AI Coding Live News

CrowdStrike: China-nexus VAPOR PANDA used the ARTEX multi-agent framework, mainly on DeepSeek, against 200+ organizations in 11 days

CrowdStrike reported on Oct 29 that the China-nexus espionage group VAPOR PANDA, starting Oct 17, ran the open-source agentic intrusion framework ARTEX with DeepSeek plus a second, unconfirmed commercial LLM, hitting more than 200 organizations in at least 14 countries from a single controller over 11 days. Post-exploitation was confirmed at nine organizations, where the BLOODALCHEMY backdoor was deployed. Planning agents used about 1.4 billion input tokens in a single intrusion, and recovered transcripts show a human operator steering the agents in Chinese at key decision points.

Key Takeaways

  • Over 11 days from Oct 17, one ARTEX controller targeted 200+ organizations in at least 14 countries, including several foreign ministries, 19 US pharma, semiconductor, defense, nuclear and finance organizations, and telecoms in the US, Belgium and the Philippines (CrowdStrike report)
  • Post-exploitation was confirmed at 9 organizations, which received the BLOODALCHEMY backdoor; at least 12 more saw password spraying and credential scanning
  • DeepSeek was the main model, plus a subscription commercial LLM CrowdStrike could not confirm; planning agents used about 1.4 billion input tokens in one intrusion
  • Recovered transcripts show an operator issuing Chinese instructions tagged as human instructions, including an order to find Windows hosts without the Falcon sensor
  • CrowdStrike says this is separate from the Storm-0940 ARTEX activity reported by Microsoft, and judges China-nexus groups will highly likely keep adopting such agent frameworks over the next 12 months
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NVIDIA Introduces EdgeWise: Edge-Cloud Collaborative Agent Framework Slashes SWE-Rebench Inference Cost by 62% Without Losing Accuracy

Executing every step of a long-horizon coding trajectory on frontier cloud LLMs incurs prohibitive inference costs. NVIDIA Research introduces EdgeWise, an edge-cloud collaborative framework that deploys a local Small Language Model (SLM) as the primary solver and uses step-level reinforcement learning to selectively delegate only critical steps to a cloud LLM. EdgeWise matches or outperforms pure-cloud LLM execution while cutting inference cost by up to 62% on SWE-Rebench and 34% on Terminal-Bench 2.1.

NVIDIA Introduces EdgeWise: Edge-Cloud Collaborative Agent Framework Slashes SWE-Rebench Inference Cost by 62% Without Losing Accuracy

Key Takeaways

  • Edge-primary selective delegation: runs a local SLM as the primary solver on edge workstations, escalating only critical reasoning steps to frontier cloud LLMs
  • Router-free step-level RL: jointly optimizes task solving and step-wise delegation decisions with fine-grained credit assignment
  • Up to 62% cost reduction: cuts inference costs by up to 62% on SWE-Rebench and 34% on Terminal-Bench 2.1 while matching or beating pure cloud LLM accuracy
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GitHub Security Lab open-sources Fuzzing Taskflow: an LLM agent runs the C/C++ fuzzing loop from harness writing to vulnerability reports

GitHub Security Lab's MIT-licensed Fuzzing Taskflow, built on its Taskflow Agent framework, takes a GitHub owner/repo and lets an agent pick entrypoints, analyze the build, write harnesses, run AFL++, iterate on coverage, and write a per-crash vulnerability report with root cause and a suggested patch. Claude Sonnet 5 is the default model; GitHub warns to run it only in disposable environments.

GitHub Security Lab open-sources Fuzzing Taskflow: an LLM agent runs the C/C++ fuzzing loop from harness writing to vulnerability reports

Key Takeaways

  • One command: ./scripts/fuzzing/run_fuzzing.sh owner/repo, ideally inside a Codespace
  • Per-iteration budgets double from 30s to 960s (~32 min per target); the loop stops after two iterations each gain <1% line coverage
  • Every harness is built twice: an .afl binary (afl-clang-lto with ASan/UBSan) for fuzzing and a .cov binary for real line/branch coverage
  • Crashes are minimized with afl-tmin, replayed under ASan, deduplicated by stack-top hash, and given one of seven verdicts
  • LLM-chosen build commands run on the host with no container, so GitHub says to use only disposable, unprivileged environments
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DAEDALUS: Bootstrapping Reusable Agent Memory via Self-Generated Tasks and Autonomous Dual-Agent Practice Without Human Oracles

LLM agents operating in novel API or terminal environments frequently repeat identical errors due to an absence of procedural memory. Existing memory bootstrapping approaches rely heavily on curated prompt playbooks or oracle verifiers that require extensive domain foresight. Illuin Technology releases DAEDALUS (arXiv:2610.08048), an autonomous dual-agent framework bootstrapping reusable memory entirely from self-generated practice without human oracles. DAEDALUS pairs an Explorer agent—which probes the execution environment to formulate curriculum-style tasks—with a Solver agent. When the solver stumbles, it extracts candidate heuristics that are committed into a persistent memory bank only after proving their efficacy across repeated verification trials. Across AppWorld, tau^2-bench, and AutomationBench, DAEDALUS boosts average success rates by up to 15.9 percentage points and raises Pass^5 rates by 2.2x over memory-less baselines, with generated procedural memories transferring seamlessly across heterogeneous foundation model families.

Key Takeaways

  • Illuin Technology open-sources DAEDALUS, bootstrapping reusable procedural memory without human labels or oracle verifiers
  • Employs an Explorer-Solver dual-agent self-play architecture to synthesize verified operational heuristics from failure traces
  • Lifts task completion by up to 15.9 points and doubles Pass^5 by 2.2x across AppWorld, tau^2-bench, and AutomationBench
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Microsoft Agent Framework: first-class ClaudeAgent wraps Claude Agent SDK for SequentialBuilder

Microsoft Agent Framework now ships a first-class ClaudeAgent over the Claude Agent SDK (pip install agent-framework-claude --pre), sharing the same BaseAgent surface as Azure OpenAI/Copilot agents so Claude drops into SequentialBuilder workflows. Built-in Read/Write/Bash/Glob, custom tools, MCP, permission modes, and streaming.

Key Takeaways

  • Install with pip install agent-framework-claude --pre for first-class ClaudeAgent.
  • Shares BaseAgent with Azure OpenAI/Copilot agents for SequentialBuilder workflows.
  • Includes Read/Write/Bash/Glob, MCP, permission modes, and streaming.
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HarnessDev: Can LLMs create and evolve their own agent harness?

_akhaliq shared HarnessDev, a paper asking whether LLMs can create and evolve their own agent harness—the runtime scaffolding for tools, loops, and execution—instead of relying only on hand-written agent frameworks. It matters for coding and research agents that need cheaper, self-improving harnesses.

Key Takeaways

  • Core question: can LLMs create and evolve an agent harness, not just use hand-written frameworks.
  • Relevant to coding/research agents that want less manual tool-loop scaffolding.
  • Paper: huggingface.co/papers/2609.01437 for methods and results.
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DeepSeek Ships V4-Flash-Vision-Exp: Multimodal Agents Near Claude Opus 4.8

DeepSeek-V4-Flash-Vision-Exp is live on the API: it matches V4-Flash on text—including agents, reasoning, and world knowledge—while jumping on multimodal agent benchmarks to near Claude Opus 4.8. A free Files API and DeepSeek Harness 0.1.1 ship with it; images bill at V4-Flash rates (up to 384 tokens each) and drop into existing agent frameworks.

Key Takeaways

  • The experimental vision model keeps V4-Flash text/agent strength while pushing multimodal agent scores close to Opus 4.8
  • Chat Completions, Messages, and Responses are supported; images mix in via base64, URLs, or the Files API
  • Files API is free with file_id reuse, and Harness 0.1.1 supports the model out of the box for agent stacks
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OpenHands 0.18 sets new open-source autonomous agent benchmark with 53.4% on SWE-bench Verified

All-Hands AI announced OpenHands 0.18, bringing architectural upgrades to its open-source software engineering agent. With a redesigned action planner, dynamic rollback search, and multi-agent code verification, OpenHands achieved a 53.4% solve rate on SWE-bench Verified, establishing a new SOTA among open-source agent frameworks.

Key Takeaways

  • Reaches a 53.4% solve rate on SWE-bench Verified, setting a record for open-source agents.
  • Integrates execution log introspection and rollback trees to avoid agent deadlock.
  • Supports Docker sandbox and cloud cluster deployment across Anthropic, OpenAI, and Ollama.
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Google Antigravity 2.5 launches async agent event bus decoupling planning, synthesis, and verification loops

Google DeepMind upgraded its coding agent framework to Antigravity 2.5. The update introduces an in-memory asynchronous event bus that decouples Planner, Coder, and Auditor agents into parallel actor loops, increasing throughput across complex distributed project builds by 70%.

Key Takeaways

  • Actor-model async event bus allows concurrent subagents to build modules in parallel.
  • Built-in semantic conflict resolution merges diffs generated by parallel worker agents.
  • Supports native dual invocation across Gemini 3.7 Pro (planning) and Flash (execution).
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Microsoft .NET Combines Copilot Coding Harness with Agent Framework for Governed Enterprise Agents

Microsoft's .NET team announced production stability for the GitHub Copilot coding harness combined with Microsoft Agent Framework across .NET and Python, featuring MCP tools, sandboxes, and approval workflows.

Key Takeaways

  • Packages enterprise agent runtimes into a governed harness featuring granular approvals and full observability;
  • First-class support for MCP tools, bash shell execution, and URL extraction across .NET and Python;
  • Integrates with Azure Container Apps sandboxes to provide secure isolation for untrusted agent code.
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