5arXiv cs.AI (Artificial Intelligence)·
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.
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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.
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.
KnowAct-GUIClaw: Self-evolving memory and skill framework for cross-platform GUI agents
Researchers introduce KnowAct-GUIClaw, a Know-Route-Act-Reflect agent framework extending the OpenClaw agent system with cross-platform GUI interaction support across Android, iOS, HarmonyOS, and Windows. The system features an experience-attributable memory system and self-evolving skill library that continuously improves from user interaction history. Using open-source Kimi-2.6 models, the framework achieves 64.1% on the MobileWorld long-horizon benchmark, reportedly outperforming closed-source agentic models including GPT-5.5 and Seed-2.0-Pro. The transferable memory and skill components provide an 8.5% improvement when applied across different base models.
ProAct: Proactive Agent Architecture Using Idle-Time Compute to Anticipate User Needs
ProAct is a proactive agent architecture that uses idle time between user interactions to predict upcoming needs, pre-fetch information, and resolve knowledge gaps before queries are issued. The system analyzes dialogue history and persistent memory to iteratively acquire relevant information in advance. Evaluated on the new ProActEval benchmark (200 scenarios, 40 domains), ProAct reduces required turns by 14.8%, user effort by 11.7%, and hallucination rates by 28.1% compared to reactive baselines. The work also achieves state-of-the-art reflective accuracy on MemBench.
ClinEnv: Interactive Multi-Stage Long-Horizon EHR Benchmark for Clinical Agent Evaluation
ClinEnv is a new interactive benchmark that evaluates LLMs as attending physicians over real inpatient admissions using a Longitudinal Inpatient Simulation paradigm. Each case is decomposed into sequential decision stages where models must query four specialized agents before committing to medications, procedures, and diagnoses. Across seven evaluated models, the best achieves only 0.31 decision F1, with a sharp gap between diagnosis recovery (0.51 F1) and management actions (0.17 F1). The benchmark uniquely measures information-acquisition process quality alongside outcome quality, exposing a gap invisible to static or outcome-only evaluations.
MedRLM: Recursive multimodal agent framework for long-context clinical decision support
MedRLM is a proposed framework for clinical decision support that uses recursive multi-agent reasoning over heterogeneous patient data including EHRs, medical images, physiological sensor streams, and clinical guidelines. Rather than single-step prompting, it decomposes patient cases into an inspectable external environment coordinated by specialized agents, with a Clinical Evidence Graph Memory and sensor-triggered deeper reasoning. The paper outlines an evaluation design using public and credentialed clinical datasets spanning radiology, ECG, ICU time series, and referral outcomes. The work targets a gap between static medical QA benchmarks and real-world longitudinal clinical workflows.
RubricsTree: Scalable hierarchical rubric framework for evaluating personal health AI agents
RubricsTree is a new evaluation framework for LLM-powered personal health agents, built around a hierarchical taxonomy of over 100 clinically-verifiable Boolean rubrics derived from 4,000 real user queries and curated with physician oversight. A context-aware router activates only relevant rubrics per query, enabling scalable yet expert-aligned evaluation. The framework outperforms strong LLM-as-a-judge baselines on expert alignment and, when used as training signal, yields up to ~66% relative gains on HealthBench across Gemini, GPT, and Qwen model families. The work addresses a concrete bottleneck in clinical deployment of health AI: the cost-quality tradeoff in evaluation.
AgentCL: A Rigorous Evaluation Framework for Continual Learning in Language Agents
AgentCL is a new benchmark and evaluation framework designed to rigorously assess continual learning in language agents, addressing gaps in existing benchmarks that focus on retrieval over long-context documents or use naive task streams with limited cross-task analysis. The framework constructs compositional task streams where earlier sub-solutions, evidence, or workflows are intentionally reusable in later tasks, contrasting them with naive streams to measure transfer gains. The authors also introduce MemProbe, a probing method that stores interactions, insights, and skills while filtering unreliable experiences during consolidation. Empirical results across coding, deep research, and language understanding tasks show that controlled streams better distinguish memory design quality, and that naive streams can mask memory-induced degradation.