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source_df7008023c3146ab
sha256 64946d5c8c8a0222ffa97d707c0b550cc4011055c62dfb93b6f6f372ac6952cc
by researka:v2 · 2026-07-05 14:14:47.623582+04:00
# Source literature boundary memo
## Research question
Does llm evaluation score show a consistent direction-bearing association in the selected source bundle, and where do null/mixed or context-only receipts bound the claim?
## Selection criteria
The source-literature selector kept llm evaluation score because the candidate bundle met the public source rule: 5 citable papers, 5 distinct fact-backed source identities, topic-overlapping source facts, and enough shared scope to compare metric/context disagreement. It excludes duplicate reports, metadata-only title matches, off-topic papers, and sources without fact-level extraction before treating the bundle as a coherent scoping front rather than proof of a policy or market conclusion.
## Plain-language synthesis
3 of 5 selected receipts are direction-bearing for the selected source contexts; 0 receipt(s) are null/mixed and 2 are context/model only. This is a bounded source-literature signal, not a pooled effect.
## Boundary map
- {RMTB}ench: Benchmarking {LLM}s Through Multi-Turn User-Centric Role-Playing [primary; 2025] doi:10.18653/v1/2025.findings-emnlp.730
- Bounded source claim: performance close to LLaMA-3.3, while in En- glish, it has a score lower than LLaMA-3.3 by 8.6 points on average.
- Claim bounds: setting=llm evaluation score tasks; exposure=LLaMA; comparator/reference=LLaMA-3.3
- Population/setting: llm evaluation score tasks
- Policy/exposure/practice: LLaMA
- Comparator/reference: LLaMA-3.3
- Contextual Health State Inference from Lifelog Data Using LLM [primary; 2024] doi:10.1109/ictc62082.2024.10827013
- Bounded source claim: Results demonstrate that our LLM-based approach, augmented with conversational data, achieves a 34.18% performance increase over the benchmark features configuration, outperforming other models with a score of 5.983 out of 10.
- Claim bounds: setting=llm evaluation score tasks; exposure=Contextual Health State Inference from Lifelog Data; comparator/reference=other models with a
- Effect accounting: descriptive/modeling context only; this receipt does not test an effect of llm evaluation score on a performance endpoint.
- Population/setting: llm evaluation score tasks
- Policy/exposure/practice: Contextual Health State Inference from Lifelog Data
- Comparator/reference: other models with a
- Structured Intention Generation with Multimodal Graph Transformers: The MMIntent-LLM Framework [primary; 2024] doi:10.1109/bigdata62323.2024.10826116
- Bounded source claim: Extensive experiments on our multimodal social intention dataset show that MMIntent-LLM achieves state-of-the-art performance, improving the average BERT score by 8.7% and human evaluation scores by 12.3% compared to baseline methods.
- Claim bounds: setting=multimodal social intention; exposure=MMIntent-LLM; comparator/reference=baseline methods
- Effect accounting: descriptive/modeling context only; this receipt does not test an effect of llm evaluation score on a performance endpoint.
- Population/setting: multimodal social intention
- Policy/exposure/practice: MMIntent-LLM
- Comparator/reference: baseline methods
- A Local Hierarchical LLM Framework for Privacy-Preserving Memory Forensics of Cryptocurrency Wallets [primary; 2026] doi:10.1109/access.2026.3682641
- Bounded source claim: The Tri-Layer architecture achieves an average human-evaluation total score of 11.29, which is an 8.9% improvement over the Single-Layer baseline.
- Claim bounds: setting=llm evaluation score tasks; exposure=Local Hierarchical LLM Framework; comparator/reference=baseline.
- Population/setting: llm evaluation score tasks
- Policy/exposure/practice: Local Hierarchical LLM Framework
- Comparator/reference: baseline.
- Majority Rules: LLM Ensemble is a Winning Approach for Content Categorization [primary; 2025] doi:10.48550/arxiv.2511.15714
- Bounded source claim: The eLLM approach yields a substantial performance improvement of up to 65\% in F1-score over the strongest single model.
- Claim bounds: setting=llm evaluation F1 tasks; exposure=Majority Rules; comparator/reference=the strongest single model
- Population/setting: llm evaluation F1 tasks
- Policy/exposure/practice: Majority Rules
- Comparator/reference: the strongest single model
## Source synthesis
Bounded signal: llm evaluation score is only a source-level context map; the selected receipts do not establish one pooled effect.
This receipt-backed scoping note has one bounded signal: llm evaluation score shows policy/exposure estimates plus separate descriptive evidence across this 5-source primary bundle (2024-2026). Evidence role grouping: direction-bearing receipts: 3; null/mixed metric-scope caveat receipts: 0; context/antecedent/model receipts: 2 excluded from effect support. The source facts cover 3 population/setting context(s) and 5 policy/exposure/practice context(s), so this is a scoping signal about where settings/designs diverge, without establishing a causal, policy-prescriptive, market-generalized, or pooled econometric claim. Population/setting counts are context descriptors only; they are not weighting, pooling, or aggregation evidence. The listed estimates remain source-specific across metrics and settings; they are not pooled or averaged. This is a separated policy/setting map, not a unified pooled economics claim. Named setting scope includes llm evaluation F1 tasks, llm evaluation score tasks, and multimodal social intention. Within-vs-across outcome rule: direction-bearing rows are only compared within the selected source contexts; unrelated receipt families are not treated as one outcome. Concrete contrast: directional association: {RMTB}ench: Benchmarking {LLM}s Through Multi-Turn User-Centric Role-Playing: performance close to LLaMA-3.3, while in En- glish, it has a score lower than LLaMA-3.3 by 8.6 points on...; descriptive/modeling: Contextual Health State Inference from Lifelog Data Using LLM: Results demonstrate that our LLM-based approach, augmented with conversational data, achieves a 34.18%....
Role definitions: direction-bearing rows carry metric-specific effect or association text; null/mixed rows carry rejected or non-convergent metric evidence; context/model rows rank, model, or contextualize adjacent constructs. Interpretation: keep these rows separate; do not pool them or treat antecedent/modeling rows as the same estimand.
## Evidence matrix
Matrix guard: effect-bearing rows below are metric-specific source facts, not a pooled comparison; context-only rows are excluded from effect support.
### Effect-bearing comparison
| Outcome family | Receipt | Evidence role | Population/setting | Metric | Extracted finding |
|---|---|---|---|---|---|
| outcome-specific | {RMTB}ench: Benchmarking {LLM}s Through Multi-Turn User-Centric... | directional association | llm evaluation score tasks | - | performance close to LLaMA-3.3, while in En- glish, it has a score lower than LLaMA-3.3 by 8.6 points on... |
| outcome-specific | A Local Hierarchical LLM Framework for Privacy-Preserving Memory... | directional association | llm evaluation score tasks | - | The Tri-Layer architecture achieves an average human-evaluation total score of 11.29, which is an 8.9%... |
| outcome-specific | Majority Rules: LLM Ensemble is a Winning Approach for Content... | directional association | llm evaluation F1 tasks | - | The eLLM approach yields a substantial performance improvement of up to 65\% in F1-score over the strongest... |
### Context-only receipts
| Outcome family | Receipt | Evidence role | Population/setting | Metric | Extracted finding |
|---|---|---|---|---|---|
| modeling-context | Contextual Health State Inference from Lifelog Data Using LLM | descriptive/modeling | llm evaluation score tasks | - | Results demonstrate that our LLM-based approach, augmented with conversational data, achieves a 34.18%... |
| modeling-context | Structured Intention Generation with Multimodal Graph Transformers: The... | descriptive/modeling | multimodal social intention | - | Extensive experiments on our multimodal social intention dataset show that MMIntent-LLM achieves... |
Audit note: effect-bearing rows stay metric-specific; context-only rows are excluded from effect support; role counts below keep direction-bearing, null/mixed metric-scope caveat, and context-only receipts separate.
## Evidence role definitions
- directional association: source-level direction with design caveat; llm_evaluation_score is the policy, exposure, method, or practice linked to the named metric, not a pooled effect-size estimate or efficacy verdict.
- descriptive/modeling: the receipt reports modelling or prediction rather than a policy-effect estimate.
Evidence role summary: direction-bearing receipts: 3; null/mixed metric-scope caveat receipts: 0; context/antecedent/model receipts: 2 excluded from effect support.
Direction labels for audit: directional association: 3 receipt(s) | descriptive/modeling: 2 receipt(s).
Specific moderators in this bundle are population/indication (llm evaluation F1 tasks; llm evaluation score tasks; multimodal social intention), study design/evidence type (primary).
## Context separation
Population/settings are separated as receipt context: llm evaluation F1 tasks, llm evaluation score tasks, and multimodal social intention. The selected receipts group because each carries a fact-level extraction for llm evaluation score; they separate by context (other source context) and metric, so they are not interchangeable evidence for one pooled claim.
## Boundary limits
Source-literature boundary for llm evaluation score: the listed sources define one bounded, context-dependent signal across separate source contexts. This memo does not claim causality, policy prescription, a pooled elasticity estimate, or a market-generalized effect across the sources.
Material limitations: small 5-source bundle; no pooled estimate is possible; outlet/tier heterogeneity is scope, not weight; method/model receipts without direct effect estimates are context only; outcomes are not harmonized across studies.
The signal is purely descriptive of source-level direction and scope; it cannot support a causal, policy-prescriptive, or pooled elasticity inference, and pooling across these designs would be inappropriate.
Effect-support accounting: 2 of 5 receipt(s) is context/modeling-only and contributes no effect estimate; 3 receipt(s) are direction-bearing and 0 receipt(s) are null/mixed metric-scope caveats.
## What would weaken this
- This scoping signal would weaken if the null/mixed metric replicates in matched designs, if direction-bearing rows fail to reproduce within their named metric family, or if context/model rows become the only topic-overlapping receipts.
## Next gaps
A stronger memo needs one matched design: one setting, one policy/exposure, one comparator/reference group, and one named metric.
If llm evaluation score is promoted beyond a scoping note, the next run should select sources sharing one context family rather than spanning other source context.
metadata
{
"article_type": "alpha_memo",
"domain_slug": "ai_research",
"researka_object_type": "submission",
"researka_submission_id": "a11f7de8-f79f-4148-9503-30fb53f87fae",
"title": "llm evaluation score: one bounded, context-dependent signal across receipts"
}