HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/hintmr-eliciting-stronger-mathematical-reasoning-in-small-language-models

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-15
Score updated: 2026-04-15
Score fresh until: 2026-05-15
References: 0
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID hintmr-eliciting-stronger-mathematical-reasoning-in-small-language-models | Route /signal-canvas/hintmr-eliciting-stronger-mathematical-reasoning-in-small-language-models

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/hintmr-eliciting-stronger-mathematical-reasoning-in-small-language-models

MCP example

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}

source_context

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  "query": "HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models",
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  "topic_slug": null,
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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

PDF: https://arxiv.org/pdf/2604.12229v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-15T17:00:06.077Z

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Watch and verify: HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

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Subject: HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

Verdict

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
Language Models Jawad Hossain University at Albany jhossain2@albany.edu Xiangyu Guo University at Buffalo xiangyug@buffalo.edu Jiawei Zhou† Stony Brook University jiawei.zhou.1@stonybrook
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
rived from the oracle hint generation process: the problem statement P, the intermediate reasoning state Wt extracted from the ground-truth solution trajectory, and the corresponding oracle-generated hint ht
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
solver to follow a more structured and reliable rea- soning trajectory, often matching the performance of LLM-guided reasoning while maintaining sig- nificantly lower token cost. In contrast, unguided reasoning (i.e.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
full solutions, GPT-5.2 was conditioned on the problem statement and the final answer to generate instructional hints. The average number of hints for AIME-2025 is 14. 4
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
2025). In addition, we evaluateFT DeepSeek-R1- Distill-Qwen-7B, a fine-tuned SLM hint generator, to examine whether it can directly solve problems beyond its primary role of producing instructional hints
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Qwen2.5-Math-7B-Instruct 75.68% (56/74)85.14%(63/74) 79.73% (59/74) 54.05% (40/74) DeepSeek-R1-Distill-Qwen-7B 64.86% (48/74) 82.43% (61/74)85.14%(63/74)68.92%(51/74) DeepSeek-R1-Distill-Llama-8B 52.70% (39/74) 71
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
The Phi-4-mini-reasoning (Xu et al., 2025) is an- other compact 3.8B-model specifically optimized for advanced mathematical reasoning and step-by- step problem-solving. Qwen2.5-Math (Yang et al.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial

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HintMR: Eliciting Stronger Mathematical Reasoning in Small Language Models

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