ARXIV:2602.20732 · EFFICIENT LLM KV CACHE MANAGEMENT · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

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

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

Q: What products could be built from this research?

Building a SaaS solution around CHESS could involve offering an API or integration with existing LLM services that suffer from latency due to long-context processing inefficiencies.

Q: What are the practical use cases?

Develop a cloud-based service that provides optimized long-context processing for enterprise document management systems, enhancing speed and efficiency in data-heavy environments.

arXiv

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory. Prior pruning methods are largely context-agnostic: their token selection ignores step-wise relevance and local semantics,…

METHOD

Full abstract

Long-context LLMs demand accurate inference at low latency, yet decoding becomes primarily constrained by KV cache as context grows. Prior pruning methods are largely context-agnostic: their token selection ignores step-wise relevance and local semantics, which undermines quality. Moreover, their irregular accesses and selection overheads yield only limited wall-clock speedups. To address this, we propose \textbf{CHESS}, an \textit{algorithm-system co-design} KV-cache management system. Algorithmically, CHESS introduces a context-aware, hierarchical selection policy that dynamically reconstructs a coherent context for the current decoding. System-wise, coarse granularity selection eliminates expensive data movement, fully realizing practical acceleration from theoretical sparsity. Extensive evaluations demonstrate that CHESS surpasses Full-KV quality using only \textbf{1\%} of the KV cache, delivers low-latency stable inference with up to \textbf{4.56$\times$} higher throughput, and consistently outperforms other strong baselines. Code is available at \href{https://anonymous.4open.science/r/CHESS-9958/}{https://anonymous.4open.science/r/CHESS/}.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive evaluations demonstrate that CHESS surpasses Full-KV quality using only \textbf{1\%} of the KV cache, delivers low-latency stable inference with up to \textbf{4.56$\times$} higher…

WHY NOW

Efficient LLM KV Cache Management 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

PainCHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

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

ARXIV:2602.20732 · EFFICIENT LLM KV CACHE MANAGEMENT · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

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

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

arXiv

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Efficient LLM KV Cache Management 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

PainCHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

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

Paper Pack

10.48550/arXiv.2602.20732

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

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; 33% coverage.

What was readable

linkedon filenot materialized8 extracted22 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

RESULT

WHY NOW

Efficient LLM KV Cache Management moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
delivers low-latency stable inference with up to 4.56x higher throughput
Implicationpartial
This is a direct quantitative result stated in the abstract and supported by the analysis.
Verificationpartial
partial
Evidencepartial
surpasses Full-KV quality using only 1% of the KV cache
Implicationpartial
This is a direct quantitative result stated in the abstract and supported by the analysis.
Verificationpartial
partial
Evidencepartial
we propose CHESS, an algorithm-system co-design KV-cache management system
Implicationpartial
This is a core description of the proposed system, explicitly stated in the abstract.
Verificationpartial
partial
Evidencepartial
CHESS introduces a context-aware, hierarchical selection policy that dynamically reconstructs a coherent context for the current decoding.
Implicationpartial
This describes the algorithmic approach of CHESS, as stated in the abstract and analysis.
Verificationpartial
partial
Evidencepartial
coarse granularity selection eliminates expensive data movement, fully realizing practical acceleration from theoretical sparsity.
Implicationpartial
This explains the system-level advantage of CHESS, as detailed in the abstract.
Verificationpartial
partial
Evidencepartial
CHESS could replace current LLM deployment strategies that are hampered by memory bandwidth limitations
Implicationpartial
This is an interpretation of the impact of CHESS, derived from the 'disruption' and 'why_it_matters' sections.
Verificationpartial
partial
Evidencepartial
The implementation may require adaptation to fit into diverse infrastructure environments
Implicationpartial
This is a potential limitation mentioned in the 'caveats' section of the analysis.
Verificationpartial
partial
Evidencepartial
there may be undiscovered edge cases where context-aware reconstruction might not perform optimally in real-world scenarios.
Implicationpartial
This is a potential limitation mentioned in the 'caveats' section of the analysis.
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

Efficient LLM KV Cache Management

Competitors

not indexed

Competitive landscape

CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.

Segment

Efficient LLM KV Cache Management

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 2602.20732 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(22)

EvolKV: Evolutionary KV Cache Compression for LLM Inference

2025Bohan Yu, Yekun Chai

NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization

2025Hyuntak Kim, Byung-Hak Kim

KeyDiff: Key Similarity-Based KV Cache Eviction for Long-Context LLM Inference in Resource-Constrained Environments

2025Junyoung Park, Dalton Jones et al.

A Survey on Personalized Alignment - The Missing Piece for Large Language Models in Real-World Applications

2025Jian Guan, Jun Wu et al.

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression

2025Payman Behnam, Yaosheng Fu et al.

ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference

2025Xiang Liu, Zhenheng Tang et al.

FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving

2025Zihao Ye, Lequn Chen et al.

LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks

2024Yushi Bai, Shangqing Tu et al.

AFlow: Automating Agentic Workflow Generation

2024Jiayi Zhang, Jinyu Xiang et al.

Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference

2024Jiaming Tang, Yilong Zhao et al.

SnapKV: LLM Knows What You are Looking for Before Generation

2024Yuhong Li, Yingbing Huang et al.

InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory

2024Chaojun Xiao, Pengle Zhang et al.

Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources

2023Yerin Hwang, Yongi-Mi Kim et al.

Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

2023Suyu Ge, Yunan Zhang et al.

Efficient Streaming Language Models with Attention Sinks

2023Guangxuan Xiao, Yuandong Tian et al.

Efficient Memory Management for Large Language Model Serving with PagedAttention

2023Woosuk Kwon, Zhuohan Li et al.

Lost in the Middle: How Language Models Use Long Contexts

2023Nelson F. Liu, Kevin Lin et al.

H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models

2023Zhenyu (Allen) Zhang, Ying Sheng et al.

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

2023Lorenz Kuhn, Y. Gal et al.

Large Language Models Can Be Easily Distracted by Irrelevant Context

2023Freda Shi, Xinyun Chen et al.

Showing 20 of 22 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 ThisLycheeDecode: Accelerating Long-Context LLM Inference via Hybrid-Head Sparse Decoding

7.0

Builds On ThisLycheeCluster: Efficient Long-Context Inference with Structure-Aware Chunking and Hierarchical KV Indexing

7.0

Builds On ThisWhere Matters More Than What: Decoding-aligned KV Cache Compression via Position-aware Pseudo Queries

7.0

Builds On ThisDepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference

7.0

Builds On ThisHybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference

7.0

Builds On ThisAdaptive Mass-Segmented KV Compression for Long-Context Reasoning

7.0

Builds On ThisMore Than a Quick Glance: Overcoming the Greedy Bias in KV-Cache Compression

5.0

Builds On ThisFrom Rigid to Dynamic: Entropy-Guided Adaptive Inference for Long-Context LLMs

0.0

Builds On ThisBeyond Speedup -- Utilizing KV Cache for Sampling and Reasoning

6.0

Builds On ThisKVSculpt: KV Cache Compression as Distillation

7.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

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: 2602.20732
Route: /paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference
Active tab: read
Artifact: chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference/paper-pack
REST build passport API/api/v1/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference/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

{
  "contract_version": "paper-r2",
  "paper_id": "de03e61b-2f2f-4e23-b9c1-91876d76a4c4",
  "arxiv_id": "2602.20732",
  "canonical_route": "/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
  "endpoints": {
    "paper_pack": "/api/v1/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference/paper-pack",
    "build_passport": "/api/v1/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference/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/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

Canonical ID chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference | Route /paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.20732"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference",
  "normalized_query": "2602.20732",
  "route": "/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
  "paper_ref": "chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
  "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: CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

/buildability/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

Watchwatch

Subject: CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

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/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

Paper ref

chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference

arXiv id

2602.20732

Freshness

Generated at

2026-03-19T21:31:49.672Z

Evidence freshness

stale

Last verification

2026-03-19T21:31:49.672Z

Sources

References

Coverage

33%

Hash state

Lineage hash

cd59f416a3aeff66d09796eef821646a6bef43e661684d9b810dc619daff2b5d

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: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed 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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference#webpage",
      "url": "https://sciencetostartup.com/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
      "name": "CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference",
      "description": "CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference#scholarlyArticle",
      "headline": "CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference",
      "description": "CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory.",
      "url": "https://sciencetostartup.com/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference",
      "sameAs": "https://arxiv.org/abs/2602.20732",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2602.20732"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-02-24T09:54:59.000Z",
      "author": [
        {
          "@type": "Person",
          "name": "Chao Fei",
          "affiliation": {
            "@type": "Organization",
            "name": "King Abdullah University of Science and Technology (KAUST)"
          }
        },
        {
          "@type": "Person",
          "name": "Guozhong Li",
          "affiliation": {
            "@type": "Organization",
            "name": "King Abdullah University of Science and Technology (KAUST)"
          }
        },
        {
          "@type": "Person",
          "name": "Chenxi Liu",
          "affiliation": {
            "@type": "Organization",
            "name": "Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences"
          }
        },
        {
          "@type": "Person",
          "name": "Panos Kalnis",
          "affiliation": {
            "@type": "Organization",
            "name": "King Abdullah University of Science and Technology (KAUST)"
          }
        }
      ],
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a3e16a32fda18fb2cc10a4073c44339be013e9f9"
          },
          "url": "https://www.semanticscholar.org/paper/a3e16a32fda18fb2cc10a4073c44339be013e9f9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "62b4867fa247172c54dfddac4f34c19ae3a25348"
          },
          "url": "https://www.semanticscholar.org/paper/62b4867fa247172c54dfddac4f34c19ae3a25348"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "8d12fc47eec8ffadc462835b6de5ab1d15391a42"
          },
          "url": "https://www.semanticscholar.org/paper/8d12fc47eec8ffadc462835b6de5ab1d15391a42"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "10088fee858ee55fa0e46eb3e31d6cf9d36861b5"
          },
          "url": "https://www.semanticscholar.org/paper/10088fee858ee55fa0e46eb3e31d6cf9d36861b5"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "f014aa430c330d263b0e7dd0fe5820a2978cac7e"
          },
          "url": "https://www.semanticscholar.org/paper/f014aa430c330d263b0e7dd0fe5820a2978cac7e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ef094815dc0118c8d1cc06d4bb3aa78bae0a3877"
          },
          "url": "https://www.semanticscholar.org/paper/ef094815dc0118c8d1cc06d4bb3aa78bae0a3877"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e712675e7d7c85ad9d95f2b7d16de4e2b57399d0"
          },
          "url": "https://www.semanticscholar.org/paper/e712675e7d7c85ad9d95f2b7d16de4e2b57399d0"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "06796ca506bb28419a734f777f069ea2f42c1eb9"
          },
          "url": "https://www.semanticscholar.org/paper/06796ca506bb28419a734f777f069ea2f42c1eb9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "53132ea6c107479d4557631299d3ed525109b464"
          },
          "url": "https://www.semanticscholar.org/paper/53132ea6c107479d4557631299d3ed525109b464"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1c7db9fb18246787fbe3de6e0eaa370ae749e795"
          },
          "url": "https://www.semanticscholar.org/paper/1c7db9fb18246787fbe3de6e0eaa370ae749e795"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1784c987e681d60c634765fe64c8d9c26f73d5ff"
          },
          "url": "https://www.semanticscholar.org/paper/1784c987e681d60c634765fe64c8d9c26f73d5ff"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "26e13e1da4f47c93c9ad0daf9cc9e2bb4ffd063d"
          },
          "url": "https://www.semanticscholar.org/paper/26e13e1da4f47c93c9ad0daf9cc9e2bb4ffd063d"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "5693cf94321d55e4b4b04f69e8fde0e74f0177d8"
          },
          "url": "https://www.semanticscholar.org/paper/5693cf94321d55e4b4b04f69e8fde0e74f0177d8"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6c323c535365e1c7cbfd9703cbec3b5650a3346b"
          },
          "url": "https://www.semanticscholar.org/paper/6c323c535365e1c7cbfd9703cbec3b5650a3346b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "fdc53c2c10742464087c0525f77e32604827a21d"
          },
          "url": "https://www.semanticscholar.org/paper/fdc53c2c10742464087c0525f77e32604827a21d"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "83b90f4a0ae4cc214eb3cc140ccfef9cd99fac05"
          },
          "url": "https://www.semanticscholar.org/paper/83b90f4a0ae4cc214eb3cc140ccfef9cd99fac05"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "1733eb7792f7a43dd21f51f4d1017a1bffd217b5"
          },
          "url": "https://www.semanticscholar.org/paper/1733eb7792f7a43dd21f51f4d1017a1bffd217b5"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e586a4591ba0303b769f2c07cbddaf1899cb72e4"
          },
          "url": "https://www.semanticscholar.org/paper/e586a4591ba0303b769f2c07cbddaf1899cb72e4"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "507465f8d46489a68a527cb5304d76bdb6c31ed9"
          },
          "url": "https://www.semanticscholar.org/paper/507465f8d46489a68a527cb5304d76bdb6c31ed9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3d68522abfadfc8ee6b7ec9edaaf91f1b2f38e5e"
          },
          "url": "https://www.semanticscholar.org/paper/3d68522abfadfc8ee6b7ec9edaaf91f1b2f38e5e"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 8
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Efficient LLM KV Cache Management"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Efficient LLM KV Cache Management",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "CHESS: Context-aware Hierarchical Efficient Semantic Selecti",
          "item": "https://sciencetostartup.com/paper/chess-context-aware-hierarchical-efficient-semantic-selection-for-long-context-llm-inference"
        }
      ]
    },
    {
      "@type": "FAQPage",
      "mainEntity": [
        {
          "@type": "Question",
          "name": "What is the startup potential of \"CHESS: Context-aware Hierarchical Efficient Semantic Selecti\"?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "CHESS optimizes long-context LLM inference by drastically reducing KV cache demands, improving throughput by over 4x with minimal memory."
          }
        },
        {
          "@type": "Question",
          "name": "What products could be built from this research?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Building a SaaS solution around CHESS could involve offering an API or integration with existing LLM services that suffer from latency due to long-context processing inefficiencies."
          }
        },
        {
          "@type": "Question",
          "name": "What are the practical use cases?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "Develop a cloud-based service that provides optimized long-context processing for enterprise document management systems, enhancing speed and efficiency in data-heavy environments."
          }
        },
        {
          "@type": "Question",
          "name": "What industries could this research disrupt?",
          "acceptedAnswer": {
            "@type": "Answer",
            "text": "CHESS could replace current LLM deployment strategies that are hampered by memory bandwidth limitations, offering significantly improved performance in long-context scenarios."
          }
        }
      ]
    }
  ]
}

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

CHESS: Context-aware Hierarchical Efficient Semantic Selection for Long-Context LLM Inference

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(22)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(22)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline