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MOTIO: Agentic Skill Engineering for Reality-Grounded Humanoid Loco-Manipulation
Abstract
We present MOTIO, an agentic skill-engineering system that turns ordinary human-action videos, reality-grounded 3D assets, motion references, and reusable robot skills into executable humanoid loco-manipulation programs. Through in-context task and contact reasoning, an agent constructs staged robot-native interactions and improves them using physics execution, multi-frame feedback, and human validation---without task-specific teleoperation or robot demonstration collection. Agentic 3D supplies metric product and environment assets, while existing whole-body, inverse-kinematics, and dexterous-hand interfaces realize the programs. Across 33 timestamped first approvals, the median recorded agent-turn time is 97.0 minutes and request-to-approval duration is 2.80 hours. Simulation-side layout and object variation prepare a long-horizon bottle-disposal skill for a continuous 101.75-second physical G1 execution in a scanned office; physical refinement reduces screw-tightening thread residual by 76.0%. Agent-generated skills additionally supervise BC/DAgger distillation into one shared reference policy, completing 18 simulated tasks under the same checkpoint without inference-time expert intervention. Project page: this https URL
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URL
https://arxiv.org/abs/2610.33446
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Perturbation-Based Data Selection for Medical Question Answering
2026-10-24 04:33:48Yuexin Wu, Dayou Yu, Vasile RusAbstract
Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate perturbation-based data selection using two signals derived from Gaussian perturbations of the final-block representation: prediction-change frequency and non-flipping margin contraction. After a shared entropy prefilter, Representation-Sensitivity Prioritization (RSP) combines the signals using automatic root-mean-square scaling, while RSP-Drop ranks candidates by contraction alone. Both selectors use only questions and answer options during selection. We evaluate them with MedGemma-4B on five medical QA datasets at retained-set budgets of 64, 128, and 256 examples. At the primary budget of 128, RSP and RSP-Drop obtain dataset-averaged Macro-F1 scores of 47.55 and 48.28, respectively, compared with 45.11 for entropy-pool-matched random selection. Their relative performance varies across datasets and budgets. A selected-set case study identifies contrasting clean-margin profiles among the examples exchanged by the two rules, while a full-pool ablation shows that entropy prefiltering is not uniformly beneficial. These retrospective results characterize the empirical utility and limitations of perturbation-based selection under a fixed adaptation protocol. Evaluation uses one backbone and benchmarks with documented training-family overlap; it does not establish clinical effectiveness.
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https://arxiv.org/abs/2610.33440
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ConMem: Counterfactual Multi-Scenario Probing for Resolving LLM Memory Misalignment
2026-10-24 04:16:37Shuning Zhang, Tenghao Ji, Jingruo Chen, Eryue Xu, Bohao Chu, Xin YiAbstract
Long-term memory is critical for autonomous Large Language Model (LLM) agents executing multi-session, long-horizon tasks. However, existing memories often inappropriately store, retain, or retrieve over-generalized, misattributed, or privacy-sensitive information, a problem we term memory misalignment. To study this problem, we construct \textbf{MemAlign}, a benchmark comprising 1,079 curated memory cases grounded in synthetic, validated multi-session conversational contexts covering diverse misalignment taxonomies. We then propose ConMem, an algorithm that reframes retention decisions as prospective counterfactual causal inference. \textbf{ConMem} synthesizes counterfactual interaction scenarios to prospectively audit downstream intervention effects across three aspects: relevant task execution, irrelevant invariance checks, and risk-sensitive boundary shifts. Experiments show that ConMem outperforms other methods, achieving 75.16\% accuracy, 64.15\% balanced accuracy, and a Macro-F1 of 65.31\%, effectively mitigating over-retention biases without sacrificing personalization utility.
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URL
https://arxiv.org/abs/2610.33432
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Bidirectional Cross-frequency Domain Interaction Network for UHD Low-Light Image Enhancement
2026-10-24 02:28:30Bo Fu, Hai Chi, Liyan Wang, Shilin Fu, Wei Wang, Kin-Man Lam, Jinshan Pan, Cong WangAbstract
Existing Ultra-High-Definition (UHD) low-light image enhancement (LLIE) methods typically rely on either spatial- or frequency-domain modeling, or independently process different frequency components for efficient restoration. However, they often fail to jointly exploit complementary spatial--frequency information and the inherent relationship between illumination and structural information across frequencies, leading to inconsistent illumination, residual noise, and detail artifacts. In this paper, we propose a novel Bidirectional Cross-frequency Domain Interaction Network (BiFreqNet) for UHD LLIE. Specifically, BiFreqNet incorporates a Dual-domain Feature Interaction (DFI) module into the multi-scale representations of a pretrained variational autoencoder (VAE) to perform reciprocal enhancement between low- and high-frequency features. DFI consists of a Spatial and Spectral-Aware Low-Frequency Enhancement (S$^2$LFE) block and a Cross-Frequency Enhancement (CFE) block. S$^2$LFE exploits high-frequency structural cues to guide low-frequency restoration and jointly models spatial and spectral information to recover global exposure and spatially varying illumination while suppressing sensor noise. CFE further leverages the enhanced low-frequency contextual information to guide adaptive high-frequency refinement, enabling faithful reconstruction of structures and fine details. Extensive experiments on four general and UHD LLIE benchmarks demonstrate that BiFreqNet achieves competitive or superior performance compared with state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33402
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AdShot: Benchmarking Multimodal Large Language Models for Video Advertisement Clipping
2026-10-24 00:14:26Wen Xie, Om Rastogi, Sai Siddhartha Vivek Dhir Rangoju, Gijs Overgoor, Yakov Bart, Sarah OstadabbasAbstract
Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process. While multimodal large language models (MLLMs) excel at general video understanding, their ability to perform specialized editorial tasks remains unexplored. We introduce AdShot, a benchmark for evaluating MLLMs on shot selection for ad clipping. It contains 823 professionally edited ad pairs (30-sec sources and 15-sec edits) across 194 brands and 17 industries, annotated with shot-level boundaries and content features (action, emotion, information, imagery). We evaluate 13 state-of-the-art video-only MLLMs and 2 audio-visual MLLMs (7B-72B parameters) on shot selection accuracy (Precision, Recall, F1), duration constraints (15s), and content preservation, including an audio ablation. We find: (1) model architecture matters more than scale, with smaller models often matching or exceeding larger ones; performance is strongest for target ads with 6-10 shots; (2) models exhibit a mean absolute duration error of 5.39s, with most tending to overshoot the 15s target, though they can still substantially reduce human editing effort; (3) all models exhibit systematic content biases, favoring information-rich and emotion-focused shots while under-selecting action and imagery. We publicly release our benchmark to facilitate research at the intersection of multimodal AI and computational advertising.
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URL
https://arxiv.org/abs/2610.33377
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AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?
2026-10-24 00:08:11Zijie Liu, Qi Yang Huang, Jinhao Duan, Yueh Che Lee, Thomas A. Joseph, Zayne Turner, Adam Seligman, Tianlong Chen, Zhaozhuo XuAbstract
Enterprise workflow agents typically rely on step-by-step interaction (Step-by-Step): the agent executes an action, observes its result, and invokes a large language model (LLM) again to decide the next step. Global orchestration (Global) instead generates a complete executable workflow in advance and executes it without further LLM calls, substantially reducing inference overhead, but it remains less accurate than Step-by-Step. We study how close Global can come to Step-by-Step and what narrows the remaining gap. We introduce AgentOrch, a benchmark that directly compares global orchestration with step-by-step interaction on 650 enterprise SOPs and 3,220 runtime variants under matched tools, environments, and evaluation. Step-by-Step remains more accurate for every evaluated model, but the gap has shrunk by about 79% from early models to the strongest recent model. We identify three routes to further narrowing this gap. Incremental workflow construction improves Global accuracy by an average of 9.98 percentage points (pp) without runtime execution feedback. Revising complete workflows with execution feedback improves all evaluated models, including 6.13 pp on held-out runtime variants. Targeted training on complete workflows improves accuracy by an average of 12.18 pp across all five trained models. Although a gap remains, global orchestration offers a promising path toward enterprise agents that deliver higher accuracy with fewer LLM calls.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33375
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Real Time Motion Detection in Video Inspired by Photonic Time Stretch
2026-10-23 22:39:22Callen MacPhee, Yiming Zhou, Bahram JalaliAbstract
Motion perception is fundamental to computer vision and physical AI, and plays a central role in robotics, autonomous vehicles, human-computer interaction, and video compression. Traditional approaches such as background subtraction, frame-differencing and optical flow algorithms often struggle in complex or dynamic scenes, typically sacrificing spatial resolution for computational tractability. Here we introduce MoViD (Motion estimation via Virtual dispersion and phase Detection), a physics-inspired algorithm grounded in the principles that govern photonic time stretch - a highly successful ultrafast realtime data acquisition platform. Drawing inspiration from the optical physics therein, MoViD treats video as a collection of optical time series waveforms associated with pixels. Motion of an object through a pixel broadens the time series spectrum and, after group delay dispersion, induces a temporal phase that exists only if there is motion. Spatiotemporal group delay dispersion templates, corresponding to dielectric functions, classify specific motion trajectories. Unlike conventional empirical phase-based motion-analysis pipelines that require narrow-band spatial channelization and pyramid reconstruction, the physics-based MoViD operates directly on the broadband video signal, vastly increasing its computational speed. The resulting algorithm achieves unprecedented performance in both motion characterization and motion magnification, providing full-resolution motion mapping in real time at nearly 200 frames per second at 4K resolution and 70 frames per second for 8K video - orders of magnitude faster than classical optical flow approaches or learning-based methods. Notably, such extreme performance enables real time motion analysis at ultrahigh resolution for motion detection and compensation in robotics and other autonomous devices.
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URL
https://arxiv.org/abs/2610.33351
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Contract-Based Agentic Systems Engineering: Delegation as Quotient for Assured System Design
2026-10-23 22:38:24Sai Sandeep Damera, Ryan Matheu, John S. BarasAbstract
A component an agent builds from a prompt can satisfy it and still break the system it joins, because the prompt omits what its neighbours assume of it. The artifact to hand over is computed, not written. A SysML v2 requirement with assumed and required constraints is an assume/guarantee contract, so when a system requirement is decomposed over fixed neighbours, the open component's requirement is the contract quotient, the weakest sound one, and again a SysML v2 requirement. Its required constraints carry the neighbours' assumptions, which a requirement for the mission alone drops and unit tests with mocked neighbours cannot reach. Signal Temporal Logic is closed under the quotient. We build the requirements, with derivation, allocation, satisfaction and verification, as a SysML~v2 model and compute the quotient from it. A case study delegates the local controller of a simulated warehouse robot, whose route crosses a worker's path and ends at a dock, to one language model under the quotient requirement, a mission-only requirement and a natural-language task, and scores the deliveries on live episodes against the system requirement from ground truth.
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URL
https://arxiv.org/abs/2610.33349
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Robot Motion as a Keyed Communication Channel
2026-10-23 21:54:52Benjamin Chang, Michael Amir, Manon Flageat, Amanda ProrokAbstract
Messages from electronic devices are conventionally received as text, audio, or radio signals. But robots move with rich, articulate motion in the real world, opening up the possibility of transmitting messages through motion itself. In this paper, we consider the problem of motion-based communication, where we seek to modify a robot's movements so as to transmit messages detectable from remote sensing (e.g., video or motion capture), without degrading policy performance. We introduce a method for messaging through motion capable of encoding arbitrary message content over short payloads - such as an agent's current intent - as noise in any pre-trained policy's actions. This brings a new kind of robustness to robot communication: this 'physical' channel complements standard wireless communications channels but does not depend on them, requiring no extra hardware nor the establishment of a direct link to the robot. We systematically characterize the space of encoding schemes and derive design heuristics, then validate them across simulated environments and real-robot deployment; on real robots running at 50 Hz, four robots jointly recover an 8-bit message at an aggregate 0.8 bits/s.
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https://arxiv.org/abs/2609.08920
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AI Teaches Humans to Handle Complexity in the Game of Go
2026-10-23 21:52:10Adamo Cerioli, Vito D. P. Servedio, Gabriele Cerioli, Antonio Villani, Carlo MettaAbstract
Strategic decision-making in complex environments requires balancing local stability against global coordination. Using the game of Go as a bounded empirical testbed, we investigate how biological and artificial agents navigate this trade-off by formalizing a network-derived metric of strategic tension defined via the spectral radius of spatial dependency matrices. Across human skill progression, tension systematically decays from novices to masters as uncoordinated tactical skirmishes give way to disciplined territorial stabilization. However, a longitudinal analysis of professional play spanning 1980--2026 reveals a striking historical paradox: following the superhuman breakthrough of AlphaGo in 2016, elite human matches display a marked surge in sustained tension. A two-dimensional projection based on move distance and captures places modern professionals close to neural engines, separating their tension from the local contact fighting of novices and from random noise. Consequently, strategic tension acts as a macroscopic order parameter for decision paradigms rather than raw computational strength. Beyond the game of Go, these findings establish a constructive paradigm for human-AI interaction, indicating how artificial systems can act as cognitive scaffolding that expands biological heuristics and elevates human capacity to navigate higher-order complexity across professional training and decision-making domains.
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https://arxiv.org/abs/2610.33323
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Active Reachability Learning for Humanoid Recoverability Monitoring
2026-10-23 21:40:04Zeyuan Feng, Baljeet Singh, Sarthak Ranjeet Kaingade, Somil Bansal, Bryan TrinhAbstract
Deploying humanoid task policies requires reliable runtime monitors that anticipate unrecoverable falls in time to trigger a fallback. We formulate this task as learning the stochastic, finite-horizon recoverability envelope of a given fallback policy. Synthesizing this monitor is challenging: existing methods assume training data naturally covers the recoverability envelope, yet acquiring such data remains a major bottleneck for contact-rich systems. Consequently, they recover only a fraction of the true envelope, leaving them prone to optimistic hallucinations for out-of-distribution (OOD) inputs. We resolve this via a unified framework that iterates between value learning and active exploration. Data exploration is reframed as a reach-avoid problem, where an uncertainty-guided explorer systematically uncovers unseen states while avoiding confidently unrecoverable regions. This loop is further supported by a combined Temporal Difference and Monte Carlo objective for accurate in-distribution value prediction and reliable uncertainty estimation. By explicitly treating the remaining high-uncertainty regions as unrecoverable, the monitor mitigates optimistic OOD hallucinations. Evaluated on stochastic benchmarks and a 29-DoF Unitree G1 humanoid, our approach drastically improves monitor coverage and accuracy and successfully transfers zero-shot to real-world hardware.
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URL
https://arxiv.org/abs/2610.33317
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SearchJev: A Calibrated System-1 Model for Search Agents
2026-10-23 21:39:04Congfeng Cao, Lipeng Zuo, Konstantinos Papakostas, Qiwei Xu, Songwei Xu, Qianru Meng, Lun Zhou, Zhaochun Ren, Yougang Lyu, Xiaohui YanAbstract
Search agents repeatedly make short decisions about relevance, evidence sufficiency, and search actions. Using generative language models for these decisions introduces latency and unreliable confidence. We present SearchJev, a calibrated System-1 model that separates search decisions from System-2 reasoning and generation. Given a search state and a decision schema, SearchJev scores legal options without autoregressive output generation. We propose Soft-Label Learning for Calibrated Decisions (SLCD) to learn decision probabilities from uncertain supervision and improve the reliability of decision confidence. In a dual-system search agent, SearchJev handles short decisions and delegates uncertain judgments to System 2, which retains planning, query generation, and answer composition. We also introduce SearchDecision-Bench, a benchmark unifying six types of search decisions for training and evaluation. On SearchDecision-Bench, SearchJev improves decision quality over same-size Qwen3.5 autoregressive models, achieves 5.2-5.3 times faster decisions, and reduces average expected calibration error by 41-74%. On BrowseComp-Plus, the dual-system agents achieve a 3.7-4.7 times speedup in active search time while improving answer accuracy from 45% to up to 54%.
Abstract (translated)
URL
https://arxiv.org/abs/2610.05107
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Responsibility Boundaries for Agentic Systems: A Comparative Evaluation of Probabilistic Agentic Programming
2026-10-23 21:31:12Khadim Lo, Soukaina Alaoui Ismaili, Jean-S\'{e}bastien DessureaultAbstract
Language-model agents combine reasoning, tools, memory, delegation, and external effects. Authority is often distributed across prompts, host code, configuration, and logs. Probabilistic Agentic Programming (PAP) makes responsibility boundaries explicit programming abstractions connecting mandate, authority, effects, and evidence. We compare PAP with programming paradigms, agent-oriented and BDI systems, workflow and policy languages, and LLM tool-use frameworks along seven dimensions. A mediation kernel formalizes checks before tool execution, operation-bound approval, resource limits, and evidence. Inductive proofs establish conditional safety for arbitrary model responses, assuming correct guards and complete mediation. A backward simulation matches graph executions to source executions in a defined sequential Touba subset. Its human checkpoints release program continuations; operation-bound approval remains a separate kernel contract. Finite-state exploration checks the model. Scripted enforcement tests exercise selected refusal paths and permitted controls. An exploratory study combines seven business cases, including two three-agent workflows, seven models from three providers, five methods, and five repetitions per case and method. Shared English instructions and exact checks of stored outcomes yield 1,225 assessed observations. Native Touba completes all 35 observations on six models and 30 on the remaining model; structured Touba ranges from 21 to 35. Native matches the highest observed count for each model, including ties with the frameworks. All paired case intervals include zero. These observed counts describe the evaluated tasks; the runtime authority boundary remains fixed. The results connect responsibility contracts, execution rules, and evidence.
Abstract (translated)
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https://arxiv.org/abs/2610.33312
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Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns
2026-10-23 21:24:33Ming-Chiang Chang, Brian DeCostAbstract
Powder X-ray diffraction (XRD) is the primary high-throughput method for resolving crystal structures, a cornerstone of materials science. As high-throughput synthesis and self-driving laboratories advance, automated XRD pattern analysis has become vital for accelerating materials discovery. However, traditional matrix factorization methods fail when lattice variations cause non-linear peak shifts. At the same time, supervised learning approaches face a combinatorial explosion when handling multi-phase mixtures. To overcome these challenges, we adopt a self-supervised Joint Embedding Predictive Architecture (JEPA) to learn representation embeddings from 1D diffraction patterns. By optimizing a masked self-prediction loss directly within the latent space, the framework captures high-level crystallographic semantics. Downstream evaluations show that lightweight attentive probes trained on frozen JEPA representations achieve space group classification and lattice parameter estimation accuracy close to much larger supervised vision transformers. Furthermore, latent probing reveals that the learned representation space naturally organizes physical transformations, isolating smooth trajectories for peak broadening. Interestingly, decoding along latent paths between single-phase crystal representations yields realistic multi-phase mixture patterns, demonstrating an emergent continuous structure learned without explicit multi-phase supervision.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33310
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The As-If Machine: Personalized counterfactuals make distant policy risks feel close
2026-10-23 21:11:11Joshua Ashkinaze, Yubo Zhou, Pengyu Chen, Avi Bhattacharya, Hyebin Park, Yanman Zhang, Stephanie Preston, Eric Gilbert, Ceren BudakAbstract
Life experiences can shape policy attitudes, but we only live one life. Citizens may therefore fail to support policies addressing risks they have never personally experienced. Narratives can make such risks immediate and personal, but personalized stories remain difficult to produce at scale. Here we ask whether AI can make distant scenarios feel concrete by simulating a participant's life as if that scenario occurred. We present the As-If Machine (AIM), a multi-agent system through which people co-write personalized counterfactual narratives---stories about their own lives under different policy futures. In two pre-registered and quota-matched experiments on science funding (N=1,007) and satellite cybersecurity (N=894), we compared AIM with two controls: reading an expert-written vignette on the same topic and co-writing with AIM about an unrelated topic. AIM increased psychological closeness---how close a risk feels to the self---by nearly identical margins in the two domains (0.36 and 0.38 standard deviations (SDs) against the vignette, 0.80 and 0.90 against unrelated writing). AIM increased psychological closeness even among participants with unfavorable views of AI. For policy attitudes, AIM's effects were 0.16 SDs against the vignette and 0.10 SDs (n.s.) against unrelated writing for science funding; the corresponding effects for satellite cybersecurity were 0.54 and 0.67 SDs. Across 23,496 human-AI autocomplete interactions, participants and the system played distinct roles: AIM supplied concrete detail that grounded the scenario, and participants supplied first-person perspective and emotion. Co-writing with the As-If Machine makes distant policy risks feel close.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33301
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Tiny Recursive Policy: Recursive Latent Refinement for Visuomotor Control
Abstract
Behaviour cloning has become a powerful approach for robotic manipulation, but many modern policies reconstruct their internal representation from a fixed observation window at each prediction and do not maintain persistent latent context throughout execution. We introduce Tiny Recursive Policy (TRP), a parameter-efficient behaviour-cloning policy that combines persistent temporal state with recursive latent refinement. At each control step, TRP repeatedly updates high- and low-level latent states using a shared computation module before decoding an action sequence, and propagates the resulting state to subsequent predictions. We evaluate TRP across LIBERO-100, MetaWorld ML45, and the LIBERO-Mem benchmarks. TRP performs competitively across multi-task and few-shot settings and shows substantial gains on long-horizon and context-dependent tasks, while using significantly fewer parameters than large generative baselines. Execution-time perturbations show that reliance on the persistent carry increases with temporal-context demand, while training with carry perturbations substantially improves robustness to both carry and visual corruptions. Ablations reveal a strong interaction between persistent state and recursive refinement: the benefit of carry depends strongly on the amount of within-step computation, and the resulting performance cannot be explained by parameter count alone.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33288
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Estimation of Propagated Risk in LLM-based Multi-Agent Systems
2026-10-23 20:46:33Shreyan Ganguly, Souradeep Chattopadhyay, Qisai Liu, Soumik SarkarAbstract
Large Language Model (LLM) based multi-agent systems introduce a distinctive reliability challenge in which actions taken by upstream agents can affect the correctness of downstream agents. While prior work has investigated error propagation and developed methods for estimating uncertainty inherited from upstream agents, existing approaches do not explicitly model how realized sender-receiver interactions contribute to downstream error risk under different sender correctness states. In this work, we study agent-level error-risk estimation from multi-agent execution traces. We propose Interaction-Effect Propagation (IE-Prop), an interaction-conditioned model that learns separate effects for correct and incorrect upstream agents. For each sender-receiver interaction, the model combines these conditional effects using the sender's estimated error risk, and recursively propagates the resulting risk estimates through the execution graph. We evaluate our approach across three multi-agent architectures and three benchmarks. Across all evaluated architectures, our approach raises the final-node AUROC over the established baseline by approximately 0.05 points on average. Furthermore, tracking risk across multiple agent steps contributes a distinct +0.03 AUROC gain in sequential systems. These results show that realized inter-agent interactions provide predictive information beyond receiver-local evidence, while the value of multi-step risk tracking depends on the system's information-flow structure.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33282
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GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation
2026-10-23 20:41:44Abdul Hannan Azad, Irvin Wang, Jiwan Jiang, Chenchen Kuai, Bofeng Cao, Keshu Wu, Yuzhou Wang, Xiaopeng Li, Yang ZhouAbstract
Engineering standards are challenging knowledge sources for large language models (LLMs) because they are hierarchical, multimodal, and densely connected through figures, tables, cross-references, and normative rules. Existing retrieval-augmented generation (RAG) methods often rely on unstructured text retrieval and struggle with questions requiring joint reasoning over textual provisions, visual specifications, and document structure. We propose GEMS-RAG (Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation), a framework for grounded reasoning over engineering standards. GEMS-RAG combines hierarchical parsing, fine-grained visual entity extraction, multimodal knowledge graph construction, and graph-grounded hybrid retrieval. Instead of treating figures as monolithic images, it decomposes them into meaningful visual entities linked to textual provisions, engineering concepts, and regulatory rules. During inference, dense, sparse, and visual retrieval are combined with graph expansion to identify relevant evidence across text, figures, and document structure. We evaluate GEMS-RAG on the Manual on Uniform Traffic Control Devices (MUTCD), a large engineering standard containing egulations, technical drawings, and standardized traffic-control devices. Results show that graph-grounded multimodal retrieval improves retrieval effectiveness and question-answering performance over text-only, multimodal, and graph-based RAG baselines. Beyond MUTCD, GEMS-RAG offers a general framework for reasoning over engineering codes, technical manuals, regulatory documents, and scientific standards in safety-critical applications.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33277
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Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments
2026-10-23 20:32:42Himani Sinhmar, Vaibhav Srivastava, Naomi Ehrich LeonardAbstract
Safe navigation in congested environments requires both collision avoidance and coordinated passage. We introduce neuromorphic decision dynamics (NDD), a decentralized, event-based controller, that provides navigation in congested spaces with guarantees on safety, liveness, robustness, and scalability. NDD makes safe-and-live deconfliction a controllable, locally tunable emergent property of local agent interactions, without requiring communication, preassigned priorities, models of neighbors' objectives or future actions, or estimates of group size or global network properties. By design, NDD couples the dynamics of internal decision and attention states with changing physical states and locally observed relative motion. Each agent continuously modulates its speed along its prescribed path, responding as soon and as much as conflict resolution requires. Agents self-organize into disjoint, locally engaged groups that resolve conflicts concurrently. When any group's conflict becomes safety-critical, decision-action feedback destabilizes collective indecision, and coordinated conflict-resolution emerges that stabilizes a safe traversal order within the group. Order persists as conflicts clear, but groups may split or merge as agents autonomously adapt to new conflicts and those with resolved conflicts resume their nominal motion. We derive explicit conditions on sensing range, feedback gains, response time scales, and actuation limits that guarantee collision avoidance and traversal-order formation in finite time with no deadlock or livelock for finite, heterogeneous agent populations of arbitrary, changing size. Computational cost scales with the number of local interactions sensed by each agent. Large-scale simulations, comparisons with existing approaches, and hardware experiments demonstrate NDD in complex congested scenarios and validate its scalability.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33271
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Image Clustering with Vision-Language Model Embeddings
2026-10-23 20:21:15Jo\~ao P. Matos-Carvalho, Nuno FachadaAbstract
Pretrained vision-language models provide image representations whose suitability for clustering may vary with feature extraction, image domain, and partitioning algorithm. We compare nine representation pipelines across seven image benchmarks and four clustering configurations, yielding 252 baseline combinations. Eight pipelines extract visual features, including two vision-only references; the ninth generates descriptions and embeds the resulting text. With agglomerative clustering (Ward linkage) fixed, Qwen3-VL achieves the highest adjusted Rand index (ARI) on five datasets, including 0.925 on MNIST, while BLIP-2 leads on CIFAR-10 and CIFAR-100 with ARIs of 0.925 and 0.591, respectively. A separate sensitivity analysis compares raw and L2-normalized embeddings at their original dimensionality across all 63 dataset-representation pairs. Normalization preserves the highest-ARI representation in every dataset, increases ARI in 35 pairs, decreases it in 26, and leaves two numerically unchanged. On the SVHN dataset, the highest ARI remains low, rising from 0.097 to 0.098 after normalization. The evaluated caption-mediated pipeline obtains relatively high ARI on isolated digits but does not consistently exceed direct visual features. These findings support selecting and evaluating complete representation-clustering pipelines for the intended domain, with explicit attention to extraction and normalization choices.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33265
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