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Uses an agentic loop architecture where the model decides when to search, what to search for, and when sufficient evidence has been gathered to answer the original query, enabling autonomous deep research without human intervention between steps.","intents":["I need to research a complex topic and get a comprehensive answer with sources without manually searching multiple times","I want an AI to autonomously investigate a question, decide what information gaps exist, and fill them through iterative searching","I need to understand a topic deeply with the AI making its own decisions about what follow-up research is necessary"],"best_for":["researchers and analysts conducting exploratory investigations","knowledge workers needing comprehensive background research on unfamiliar domains","teams building autonomous research agents that must operate without human guidance between steps"],"limitations":["Long-horizon reasoning adds latency — multi-step research tasks may take 30-60+ seconds depending on query complexity","Agentic loop depth is not user-configurable — cannot directly control max iterations or reasoning steps","May perform redundant searches if the model's search strategy is inefficient for a particular domain","No built-in fact-checking or source credibility ranking — relies on model's training data for accuracy assessment"],"requires":["API access via OpenRouter or direct Alibaba endpoint","Sufficient context window to maintain multi-step reasoning state (model supports extended context)","Query phrased as an open-ended research question rather than factual lookup"],"input_types":["natural language research queries","open-ended questions requiring multi-source investigation","exploratory prompts without predefined answer structure"],"output_types":["synthesized research summaries","structured findings with source attribution","reasoning traces showing search decisions and refinements"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_1","uri":"capability://planning.reasoning.iterative.search.refinement.with.model.directed.queries","name":"iterative-search-refinement-with-model-directed-queries","description":"The model autonomously generates search queries based on information gaps identified during reasoning, executes searches, evaluates results, and decides whether to refine the search strategy or proceed to synthesis. This differs from simple retrieval by having the model control the search loop — it determines query reformulation, decides when to pivot search strategy, and identifies when sufficient information has been gathered, implementing a feedback loop between reasoning and information retrieval.","intents":["I want the AI to automatically refine its search strategy when initial results don't answer the question","I need the model to identify what information is missing and search for it without me specifying each search","I want research that adapts its search approach based on what it learns from previous results"],"best_for":["exploratory research where the answer structure is unknown upfront","domains where initial search terms may not yield relevant results and strategy pivoting is necessary","users who want to avoid manually iterating through multiple searches"],"limitations":["Search quality depends on the model's ability to formulate effective queries — poor query generation leads to irrelevant results","No explicit control over search parameters (date ranges, source types, result count) — all determined by model decisions","May enter infinite loops if the model repeatedly generates similar queries without recognizing diminishing returns","Search result ranking is opaque — model's evaluation of result relevance is not exposed to the user"],"requires":["Integration with a search backend (web search, knowledge base, or document index)","Model context window large enough to maintain search history and reasoning state across iterations","Query formulated as an open-ended research question"],"input_types":["natural language research questions","exploratory prompts","complex multi-faceted queries"],"output_types":["final synthesized answer","search history with queries executed","source citations and relevance assessments"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_2","uri":"capability://planning.reasoning.multi.source.information.synthesis.with.conflict.resolution","name":"multi-source-information-synthesis-with-conflict-resolution","description":"Aggregates information from multiple search results and sources, identifies contradictions or conflicting claims, and synthesizes a coherent answer by reasoning about source credibility, recency, and relevance. The model maintains awareness of source provenance throughout reasoning and explicitly addresses conflicts rather than simply merging results, producing a unified narrative that acknowledges uncertainty where sources disagree.","intents":["I need a comprehensive answer that reconciles conflicting information from different sources","I want the AI to identify when sources disagree and explain why rather than picking one answer","I need research that shows me how different perspectives or sources relate to each other"],"best_for":["research on controversial or evolving topics where sources naturally conflict","domains with multiple authoritative sources that may have different perspectives","users who need to understand the landscape of information rather than a single definitive answer"],"limitations":["Conflict resolution quality depends on model's training data and reasoning capability — may not correctly assess source credibility","No explicit framework for weighting sources (e.g., peer-reviewed vs. news vs. blogs) — weighting is implicit in model behavior","May over-synthesize by finding false consensus where sources actually disagree","Cannot access real-time source metadata (publication date, author credentials) — relies on information embedded in search results"],"requires":["Multiple search results or sources to synthesize","Sufficient context window to maintain information from all sources during reasoning","Query that naturally has multiple valid perspectives or sources"],"input_types":["research questions with potentially conflicting answers","topics with multiple authoritative sources","exploratory queries where perspective diversity is valuable"],"output_types":["synthesized answer acknowledging multiple perspectives","explicit