Human Feedback Types
provisional (inferred)None explicit
No explicit feedback protocol extracted.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
HFEPX · Eval paper review
Enhancing Multilingual Reasoning via Steerable Model Merging
Zhuoran Li, Rui Xu, Jian Yang, Junnan Liu +7 more
Published
Jun 17, 2026
Citations
0
Trust level
Provisional
Usefulness score
Unavailable
Extraction confidence
0% (Provisional)
Derived from abstract and metadata only.
Signals refreshed
Jun 17, 2026
Should you rely on this paper?
Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.
This page is a lightweight research summary built from the abstract and metadata while deeper extraction catches up.
Abstract-only analysis, low confidence
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
- This page is still relying on abstract and metadata signals, not a fuller protocol read.
Best use
Background context only
Use if you need
A provisional background reference while structured extraction finishes.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This page is still relying on abstract and metadata signals, not a fuller protocol read.
Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
Unavailable
Provisional (processing)
Eval-fit score is unavailable until extraction completes.
If you are doing eval pipeline work, start here
Abstract
Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
provisional (inferred)None explicit
Validate eval design from full paper text.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
Quality Controls
provisional (inferred)Not reported
No explicit QC controls found.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
Benchmarks / Datasets
provisional (inferred)Not extracted
No benchmark anchors detected.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
Reported Metrics
provisional (inferred)Not extracted
No metric anchors detected.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
Rater Population
provisional (inferred)Unknown
Rater source not explicitly reported.
"Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model."
Human feedback details
This page is using abstract-level cues only right now. Treat the signals below as provisional.
- Potential human-data signal: No explicit human-data keywords detected.
- Potential benchmark anchors: No benchmark names detected in abstract.
- Abstract highlights: 3 key sentence(s) extracted below.
Evaluation details
Evaluation fields are inferred from the abstract only.
- Potential evaluation modes: No explicit eval keywords detected.
- Potential metric signals: No metric keywords detected.
- Confidence: Provisional (metadata-only fallback).
Research brief
Metadata summary
Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model.
- It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models.
- However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.