{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7","slug":"xiangyue-zhang--auto-deep-researcher-24x7","name":"auto-deep-researcher-24x7","type":"agent","url":"https://arxiv.org/abs/2604.05854","page_url":"https://unfragile.ai/xiangyue-zhang--auto-deep-researcher-24x7","categories":["automation","research"],"tags":["ai-agent","autonomous-agent","claude-code","deep-learning","experiment-automation","gpu","hyperparameter-tuning","llm-agent","machine-learning","mlops","pytorch","research-automation"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_0","uri":"capability://automation.workflow.autonomous.research.loop.orchestration","name":"autonomous-research-loop-orchestration","description":"Implements a persistent state machine (ResearchLoop in core/loop.py) that coordinates the THINK → EXECUTE → REFLECT lifecycle across multiple experiment cycles. The loop maintains cycle counters, manages graceful shutdowns, and orchestrates transitions between Leader and Worker agents while tracking experiment state across 30+ day runs without human intervention. Uses a cycle-persistence mechanism to resume from checkpoints and prevent context window bloat.","intents":["Run deep learning experiments autonomously for weeks without manual intervention","Maintain experiment state across multiple hypothesis-test cycles","Coordinate agent actions in a deterministic sequence without manual prompting"],"best_for":["ML researchers automating hyperparameter search and model iteration","Teams running long-horizon experiments on shared GPU clusters","Solo developers prototyping autonomous research workflows"],"limitations":["Requires stable GPU availability — cloud preemption will interrupt cycles","No built-in distributed coordination — single-machine only","Cycle persistence relies on local filesystem; no cloud state sync"],"requires":["Python 3.10+","Anthropic API key for Claude agent communication","Local GPU or cloud GPU with persistent storage","PyYAML for configuration management"],"input_types":["natural language research goal","YAML configuration file","existing codebase directory"],"output_types":["experiment results (metrics, logs)","modified training code","cycle state checkpoint"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_1","uri":"capability://automation.workflow.zero.cost.experiment.monitoring","name":"zero-cost-experiment-monitoring","description":"Replaces LLM polling with system-level monitoring (monitor.py) using os.kill checks, nvidia-smi GPU telemetry, and log tailing to track training progress without invoking the LLM. The agent 'sleeps' during GPU training and only wakes to parse structured logs and system metrics, reducing operational costs by over 90% compared to continuous LLM-based monitoring. Integrates with PyTorch training loops via log file parsing and GPU process introspection.","intents":["Monitor long-running training jobs without accumulating LLM costs","Detect training failures (OOM, NaN loss) via system signals instead of LLM inference","Track GPU utilization and memory pressure to decide when to launch next experiment"],"best_for":["Cost-conscious researchers running 24/7 experiments on limited budgets","Teams with GPU clusters where LLM polling would exceed compute savings","Autonomous agents designed for long-horizon (weeks-long) autonomy"],"limitations":["Requires structured log output from training code — custom formatters needed for non-standard frameworks","nvidia-smi parsing is GPU-vendor-specific; no support for TPU or custom accelerators","Cannot detect subtle training issues (e.g., mode collapse in GANs) without explicit log signals","Log tailing adds ~100-500ms latency to failure detection"],"requires":["NVIDIA GPU with nvidia-smi CLI tool","Training code that writes structured logs (JSON or CSV)","Linux/Unix OS for os.kill and process introspection","Read access to training log files"],"input_types":["GPU process ID","log file path","nvidia-smi output (JSON or text)"],"output_types":["training status (running/failed/completed)","GPU memory usage percentage","loss/metric values extracted from logs","failure reason (OOM, NaN, timeout)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_10","uri":"capability://code.generation.editing.pytorch.and.tensorflow.experiment.execution","name":"pytorch-and-tensorflow-experiment-execution","description":"Provides native integration with PyTorch and TensorFlow training loops, allowing the Code Worker to generate and execute training scripts that use these frameworks. The system handles GPU allocation, device management, and training process spawning via subprocess calls. Experiment results (metrics, checkpoints) are automatically logged to structured formats (JSON, CSV) that the monitor can parse.","intents":["Execute PyTorch and TensorFlow training jobs autonomously","Manage GPU device allocation and training process lifecycle","Capture training metrics and checkpoints for analysis"],"best_for":["ML researchers using PyTorch or TensorFlow as primary frameworks","Teams with standardized training code (e.g., PyTorch Lightning, Hugging Face)","Projects where training is the bottleneck (not data preprocessing)"],"limitations":["Only supports PyTorch and TensorFlow — no JAX, MXNet, or other frameworks","Assumes training code is well-structured and can be modified programmatically","No built-in distributed training support — single-GPU or single-node only","Checkpoint management is manual — no automatic checkpoint cleanup or versioning"],"requires":["PyTorch or TensorFlow installed in Python environment","CUDA/cuDNN for GPU support","Training code that outputs metrics to structured logs"],"input_types":["training script (Python file)","hyperparameters (JSON/YAML)","dataset path (local or cloud)"],"output_types":["training process (subprocess handle)","metrics log (JSON/CSV)","model checkpoint (PyTorch .pt or TensorFlow .pb)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_11","uri":"capability://search.retrieval.literature.search.and.research.discovery","name":"literature-search-and-research-discovery","description":"The Writing Worker agent has access to literature search tools (e.g., arXiv API, Google Scholar) to discover relevant papers and research directions. When generating ideas or analyzing results, the agent can query the literature to find similar work, identify gaps, or validate hypotheses against published results. Search results are summarized and fed back to the Leader for decision-making.","intents":["Discover relevant papers and research directions autonomously","Validate hypotheses against published results","Identify gaps in literature to guide new research directions"],"best_for":["Researchers automating literature review as part of the research cycle","Teams exploring novel domains where manual literature review is time-consuming","Projects where research direction should be informed by recent publications"],"limitations":["Literature search results are limited by API availability (arXiv, Google Scholar may rate-limit)","Search quality depends on query formulation — generic queries return too many results","Paper summaries are LLM-generated and may miss important details","No access to paywalled papers (IEEE, ACM) — only open-access content"],"requires":["arXiv API access (free, no key required)","Optional: Google Scholar API key","Anthropic Claude API for summarization"],"input_types":["search query (natural language or keywords)","optional: filters (year, author, venue)"],"output_types":["list of relevant papers (title, authors, abstract)","summarized insights (natural language)","links to papers (arXiv URLs)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_12","uri":"capability://code.generation.editing.happy.coder.integration.for.interactive.development","name":"happy-coder-integration-for-interactive-development","description":"Integrates with Happy Coder (Claude Code's interactive development environment) to allow humans to inspect and modify agent-generated code in real-time. When the Code Worker generates changes, they can be reviewed in Happy Coder before being applied to the training codebase. This provides a safety checkpoint and allows developers to understand agent reasoning.","intents":["Review agent-generated code changes before applying them","Understand and debug agent modifications interactively","Maintain code quality and safety in autonomous code generation"],"best_for":["Teams that want human oversight of agent code changes","Developers debugging agent behavior or code generation issues","Projects where code safety and quality are critical"],"limitations":["Requires manual review — defeats the purpose of full autonomy","Happy Coder integration is Claude-specific — not portable to other LLMs","Review bottleneck can slow down experiment cycles if humans are unavailable","No automatic conflict resolution — human must decide between agent suggestion and existing code"],"requires":["Claude Code / Happy Coder environment","Anthropic API key","Human reviewer available during experiment cycles"],"input_types":["proposed code changes (Python files)","diff summary (what changed)"],"output_types":["approved/rejected status (boolean)","human feedback (natural language)","modified code (if human made edits)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_13","uri":"capability://automation.workflow.cycle.based.experiment.batching.and.scheduling","name":"cycle-based-experiment-batching-and-scheduling","description":"Organizes experiments into discrete cycles, where each cycle consists of hypothesis generation, code modification, training execution, and result analysis. The ResearchLoop (loop.py) manages cycle transitions and maintains a cycle counter for persistence. This batching approach allows the agent to group related experiments and make strategic decisions at cycle boundaries rather than continuously.","intents":["Organize experiments into logical batches for coherent research progression","Make strategic decisions at cycle boundaries (e.g., pivot vs continue)","Enable checkpoint-based resumption if the agent is interrupted"],"best_for":["Long-horizon research projects with multiple hypothesis iterations","Teams that want clear experiment organization and decision points","Autonomous agents that need to balance exploration and exploitation"],"limitations":["Cycle boundaries are fixed — cannot adapt to experiment duration (e.g., if training takes longer than expected)","No built-in cycle prioritization — all cycles are treated equally","Cycle state is local; no distributed coordination across multiple machines","No mechanism to parallelize cycles — sequential execution only"],"requires":["Cycle counter persistence (file or database)","Defined cycle structure (hypothesis → code → train → analyze)","Checkpoint mechanism for resumption"],"input_types":["cycle number (integer)","cycle state (JSON/YAML)"],"output_types":["next cycle number (integer)","cycle results (metrics, decisions)","checkpoint for resumption"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_2","uri":"capability://planning.reasoning.leader.worker.agent.specialization","name":"leader-worker-agent-specialization","description":"Implements a two-tier agent architecture (AgentDispatcher in agents.py) where a persistent Leader agent maintains high-level research strategy and cycle state, while stateless specialized Workers (Idea, Code, Writing) execute specific tasks with minimal, role-specific toolsets. The Leader coordinates which Worker to invoke and when, ensuring only one Worker is active at a time to minimize parallel LLM costs. Each Worker has a tailored prompt and tool registry optimized for its domain (e.g., Code Worker has PyTorch/TensorFlow tools, Writing Worker has literature search tools).","intents":["Reduce per-task LLM prompt overhead by specializing agent roles","Maintain research strategy consistency across multiple experiment cycles","Minimize concurrent LLM calls to keep costs predictable and low"],"best_for":["Research teams automating multi-phase workflows (hypothesis → code → experiment → analysis)","Projects requiring strict cost control where parallel agents would exceed budget","Developers building domain-specific autonomous agents with role-based task decomposition"],"limitations":["Stateless Workers cannot maintain context across their invocations — all state must be passed via Leader","Sequential execution (one Worker at a time) means slower wall-clock time compared to parallel agents","Adding new Worker types requires defining new prompt templates and tool registries","No inter-Worker communication — all coordination flows through Leader"],"requires":["Anthropic Claude API with tool_use capability","Defined prompt templates for each Worker role","Tool registry mapping (tools.py) for each Worker type","Leader agent state persistence mechanism"],"input_types":["research goal (natural language)","previous cycle results (JSON/YAML)","Worker-specific task description"],"output_types":["Worker action result (code, idea, or written content)","updated research strategy (Leader output)","next Worker to invoke (dispatch decision)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_3","uri":"capability://memory.knowledge.two.tier.fixed.memory.system","name":"two-tier-fixed-memory-system","description":"Enforces a strict memory budget (~5,000 characters total) split across two tiers: Tier 1 (PROJECT_BRIEF.md) is a frozen, immutable project reference containing the original research goal and constraints, while Tier 2 (MEMORY_LOG.md) is a rolling log of milestones, decisions, and experiment results that undergoes aggressive auto-compaction. When Tier 2 exceeds budget, the MemoryManager (memory.py) summarizes old entries into condensed milestone summaries and removes redundant logs, preventing context window bloat over weeks of operation.","intents":["Prevent context window overflow during 30+ day autonomous runs","Maintain research continuity while keeping LLM token cost constant per cycle","Preserve original research intent (Tier 1) while compacting operational history (Tier 2)"],"best_for":["Long-horizon autonomous agents (weeks to months of operation)","Cost-sensitive research teams with fixed LLM budgets","Projects where context window growth would exceed API limits"],"limitations":["5,000 character budget is aggressive — complex projects may lose detail during compaction","Compaction is lossy; detailed experiment logs are summarized and cannot be fully recovered","Requires manual tuning of compaction thresholds per project type","No versioning of compacted logs — only the latest summary is retained"],"requires":["Markdown file storage (PROJECT_BRIEF.md, MEMORY_LOG.md)","Compaction algorithm implementation (summarization logic)","Character count tracking and threshold enforcement"],"input_types":["research goal (initial, for Tier 1)","experiment results (JSON/YAML, for Tier 2 logging)","decision rationale (natural language)"],"output_types":["compacted memory log (Markdown)","milestone summary (condensed text)","memory budget status (characters used / available)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_4","uri":"capability://automation.workflow.gpu.detection.and.availability.management","name":"gpu-detection-and-availability-management","description":"Provides real-time GPU telemetry via detect.py, querying nvidia-smi to determine GPU utilization, memory availability, and process status. The system uses this data to decide whether a training run can be safely launched (e.g., waiting if GPU memory is insufficient) and to track which GPUs are available. Integrates with the research loop to prevent resource conflicts and gracefully queue experiments when GPUs are saturated.","intents":["Determine if GPU resources are available before launching a training job","Prevent out-of-memory (OOM) errors by checking available GPU memory","Track GPU utilization across multiple concurrent experiments"],"best_for":["Multi-GPU clusters where resource contention is common","Autonomous agents running on shared cloud GPUs (Aliyun PAI-DSW, Lambda Labs, etc.)","Research teams needing predictable resource allocation"],"limitations":["nvidia-smi parsing is fragile — output format varies across NVIDIA driver versions","No support for non-NVIDIA accelerators (AMD ROCm, Intel Arc, TPU)","GPU memory estimates are conservative; actual available memory may differ due to fragmentation","No cross-machine GPU discovery — single-node only"],"requires":["NVIDIA GPU with nvidia-smi CLI tool","NVIDIA driver installed and in PATH","Linux/Unix OS (Windows support via WSL2)"],"input_types":["GPU index (0, 1, 2, ...)","required memory in GB","optional: process ID to check"],"output_types":["GPU utilization percentage","available memory in GB","process list on GPU","boolean: can_launch (true/false)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_5","uri":"capability://automation.workflow.cloud.gpu.keep.alive.mechanism","name":"cloud-gpu-keep-alive-mechanism","description":"Implements keeper.py to prevent cloud IDE environments (e.g., Aliyun PAI-DSW, Kaggle Notebooks) from reclaiming GPU resources due to inactivity timeouts. The keeper sends periodic heartbeat signals (e.g., dummy GPU operations, log writes) to signal that the session is still active, allowing long-running experiments to continue without interruption. This is essential for autonomous agents running on managed cloud platforms with strict idle-time policies.","intents":["Keep cloud GPU sessions alive during long training runs without manual interaction","Prevent session timeout and resource reclamation on managed cloud platforms","Enable 24/7 autonomous experiments on cloud IDEs with strict idle policies"],"best_for":["Researchers using managed cloud GPU services (Aliyun PAI-DSW, Kaggle, Colab Pro)","Teams without dedicated on-premise GPU clusters","Autonomous agents deployed on ephemeral cloud environments"],"limitations":["Cloud-specific implementation — heartbeat signals vary by platform (Aliyun vs Kaggle vs Colab)","Heartbeat overhead adds ~1-5% GPU utilization even during idle training","Some cloud providers (e.g., Colab) have hard limits on session duration (12-24 hours) that keeper cannot bypass","Requires write access to cloud storage or GPU device for heartbeat signals"],"requires":["Cloud GPU environment (Aliyun PAI-DSW, Kaggle, Colab, etc.)","Platform-specific API or CLI access for heartbeat signals","Periodic task scheduler (e.g., threading.Timer or APScheduler)"],"input_types":["cloud platform identifier (string)","heartbeat interval in seconds","optional: custom heartbeat function"],"output_types":["heartbeat status (success/failure)","session remaining time (if available)","logs of keep-alive signals"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_6","uri":"capability://planning.reasoning.hypothesis.generation.and.idea.refinement","name":"hypothesis-generation-and-idea-refinement","description":"The Idea Worker agent (specialized prompt in agents.py) generates new research hypotheses and experiment ideas based on previous cycle results. It uses a tool registry that includes literature search capabilities and prior experiment analysis to propose next steps (e.g., 'try lower learning rate', 'test batch normalization'). The Leader agent evaluates these ideas and decides which to pursue, creating a feedback loop that drives iterative research.","intents":["Automatically generate new hypotheses based on previous experiment outcomes","Propose hyperparameter changes or architectural modifications to improve results","Discover research directions without manual literature review"],"best_for":["ML researchers automating hyperparameter search and architecture exploration","Teams exploring large hypothesis spaces (e.g., NAS, AutoML)","Solo developers prototyping research ideas quickly"],"limitations":["Idea generation is constrained by the LLM's training data — novel ideas outside common patterns are rare","No built-in domain expertise — generic ideas may not apply to specialized domains (e.g., quantum ML)","Requires previous experiment results to be well-formatted; noisy logs reduce idea quality","No mechanism to avoid redundant ideas across cycles — may re-propose same changes"],"requires":["Anthropic Claude API with tool_use capability","Previous experiment results (metrics, logs) in structured format","Optional: literature search API (e.g., arXiv, Google Scholar)"],"input_types":["previous cycle results (JSON with metrics)","current model architecture (code or description)","research goal (natural language)"],"output_types":["list of proposed ideas (natural language)","rationale for each idea (explanation)","priority ranking (high/medium/low)"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_7","uri":"capability://code.generation.editing.code.generation.and.experiment.modification","name":"code-generation-and-experiment-modification","description":"The Code Worker agent (specialized prompt in agents.py) modifies training code based on the Leader's directives and Idea Worker's proposals. It has access to Claude Code capabilities (file editing, code execution) and a tool registry including PyTorch/TensorFlow utilities, allowing it to implement changes like hyperparameter tuning, architecture modifications, or data augmentation. The Code Worker reads the current codebase, applies changes, and validates syntax before returning modified code to the Leader.","intents":["Automatically modify training code to implement new hypotheses","Implement hyperparameter changes, architecture tweaks, or data pipeline modifications","Validate code changes before launching experiments"],"best_for":["ML researchers automating code changes across experiment cycles","Teams with standardized training code structures (e.g., PyTorch Lightning, Hugging Face Transformers)","Projects where code modifications are frequent and repetitive"],"limitations":["Code generation quality depends on codebase structure — messy or non-standard code is harder to modify","Claude Code has context limits — very large codebases (>100K lines) may exceed token budget","No built-in version control — code changes are not automatically committed or diffed","Syntax validation is local; runtime errors (e.g., shape mismatches) are only caught during training"],"requires":["Anthropic Claude API with tool_use and code execution capability","Python 3.10+ environment with PyTorch/TensorFlow installed","Read/write access to training code files","Optional: Git repository for version control"],"input_types":["current training code (Python files)","modification directive (natural language or structured)","hyperparameter changes (JSON/YAML)"],"output_types":["modified training code (Python files)","diff summary (what changed)","validation status (syntax OK / errors)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_8","uri":"capability://data.processing.analysis.experiment.result.analysis.and.reflection","name":"experiment-result-analysis-and-reflection","description":"After each training run completes, the system parses experiment logs and metrics (via monitor.py) and feeds them to the Leader agent for reflection. The Leader analyzes whether the hypothesis was validated, identifies failure modes (e.g., 'loss diverged', 'accuracy plateaued'), and decides the next research direction. This reflection step is critical for the iterative research cycle and informs the next hypothesis generation.","intents":["Automatically analyze experiment results and extract insights","Identify failure modes and root causes (e.g., learning rate too high, data quality issues)","Decide whether to continue with current direction or pivot to new hypothesis"],"best_for":["Autonomous research agents that need to interpret experiment outcomes","Teams running many experiments and needing automated result analysis","Projects with well-defined success metrics (e.g., accuracy, loss)"],"limitations":["Analysis quality depends on log structure — unstructured logs are hard to parse","Cannot detect subtle issues (e.g., overfitting, distribution shift) without explicit metrics","Reflection is limited by the LLM's ability to reason about domain-specific problems","No built-in statistical significance testing — may over-interpret noisy results"],"requires":["Structured experiment logs (JSON, CSV, or TensorBoard format)","Defined success metrics (accuracy, loss, F1, etc.)","Anthropic Claude API for reflection"],"input_types":["training logs (JSON/CSV with metrics)","final model checkpoint (optional)","original hypothesis (natural language)"],"output_types":["analysis summary (natural language)","success/failure verdict (boolean)","identified issues (list of failure modes)","recommendation for next step (natural language)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-xiangyue-zhang--auto-deep-researcher-24x7__cap_9","uri":"capability://planning.reasoning.directive.system.for.human.in.the.loop.control","name":"directive-system-for-human-in-the-loop-control","description":"Implements a directive system that allows humans to inject high-level commands into the autonomous loop without stopping it. Directives are natural language instructions (e.g., 'try learning rate 0.001', 'focus on reducing overfitting') that the Leader agent reads and incorporates into its decision-making. Directives are stored in a queue and processed at the start of each cycle, enabling real-time human guidance without interrupting the agent.","intents":["Provide human guidance to autonomous agent without stopping experiments","Steer research direction based on domain expertise or new insights","Enable collaborative human-AI research workflows"],"best_for":["Research teams where humans want to guide autonomous agents in real-time","Projects where domain expertise is critical and should override agent decisions","Hybrid workflows combining human intuition with autonomous execution"],"limitations":["Directives are processed at cycle boundaries — immediate changes require stopping the agent","No priority system — all directives are treated equally; conflicting directives may confuse the agent","Directive parsing is natural language — ambiguous instructions may be misinterpreted","No audit trail of which directives were followed and which were ignored"],"requires":["Directive queue storage (file, database, or API)","Natural language parsing to extract intent from directives","Integration with Leader agent prompt"],"input_types":["natural language directive (string)","optional: priority level (high/medium/low)","optional: deadline (cycle number or timestamp)"],"output_types":["directive acknowledgment (boolean)","execution status (pending/executed/ignored)","rationale if ignored (natural language)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":40,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","Anthropic API key for Claude agent communication","Local GPU or cloud GPU with persistent storage","PyYAML for configuration management","NVIDIA GPU with nvidia-smi CLI tool","Training code that writes structured logs (JSON or CSV)","Linux/Unix OS for os.kill and process introspection","Read access to training log files","PyTorch or TensorFlow installed in Python environment","CUDA/cuDNN for GPU support"],"failure_modes":["Requires stable GPU availability — cloud preemption will interrupt cycles","No built-in distributed coordination — single-machine only","Cycle persistence relies on local filesystem; no cloud state sync","Requires structured log output from training code — custom formatters needed for non-standard frameworks","nvidia-smi parsing is GPU-vendor-specific; no support for TPU or custom accelerators","Cannot detect subtle training issues (e.g., mode collapse in GANs) without explicit log signals","Log tailing adds ~100-500ms latency to failure detection","Only supports PyTorch and TensorFlow — no JAX, MXNet, or other frameworks","Assumes training code is well-structured and can be modified programmatically","No built-in distributed training support — single-GPU or single-node only","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3879536573847189,"quality":0.4,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-05-06T15:12:23.810Z","last_scraped_at":"2026-05-03T13:57:09.057Z","last_commit":"2026-04-22T10:29:54Z"},"community":{"stars":764,"forks":61,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=xiangyue-zhang--auto-deep-researcher-24x7","compare_url":"https://unfragile.ai/compare?artifact=xiangyue-zhang--auto-deep-researcher-24x7"}},"signature":"pIQtBQBa6h2ievmoJDepNfFarr7CY7+Z8fk4Dd7IdXjJhl1cb7lyoJIj0rJfgU+Is+z04I7T/nMPXdQHZ9MmBQ==","signedAt":"2026-06-20T08:42:39.743Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/xiangyue-zhang--auto-deep-researcher-24x7","artifact":"https://unfragile.ai/xiangyue-zhang--auto-deep-researcher-24x7","verify":"https://unfragile.ai/api/v1/verify?slug=xiangyue-zhang--auto-deep-researcher-24x7","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}