conflict identification and resolution reasoning","source attribution with credibility assessment"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_3","uri":"capability://planning.reasoning.extended.context.reasoning.with.sparse.activation","name":"extended-context-reasoning-with-sparse-activation","description":"Maintains coherent reasoning across extended context windows by using a mixture-of-experts (MoE) architecture where only 3 billion of 30 billion parameters activate per token, reducing computational overhead while preserving reasoning depth. This sparse activation pattern allows the model to process longer reasoning chains, maintain state across multiple research iterations, and synthesize information from numerous sources without the latency and memory penalties of dense full-parameter models.","intents":["I need the AI to reason over a long research process without losing context or coherence","I want deep reasoning on complex topics without waiting for dense model inference","I need to maintain conversation state across many research iterations efficiently"],"best_for":["long-horizon research tasks requiring 10+ reasoning steps","applications where latency is critical but reasoning depth is also required","teams building research agents that need to scale to many concurrent requests"],"limitations":["Sparse activation may cause some reasoning steps to be less thorough than a dense model — not all parameters are engaged for every token","Expert selection in MoE architecture is not user-controllable — cannot force specific parameter subsets to activate","Extended context reasoning still has practical limits — very long contexts (100k+ tokens) may show degradation","Sparse activation introduces non-determinism in some inference patterns — identical inputs may produce slightly different outputs"],"requires":["API client supporting streaming or long-context responses","Sufficient network bandwidth for extended token generation","Queries that benefit from long-horizon reasoning (short queries may not see efficiency gains)"],"input_types":["complex multi-step research queries","long conversation histories","queries requiring synthesis of many information sources"],"output_types":["extended reasoning traces","long-form synthesized answers","multi-step research narratives"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_4","uri":"capability://planning.reasoning.autonomous.task.decomposition.for.complex.queries","name":"autonomous-task-decomposition-for-complex-queries","description":"Automatically breaks down complex, multi-faceted research questions into sub-tasks, executes them in a logical sequence, and combines results into a coherent final answer. The model identifies task dependencies, determines optimal execution order, and manages state across sub-tasks without explicit user guidance on decomposition strategy. This enables handling of queries that would normally require manual step-by-step prompting.","intents":["I have a complex question with multiple parts and want the AI to figure out how to break it down","I need research on a topic that requires understanding several prerequisite concepts first","I want the AI to autonomously plan and execute a research strategy without me specifying each step"],"best_for":["complex research questions with implicit task structure","domains where understanding prerequisites is necessary before answering the main question","users who want to avoid manually specifying research steps"],"limitations":["Decomposition quality depends on model's reasoning — may miss important sub-tasks or create unnecessary ones","No explicit control over decomposition strategy — cannot override the model's task breakdown","Task dependencies are inferred implicitly — no explicit dependency graph is exposed","May execute tasks sequentially when parallel execution would be more efficient"],"requires":["Complex query with multiple implicit components","Sufficient context window to maintain task state and results","Query phrased as an open-ended research question"],"input_types":["complex multi-faceted research questions","questions requiring understanding of prerequisites","exploratory queries with implicit structure"],"output_types":["decomposed task list with execution order","results for each sub-task","final synthesized answer combining sub-task results"],"categories":["planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_5","uri":"capability://planning.reasoning.streaming.research.progress.with.intermediate.reasoning.visibility","name":"streaming-research-progress-with-intermediate-reasoning-visibility","description":"Streams research progress and intermediate reasoning steps to the user in real-time, allowing visibility into what searches are being executed, what information gaps are being identified, and how the model is synthesizing results. Rather than waiting for a final answer, users see the research process unfold, including search queries executed, results evaluated, and reasoning about next steps, enabling early intervention if the research direction is incorrect.","intents":["I want to see what searches the AI is doing and intervene if it's going in the wrong direction","I need visibility into the research process, not just the final answer","I want to understand how the AI decided to research this topic and what it learned at each step"],"best_for":["interactive research workflows where user guidance may be needed mid-process","educational contexts where understanding the research process is as important as the answer","quality assurance scenarios where research decisions need to be auditable"],"limitations":["Streaming intermediate steps adds latency to final answer delivery — users see progress but wait longer for completion","Intermediate reasoning may be verbose or include dead-end explorations that don't contribute to final answer","No built-in mechanism to pause and redirect research mid-stream — streaming is one-way from model to user","Intermediate steps may be incomplete or change as the model refines its reasoning"],"requires":["API client supporting streaming responses","UI capable of displaying streaming text and structured intermediate results","Network connection stable enough for long-duration streaming"],"input_types":["research queries","exploratory questions"],"output_types":["streaming intermediate reasoning steps","search queries executed","results evaluated","final synthesized