ARXIV:2602.15028 · LLM PRIVACY AND PERSONALIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

arXiv

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs. We introduce a large-scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality…

METHOD

Full abstract

Large language models (LLMs) are increasingly deployed in privacy-critical and personalization-oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large-scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality and privacy protection in LLMs. The benchmark comprises approximately 29,000 instances with context lengths ranging from 1K to 256K tokens, yielding a total of 377K evaluation questions. It jointly evaluates personalization performance and privacy risks across diverse scenarios, enabling controlled analysis of long-context model behavior. Extensive evaluations across state-of-the-art LLMs reveal consistent performance degradation in both personalization and privacy as context length increases. We further provide a theoretical analysis of attention dilution under context scaling, explaining this behavior as an inherent limitation of soft attention in fixed-capacity Transformers. The empirical and theoretical findings together suggest a general scaling gap in current models -- long context, less focus. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization. Code and data are available at https://github.com/SafeRL-Lab/PAPerBench

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization.

WHY NOW

LLM Privacy and Personalization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainA benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

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

ARXIV:2602.15028 · LLM PRIVACY AND PERSONALIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

arXiv

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization.

WHY NOW

LLM Privacy and Personalization moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainA benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

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

Paper Pack

10.48550/arXiv.2602.15028

Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

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 fallback51 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

ScienceToStartup currently rates this 5.0/10 on the public viability pass. We release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization.

WHY NOW

LLM Privacy and Personalization moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs. We introduce a large-scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality and privacy protection in LLMs.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Large language models (LLMs) are increasingly deployed in privacy-critical and personalization-oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large-scale benchmark, PAPerBench, to systematically study how increasing context length influences both personalization quality and privacy protection in LLMs.
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 release the benchmark to support reproducible evaluation and future research on scalable privacy and personalization.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
LLM Privacy and Personalization moved forward this cycle; last verified April 2026. Public score 5.0/10.
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

LLM Privacy and Personalization

Competitors

not indexed

Competitive landscape

A benchmark suite for evaluating the effects of long context lengths on privacy and personalization in LLMs.

Segment

LLM Privacy and Personalization

Adoption evidence

No public code link in the paper record yet

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 2602.15028 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(51)

Recursive Language Models

2025Alex L. Zhang, Tim Kraska et al.

AccidentBench: Benchmarking Multimodal Understanding and Reasoning in Vehicle Accidents and Beyond

2025Shangding Gu, Xiaohan Wang et al.

Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents

2025Shouju Wang, Fenglin Yu et al.

LLM-as-a-Judge for Privacy Evaluation? Exploring the Alignment of Human and LLM Perceptions of Privacy in Textual Data

2025Stephen Meisenbacher, Alexandra Klymenko et al.

Agentic Web: Weaving the Next Web with AI Agents

2025Yingxuan Yang, Mulei Ma et al.

Benchmarking LLM Privacy Recognition for Social Robot Decision Making

2025Dakota Sullivan, Shirley Zhang et al.

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

2025Gheorghe Comanici, Eric Bieber et al.

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices

2025Xiaopei Chen, Liang Li et al.

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

2025P. Chhikara, Dev Khant et al.

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

2025Bowen Jiang, Zhuoqun Hao et al.

A Survey of Personalization: From RAG to Agent

2025Xiaopeng Li, Pengyue Jia et al.

Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation

2025Teng Shi, Jun Xu et al.

Personalized Language Models via Privacy-Preserving Evolutionary Model Merging

2025Kyuyoung Kim, Jinwoo Shin et al.

A Survey on Federated Fine-tuning of Large Language Models

2025Yebo Wu, Chunlin Tian et al.

Personalized Generation In Large Model Era: A Survey

2025Yiyan Xu, Jinghao Zhang et al.

Unveiling Privacy Risks in LLM Agent Memory

2025Bo Wang, Weiyi He et al.

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

2025Siyan Zhao, Mingyi Hong et al.

Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents

2024Yuyou Gan, Yong Yang et al.

Personalization of Large Language Models: A Survey

2024Zhehao Zhang, Ryan A. Rossi et al.

GPT-4o System Card

2024OpenAI Aaron Hurst, Adam Lerer et al.

Showing 20 of 51 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 ThisDense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

0.0

Builds On ThisPolicyLong: Towards On-Policy Context Extension

4.0

Builds On ThisEmergence of Context Characteristics Sensitivity in Large Language Models

0.0

Builds On ThisReasoning Shift: How Context Silently Shortens LLM Reasoning

3.0

Builds On ThisAn Evaluation of Context Length Extrapolation in Long Code via Positional Embeddings and Efficient Attention

2.0

Builds On ThisRe-Centering Humans in LLM Personalization

3.0

Builds On ThisPersonalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs

0.0

Commercially relevant

Higher ViabilityStacked from One: Multi-Scale Self-Injection for Context Window Extension

6.0

Higher ViabilityDo LLMs Know What Is Private Internally? Probing and Steering Contextual Privacy Norms in Large Language Model Representations

8.0

Higher ViabilityHow to Train Your Long-Context Visual Document Model

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: 2602.15028
Route: /paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization
Active tab: read
Artifact: long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization/paper-pack
REST build passport API/api/v1/paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization/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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  "arxiv_id": "2602.15028",
  "canonical_route": "/paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization",
  "endpoints": {
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    "build_passport": "/api/v1/paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization/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/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

Canonical ID long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization | Route /paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

MCP example

{
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  "arguments": {
    "arxiv_id": "2602.15028"
  }
}

source_context

{
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  "mode": "paper",
  "query": "Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization",
  "normalized_query": "2602.15028",
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  "paper_ref": "long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization",
  "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

Watch and verify: Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

/buildability/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

Watchwatch

Subject: Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

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/long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

Paper ref

long-context-less-focus-a-scaling-gap-in-llms-revealed-through-privacy-and-personalization

arXiv id

2602.15028

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

bd05640d64410d74d76c49635c44df33992cad7b6bc4d76bfab21cc92189bebb

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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Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization

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References(51)

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Brief

Experiment plan

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Translational engineer

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GTM lead

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