinguishes FlowPath from prior unconstrained learnable path models. Empirical evaluations on 18 benchmark datasets and a real-world case study demonstrate that FlowPath consistently achieves statistically significant improvements in classification accuracy over baselines using fixed interpolants or non-invertible architectures. These results highlight the importance of modeling not only the dynamics along the path but also the geometry of the path itself, offering a robust and generalizable solution for learning from irregular time series.

YongKyung Oh, Dong-Young Lim, Sungil Kim

Medical & Imaging Informatics (MII) Group, University of California, Los Angeles (UCLA), CA, USA · Department of Industrial Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea · Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea

Time-Series Classification Invertible Flows Data Manifold Time Series Path

Aug 30, 2025cs.DB

Access Paths for Efficient Ordering with Large Language Models

In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations. First, we propose several improvements to existing semantic sorting algorithms and introduce a semantic-aware external merge sort algorithm. Our extensive evaluation reveals that no single implementation offers universal optimality on all datasets. From our evaluations, we observe a general scaling relationship between sorting cost and the ordering quality for comparison-based algorithms. Building on these insights, we design a budget-aware optimizer that utilizes heuristic rules, LLM-as-Judge evaluation, and consensus aggregation to dynamically select the near-optimal access path for LLM ORDER BY. In our extensive evaluations, our optimizer consistently achieves ranking accuracy on par with or superior to the best static methods across all benchmarks. We believe that this work provides foundational insights into the principled optimization of semantic operators essential for building robust, large-scale LLM-powered analytic systems.

Fuheng Zhao, Jiayue Chen, Yiming Pan +7

1Snowflake Inc. · University of Chicago · 3UC Los Angeles +1

Order Matters Order Token Budget Allocation Path

Mar 22, 2024cs.CV

GCN-DevLSTM: Path Development for Skeleton-Based Action Recognition

Skeleton-based action recognition (SAR) in videos is an important but challenging task in computer vision. The recent state-of-the-art (SOTA) models for SAR are primarily based on graph convolutional neural networks (GCNs), which are powerful in extracting the spatial information from skeleton data. However, their ability to capture temporal dynamics remains limited. To address this, we propose the G-Dev layer, which leverages path development-a principled and parsimonious representation for sequential data based on Lie group structures-to enhance temporal modeling. By integrating the G-Dev layer, the proposed DevLSTM module summarizes local temporal dynamics, reducing the time dimension while retaining high-frequency information. It can be conveniently applied to any temporal graph data, complementing existing advanced GCN-based models. Our empirical studies on the NTU-60, NTU-120 and Chalearn2013 datasets demonstrate that our proposed GCN-DevLSTM network consistently improves the strong GCN baseline models and achieves competitive performance. The code repository is publicly available at https://github.com/DeepIntoStreams/GCN-DevLSTM.

Lei Jiang, Weixin Yang, Xin Zhang +1

University College London · University of Oxford · South China University of Technology

Skeleton-Based Action Recognition Temporal Graph Neural Networks Cnn-Lstm Latent Motion Convolutional Neural Networks Pathways+1

Date pendingcs.AI

Relevance Is Not Permission: Localizing and Controlling Metric-Facing Attention Contributions

Attention identifies items relevant to a current query, but does not separately determine whether their value contributions support the prediction. We propose Warrant, a unified method for locating and controlling metric-facing attention contributions. Warrant identifies and exposes the item-wise contribution path that reaches the reported metric, then applies current-query-conditioned permission on that same path. Full Warrant improves the primary metric in 27 of 32 model-dataset comparisons across CTDG, MTPP, RAG, STPP, and TKG. Exact item-removal analysis in five representative settings finds near-zero correlation between attention and marginal prediction utility; even the highest-attention item reduces target utility in 43.5-54.4% of examples. Decomposition over the complete benchmark shows that the contributions of path exposure and learned permission vary by task. In a five-seed HotpotQA analysis, the opened path assigns more attention mass to distractors than to gold evidence, whereas learned permission preserves gold contributions, suppresses distractor contributions, and recovers evidence ranking in four of five seeds. These results show why attention-selected contributions must be localized and authorized again on the metric-facing path.

Minwoo Yu, Young-guk Ha

Smart Computing Laboratory, Department of Computer Science and Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea

Relevance Value Kernel-Resident Tool Governance Gateway Path