answer"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_6","uri":"capability://planning.reasoning.context.aware.follow.up.question.generation","name":"context-aware-follow-up-question-generation","description":"Automatically identifies gaps in the current research and generates follow-up questions that would deepen understanding or fill missing information. The model maintains awareness of what has been learned so far and what remains unclear, suggesting natural next questions that build on previous research rather than starting fresh. This enables continuous research refinement without users having to manually think of follow-up questions.","intents":["I want the AI to suggest what I should research next to deepen my understanding","I need to know what information gaps remain after initial research","I want the AI to guide me toward comprehensive understanding by suggesting follow-up questions"],"best_for":["exploratory research where the full scope of a topic is unknown upfront","educational contexts where guided learning is valuable","research workflows where iterative deepening is more effective than single-pass research"],"limitations":["Follow-up questions are generated by the model and may not align with user's actual information needs","No mechanism to prioritize follow-up questions by importance or relevance to user's goals","Generated questions may be obvious or trivial if the model's gap analysis is shallow","Cannot distinguish between gaps that are important vs. gaps that are tangential"],"requires":["Research history or context from previous queries","Sufficient context window to maintain awareness of what has been learned","User willingness to engage with suggested follow-up questions"],"input_types":["research query with prior context","conversation history showing previous research"],"output_types":["suggested follow-up questions","explanation of why each question would be valuable","prioritization or ranking of follow-up questions"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-alibaba-tongyi-deepresearch-30b-a3b__cap_7","uri":"capability://tool.use.integration.api.access.via.openrouter.with.provider.agnostic.integration","name":"api-access-via-openrouter-with-provider-agnostic-integration","description":"Provides access to the Tongyi DeepResearch model through OpenRouter's unified API interface, enabling integration without direct Alibaba endpoint management. OpenRouter abstracts provider-specific details (authentication, rate limiting, error handling) behind a standard REST API, allowing developers to integrate the model using familiar HTTP patterns and switch providers without code changes. Supports streaming responses, token counting, and standard LLM API conventions.","intents":["I want to integrate Tongyi DeepResearch into my application without managing Alibaba API credentials directly","I need a standard LLM API interface that works with my existing integration code","I want to be able to switch between different research models without rewriting integration code"],"best_for":["developers building applications that need research capabilities","teams using OpenRouter for multi-provider LLM access","applications where provider abstraction reduces vendor lock-in"],"limitations":["OpenRouter adds a network hop and potential latency compared to direct Alibaba API access","OpenRouter pricing may include markup over direct Alibaba pricing","Some Alibaba-specific features or parameters may not be exposed through OpenRouter's abstraction","Rate limiting and quota management is handled by OpenRouter, not directly by Alibaba"],"requires":["OpenRouter API key","HTTP client library (curl, requests, fetch, etc.)","Network access to OpenRouter endpoints"],"input_types":["text prompts","research queries","conversation histories"],"output_types":["text responses","streaming token streams","structured metadata (token counts, model info)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or direct Alibaba endpoint","Sufficient context window to maintain multi-step reasoning state (model supports extended context)","Query phrased as an open-ended research question rather than factual lookup","Integration with a search backend (web search, knowledge base, or document index)","Model context window large enough to maintain search history and reasoning state across iterations","Query formulated as an open-ended research question","Multiple search results or sources to synthesize","Sufficient context window to maintain information from all sources during reasoning","Query that naturally has multiple valid perspectives or sources","API client supporting streaming or long-context responses"],"failure_modes":["Long-horizon reasoning adds latency — multi-step research tasks may take 30-60+ seconds depending on query complexity","Agentic loop depth is not user-configurable — cannot directly control max iterations or reasoning steps","May perform redundant searches if the model's search strategy is inefficient for a particular domain","No built-in fact-checking or source credibility ranking — relies on model's training data for accuracy assessment","Search quality depends on the model's ability to formulate effective queries — poor query generation leads to irrelevant results","No explicit control over search parameters (date ranges, source types, result count) — all determined by model decisions","May enter infinite loops if the model repeatedly generates similar queries without recognizing diminishing returns","Search result ranking is opaque — model's evaluation of result relevance is not exposed to the user","Conflict resolution quality depends on model's training data and reasoning capability — may not correctly assess source credibility","No explicit framework for weighting sources (e.g., peer-reviewed vs. news vs. blogs) — weighting is implicit in model behavior","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=alibaba-tongyi-deepresearch-30b-a3b","compare_url":"https://unfragile.ai/compare?artifact=alibaba-tongyi-deepresearch-30b-a3b"}},"signature":"vqC5SvrrKzOK0+QHlhJLXNE27wc/Tcp3CW6H7QPlQPSoavJhzqHs+VdjXv9QsoYGMLnkfm0qgwQasB070e7fBQ==","signedAt":"2026-06-21T20:15:52.164Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alibaba-tongyi-deepresearch-30b-a3b","artifact":"https://unfragile.ai/alibaba-tongyi-deepresearch-30b-a3b","verify":"https://unfragile.ai/api/v1/verify?slug=alibaba-tongyi-deepresearch-30b-a3b","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"}}