CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering

degraded

Proof freshness: stale
Proof status: failed
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 has proof data, but the latest verification did not complete cleanly.

Agent Handoff

Canonical ID compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering | Route /signal-canvas/compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering",
    "query_text": "Summarize CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering",
  "normalized_query": "2602.05728",
  "route": "/signal-canvas/compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering",
  "paper_ref": "compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: degraded

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: stale

Source paper: CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

PDF: https://arxiv.org/pdf/2602.05728v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

Signal Canvas receipt window

Watch and verify: CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

/buildability/compactrag-reducing-llm-calls-and-token-overhead-in-multi-hop-question-answering

Watchwatch

Subject: CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Notably, during inference, the LLM is invoked only twice in total - once for sub-question decomposition and once for final answer synthesis - regardless of the number of reasoning hops.
Implicationpartial
Explicitly stated in the abstract with clear numeric specification
Verificationpartial
partial
Evidencepartial
Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines
Implicationpartial
Directly stated in abstract with specific dataset names and comparative language
Verificationpartial
partial
Evidencepartial
CompactRAG achieves competitive accuracy while substantially reducing token consumption compared to iterative RAG baselines
Implicationpartial
Explicitly stated in abstract with clear comparative claim
Verificationpartial
partial
Evidencepartial
In the offline stage, an LLM reads the corpus once and converts it into an atomic QA knowledge base, which represents knowledge as minimal, fine-grained question-answer pairs.
Implicationpartial
Directly described in abstract with specific technical details
Verificationpartial
partial
Evidencepartial
resolved through dense retrieval followed by RoBERTa-based answer extraction
Implicationpartial
Strongly implied in abstract and analysis, though not explicitly naming RoBERTa in abstract
Verificationpartial
partial
Evidencepartial
its efficiency depends on the quality of the initial corpus transformation, and the offline processing can be computationally intensive upfront
Implicationpartial
Directly stated in analysis section with clear limitations
Verificationpartial
partial
Evidencepartial
highlighting a cost-efficient and practical approach to multi-hop reasoning over large knowledge corpora
Implicationpartial
Directly stated in abstract but includes subjective evaluation terms
Verificationpartial
partial
Evidencepartial
the success of sub-question decomposition accuracy could vary depending on the complexity of input questions
Implicationpartial
Directly stated in analysis section as a caveat
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

CompactRAG: Reducing LLM Calls and Token Overhead in Multi-Hop Question Answering

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface