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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.
Practitioner guide to LangGraph for stateful multi-step business process agents
An arXiv preprint presents a practitioner-oriented guide to LangGraph, a low-level orchestration framework for stateful agentic workflows, with three executable recipes: SQL analytics with repair loops, agentic RAG with evidence gating, and human-in-the-loop policy review with interrupt/checkpoint recovery. The paper positions LangGraph by workflow-complexity fit rather than as a universal default, contrasting it with ReAct loops, schema-first tools, and DSPy for different use cases. The work targets engineers building long-running, auditable business process agents and explains how typed state, conditional routing, retries, and checkpoints become explicit product behavior.
HULAT2 multi-agent LangGraph system for Spanish Easy-to-Read text simplification at MER-TRANS 2026
Researchers from HULAT2-UC3M describe their submission to the MER-TRANS 2026 shared task on multilingual Easy-to-Read translation, using a LangGraph-based multi-agent workflow combining Gemini 2.5 Flash and RigoChat-7B-v2. The best run (RUN1) achieved a SARI score of 44.05 using Event-Condition-Action routing and internal quality signals, outperforming a LoRA-adapted generate-evaluate-regenerate baseline. Results show signal-guided multi-agent routing outperforms linear regeneration, while adding lexical support did not automatically improve reference-based scores.
MESA framework proactively ranks vulnerable communication channels in multi-agent systems
Researchers introduce MESA, a label-free framework for prioritizing security-critical communication edges in multi-agent systems (MAS) before attacks are observed. The framework combines six graph-theoretic metrics with two dynamic probes (ablation and masking) to rank edges by compromise risk, without requiring attack traces. Evaluated across three MAS scenarios, eight network topologies, and five open-source LLMs, MESA achieves mean Spearman ρ=+0.60 correlation with empirical per-edge attack success, and monitoring the top 10% of ranked edges intercepts roughly 3x more successful attacks than random allocation. The work highlights that attack impact in MAS is highly concentrated — a single compromised edge can account for up to 75% of total attack success.
DeerFlow 2.0 launches as open-source agent harness; Anthropic sues Pentagon over AI blacklist; Google releases Gemini Embedding 2
ByteDance released DeerFlow 2.0, an open-source agent harness built on LangGraph/LangChain that orchestrates parallel sub-agents with sandboxed Docker environments, progressive skill-loading, and persistent memory for complex workflows. Anthropic filed two lawsuits against the U.S. Pentagon contesting a supply-chain risk blacklist tied to its refusal to remove guardrails preventing Claude's use in autonomous weapons and domestic surveillance, with potential multi-billion dollar revenue impact. Google released Gemini Embedding 2, a multimodal embedding model unifying text, images, video, audio, and PDFs in a single vector space, succeeding the text-only predecessor. Meta acquired Moltbook, an agent-to-agent social platform built around the OpenClaw framework, while OpenAI hired OpenClaw's creator and acquired AI security testing platform Promptfoo.