Evidence Receipt. Related Resources.
Parameter-Efficient Quality Estimation via Frozen Recursive Models
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Canonical route: /signal-canvas/parameter-efficient-quality-estimation-via-frozen-recursive-models
- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 8/10
- 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.
Agent Handoff
Parameter-Efficient Quality Estimation via Frozen Recursive Models
Canonical ID parameter-efficient-quality-estimation-via-frozen-recursive-models | Route /signal-canvas/parameter-efficient-quality-estimation-via-frozen-recursive-models
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/parameter-efficient-quality-estimation-via-frozen-recursive-modelsMCP example
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"query_text": "Summarize Parameter-Efficient Quality Estimation via Frozen Recursive Models"
}
}source_context
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"paper_ref": "parameter-efficient-quality-estimation-via-frozen-recursive-models",
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}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
TRM's recursive mechanisms do not transfer to QE. External iteration hurts performance, and internal recursion offers only narrow benefits.
ImplicationpartialDirectly stated in abstract with clear negative finding
Verificationpartialpartial
- Evidencepartial
frozen pretrained embeddings match fine-tuned performance while reducing trainable parameters by 37× (7M vs 262M).
ImplicationpartialExplicitly stated with precise numeric comparison in abstract
Verificationpartialpartial
- Evidencepartial
TRM-QE with frozen XLM-R embeddings achieves a Spearman's correlation of 0.370, matching fine-tuned variants (0.369)
ImplicationpartialDirect numeric results provided in abstract with specific correlation values
Verificationpartialpartial
- Evidencepartial
On Hindi and Tamil, frozen TRM-QE outperforms MonoTransQuest (560M parameters) with 80× fewer trainable parameters.
ImplicationpartialSpecific language pairs and parameter reduction explicitly stated
Verificationpartialpartial
- Evidencepartial
representation quality dominates architectural choices
ImplicationpartialDirectly stated as a key finding in abstract
Verificationpartialpartial
- Evidencepartial
weight sharing combined with frozen embeddings enables parameter efficiency for QE.
ImplicationpartialConclusion directly stated in abstract based on experimental results
Verificationpartialpartial
- Evidencepartial
Experiments on $8$ language pairs on a low-resource QE dataset
ImplicationpartialExplicit mention of experimental scope and dataset type
Verificationpartialpartial
- Evidencepartial
using a three-phase methodology
ImplicationpartialDirect description of methodology in abstract
Verificationpartialpartial
