ARXIV:2602.15012 · PERSONALIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Cold-Start Personalization via Training-Free Priors from Structured World Models

arXiv

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction. The core challenge is a routing problem: each task admits dozens of preference dimensions, yet individual users…

METHOD

Full abstract

Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each task admits dozens of preference dimensions, yet individual users care about only a few, and which ones matter depends on who is asking. With a limited question budget, asking without structure will miss the dimensions that matter. Reinforcement learning is the natural formulation, but in multi-turn settings its terminal reward fails to exploit the factored, per-criterion structure of preference data, and in practice learned policies collapse to static question sequences that ignore user responses. We propose decomposing cold-start elicitation into offline structure learning and online Bayesian inference. Pep (Preference Elicitation with Priors) learns a structured world model of preference correlations offline from complete profiles, then performs training-free Bayesian inference online to select informative questions and predict complete preference profiles, including dimensions never asked about. The framework is modular across downstream solvers and requires only simple belief models. Across medical, mathematical, social, and commonsense reasoning, Pep achieves 80.8% alignment between generated responses and users' stated preferences versus 68.5% for RL, with 3-5x fewer interactions. When two users give different answers to the same question, Pep changes its follow-up 39-62% of the time versus 0-28% for RL. It does so with ~10K parameters versus 8B for RL, showing that the bottleneck in cold-start elicitation is the capability to exploit the factored structure of preference data.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Across medical, mathematical, social, and commonsense reasoning, Pep achieves 80.8% alignment between generated responses and users' stated preferences versus 68.5% for RL, with 3-5x…

WHY NOW

Personalization 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

PainDevelop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

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

ARXIV:2602.15012 · PERSONALIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Cold-Start Personalization via Training-Free Priors from Structured World Models

arXiv

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Personalization 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

PainDevelop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

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

Paper Pack

10.48550/arXiv.2602.15012

Cold-Start Personalization via Training-Free Priors from Structured World Models

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

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 fallback68 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

Personalization 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
Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction. The core challenge is a routing problem: each task admits dozens of preference dimensions, yet individual users care about only a few, and which ones matter depends on who is asking.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Cold-start personalization requires inferring user preferences through interaction when no user-specific historical data is available. The core challenge is a routing problem: each task admits dozens of preference dimensions, yet individual users care about only a few, and which ones matter depends on who is asking.
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. Across medical, mathematical, social, and commonsense reasoning, Pep achieves 80.8% alignment between generated responses and users' stated preferences versus 68.5% for RL, with 3-5x fewer interactions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Personalization 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

Personalization

Competitors

not indexed

Competitive landscape

Develop a cold-start personalization tool using structured world models for effective preference inference with minimal user interaction.

Segment

Personalization

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 2602.15012 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

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Bluesky

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PDF

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

References(68)

Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn LLM Agents

2025Guoqing Wang, Sunhao Dai et al.

Asking Clarifying Questions for Preference Elicitation With Large Language Models

2025Ali Montazeralghaem, Guy Tennenholtz et al.

Personalized Reasoning: Just-In-Time Personalization and Why LLMs Fail At It

2025Shuyue Stella Li, Avinandan Bose et al.

Bayesian preference elicitation for decision support in multiobjective optimization

2025Felix Huber, Sebastian Rojas-Gonzalez et al.

PrefPalette: Personalized Preference Modeling with Latent Attributes

2025Shuyue Stella Li, Melanie Sclar et al.

Interactive Reasoning: Visualizing and Controlling Chain-of-Thought Reasoning in Large Language Models

2025Rock Yuren Pang, K. J. K. Feng et al.

Reinforcing Multi-Turn Reasoning in LLM Agents via Turn-Level Reward Design

2025Quan Wei, Siliang Zeng et al.

LoRe: Personalizing LLMs via Low-Rank Reward Modeling

2025Avinandan Bose, Zhihan Xiong et al.

Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale

2025Bowen Jiang, Zhuoqun Hao et al.

QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?

2025Belinda Z. Li, Been Kim et al.

ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning

2025Shuyue Stella Li, Jimin Mun et al.

Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs

2025Siyan Zhao, Mingyi Hong et al.

CollabLLM: From Passive Responders to Active Collaborators

2025Shirley Wu, Michel Galley et al.

Accelerated Preference Elicitation with LLM-Based Proxies

2025David Huang, F. J. Cossío et al.

Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration

2024Avinandan Bose, Zhihan Xiong et al.

Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning

2024S. Poddar, Yanming Wan et al.

MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

2024Shuyue Stella Li, Vidhisha Balachandran et al.

Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

2024Shenao Zhang, Donghan Yu et al.

Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation

2024David Eric Austin, A. Korikov et al.

Bayesian Preference Elicitation with Language Models

2024Kunal Handa, Yarin Gal et al.

Showing 20 of 68 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 ThisStatistical Priors for Implicit Preferences: Decoupling Skill Selection as a Local Harness in Personal Agents

0.0

Commercially relevant

Higher ViabilitySparse Personalized Text Generation with Multi-Trajectory Reasoning

7.0

Higher ViabilityLearning to Reason for Multi-Step Retrieval of Personal Context in Personalized Question Answering

7.0

Higher ViabilityTowards Realistic Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions

5.0

Higher ViabilityLatent Preference Modeling for Cross-Session Personalized Tool Calling

6.0

Higher ViabilityTraining LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering

7.0

Higher ViabilityPlan Before Search: Search Agents Need Plan

7.0

Higher ViabilityLearning Personalized Agents from Human Feedback

9.0

Higher ViabilitySmall Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

6.0

Higher ViabilityTrain at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model

7.0

Conflicting

none indexed

Related Resources

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: 2602.15012
Route: /paper/cold-start-personalization-via-training-free-priors-from-structured-world-models
Active tab: read
Artifact: cold-start-personalization-via-training-free-priors-from-structured-world-models

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/cold-start-personalization-via-training-free-priors-from-structured-world-models/paper-pack
REST build passport API/api/v1/paper/cold-start-personalization-via-training-free-priors-from-structured-world-models/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/cold-start-personalization-via-training-free-priors-from-structured-world-models

stale

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

Cold-Start Personalization via Training-Free Priors from Structured World Models

Canonical ID cold-start-personalization-via-training-free-priors-from-structured-world-models | Route /paper/cold-start-personalization-via-training-free-priors-from-structured-world-models

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/cold-start-personalization-via-training-free-priors-from-structured-world-models

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

Not build-ready: Cold-Start Personalization via Training-Free Priors from Structured World Models

/buildability/cold-start-personalization-via-training-free-priors-from-structured-world-models

Ignoreblocked

Subject: Cold-Start Personalization via Training-Free Priors from Structured World Models

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/cold-start-personalization-via-training-free-priors-from-structured-world-models

Paper ref

cold-start-personalization-via-training-free-priors-from-structured-world-models

arXiv id

2602.15012

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

5316663e7c5bdfb3c509863005cb0e4c32c5e881243b2bd1d0ede4f76d207f25

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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Cold-Start Personalization via Training-Free Priors from Structured World Models

Cold-Start Personalization via Training-Free Priors from Structured World Models

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References(68)

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Brief

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(68)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline