ARXIV:2602.14890 · AI REASONING AND INFERENCE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Lifted Relational Probabilistic Inference via Implicit Learning

arXiv

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models. We study the problem of answering queries in a first-order relational probabilistic logic through a joint effort…

METHOD

Full abstract

Reconciling the tension between inductive learning and deductive reasoning in first-order relational domains is a longstanding challenge in AI. We study the problem of answering queries in a first-order relational probabilistic logic through a joint effort of learning and reasoning, without ever constructing an explicit model. Traditional lifted inference assumes access to a complete model and exploits symmetry to evaluate probabilistic queries; however, learning such models from partial, noisy observations is intractable in general. We reconcile these two challenges through implicit learning to reason and first-order relational probabilistic inference techniques. More specifically, we merge incomplete first-order axioms with independently sampled, partially observed examples into a bounded-degree fragment of the sum-of-squares (SOS) hierarchy in polynomial time. Our algorithm performs two lifts simultaneously: (i) grounding-lift, where renaming-equivalent ground moments share one variable, collapsing the domain of individuals; and (ii) world-lift, where all pseudo-models (partial world assignments) are enforced in parallel, producing a global bound that holds across all worlds consistent with the learned constraints. These innovations yield the first polynomial-time framework that implicitly learns a first-order probabilistic logic and performs lifted inference over both individuals and worlds.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. These innovations yield the first polynomial-time framework that implicitly learns a first-order probabilistic logic and performs lifted inference over both individuals and worlds.

WHY NOW

AI Reasoning and Inference 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

PainDevelops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

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

ARXIV:2602.14890 · AI REASONING AND INFERENCE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Lifted Relational Probabilistic Inference via Implicit Learning

arXiv

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

AI Reasoning and Inference 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

PainDevelops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

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

Paper Pack

10.48550/arXiv.2602.14890

Lifted Relational Probabilistic Inference via Implicit Learning

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

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 fallback33 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 Reasoning and Inference 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
Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models. We study the problem of answering queries in a first-order relational probabilistic logic through a joint effort of learning and reasoning, without ever constructing an explicit model.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Reconciling the tension between inductive learning and deductive reasoning in first-order relational domains is a longstanding challenge in AI. We study the problem of answering queries in a first-order relational probabilistic logic through a joint effort of learning and reasoning, without ever constructing an explicit model.
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. These innovations yield the first polynomial-time framework that implicitly learns a first-order probabilistic logic and performs lifted inference over both individuals and worlds.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
AI Reasoning and Inference 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 Reasoning and Inference

Competitors

not indexed

Competitive landscape

Develops a polynomial-time framework for implicit learning and lifted inference in first-order probabilistic logic without explicit models.

Segment

AI Reasoning and Inference

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 2602.14890 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(33)

Polynomial-Time Relational Probabilistic Inference in Open Universes

2025Luise Ge, Brendan Juba et al.

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

2025Adam Suma, Samuel Dauncey

When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1

2024R. Thomas McCoy, Shunyu Yao et al.

Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve

2023R. Thomas McCoy, Shunyu Yao et al.

Large Language Models Are Not Strong Abstract Reasoners

2023Gaël Gendron, Qiming Bao et al.

Faith and Fate: Limits of Transformers on Compositionality

2023Nouha Dziri, Ximing Lu et al.

Sparks of Artificial General Intelligence: Early experiments with GPT-4

2023Sébastien Bubeck, Varun Chandrasekaran et al.

A Faster Interior-Point Method for Sum-of-Squares Optimization

2022Shunhua Jiang, Bento Natura et al.

Solving SDP Faster: A Robust IPM Framework and Efficient Implementation

2021Baihe Huang, Shunhua Jiang et al.

Learning Implicitly with Noisy Data in Linear Arithmetic

2020Alexander Philipp Rader, I. Mocanu et al.

Inductive logic programming at 30: a new introduction

2020Andrew Cropper, Sebastijan Dumancic

Polynomial-Time Implicit Learnability in SMT

2020I. Mocanu, Vaishak Belle et al.

Implicitly Learning to Reason in First-Order Logic

2019Vaishak Belle, Brendan Juba

Neural Markov Logic Networks

2019G. Marra, Ondřej Kuželka

Polynomial-time probabilistic reasoning with partial observations via implicit learning in probability logics

2018Brendan Juba

DeepProbLog: Neural Probabilistic Logic Programming

2018Robin Manhaeve, Sebastijan Dumancic et al.

End-to-end Differentiable Proving

2017Tim Rocktäschel, Sebastian Riedel

Logic Tensor Networks for Semantic Image Interpretation

2017Ivan Donadello, L. Serafini et al.

Open-Universe Weighted Model Counting

2017Vaishak Belle

Moment problem in infinitely many variables

2014M. Ghasemi, S. Kuhlmann et al.

Showing 20 of 33 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 WorkLearning Lifted Action Models from Unsupervised Visual Traces

3.0

Prior WorkEnhanced and Efficient Reasoning in Large Learning Models

3.0

Extension

Builds On ThisDifferentiable Learning of Lifted Action Schemas for Classical Planning

1.0

Builds On ThisHypothesis Generation and Inductive Inference in Children and Language Models

0.0

Builds On ThisParallel Lifted Planning via Semi-Naive Datalog Evaluation

2.0

Commercially relevant

Higher ViabilityStructured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants

7.0

Higher ViabilityEvaluating Relational Reasoning in LLMs with REL

4.0

Higher ViabilityAbductive Reasoning with Probabilistic Commonsense

7.0

Higher ViabilityContinual learning and refinement of causal models through dynamic predicate invention

5.0

Higher ViabilityAuto-Relational Reasoning

4.0

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: 2602.14890
Route: /paper/lifted-relational-probabilistic-inference-via-implicit-learning
Active tab: read
Artifact: lifted-relational-probabilistic-inference-via-implicit-learning

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/lifted-relational-probabilistic-inference-via-implicit-learning/paper-pack
REST build passport API/api/v1/paper/lifted-relational-probabilistic-inference-via-implicit-learning/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/lifted-relational-probabilistic-inference-via-implicit-learning

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

Lifted Relational Probabilistic Inference via Implicit Learning

Canonical ID lifted-relational-probabilistic-inference-via-implicit-learning | Route /paper/lifted-relational-probabilistic-inference-via-implicit-learning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/lifted-relational-probabilistic-inference-via-implicit-learning

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: Lifted Relational Probabilistic Inference via Implicit Learning

/buildability/lifted-relational-probabilistic-inference-via-implicit-learning

Ignoreblocked

Subject: Lifted Relational Probabilistic Inference via Implicit Learning

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/lifted-relational-probabilistic-inference-via-implicit-learning

Paper ref

lifted-relational-probabilistic-inference-via-implicit-learning

arXiv id

2602.14890

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

711eeea790980cf190845ca65fa7d8dc1e37ae87080d78d6157a708a4e90f706

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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Lifted Relational Probabilistic Inference via Implicit Learning

Lifted Relational Probabilistic Inference via Implicit Learning

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