4arXiv cs.LG (Machine Learning)·
VetClaw: Edge-cloud multimodal agentic system for veterinary disease screening
VetClaw is a multimodal agentic system that combines an edge camera module with a server-hosted vision-language model for zero-shot veterinary disease classification. The architecture separates agent interaction (OpenClaw) from workflow orchestration (LangGraph), enabling stateful screening with safety checks, conditional routing, and failure handling. Results show that image-only VLM prediction is limited, but symptom-guided and multimodal inputs improve zero-shot classification performance. The paper demonstrates a practical pattern for deploying agentic AI pipelines in constrained, safety-sensitive edge-cloud settings.
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SpatialClaw: Code-as-action interface for agentic 3D/4D spatial reasoning with VLMs
SpatialClaw is a training-free framework that uses code execution as the action interface for vision-language model agents performing spatial reasoning tasks. The system maintains a stateful Python kernel with perception and geometry primitives, allowing the VLM to write iterative executable cells conditioned on prior outputs rather than committing to a full strategy upfront. Evaluated across 20 spatial reasoning benchmarks covering static and dynamic 3D/4D tasks, SpatialClaw achieves 59.9% average accuracy, outperforming the prior state-of-the-art spatial agent by +11.2 points across six VLM backbones.
MaskClaw: Edge-Side Privacy Arbitration System for GUI Agents with Behavior-Driven Skill Evolution
MaskClaw is an edge-side privacy arbitration framework for GUI agents that intercepts screenshots before they leave a trusted environment, applying Allow/Mask/Ask decisions based on local visual evidence and user-specific policy memory. The system addresses the gap where static PII detectors miss context-dependent privacy boundaries and cloud-side VLMs may upload raw screens before deciding what to protect. The authors introduce P-GUI-Evo, a new benchmark built from real UI patterns and sanitized labels, and demonstrate that pattern matching, cloud reasoning, and routing alone each exhibit systematic failure modes. The artifact is open-sourced on GitHub.
HealthClaw: Self-evolving agent architecture for longitudinal personal health management
Researchers introduce HealthClaw, an open-source agent architecture that maintains and updates a structured longitudinal memory across repeated health interactions, separating shared medical knowledge from private per-user profiles, procedures, and episodic traces. Evaluated on a synthetic year-long benchmark and nine biomedical tasks, HealthClaw improves answer accuracy from 0.2% to 45.7% over current-query prompting while reducing prompt-side context exposure by 71.7% versus full-history prompting. Privacy probes show fewer unsafe disclosures than baselines. The authors note that offline benchmark results require prospective clinical validation before deployment claims.
ClinFusion: Vision-centric multimodal LLM for holistic medical image understanding
Researchers introduce ClinFusion, a multimodal LLM system designed for clinical medical understanding, featuring a cascaded vision encoder architecture (Cascade Spatial-Aware Locality Fusion) that handles both 2D and native 3D medical images within a unified encoder. The system is evaluated on a new benchmark suite including MedIF-Bench and a region-of-interest-grounded report generation metric, claiming state-of-the-art results on 20 of 24 benchmarks against open-source medical MLLMs and outperforming GPT-5.2 and Gemini-3-Flash on 13 of 16 multimodal benchmarks. Blinded evaluation by board-certified radiologists confirms ClinFusion produces the highest-ranked radiology reports, and the proposed RoI-grounded metric shows the strongest correlation with expert judgment among automatic metrics tested.
VulnClaw: AI agent + MCP toolchain for automated penetration testing workflows
VulnClaw is an open-source Python project that orchestrates an AI agent pipeline using MCP tooling and LLMs to automate the full penetration testing lifecycle: information gathering, vulnerability discovery, exploitation, and report generation via natural language input. The project has accumulated 1,049 GitHub stars with 105 added today, indicating notable community traction. It represents a concrete application of the MCP protocol to security automation.
Claw-Anything: Benchmark for Always-On Personal Assistants with Broad Digital World Access
Claw-Anything is a new benchmark designed to evaluate LLM agents acting as always-on personal assistants with access to long-horizon activity histories, interdependent backend services, and multi-device GUI/CLI interaction. The benchmark simulates months of user activity to create complex, noisy world states and evaluates both reactive and proactive assistance. GPT-5.5 achieves only 34.5% pass@1, revealing a substantial capability gap versus prior narrower benchmarks. An accompanying automated data-generation pipeline produces 2,000 training environments and yields a 23.7% improvement over the base model.
CamVLA: Calibration-free view-robust Vision-Language-Action model for robot deployment
Researchers from Alibaba DAMO Academy introduce CamVLA, a Vision-Language-Action model that eliminates the need for explicit camera calibration during robot deployment. The model decouples manipulation controls from camera geometry by predicting camera-centric end-effector actions and a 6-DoF hand-eye matrix, composing them into robot base-frame actions via deterministic geometric transformation. Operating on a single monocular RGB image without depth or calibration data, CamVLA improves success rates across diverse unseen viewpoints in both simulation and real-world evaluations.
ClaMPAPP: Hybrid LLM-ML system uses language models as interfaces for pediatric appendicitis diagnosis
Researchers introduce ClaMPAPP, a hybrid clinical decision support system that uses an LLM solely for structured feature extraction from free-text clinical notes, then passes validated features to an XGBoost classifier for final diagnosis. Evaluated on two independent German pediatric appendicitis cohorts, ClaMPAPP outperformed end-to-end LLM baselines on diagnostic performance and showed greater robustness to narrative reordering. The work formalizes an 'LLM-as-interface, ML-as-predictor' design pattern that separates natural-language usability from predictive inference, offering a more auditable pathway for clinical AI.