ARXIV:2601.21916 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Agentic RAG: Joint Optimization for Dynamic AI Workflows

arXiv

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic…

METHOD

Full abstract

The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reasoning. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic planning while treating executors as frozen, black-box tools. We identify that this \textit{decoupled optimization} creates a ``strategic-operational mismatch,'' where sophisticated planning strategies fail to materialize due to unadapted local executors, often leading to negative performance gains despite increased system complexity. In this paper, we propose \textbf{JADE} (\textbf{J}oint \textbf{A}gentic \textbf{D}ynamic \textbf{E}xecution), a unified framework for the joint optimization of planning and execution within dynamic, multi-turn workflows. By modeling the system as a cooperative multi-agent team unified under a single shared backbone, JADE enables end-to-end learning driven by outcome-based rewards. This approach facilitates \textit{co-adaptation}: the planner learns to operate within the capability boundaries of the executors, while the executors evolve to align with high-level strategic intent. Empirical results demonstrate that JADE transforms disjoint modules into a synergistic system, yielding remarkable performance improvements via joint optimization and enabling a flexible balance between efficiency and effectiveness through dynamic workflow orchestration.

RESULT

ScienceToStartup currently rates this 6.0/10 on the public viability pass. By modeling the system as a cooperative multi-agent team unified under a single shared backbone, JADE enables end-to-end learning driven by outcome-based rewards.

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainDevelop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2601.21916 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Agentic RAG: Joint Optimization for Dynamic AI Workflows

arXiv

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Blocked on Code›Score6.0Evidence unverified

Opportunity summary

Pain Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score6.0

PainDevelop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2601.21916

JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback35 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

6.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic planning while treating executors as frozen, black-box tools.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reasoning. However, existing paradigms face a critical dichotomy: they either optimize modules jointly within rigid, fixed-graph architectures, or empower dynamic planning while treating executors as frozen, black-box tools.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. By modeling the system as a cooperative multi-agent team unified under a single shared backbone, JADE enables end-to-end learning driven by outcome-based rewards.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Agents

Competitors

not indexed

Competitive landscape

Develop a joint optimization framework for dynamic agentic RAG workflows to improve performance and efficiency.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

6.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2601.21916 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(35)

Reference metadata pending (c8f5f2148a6d60aedbb6530754438f3ce7abd2e6)

Reference metadata pending (ef896f673dd123d9812ebfe5316c1f57d02848fe)

Reference metadata pending (917ef1e14deebda9850e66a48017588f143dd4da)

Reference metadata pending (ec7ac137ceb25a4c04a11d33de784016990d0524)

Reference metadata pending (191cf833a0b9b5232ea34e055fe9ffc6b04decea)

Reference metadata pending (8b1738b888e3397bb22a8cf2f4d89721363b388b)

Reference metadata pending (4298a7bca88001f5df2d1ae15cca186d46271dc5)

Reference metadata pending (1fcaafeb72142fe3d1a5d698a072d69778d244b0)

Reference metadata pending (78f85a3a4e9d1b83ac33179c777e6eb2d756be82)

Reference metadata pending (5e5c2b08231678dcb33b1f00bc2dd5bb7045bcd5)

Reference metadata pending (3e2cbbf5f677f372cbb21969fe268a9b8a1ad64a)

Reference metadata pending (d8cbe0bfcc9c2eb1714c0e5be18a6ecaf50ea7a5)

Reference metadata pending (8051818817a9a3815be6623a679d4a7f5a7b7964)

Reference metadata pending (54fb839f621e3fe787437ab8ca5f37e7e4726bfe)

Reference metadata pending (e8a9b5a32836af499dcb9ccdee1008638309a6cc)

Reference metadata pending (e5e8c6ac537e0f5b5db14170bc232d6f9e641bbc)

Reference metadata pending (f65ecb65d00f2e69a49465debfdd78efa0838cec)

Reference metadata pending (ddbd8fe782ac98e9c64dd98710687a962195dd9b)

Reference metadata pending (f743287be3ced6757de7ecb26d03815b22cd737b)

Reference metadata pending (f208ea909fa7f54fea82def9a92fd81dfc758c39)

Showing 20 of 35 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkA2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning

6.0

Extension

Builds On ThisAgent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval

0.0

Commercially relevant

Higher ViabilityExperience as a Compass: Multi-agent RAG with Evolving Orchestration and Agent Prompts

7.0

Higher ViabilitySTRUCTUREDAGENT: Planning with AND/OR Trees for Long-Horizon Web Tasks

7.0

Higher ViabilityType-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

10.0

Higher ViabilityExplainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back

8.0

Higher ViabilityBridging Large-Model Reasoning and Real-Time Control via Agentic Fast-Slow Planning

7.0

Higher ViabilityHarmonizing Real-Time Constraints and Long-Horizon Reasoning: An Asynchronous Agentic Framework for Dynamic Scheduling

7.0

Conflicting

Competing ApproachAdaptive Multimodal Agents-Based Framework for Automatic Workflow Execution

4.0

Competing ApproachEngineering Robustness into Personal Agents with the AI Workflow Store

2.0

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Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2601.21916
Route: /paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag
Active tab: read
Artifact: jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag/paper-pack
REST build passport API/api/v1/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "arxiv_id": "2601.21916",
  "canonical_route": "/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag",
  "endpoints": {
    "paper_pack": "/api/v1/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag/paper-pack",
    "build_passport": "/api/v1/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

stale

Proof freshness: stale
Proof status: unverified
Display score: 6/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG

Canonical ID jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag | Route /paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2601.21916"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG",
  "normalized_query": "2601.21916",
  "route": "/paper/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag",
  "paper_ref": "jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Agentic RAG: Joint Optimization for Dynamic AI Workflows

/buildability/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

Watchwatch

Subject: Agentic RAG: Joint Optimization for Dynamic AI Workflows

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

Paper ref

jade-bridging-the-strategic-operational-gap-in-dynamic-agentic-rag

arXiv id

2601.21916

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

ca63c4e452ddfe2c60fa5735cb1d40bf2442511a6e43e53b1eadac931a2ae719

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Agentic RAG: Joint Optimization for Dynamic AI Workflows

Agentic RAG: Joint Optimization for Dynamic AI Workflows

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Claim map

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References(35)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline