ARXIV:2602.14968 · AI FOR ROBOTICS SIMULATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

arXiv

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between…

METHOD

Full abstract

Automatically generating interactive 3D environments is crucial for scaling up robotic data collection in simulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing. Compared to classical 3D layout generation, producing complex physical scenes introduces additional challenges: (a) higher object density and complexity (e.g., a small shelf may hold dozens of books), (b) richer supporting relationships and compact spatial layouts, and (c) the need to accurately model both spatial placement and physical properties. To address these challenges, we propose PhyScensis, an LLM agent-based framework powered by a physics engine, to produce physically plausible scene configurations with high complexity. Specifically, our framework consists of three main components: an LLM agent iteratively proposes assets with spatial and physical predicates; a solver, equipped with a physics engine, realizes these predicates into a 3D scene; and feedback from the solver informs the agent to refine and enrich the configuration. Moreover, our framework preserves strong controllability over fine-grained textual descriptions and numerical parameters (e.g., relative positions, scene stability), enabled through probabilistic programming for stability and a complementary heuristic that jointly regulates stability and spatial relations. Experimental results show that our method outperforms prior approaches in scene complexity, visual quality, and physical accuracy, offering a unified pipeline for generating complex physical scene layouts for robotic manipulation.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment),…

WHY NOW

AI for Robotics Simulation moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainA framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

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

ARXIV:2602.14968 · AI FOR ROBOTICS SIMULATION · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

arXiv

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

AI for Robotics Simulation moved forward this cycle; last verified April 2026. Public score 3.0/10.

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

Opportunity summary

Score3.0

PainA framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

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

Paper Pack

10.48550/arXiv.2602.14968

PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

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 fallback48 indexednot indexed

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

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

AI for Robotics Simulation moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Automatically generating interactive 3D environments is crucial for scaling up robotic data collection in simulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
AI for Robotics Simulation moved forward this cycle; last verified April 2026. Public score 3.0/10.
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

AI for Robotics Simulation

Competitors

not indexed

Competitive landscape

A framework using a physics engine and LLMs to generate complex physical 3D scenes for robotic manipulation.

Segment

AI for Robotics Simulation

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.14968 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(48)

ArtiScene: Language-Driven Artistic 3D Scene Generation Through Image Intermediary

2025Zeqi Gu, Yin Cui et al.

PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes

2025Ahmed Abdelreheem, Filippo Aleotti et al.

Steerable Scene Generation with Post Training and Inference-Time Search

2025Nicholas Pfaff, Hongkai Dai et al.

Scenethesis: A Language and Vision Agentic Framework for 3D Scene Generation

2025Lu Ling, Chen-Hsuan Lin et al.

HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation

2025Wenqi Dong, Bangbang Yang et al.

Imperative vs. Declarative Programming Paradigms for Open-Universe Scene Generation

2025Maxim Gumin, Do Heon Han et al.

HSM: Hierarchical Scene Motifs for Multi-Scale Indoor Scene Generation

2025Hou In Derek Pun, Hou In Ivan Tam et al.

ArticuBot: Learning Universal Articulated Object Manipulation Policy via Large Scale Simulation

2025Yufei Wang, Ziyu Wang et al.

3D-Generalist: Self-Improving Vision-Language-Action Models for Crafting 3D Worlds

2025Fan-Yun Sun, Shengguang Wu et al.

LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models

2024Fan-Yun Sun, Weiyu Liu et al.

Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical 2D Inpainting

2024Yian Wang, Xiaowen Qiu et al.

GPT-4o System Card

2024OpenAI Aaron Hurst, Adam Lerer et al.

Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

2024Matt Deitke, Christopher Clark et al.

ClutterGen: A Cluttered Scene Generator for Robot Learning

2024Yinsen Jia, Boyuan Chen

Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation

2024Alexander R. E. Raistrick, Lingjie Mei et al.

Physically Compatible 3D Object Modeling from a Single Image

2024Minghao Guo, Bohan Wang et al.

PhyScene: Physically Interactable 3D Scene Synthesis for Embodied AI

2024Yandan Yang, Baoxiong Jia et al.

Physical Property Understanding from Language-Embedded Feature Fields

2024Albert J. Zhai, Yuan Shen et al.

Evaluating Text-to-Visual Generation with Image-to-Text Generation

2024Zhiqiu Lin, Deepak Pathak et al.

Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians

2024Licheng Zhong, Hong-Xing Yu et al.

Showing 20 of 48 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 WorkHetScene: Heterogeneity-Aware Diffusion for Dense Indoor Scene Generation

3.0

Extension

Builds On ThisPhysScene: A Scene Graph Dataset for Scientific Visual Reasoning in Physics Experiments

0.0

Commercially relevant

Higher ViabilityCoding Agent Is Good As World Simulator

8.0

Higher ViabilitySpatialGrammar: A Domain-Specific Language for LLM-Based 3D Indoor Scene Generation

4.0

Higher ViabilityThink like a Scientist: Physics-guided LLM Agent for Equation Discovery

7.0

Higher ViabilityPARSE: Part-Aware Relational Spatial Modeling

7.0

Higher ViabilitySceneAssistant: A Visual Feedback Agent for Open-Vocabulary 3D Scene Generation

8.0

Higher ViabilityIntelligent Co-Design: An Interactive LLM Framework for Interior Spatial Design via Multi-Modal Agents

8.0

Higher ViabilityPhyDrawGen: Physically Grounded Diagram Generation from Natural Language

8.0

Higher ViabilityCutscene Agent: An LLM Agent Framework for Automated 3D Cutscene Generation

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: 2602.14968
Route: /paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement
Active tab: read
Artifact: physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement/paper-pack
REST build passport API/api/v1/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement/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

{
  "contract_version": "paper-r2",
  "paper_id": "07ceea9e-f184-41c4-849f-ad67c09b3014",
  "arxiv_id": "2602.14968",
  "canonical_route": "/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement",
  "endpoints": {
    "paper_pack": "/api/v1/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement/paper-pack",
    "build_passport": "/api/v1/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement/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/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
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

PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

Canonical ID physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement | Route /paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.14968"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement",
  "normalized_query": "2602.14968",
  "route": "/paper/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement",
  "paper_ref": "physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement",
  "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

Not build-ready: PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

/buildability/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

Ignoreblocked

Subject: PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

Paper ref

physcensis-physics-augmented-llm-agents-for-complex-physical-scene-arrangement

arXiv id

2602.14968

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

0c8f7776aaddc9b3cebc53ac607fc42569b0cbc85d53a14a7ddde34dff18b9c2

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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PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

PhyScensis: Physics-Augmented LLM Agents for Complex Physical Scene Arrangement

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References(48)

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Brief

Experiment plan

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline