ARXIV:2602.18262 · INTERPRETABILITY TOOLS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Simplifying Outcomes of Language Model Component Analyses with ELIA

Q: What is the startup potential of "Simplifying Outcomes of Language Model Component Analyses wi"?

ELIA simplifies complex language model analyses with an interactive tool powered by AI-generated explanations for non-experts.

Q: What products could be built from this research?

The product can be offered as a web-based tool where users upload their models or use predefined ones for analysis, providing interactive visualizations and AI-generated explanations of model behaviors.

Q: What are the practical use cases?

ELIA can be positioned as a SaaS platform for enterprises, enabling data teams and non-technical stakeholders to understand AI model decisions, enhancing transparency and compliance in sectors like finance and healthcare.

Q: What industries could this research disrupt?

ELIA could replace proprietary, less approachable interpretability tools, widening the user base to include non-experts who require insights into AI model decisions, potentially challenging existing tools like LIT and BertViz.

arXiv

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Blocked on Code›Score5.0Evidence partial

Opportunity summary

Pain ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Evidence 0 refs | 0 sources | 50% coverage

Blocker Evidence partial

Open Build Read PDF Signal Canvas Track

PROBLEM

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application…

METHOD

Full abstract

While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap, limiting their use to specialists. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application that simplifies the outcomes of various language model component analyses for a broader audience. The system integrates three key techniques -- Attribution Analysis, Function Vector Analysis, and Circuit Tracing -- and introduces a novel methodology: using a vision-language model to automatically generate natural language explanations (NLEs) for the complex visualizations produced by these methods. The effectiveness of this approach was empirically validated through a mixed-methods user study, which revealed a clear preference for interactive, explorable interfaces over simpler, static visualizations. A key finding was that the AI-powered explanations helped bridge the knowledge gap for non-experts; a statistical analysis showed no significant correlation between a user's prior LLM experience and their comprehension scores, suggesting that the system reduced barriers to comprehension across experience levels. We conclude that an AI system can indeed simplify complex model analyses, but its true power is unlocked when paired with thoughtful, user-centered design that prioritizes interactivity, specificity, and narrative guidance.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We conclude that an AI system can indeed simplify complex model analyses, but its true power is unlocked when paired with thoughtful, user-centered design…

WHY NOW

Interpretability Tools moved forward this cycle; last verified April 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.

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

Opportunity summary

Score5.0

PainELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Evidence0 refs | 0 sources | 50% coverage

Blockermissing authors

Analysis summary

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

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

ARXIV:2602.18262 · INTERPRETABILITY TOOLS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE

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

Simplifying Outcomes of Language Model Component Analyses with ELIA

arXiv

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Blocked on Code›Score5.0Evidence partial

Opportunity summary

Pain ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Evidence 0 refs | 0 sources | 50% coverage

Blocker Evidence partial

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Interpretability Tools moved forward this cycle; last verified April 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.

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

Opportunity summary

Score5.0

PainELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Evidence0 refs | 0 sources | 50% coverage

Blockermissing authors

Analysis summary

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

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

Paper Pack

10.48550/arXiv.2602.18262

Simplifying Outcomes of Language Model Component Analyses with ELIA

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

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

partial

0 refs; 0 sources; 50% coverage.

What was readable

linkedon filenot materializedderived fallback21 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

Interpretability Tools moved forward this cycle; last verified April 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application that simplifies the outcomes of various language model component analyses for a broader audience.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap, limiting their use to specialists. We address this challenge by designing, building, and evaluating ELIA (Explainable Language Interpretability Analysis), an interactive web application that simplifies the outcomes of various language model component analyses for a broader audience.
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. We conclude that an AI system can indeed simplify complex model analyses, but its true power is unlocked when paired with thoughtful, user-centered design that prioritizes interactivity, specificity, and narrative guidance. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Interpretability Tools moved forward this cycle; last verified April 2026. Public score 5.0/10. Implementation evidence is present through a linked repository.
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

Interpretability Tools

Competitors

not indexed

Competitive landscape

ELIA provides a user-friendly interactive web app to simplify and explain language model analyses to non-experts.

Segment

Interpretability Tools

Adoption evidence

Public code linked for build inspection

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 2602.18262 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(21)

Interpreting Language Models Through Concept Descriptions: A Survey

2025Nils Feldhus, Laura Kopf

Because we have LLMs, we Can and Should Pursue Agentic Interpretability

2025Been Kim, John Hewitt et al.

Circuit-Tracer: A New Library for Finding Feature Circuits

2025Michael P. Hanna, Mateusz Piotrowski et al.

On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs

2024Nitay Calderon, Roi Reichart

Transcoders Find Interpretable LLM Feature Circuits

2024Jacob Dunefsky, Philippe Chlenski et al.

Mechanistic Interpretability for AI Safety - A Review

2024Leonard Bereska, E. Gavves

LM Transparency Tool: Interactive Tool for Analyzing Transformer Language Models

2024Igor Tufanov, Karen Hambardzumyan et al.

OLMo: Accelerating the Science of Language Models

2024Dirk Groeneveld, Iz Beltagy et al.

On Measuring Faithfulness or Self-consistency of Natural Language Explanations

2023Letitia Parcalabescu, Anette Frank

Sociotechnical Safety Evaluation of Generative AI Systems

2023Laura Weidinger, Maribeth Rauh et al.

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods

2023Fred Zhang, Neel Nanda

FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

2023Sewon Min, Kalpesh Krishna et al.

Inseq: An Interpretability Toolkit for Sequence Generation Models

2023Gabriele Sarti, Nils Feldhus et al.

Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

2022Kevin Wang, Alexandre Variengien et al.

Human Interpretation of Saliency-based Explanation Over Text

2022Hendrik Schuff, Alon Jacovi et al.

The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models

2020Ian Tenney, James Wexler et al.

Attention is not not Explanation

2019Sarah Wiegreffe, Yuval Pinter

A Multiscale Visualization of Attention in the Transformer Model

2019Jesse Vig

Attention is not Explanation

2019Sarthak Jain, Byron C. Wallace

Sanity Checks for Saliency Maps

2018Julius Adebayo, J. Gilmer et al.

Showing 20 of 21 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 WorkUsing Large Language Models and Knowledge Graphs to Improve the Interpretability of Machine Learning Models in Manufacturing

5.0

Prior WorkTell Me Why: Designing an Explainable LLM-based Dialogue System for Student Problem Behavior Diagnosis

5.0

Extension

Builds On ThisTowards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures

3.0

Builds On ThisInterpretative Interfaces: Designing for AI-Mediated Reading Practices and the Knowledge Commons

2.0

Builds On ThisHuman Decision-Making with Persuasive and Narrative LLM Explanations

3.0

Builds On ThisCeci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems

4.0

Commercially relevant

Higher ViabilityA Two-Stage LLM Framework for Accessible and Verified XAI Explanations

7.0

Higher ViabilityExplaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

7.0

Higher ViabilityLexara: A User-Centered Toolkit for Evaluating Large Language Models for Conversational Visual Analytics

7.0

Higher ViabilityBoolXLLM: LLM-Assisted Explainability for Boolean Models

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: 2602.18262
Route: /paper/simplifying-outcomes-of-language-model-component-analyses-with-elia
Active tab: read
Artifact: simplifying-outcomes-of-language-model-component-analyses-with-elia

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/simplifying-outcomes-of-language-model-component-analyses-with-elia/paper-pack
REST build passport API/api/v1/paper/simplifying-outcomes-of-language-model-component-analyses-with-elia/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/simplifying-outcomes-of-language-model-component-analyses-with-elia

stale

Proof freshness: stale
Proof status: partial
Display score: 5/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 50%

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

Simplifying Outcomes of Language Model Component Analyses with ELIA

Canonical ID simplifying-outcomes-of-language-model-component-analyses-with-elia | Route /paper/simplifying-outcomes-of-language-model-component-analyses-with-elia

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/simplifying-outcomes-of-language-model-component-analyses-with-elia

MCP example

{
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  }
}

source_context

{
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  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Simplifying Outcomes of Language Model Component Analyses with ELIA

/buildability/simplifying-outcomes-of-language-model-component-analyses-with-elia

Watchwatch

Subject: Simplifying Outcomes of Language Model Component Analyses with ELIA

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/simplifying-outcomes-of-language-model-component-analyses-with-elia

Paper ref

simplifying-outcomes-of-language-model-component-analyses-with-elia

arXiv id

2602.18262

Freshness

Generated at

2026-03-19T18:48:05.835Z

Evidence freshness

stale

Last verification

2026-03-19T18:48:05.835Z

Sources

References

Coverage

50%

Hash state

Lineage hash

c606f271b2d903fd103ee40d8c970202c9fcacde23765f44dfc16314c3094c8b

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: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

references

distribution_readiness_scores

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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Simplifying Outcomes of Language Model Component Analyses with ELIA

Simplifying Outcomes of Language Model Component Analyses with ELIA

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