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ARXIV:2604.02113 · LLM CONTROL · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.02113LLM CONTROLSUBMITTED 03 APR · 20:30 UTCFRESHNESS STALEHaomin Zhuang · Hojun Yoo · Xiaonan Luo · Kehan Guo · Xiangliang Zhang · arXiv
A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks.
Opportunity summary
Pain A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks.
Evidence 0 refs | 0 sources | 67% coverage
Blocker Evidence partial
A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks. For behaviors that can be toggled via prompts, this is straightforward.
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models, but constructing effective vectors requires identifying genuine behavioral signals in the model's hidden states. For behaviors that can be toggled…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that this assumption is overwhelmingly wrong: across 541 keyword-detected boundaries, 93.3\% are behaviorally unstable, failing to reproduce the detected behavior under re-generation…
LLM Control moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks.
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10.48550/arXiv.2604.02113A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks.
Abstract
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models, but constructing effective vectors requires identifying genuine behavioral signals in the model's hidden states. For behaviors that can be toggled via prompts, this is straightforward. However, many reasoning behaviors -- such as self-reflection -- emerge spontaneously and resist prompt-level control. Current methods detect these behaviors through keyword matching in chain-of-thought traces, implicitly assuming that every detected boundary encodes a genuine behavioral signal. We show that this assumption is overwhelmingly wrong: across 541 keyword-detected boundaries, 93.3\% are behaviorally unstable, failing to reproduce the detected behavior under re-generation from the same prefix. We develop a probabilistic model that formalizes intrinsic reasoning behaviors as stochastic events with context-dependent trigger probabilities, and show that unstable boundaries dilute the steering signal. Guided by this analysis, we propose stability filtering, which retains only boundaries where the model consistently reproduces the target behavior. Combined with a content-subspace projection that removes residual question-specific noise, our method achieves 0.784 accuracy on MATH-500 (+5.0 over the strongest baseline). The resulting steering vectors transfer across models in the same architecture family without re-extraction, improving Nemotron-Research-Reasoning-1.5B (+5.0) and DeepScaleR-1.5B-Preview (+6.0). Code is available at https://github.com/zhmzm/stability-steering.
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partial0 refs; 0 sources; 67% coverage.
What was readable
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Dimensions overall score 7.0
PROBLEM
A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks. For behaviors that can be toggled via prompts, this is straightforward.
METHOD
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models, but constructing effective vectors requires identifying genuine behavioral signals in the model's hidden states. For behaviors that can be toggled via prompts, this is...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that this assumption is overwhelmingly wrong: across 541 keyword-detected boundaries, 93.3\% are behaviorally unstable, failing to reproduce the detected behavior under re-generation from the same...
WHY NOW
LLM Control moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
We show that this assumption is overwhelmingly wrong: across 541 keyword-detected boundaries, 93.3% are behaviorally unstable
Explicitly stated in abstract with specific numeric evidence (541 boundaries, 93.3% failure rate)
partial
our method achieves 0.784 accuracy on MATH-500 (+5.0 over the strongest baseline)
Directly stated in abstract with specific numeric results
partial
The resulting steering vectors transfer across models in the same architecture family without re-extraction, improving Nemotron-Research-Reasoning-1.5B (+5.0) and DeepScaleR-1.5B-Preview (+6.0)
Explicitly stated in abstract with specific model improvements
partial
we propose stability filtering, which retains only boundaries where the model consistently reproduces the target behavior
Directly described in abstract as the proposed solution to identified problem
partial
many reasoning behaviors -- such as self-reflection -- emerge spontaneously and resist prompt-level control
Explicitly stated in abstract as motivation for the research
partial
unstable boundaries dilute the steering signal
Directly stated in abstract as a key finding from the analysis
partial
We develop a probabilistic model that formalizes intrinsic reasoning behaviors as stochastic events with context-dependent trigger probabilities
Explicitly stated in abstract as a methodological contribution
partial
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models
Directly stated in the opening sentence of the abstract
partial
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A method to reliably select steering vectors for controlling LLM reasoning by filtering unstable behavioral signals, improving performance on math and reasoning tasks.
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LLM Control
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