ARXIV:2602.11574 · AGENT OPTIMIZATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Learning to Configure Agentic AI Systems

arXiv

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to…

METHOD

Full abstract

Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries. We formulate agent configuration as a query-wise decision problem and introduce ARC (Agentic Resource & Configuration learner), which learns a light-weight hierarchical policy using reinforcement learning to dynamically tailor these configurations. Across multiple benchmarks spanning reasoning and tool-augmented question answering, the learned policy consistently outperforms strong hand-designed and other baselines, achieving up to 25% higher task accuracy while also reducing token and runtime costs. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.

WHY NOW

Agent Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainIntroduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

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

ARXIV:2602.11574 · AGENT OPTIMIZATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Learning to Configure Agentic AI Systems

arXiv

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agent Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainIntroduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

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

Paper Pack

10.48550/arXiv.2602.11574

Learning to Configure Agentic AI Systems

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

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; 33% coverage.

What was readable

linkedon filenot materializedderived fallback30 indexednot indexed

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

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agent Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Configuring LLM-based agent systems involves choosing workflows, tools, token budgets, and prompts from a large combinatorial design space, and is typically handled today by fixed large templates or hand-tuned heuristics. This leads to brittle behavior and unnecessary compute, since the same cumbersome configuration is often applied to both easy and hard input queries.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results demonstrate that learning per-query agent configurations is a powerful alternative to "one size fits all" designs.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agent Optimization moved forward this cycle; last verified April 2026. Public score 7.0/10.
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

Agent Optimization

Competitors

not indexed

Competitive landscape

Introduce dynamic AI system configurations using ARC to outperform traditional templates by optimizing token usage and accuracy.

Segment

Agent Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.11574 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(30)

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

2025Lakshya A Agrawal, Shangyin Tan et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

Token-Budget-Aware LLM Reasoning

2024Tingxu Han, Zhenting Wang et al.

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

2024John Yang, Carlos E. Jimenez et al.

Iterative Reasoning Preference Optimization

2024Richard Yuanzhe Pang, Weizhe Yuan et al.

GAIA: a benchmark for General AI Assistants

2023G. Mialon, Clémentine Fourrier et al.

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

2023Huiqiang Jiang, Qianhui Wu et al.

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines

2023O. Khattab, Arnav Singhvi et al.

Demystifying CLIP Data

2023Hu Xu, Saining Xie et al.

Large Language Models as Optimizers

2023Chengrun Yang, Xuezhi Wang et al.

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

2023Qingyun Wu, Gagan Bansal et al.

AgentBench: Evaluating LLMs as Agents

2023Xiao Liu, Hao Yu et al.

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

2023Yujia Qin, Shi Liang et al.

WebArena: A Realistic Web Environment for Building Autonomous Agents

2023Shuyan Zhou, Frank F. Xu et al.

Lost in the Middle: How Language Models Use Long Contexts

2023Nelson F. Liu, Kevin Lin et al.

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

2023Lingjiao Chen, M. Zaharia et al.

Sigmoid Loss for Language Image Pre-Training

2023Xiaohua Zhai, Basil Mustafa et al.

Toolformer: Language Models Can Teach Themselves to Use Tools

2023Timo Schick, Jane Dwivedi-Yu et al.

Text Embeddings by Weakly-Supervised Contrastive Pre-training

2022Liang Wang, Nan Yang et al.

ReAct: Synergizing Reasoning and Acting in Language Models

2022Shunyu Yao, Jeffrey Zhao et al.

Showing 20 of 30 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 WorkAutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents

7.0

Prior WorkReinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue

7.0

Prior WorkAgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents

7.0

Prior WorkAdaptive Latent Agentic Reasoning

7.0

Extension

Builds On ThisAres: Adaptive Reasoning Effort Selection for Efficient LLM Agents

4.0

Builds On ThisComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks

6.0

Builds On ThisCalibrate-Then-Act: Cost-Aware Exploration in LLM Agents

3.0

Builds On ThisRethinking Agentic Reinforcement Learning In Large Language Models

2.0

Commercially relevant

Higher ViabilityAutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration -- Learning from Cheap, Optimizing Expensive

8.0

Higher ViabilityAgentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design

9.0

Conflicting

none indexed

Owned Distribution

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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: 2602.11574
Route: /paper/learning-to-configure-agentic-ai-systems
Active tab: read
Artifact: learning-to-configure-agentic-ai-systems

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/learning-to-configure-agentic-ai-systems/paper-pack
REST build passport API/api/v1/paper/learning-to-configure-agentic-ai-systems/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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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/learning-to-configure-agentic-ai-systems

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

Learning to Configure Agentic AI Systems

Canonical ID learning-to-configure-agentic-ai-systems | Route /paper/learning-to-configure-agentic-ai-systems

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/learning-to-configure-agentic-ai-systems

MCP example

{
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source_context

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Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Learning to Configure Agentic AI Systems

/buildability/learning-to-configure-agentic-ai-systems

Watchwatch

Subject: Learning to Configure Agentic AI Systems

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/learning-to-configure-agentic-ai-systems

Paper ref

learning-to-configure-agentic-ai-systems

arXiv id

2602.11574

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

33%

Hash state

Lineage hash

d8bf05aa36fe2a51828df0e7619f266edd19ce998a21196c50ceb6cf46dccee8

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: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed 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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Learning to Configure Agentic AI Systems

Learning to Configure Agentic AI Systems

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(30)

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