Stay ahead of AI. Curated news, research papers, tools, and models distilled into daily briefings. https://aibrief.ai en-us Tue, 10 Nov 2026 18:33:08 GMT https://aibrief.ai/icon.png https://aibrief.ai Google Cloud has launched Gemini for healthcare, a specialized AI agent designed to orchestrate complex workflows across fragmented clinical, financial, and administrative systems in healthcare. It extends the universal Gemini agent with enterprise-grade security, identity management, and role-based access control to automate multi-step tasks such as pre-visit chart preparation and claim authorization, reducing manual effort and claim denials. https://cloud.google.com/blog/products/ai-machine-learning/gemini-for-healthcare/ Tue, 10 Nov 2026 14:15:26 GMT agent https://aibrief.ai/feed/introducing-gemini-for-healthcare-r7l3qj Google Cloud has introduced Gemini for healthcare, a specialized AI product designed to integrate clinical data across systems and streamline administrative workflows in medical environments. The solution aims to reduce clinician burnout by automating routine tasks and improving data accessibility within healthcare organizations. https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/gemini-for-healthcare/ Tue, 10 Nov 2026 14:15:16 GMT product https://aibrief.ai/feed/google-cloud-launches-gemini-for-healthcare-cu21dg Google and Adobe have integrated Adobe's creative and productivity tools into Google's ecosystem, enabling users to edit PDFs directly within Google Drive and leverage Adobe's AI capabilities within the Gemini AI assistant. This collaboration enhances workflow efficiency by unifying access to advanced editing and AI features across platforms. https://blog.google/company-news/inside-google/company-announcements/adobe-google-partnership-integration/ Tue, 10 Nov 2026 14:15:09 GMT product https://aibrief.ai/feed/were-bringing-adobes-creative-and-productivity-tools-closer-to-your-everyday-goo-y3yf8 Version 2.2.7 of OpenCode introduces intelligent model routing via GitHub Copilot Auto, enhancing AI efficiency by letting Copilot's router select models at discounted rates. Key UX improvements include enhanced side panel tab management, session read/unread tracking, and better file reading and shell feedback. Breaking changes include the removal of `ImageResponse.image` and reduced waiting for plugin-loaded endpoints, improving responsiveness. https://github.com/anomalyco/opencode/releases/tag/v2.2.7 Tue, 10 Nov 2026 12:30:18 GMT agent https://aibrief.ai/feed/opencode-v227-mjwpsx DeepMind's AlphaProof Code agents generated over three million lines of formal proofs in the Lean theorem prover to verify the core encryption logic in Signal's messaging protocol. This demonstrates a significant advancement in using AI to perform deep, automated verification of complex software systems. https://deepmind.google/blog/introducing-alphaproof-code/ Tue, 10 Nov 2026 12:15:10 GMT agent https://aibrief.ai/feed/introducing-alphaproof-code-a-breakthrough-in-verifying-complex-software-at-dept-32rfzz This release introduces typed async function tools support in the OpenAI Python client, enhancing developer experience for building AI applications with structured, type-safe asynchronous tool usage. It also fixes an issue with microphone audio encoding, ensuring recordings are properly formatted as valid PCM WAV files. https://github.com/openai/openai-python/releases/tag/v3.49.0 Tue, 10 Nov 2026 08:30:11 GMT model https://aibrief.ai/feed/openai-python-v3490-uqf8ki WorkflowArena introduces a benchmark for evaluating how well large language models orchestrate multi-agent workflows in complex, long-horizon tasks across diverse professional domains. It measures both the quality of final deliverables and the structure of delegation decisions made by agents during execution. https://arxiv.org/abs/2611.12856 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/workflowarena-benchmarking-executable-workflow-orchestration-in-llm-based-multi--909pes The study reveals a consistent probit-linear relationship between in-domain and out-of-domain persona behaviors across diverse models and fine-tuning settings. This relationship allows prediction and control of unintended behavioral transfer by adjusting fine-tuning strategies, particularly layer selection. The findings offer a unified quantitative framework for managing emergent misalignment during model adaptation. https://arxiv.org/abs/2611.12864 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/is-persona-generalization-predictable-9c6oiu The paper introduces a framework to detect and mitigate reward-driven misalignment in reinforcement learning by identifying verifiable reward-context conflicts that lead to unsafe shortcuts. It enables scalable generation of interactive environments and uses execution history to provide compliance feedback, improving safety without sacrificing task performance. https://arxiv.org/abs/2611.12865 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/shortcuts-and-boundaries-verifiable-rl-misalignment-and-contextual-safety-learni-p7gqyv The study examines where and how unimodal language and vision models develop aligned representations, finding that alignment emerges across layers and dimensions, especially in deeper layers. It confirms that the Platonic Representation Hypothesis holds for newer models when training data is clean, and shows that unimodal models can achieve higher cross-modal alignment than explicitly trained vision-language models, though still lag behind same-modality alignment. https://arxiv.org/abs/2611.12869 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/where-does-alignment-emerge-and-what-does-it-mean-a-closer-look-at-the-platonic--o6nk8k The paper introduces SWAM, a world action model that maintains language-video alignment while learning actions from diverse data sources. It demonstrates that combining egocentric videos with UMI and robot demonstrations improves control generalization, and that careful supervision design and model scaling yield strong performance on both simulated and real-world tasks. https://arxiv.org/abs/2611.12870 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/swam-scaling-world-action-models-with-cross-modal-alignment-mb3moe The paper introduces Vendi Anomaly Scores (VAS), a non-parametric anomaly detection method that identifies outliers by measuring how much a data point affects the overall diversity of the dataset when removed. VAS operates without hyperparameters, avoids density estimation and partitioning, and scales linearly with data size, achieving state-of-the-art performance across diverse embedding datasets and large-scale ImageNet experiments. https://arxiv.org/abs/2611.12871 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/vendi-anomaly-scores-for-efficient-and-accurate-anomaly-detection-42g90h HiVe-Mem introduces a novel multimodal memory framework that enhances long-term interaction in AI agents by combining horizontal memory expansion with vertical evidence composition. It uses a relational graph to expand retrieved memory seeds and dynamically selects relevant evidence forms per query, improving retrieval accuracy and reducing token usage by 90% compared to prior methods. https://arxiv.org/abs/2611.12872 Tue, 10 Nov 2026 08:02:16 GMT paper https://aibrief.ai/feed/hive-mem-interleaving-horizontal-memory-expansion-and-vertical-evidence-composit-re0qt3 Click3R enables interactive correction of 3D reconstructions by incorporating sparse user-provided point correspondences into feed-forward models. It uses a lightweight adapter to refine geometry, reducing errors in difficult cases like symmetry or textureless areas with minimal user input. https://arxiv.org/abs/2611.12875 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/click3r-interactive-stereo-3d-reconstruction-with-sparse-correspondence-clicks-u6oew1 The paper introduces BAQ-TD, an algorithm that enables efficient policy evaluation in reinforcement learning under communication constraints by combining batching and adaptive quantization. It achieves near-optimal convergence rates with only O(d log T) bits transmitted over T time steps, significantly reducing communication costs compared to prior methods. https://arxiv.org/abs/2611.12878 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/adaptive-quantization-preserves-fast-rates-for-td-learning-over-rate-limited-cha-5c6l4t The paper introduces a unified generative model that predicts joint 3D hand and object motion from internet videos, using masked spatiotemporal completion and flow matching. It recovers 3D trajectories from unstructured video data and incorporates contact constraints to maintain realistic grasps during prediction. https://arxiv.org/abs/2611.12879 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/grasping-the-future-joint-3d-hand-object-forecasting-from-internet-videos-kue243 KC-Maps is a neural network approach that learns the transition distribution of stochastic differential equations without requiring full trajectories. It uses only marginal samples and known drift/diffusion terms, enforcing boundary conditions and the Kolmogorov-Chapman equation during training. The method enables fast, trajectory-free simulation of molecular dynamics with significant speedups over traditional methods. https://arxiv.org/abs/2611.12880 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/kolmogorov-chapman-maps-trajectory-free-learning-for-simulating-molecular-dynami-mkd1th The paper identifies specific attention heads in multimodal large language models responsible for deciding which modality to trust during conflicts between visual and textual inputs. It introduces a training-free method to steer these heads at inference time, enabling precise control over modality reliance without retraining. The approach works across diverse benchmarks and remains stable under varying calibration conditions. https://arxiv.org/abs/2611.12883 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/beyond-modality-preference-controlling-multimodal-arbitration-under-modality-con-399aps The paper investigates data allocation strategies for improving model robustness across subpopulations, showing that balanced training data is not the only optimal configuration. It identifies multiple imbalanced distributions that perform as well as balanced ones and proposes gradient-based methods to efficiently adjust subgroup proportions for better worst-group performance. https://arxiv.org/abs/2611.12884 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/guiding-data-allocation-for-robust-subpopulation-generalization-r4yci2 The paper introduces World In-Context Learning (WICL), a method where a frozen model uses simulation to rehearse tasks from a single demonstration, storing task knowledge as semantic memory rather than updating weights. RoboScript, the system built on WICL, achieves high success rates across 50 manipulation tasks without gradient updates, outperforming trained policies and generalizing across visual domains and physical robots. https://arxiv.org/abs/2611.12885 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/world-in-context-learning-for-agentic-robot-manipulation-ybcav6 Reframe is a post-training framework for visual agents that improves tool use in reinforcement learning by aligning both observations and actions. It uses two stages: GD-GRPO trains policies to rely on tool-returned observations through evidence-aware rewards and input masking, while OG-MOPD repairs flawed actions mid-rollout using canonical replacements. The method boosts performance across 14 benchmarks without needing task-specific supervised fine-tuning. https://arxiv.org/abs/2611.12886 Tue, 10 Nov 2026 08:02:15 GMT paper https://aibrief.ai/feed/reframe-observation-aligned-multi-teacher-on-policy-distillation-for-agentic-vis-r55s9h OpenHands v1.38.0 introduces enhanced automation capabilities, including support for plugin configuration editing within automation workflows and the ability to run prompt and plugin presets from agent profiles. Key UI improvements include a new Paper theme, semantic budget visualization, and refined chat interactions with resolved banners and improved focus handling. The release also strengthens enterprise features with license banner visibility and better context preservation during automation exports. https://github.com/OpenHands/OpenHands/releases/tag/v1.38.0 Tue, 10 Nov 2026 06:30:19 GMT agent https://aibrief.ai/feed/openhands-v1380-4ovxvr The v1.58.1 release of block/goose includes two bug fixes addressing compatibility and authentication issues. One fix adjusts a compaction prompt to resolve incompatibility with Opus 5.5's distillation process, while another ensures Databricks OAuth tokens are refreshed before expiration to maintain uninterrupted access. https://github.com/aaif-goose/goose/releases/tag/v1.58.1 Tue, 10 Nov 2026 04:30:04 GMT agent https://aibrief.ai/feed/goose-v1581-588gp1 The study introduces BanglaCV-Probe, a benchmark assessing how models understand Bangla compound verbs formed by a main verb and a vector verb. Encoders effectively distinguish between literal and compositional meanings, while LLMs often fail to interpret vector verbs beyond their surface form, especially in entailment and translation tasks. https://arxiv.org/abs/2611.10909 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/pole-or-vector-encoders-capture-bangla-compound-verbs-but-llms-read-them-literal-79fdtv MemSurg introduces a structured memory graph that stores timestamped surgical observations, including entity locations, motion, and interactions, enabling temporal and relational reasoning across procedure stages. It enhances scene and workflow understanding in robot-assisted surgery by retrieving relevant historical context during inference, improving performance across multiple benchmarks. https://arxiv.org/abs/2611.10917 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/remembering-to-reason-structured-memory-for-scene-and-workflow-understanding-in--87hvq The paper introduces a prescriptive learning approach for binary decision-making that jointly optimizes linear decision rules and feasible assignments across scenarios, ensuring feasibility without relying on predictive labels. It extends this with a distributionally robust formulation using a Wasserstein-based ambiguity set to maintain feasibility under distributional shifts, improving performance in out-of-sample tests. https://arxiv.org/abs/2611.10923 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/prescriptive-learning-of-linear-policies-with-feasibility-guarantees-for-binary--n68w4e DeepFinance introduces a comprehensive benchmark for evaluating AI models on real-world private equity and investment banking tasks, requiring 27.1 hours of human effort on average. It assesses models across 21 end-to-end tasks using 19 files per task and 84 expert-defined criteria, revealing that even top models fail to consistently apply correct reasoning and judgment. https://arxiv.org/abs/2611.10926 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/deepfinance-can-ai-diligence-a-100mm-deal-gd9kmb Surname-based race imputation in voter files systematically misclassifies women, especially those in interracial marriages, due to surname changes after marriage. Using self-reported race data from six U.S. states, the study shows this leads to inflated gender gaps in political behavior estimates, particularly for Asian and Hispanic women. Correcting for this by using maiden names or excluding married surnames significantly improves accuracy and reduces bias. https://arxiv.org/abs/2611.10931 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/gendered-misclassification-from-surname-based-race-imputation-3vzkef The sudsho agent is a rule-based system that plays language-based economic games without using large language models during gameplay. It achieves high performance by targeting percentile rankings relative to a historical pool of prior plays, using clamped equilibrium values to guide decisions. The agent scored 2056 overall across over 117,000 games with zero inference cost and ranked 26th out of 71 accounts. https://arxiv.org/abs/2611.10932 Tue, 10 Nov 2026 04:01:31 GMT robotics https://aibrief.ai/feed/beating-the-pool-not-the-game-a-white-box-equilibrium-agent-for-language-based-e-lpgw63 A small transformer was trained to model the structure of the affine A2 Coxeter group, a mathematical group representing symmetries of a triangular tiling. Analysis of the model's attention patterns led to a new conjecture in word theory for this group, demonstrating that neural networks can uncover deep mathematical structure. https://arxiv.org/abs/2611.10936 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/can-small-transformers-learn-the-language-of-coxeter-groups-9m9idm BRACE is a hierarchical reinforcement learning framework that enables safe navigation for autonomous systems under unknown, spatially varying disturbances by using belief estimates from model errors to adapt reference trajectories. It combines nominal and belief-guided residual control terms, trained with a likelihood-ratio advantage reweighting method, and demonstrates improved performance over MPC and RL baselines in simulation and hardware experiments. https://arxiv.org/abs/2611.10938 Tue, 10 Nov 2026 04:01:31 GMT robotics https://aibrief.ai/feed/brace-belief-guided-residual-adaptive-control-for-safe-navigation-under-unknown--gvpmzu Large language models can generate effective data transformation programs for computer vision without relying on predefined augmentation spaces. This semantic synthesis approach achieves superior performance with far fewer candidate evaluations than brute-force search, reducing computational cost by up to 600x. https://arxiv.org/abs/2611.10940 Tue, 10 Nov 2026 04:01:31 GMT paper https://aibrief.ai/feed/semantic-synthesis-over-brute-force-search-llm-guided-transformation-design-for--5laxw7 COUPLED FLOW is a neural operator framework designed for multi-physics PDE systems, using a structure-constrained flow matching approach to model cross-process interactions. It learns process-specific velocity fields conditioned on the full system state, enabling accurate one-step predictions without iterative simulation. A novel mean-flow consistency identity and posterior Gaussian process-based sampling improve training quality and capture richer correlations. https://arxiv.org/abs/2611.10945 Tue, 10 Nov 2026 04:01:30 GMT paper https://aibrief.ai/feed/coupledflow-one-step-neural-operators-for-coupled-multi-physics-pdes-6lt2v3 The paper introduces vine sampling to improve behavior cloning data collection for diffusion policies by querying the demonstrator multiple times at the same observation. This helps the policy learn multi-modal action distributions instead of memorizing single actions. Experiments show better alignment with the demonstrator's conditional distribution and improved performance in test-time search and policy iteration. https://arxiv.org/abs/2611.10949 Tue, 10 Nov 2026 04:01:30 GMT robotics https://aibrief.ai/feed/explicitly-capturing-demonstrator-multi-modality-for-distillation-into-diffusion-v9z1qh The study evaluates cyber-physical attack detection in smart grids using a time-windowed fusion Random Forest with interpretable design, revealing that standard validation methods overestimate performance. Real-world attack scenarios and physics-aware evasion tests expose vulnerabilities in both cyber and physical observation layers, with a rate-invariant physics-manifold test proving more robust than temporal forecasting methods. https://arxiv.org/abs/2611.10950 Tue, 10 Nov 2026 04:01:30 GMT paper https://aibrief.ai/feed/interpretable-cyber-physical-attack-detection-in-smart-grids-a-leakage-controlle-80ymiw The article outlines an open-source AI-native stack built on Kubernetes that enables scalable, efficient deployment of open-weight models for production use. It addresses challenges in distributed inference, reinforcement learning, and agent execution by optimizing hardware utilization and stateful routing, with practical manifests for implementation. https://cloud.google.com/blog/topics/developers-practitioners/the-open-ai-native-stack-on-kubernetes-from-open-models-to-distributed-inference-and-agents/ Tue, 10 Nov 2026 02:15:06 GMT infra https://aibrief.ai/feed/the-open-ai-native-stack-on-kubernetes-from-open-models-to-distributed-inference-cc03m4 Decision models have emerged as a cost-efficient alternative to language models for AI inference tasks, handling classification, scoring, and choice selection with significantly lower latency and cost. On AI Gateway, they accounted for 26% of requests in October while contributing only 0.53% of total spend, with TypeSafe AI's Jev model dominating usage volume despite low cost. High-volume AI teams are increasingly adopting decision models, with adoption rates rising sharply with scale. https://vercel.com/blog/ai-gateway-production-index-november-2026 Tue, 10 Nov 2026 00:39:40 GMT model https://aibrief.ai/feed/decision-models-take-26-of-requests-anthropic-now-wins-new-teams-21-umpm2o NVIDIA Holoscan 5 enables real-time gravity compensation for robotic arms by executing inverse dynamics calculations continuously, allowing the arm to feel weightless during manual guidance. This capability supports applications like robot learning from demonstration and human-robot collaboration by precisely balancing gravitational forces using motor torque control. https://developer.nvidia.com/blog/make-a-robot-arm-feel-weightless-with-nvidia-holoscan-5/ Tue, 10 Nov 2026 00:39:31 GMT robotics https://aibrief.ai/feed/make-a-robot-arm-feel-weightless-with-nvidia-holoscan-5-iht9tl Eval-driven development is a methodology for building AI agents by defining evaluation criteria upfront, enabling systematic comparison of different agent designs based on consistent metrics. This approach emphasizes writing evaluations before implementation to guide development and measure performance objectively. https://www.langchain.com/blog/what-is-eval-driven-development Tue, 10 Nov 2026 00:39:25 GMT agent https://aibrief.ai/feed/what-is-eval-driven-development-how-to-improve-ai-agents-rgewow FlowAlign introduces a novel method for aligning source modality representations to a fixed target space by leveraging conditional flow fields during training to guide alignment. It improves both instance-level correspondence and global structure preservation, outperforming existing approaches in multimodal alignment tasks across synthetic, single-cell multiome, and image-text data. https://arxiv.org/abs/2611.09863 Tue, 10 Nov 2026 00:39:22 GMT paper https://aibrief.ai/feed/flow-guided-target-space-alignment-via-path-consistency-19t1u1 Grasp-TAGS introduces a task-aware grasp selection method that uses critic distillation to rank grasps before contact, enabling mobile manipulators to choose optimal initial grasps for long-horizon object rearrangement. It trains a single policy across multiple grasps and distills the critic into a lightweight utility network for fast inference. https://arxiv.org/abs/2611.09864 Tue, 10 Nov 2026 00:39:21 GMT robotics https://aibrief.ai/feed/grasp-tags-task-aware-grasp-selection-via-critic-distillation-for-prehensile-loc-cg788b GRAC is a novel framework that uses graph-based representations of surgical scenes to improve automated captioning by retrieving and adapting relevant past examples. It enhances both language quality and factual accuracy by comparing instrument-tissue interactions across scenes during caption generation. https://arxiv.org/abs/2611.09866 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/grac-graph-based-retrieval-augmented-captioning-for-surgical-scene-description-e26289 CodeQuant2 is a quantization framework designed for low-precision large reasoning models that addresses activation outliers during early decoding stages. It uses adaptive clustering, rotation-scaling for outlier mitigation, and dynamic codebook sizing per layer, improving accuracy and speed during inference. https://arxiv.org/abs/2611.09868 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/codequant2-enhanced-outlier-mitigation-and-adaptive-clustering-for-low-precision-jyzfae The paper introduces PC-Dr.DPO, a method that improves the reliability of pseudo-labels in test-time reinforcement learning by conditioning robust reweighting on prompt-specific vote reliability. It reduces sensitivity to finite-rollout variation and includes a cache mechanism to prevent forgetting during online adaptation. The approach achieves state-of-the-art performance across diverse reasoning benchmarks and model architectures. https://arxiv.org/abs/2611.09872 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/on-the-reliability-of-majority-vote-pseudo-labels-in-test-time-reinforcement-lea-b6s47x The paper introduces a constrained moving horizon estimator that locates contact points and estimates contact forces on tendon-driven continuum robots using only tendon measurements. It proves local exponential strong detectability when obstacle geometry is known and demonstrates real-time performance with high accuracy in simulations, outperforming tuned extended Kalman filters under model uncertainty and errors. https://arxiv.org/abs/2611.09877 Tue, 10 Nov 2026 00:39:21 GMT robotics https://aibrief.ai/feed/locating-contact-on-tendon-driven-continuum-robots-from-tendon-measurements-alon-pl7so4 The paper introduces graded convolutional networks that achieve scale invariance through a geometric retraction onto a weighted sphere, avoiding reliance on scale normalization. This approach maintains high accuracy under unknown Gaussian blur, outperforming scale-normalized derivative networks and existing Gaussian derivative networks on MNIST benchmarks. https://arxiv.org/abs/2611.09878 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/graded-convolutional-networks-scale-invariance-without-the-scale-and-robustness--glk8c7 ZeoBench is a benchmark for self-supervised learning on 3D representations of all-silica zeolites, evaluating multiple pretraining strategies on adsorption property prediction tasks. It demonstrates that 3D self-supervised models outperform hand-crafted features and supervised baselines, especially in low-data settings, with convolutional networks showing superior performance over vision transformers. https://arxiv.org/abs/2611.09880 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/zeobench-a-benchmark-for-self-supervised-learning-on-3d-zeolite-representations-ss7whe FlowMoP introduces a conditional flow matching framework for stochastic multi-person motion prediction, jointly modeling diverse future trajectories and poses using a B-spline reparameterization and dual-stream transformer. It achieves state-of-the-art results on CMU-Mocap and 3DPW while producing full predictive distributions, and establishes the first motion prediction baseline on WorldPose. https://arxiv.org/abs/2611.09882 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/flowmop-stochastic-multi-person-motion-prediction-3qm3sd The study establishes that single-layer ReLU networks with bounded neuron count form a weakly closed set in Sobolev spaces, ensuring existence of minimizers for various learning problems. In contrast, two-layer ReLU networks with bounded widths are not closed in Sobolev or continuous function spaces, refuting a prior conjecture. https://arxiv.org/abs/2611.09887 Tue, 10 Nov 2026 00:39:21 GMT paper https://aibrief.ai/feed/on-the-non-closedness-of-the-set-of-relu-networks-w9a6dy The paper presents a scenario orchestrator that dynamically adjusts non-ego traffic actors in real time during autonomous driving simulations to ensure intended interactions occur, even when the ego vehicle behaves unexpectedly. It uses constrained optimization to re-time, re-route, and reassign roles of traffic agents while minimizing interventions and preserving traffic realism. Experiments show this method achieves more reliable scenario realization than fixed or learned actor approaches. https://arxiv.org/abs/2611.09888 Tue, 10 Nov 2026 00:39:21 GMT robotics https://aibrief.ai/feed/scenario-orchestration-for-closed-loop-policy-evaluation-of-autonomous-driving-9q35m1