ARXIV:2603.18855 · WIRELESS COMMUNICATION OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

Xiucheng Wang · Yue Zhang · Nan Cheng · arXiv

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with…

METHOD

Full abstract

Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation. However, LLMs lack the precision required for physical-layer optimization, and the scarcity of wireless training data makes domain-specific fine-tuning impractical. We propose BeamAgent, an LLM-aided MIMO beamforming framework that explicitly decouples semantic intent parsing from numerical optimization. The LLM serves solely as a semantic translator that converts natural language descriptions into structured spatial constraints. A dedicated gradient-based optimizer then jointly solves the discrete base station site selection and continuous precoding design through an alternating optimization algorithm. A scene-aware prompt enables grounded spatial reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification. A penalty-based loss function enforces dark-zone power constraints while releasing optimization degrees of freedom for bright-zone gain maximization. Experiments on a ray-tracing-based urban MIMO scenario show that BeamAgent achieves a bright-zone power of 84.0\,dB, outperforming exhaustive zero-forcing by 7.1 dB under the same dark-zone constraint. The end-to-end system reaches within 3.3 dB of the expert upper bound, with the full optimization completing in under 2 s on a laptop.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. A scene-aware prompt enables grounded spatial reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification.

WHY NOW

Wireless Communication Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainAn LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

ARXIV:2603.18855 · WIRELESS COMMUNICATION OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

Xiucheng Wang · Yue Zhang · Nan Cheng · arXiv

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Wireless Communication Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainAn LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Evidence0 refs | 0 sources | 17% coverage

Blockerno shell-level blocker reported

Analysis summary

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.18855

BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

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 fallback40 indexednot indexed

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

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Wireless Communication Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Integrating large language models (LLMs) into wireless communication optimization is a promising yet challenging direction. Existing approaches either use LLMs as black-box solvers or code generators, tightly coupling them with numerical computation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. A scene-aware prompt enables grounded spatial reasoning without fine-tuning, and a multi-round interaction mechanism with dual-layer intent classification ensures robust constraint verification.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Wireless Communication Optimization moved forward this cycle; last verified April 2026. Public score 5.0/10.
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

Wireless Communication Optimization

Competitors

not indexed

Competitive landscape

An LLM-aided framework that translates natural language into structured spatial constraints for wireless communication optimization, achieving significant performance gains.

Segment

Wireless Communication Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.18855 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

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PDF

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

References(40)

Wireless Power Control Based on Large Language Models

2026Jiacheng Wang, Yucheng Sheng et al.

WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networks

2026Jingwen Tong, Zijian Li et al.

Bridging 6G IoT and AI: LLM-Based Efficient Approach for Physical Layer's Optimization Tasks

2026Ahsan Mehmood, Naveed Ul Hassan et al.

ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks

2026Haoyun Li, Mingkun Xiao et al.

Physics-Guided Language Model Via Low-Rank Adaptation for Path Loss Prediction

2026Xianli Feng, Jun Xiong et al.

Base Station Deployment under EMF constrain by Deep Reinforcement learning

2025Mohammed Mallik, G. Villemaud

A Comprehensive Survey on LLM‐Based Network Management and Operations

2025Jibum Hong, Nguyen Van Tu et al.

LLM4MG: Adapting Large Language Model for Multipath Generation via Synesthesia of Machines

2025Ziwei Huang, Shiliang Lu et al.

Intent-Driven Network Management with Multi-Agent LLMs: The Confucius Framework

2025Zhaodong Wang, Samuel Lin et al.

RadioDiff-3D: A 3D× 3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication

2025Xiucheng Wang, Qiming Zhang et al.

TelePlanNet: An AI-Driven Framework for Efficient Telecom Network Planning

2025Zongyuan Deng, Yujie Cai et al.

RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Construction

2025Xiucheng Wang, Qiming Zhang et al.

A Recent Survey on Radio Map Estimation Methods for Wireless Networks

2025Bin Feng, Meng Zheng et al.

WirelessGPT: A Generative Pre-Trained Multi-Task Learning Framework for Wireless Communication

2025Tingting Yang, Ping Zhang et al.

Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

2025Hyeonho Noh, B. Shim et al.

Large Language Model-Based Wireless Network Design

2024Kehai Qiu, Stefanos Bakirtzis et al.

WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks

2024Jingwen Tong, Jiawei Shao et al.

Intent-Based Management of Next-Generation Networks: an LLM-Centric Approach

2024Abdelkader Mekrache, Adlen Ksentini et al.

RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction

2024Xiucheng Wang, Keda Tao et al.

Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control

2024Hao Zhou, Chengming Hu et al.

Showing 20 of 40 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

none indexed

Extension

Builds On ThisBypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays

4.0

Builds On ThisEnergy Saving for Cell-Free Massive MIMO Networks: A Multi-Agent Deep Reinforcement Learning Approach

4.0

Commercially relevant

Higher ViabilityComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks

6.0

Higher ViabilityLearning to Reflect: Hierarchical Multi-Agent Reinforcement Learning for CSI-Free mmWave Beam-Focusing

7.0

Higher ViabilityLook Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity

7.0

Higher ViabilityEnhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

6.0

Higher ViabilityAgentic AI for Embodied-enhanced Beam Prediction in Low-Altitude Economy Networks

7.0

Higher ViabilityLarge Language Models as Optimization Controllers: Adaptive Continuation for SIMP Topology Optimization

7.0

Higher ViabilityLearning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors

7.0

Higher ViabilityOptimizing Service Operations via LLM-Powered Multi-Agent Simulation

7.0

Conflicting

none indexed

Owned Distribution

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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: 2603.18855
Route: /paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection
Active tab: read
Artifact: beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection/paper-pack
REST build passport API/api/v1/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection/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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  "paper_id": "48df3148-1dc1-48f8-95a3-5327a2c52d4f",
  "arxiv_id": "2603.18855",
  "canonical_route": "/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection",
  "endpoints": {
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    "build_passport": "/api/v1/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection/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/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

Canonical ID beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection | Route /paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.18855"
  }
}

source_context

{
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  "mode": "paper",
  "query": "BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding",
  "normalized_query": "2603.18855",
  "route": "/paper/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection",
  "paper_ref": "beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection",
  "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: BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

/buildability/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

Watchwatch

Subject: BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

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/beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

Paper ref

beamagent-llm-aided-mimo-beamforming-with-decoupled-intent-parsing-and-alternating-optimization-for-joint-site-selection

arXiv id

2603.18855

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

7f9acc8f8e7c98b727e516f06db7d2881f1c9a2365e961f4e6987a168c8f7459

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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BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

BeamAgent: LLM-Aided MIMO Beamforming with Decoupled Intent Parsing and Alternating Optimization for Joint Site Selection and Precoding

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