SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models

• URL: http://arxiv.org/abs/2607.10296v1
• Date: Sat, 11 Jul 2026 12:47:27 GMT
• Title: SPARK: Susceptibility-Guided Profiling and Steering of Latent Reasoning States in Large Language Models
• Abstract summary: We introduce SPARK, which uses hidden-state response to diagnose whether a model enters an effective reasoning state.<n>Steering can serve not only as a diagnostic signal for reasoning failures, but also as a practical guide for targeted test-time intervention.
• Score: 26.849417177804302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning failures in large language models (LLMs) are usually evaluated from final answers, but a wrong answer does not reveal why the model failed. The same incorrect output may reflect missing capability, an unstable reasoning trajectory, or a failure to activate a reasoning state that is already available in the frozen model. Existing prompting and benchmark-based evaluation methods mostly operate at the output level, while generic activation-steering methods typically apply global directions without diagnosing which examples require intervention. In this paper, we introduce SPARK, which uses hidden-state response to diagnose whether a model internally enters an effective reasoning state and to guide lightweight test-time steering. The key observation is that raw hidden-state susceptibility is strongly confounded by prompt length, especially in programmatic and algorithmic reasoning where harder serialized instances naturally become longer. SPARK therefore uses length-controlled susceptibility to separate input-scale effects from residual reasoning activation, and combines this signal with cross-layer coordination to select reasoning-active anchors and under-activated hard examples. We use FRONTIER-4.5K as a controlled programmatic reasoning suite for latent profiling and difficulty-aware analysis, and evaluate SPARK-Steering on GSM8K and MATH-500 with forward-only benchmark profiling. Our method improves Qwen3 series models consistently; on MATH-500, accuracy rises from 82.0% to 84.6% for Qwen3-4B and from 82.4% to 85.6% for Qwen3-8B. These results suggest that susceptibility can serve not only as a diagnostic signal for reasoning failures, but also as a practical guide for targeted test-time intervention.

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