Agents20d ago
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.
Agents21d ago
GUI-HARVEST: Self-Improving GUI Agents via Evidence-Driven Harness Evolution Lifts OSWorld by 12.3 Points and GPT-5 by 13.8%
The executable harness surrounding a GUI model governs how multimodal visual observations are assembled, how mouse/keyboard actions are executed, and how error recovery and termination gates operate. Automatically evolving this harness with frozen base models presents coupled challenges: grounding error diagnosis in visual UI transitions, attributing failures amid execution variability, and translating multi-task failure modes into reliable runtime code edits. Researchers introduce GUI-HARVEST (arXiv:2610.00948), an evidence-driven harness optimizer. GUI-HARVEST synchronizes model intentions with before-and-after screenshots, treats repeated rollouts as joint evidence units, and abstracts recurring failure patterns into bounded Python code edits applied directly to the harness. Evaluated on OSWorld-Verified across six general and GUI-specialized backbones, Qwen3-VL-32B-Instruct improves by 12.33 points. Remarkably, transferring the evolved harness to WindowsAgentArena without further optimization elevates GPT-5 by 13.87 percentage points at 50 steps, decisively outperforming Self-Harness and Meta-Harness. Code is available at github.com/GaryYang12345/GUI-HARVEST.
Tooling6d ago
VS Code 1.143 makes the Copilot SDK-based harness the default, adds multi-model comparison and on-device MAI Code 1.1 Flash
Microsoft shipped Visual Studio Code 1.143. The Copilot harness, built on the Copilot SDK, is now the default for new sessions and works across the editor and Agents window. A Compare Models preview runs several models on one task with an optional Judge model, an experimental mode runs MAI Code 1.1 Flash on a local NVIDIA GPU, and large chats open much faster.
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