ARXIV:2603.10913 · EMBEDDINGS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

LLM2Vec-Gen: Generative Embeddings from Large Language Models

arXiv

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability. However, embedding tasks require mapping diverse inputs to similar outputs.

METHOD

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs.

Full abstract

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs. Typically, this input-output is addressed by training embedding models with paired data using contrastive learning. In this work, we propose a novel self-supervised approach, LLM2Vec-Gen, which adopts a different paradigm: rather than encoding the input, we learn to represent the model's potential response. Specifically, we add trainable special tokens to the LLM's vocabulary, append them to input, and optimize them to represent the LLM's response in a fixed-length sequence. Training is guided by the LLM's own completion for the query, along with an unsupervised embedding teacher that provides distillation targets. This formulation helps to bridge the input-output gap and transfers LLM capabilities such as safety alignment and reasoning to embedding tasks. Crucially, the LLM backbone remains frozen and training requires only unlabeled queries. LLM2Vec-Gen achieves state-of-the-art self-supervised performance on the Massive Text Embedding Benchmark (MTEB), improving by 9.3% over the best unsupervised embedding teacher. We also observe up to 43.2% reduction in harmful content retrieval and 29.3% improvement in reasoning capabilities for embedding tasks. Finally, the learned embeddings are interpretable and can be decoded into text to reveal their semantic content.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. LLM2Vec-Gen achieves state-of-the-art self-supervised performance on the Massive Text Embedding Benchmark (MTEB), improving by 9.3% over the best unsupervised embedding teacher.

WHY NOW

Embeddings moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainLLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

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

ARXIV:2603.10913 · EMBEDDINGS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

LLM2Vec-Gen: Generative Embeddings from Large Language Models

arXiv

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs.

Full abstract

RESULT

WHY NOW

Embeddings moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Opportunity summary

Score8.0

PainLLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

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

Paper Pack

10.48550/arXiv.2603.10913

LLM2Vec-Gen: Generative Embeddings from Large Language Models

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

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 materialized8 extracted30 indexednot indexed

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

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs.

RESULT

WHY NOW

Embeddings moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
LLM2Vec-Gen achieves state-of-the-art self-supervised performance on the Massive Text Embedding Benchmark (MTEB), improving by 9.3% over the best unsupervised embedding teacher.
Implicationpartial
Directly stated in abstract with specific numeric improvement
Verificationpartial
partial
Evidencepartial
We also observe up to 43.2% reduction in harmful content retrieval
Implicationpartial
Directly stated in abstract with specific numeric result
Verificationpartial
partial
Evidencepartial
29.3% improvement in reasoning capabilities for embedding tasks
Implicationpartial
Directly stated in abstract with specific numeric improvement
Verificationpartial
partial
Evidencepartial
rather than encoding the input, we learn to represent the model's potential response
Implicationpartial
Directly stated in abstract describing the core method
Verificationpartial
partial
Evidencepartial
we add trainable special tokens to the LLM's vocabulary, append them to input, and optimize them to represent the LLM's response in a fixed-length sequence
Implicationpartial
Directly stated in abstract describing technical implementation
Verificationpartial
partial
Evidencepartial
Crucially, the LLM backbone remains frozen and training requires only unlabeled queries
Implicationpartial
Directly stated in abstract describing training requirements
Verificationpartial
partial
Evidencepartial
This formulation helps to bridge the input-output gap and transfers LLM capabilities such as safety alignment and reasoning to embedding tasks
Implicationpartial
Directly stated in abstract but requires inference that the method enables this transfer
Verificationpartial
partial
Evidencepartial
the learned embeddings are interpretable and can be decoded into text to reveal their semantic content
Implicationpartial
Directly stated in abstract describing an important property of the embeddings
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

LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs.

Materials

PDF linked

Markets

Embeddings

Competitors

not indexed

Competitive landscape

LLM2Vec-Gen leverages self-supervised learning to create generative embeddings from large language models, enhancing performance and interpretability.

Segment

Embeddings

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.10913 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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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(30)

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

2026Benno Krojer, Shravan Nayak et al.

CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning

2025Jie He, R. Bai et al.

Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement

2025Yu-Che Tsai, Kuan-Yu Chen et al.

LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures

2025Hai Huang, Yann LeCun et al.

Causal2Vec: Improving Decoder-only LLMs as Versatile Embedding Models

2025Ailiang Lin, Zhuoyu Li et al.

Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

2025Yanzhao Zhang, Mingxin Li et al.

GEM: Empowering LLM for both Embedding Generation and Language Understanding

2025Caojin Zhang, Qiang Zhang et al.

Exploiting Instruction-Following Retrievers for Malicious Information Retrieval

2025Parishad BehnamGhader, Nicholas Meade et al.

SafeArena: Evaluating the Safety of Autonomous Web Agents

2025Ada Defne Tur, Nicholas Meade et al.

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training

2024Nathan Lambert, Jacob Daniel Morrison et al.

GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings

2024Raghuveer Thirukovalluru, Bhuwan Dhingra

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

2024Hongjin Su, Howard Yen et al.

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

2024Chankyu Lee, Rajarshi Roy et al.

xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token

2024Xin Cheng, Xun Wang et al.

LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

2024Parishad BehnamGhader, Vaibhav Adlakha et al.

Generative Representational Instruction Tuning

2024Niklas Muennighoff, Hongjin Su et al.

Answer is All You Need: Instruction-following Text Embedding via Answering the Question

2024Letian Peng, Yuwei Zhang et al.

Improving Text Embeddings with Large Language Models

2023Liang Wang, Nan Yang et al.

Scaling Sentence Embeddings with Large Language Models

2023Ting Jiang, Shaohan Huang et al.

Universal and Transferable Adversarial Attacks on Aligned Language Models

2023Andy Zou, Zifan Wang 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 WorkVidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval

8.0

Extension

Builds On ThisPromptEmbedder:: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting

7.0

Builds On ThisOne Model Is Enough: Native Retrieval Embeddings from LLM Agent Hidden States

7.0

Builds On ThisF2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World

3.0

Builds On ThisLLMs can construct powerful representations and streamline sample-efficient supervised learning

7.0

Builds On ThisTSEmbed: Unlocking Task Scaling in Universal Multimodal Embeddings

5.0

Builds On ThisLOCUS: Low-Dimensional Model Embeddings for Efficient Model Exploration, Comparison, and Selection

5.0

Builds On ThisLLM Self-Recognition: Steering and Retrieving Activation Signatures

0.0

Builds On ThisBidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs

7.0

Builds On ThisLanguage on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality

7.0

Commercially relevant

none indexed

Conflicting

none indexed

Related Resources

Semantic Embeddings(glossary)
How can I measure the fairness of LLM embeddings?(question)
How can I optimize word embeddings like Word2Vec or GloVe for specific tasks?(question)
What are the best strategies for combining visual and textual embeddings in retrieval?(question)

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: 2603.10913
Route: /paper/llm2vec-gen-generative-embeddings-from-large-language-models
Active tab: read
Artifact: llm2vec-gen-generative-embeddings-from-large-language-models

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/llm2vec-gen-generative-embeddings-from-large-language-models/paper-pack
REST build passport API/api/v1/paper/llm2vec-gen-generative-embeddings-from-large-language-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/llm2vec-gen-generative-embeddings-from-large-language-models

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
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

LLM2Vec-Gen: Generative Embeddings from Large Language Models

Canonical ID llm2vec-gen-generative-embeddings-from-large-language-models | Route /paper/llm2vec-gen-generative-embeddings-from-large-language-models

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/llm2vec-gen-generative-embeddings-from-large-language-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

Watch and verify: LLM2Vec-Gen: Generative Embeddings from Large Language Models

/buildability/llm2vec-gen-generative-embeddings-from-large-language-models

Watchwatch

Subject: LLM2Vec-Gen: Generative Embeddings from Large Language Models

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/llm2vec-gen-generative-embeddings-from-large-language-models

Paper ref

llm2vec-gen-generative-embeddings-from-large-language-models

arXiv id

2603.10913

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

c0551d560855be86d72ced71ef29bd725054eb38a731c79b7f71941050297e51

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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LLM2Vec-Gen: Generative Embeddings from Large Language Models

LLM2Vec-Gen: Generative Embeddings from Large Language Models

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Claim map

Constellation map

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Buzz

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References(30)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline