Reliable Control-Point Selection for Steering Reasoning in Large Language Models

Reliable Control-Point Selection for Steering Reasoning in Large Language Models | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/reliable-control-point-selection-for-steering-reasoning-in-large-language-models

stale

Proof freshness: stale
Proof status: partial
Display score: 7/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 67%

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 reliable-control-point-selection-for-steering-reasoning-in-large-language-models | Route /signal-canvas/reliable-control-point-selection-for-steering-reasoning-in-large-language-models

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curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/reliable-control-point-selection-for-steering-reasoning-in-large-language-models

MCP example

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source_context

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  "query": "Reliable Control-Point Selection for Steering Reasoning in Large Language Models",
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  "topic_slug": null,
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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Reliable Control-Point Selection for Steering Reasoning in Large Language Models

PDF: https://arxiv.org/pdf/2604.02113v1

Repository: https://github.com/zhmzm/stability-steering

Source count: Pending verification

Coverage: 67%

Last proof check: 2026-04-03T20:30:26.398Z

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Subject: Reliable Control-Point Selection for Steering Reasoning in Large Language Models

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4/4/2026

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We show that this assumption is overwhelmingly wrong: across 541 keyword-detected boundaries, 93.3% are behaviorally unstable
Implicationpartial
Explicitly stated in abstract with specific numeric evidence (541 boundaries, 93.3% failure rate)
Verificationpartial
partial
Evidencepartial
our method achieves 0.784 accuracy on MATH-500 (+5.0 over the strongest baseline)
Implicationpartial
Directly stated in abstract with specific numeric results
Verificationpartial
partial
Evidencepartial
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)
Implicationpartial
Explicitly stated in abstract with specific model improvements
Verificationpartial
partial
Evidencepartial
we propose stability filtering, which retains only boundaries where the model consistently reproduces the target behavior
Implicationpartial
Directly described in abstract as the proposed solution to identified problem
Verificationpartial
partial
Evidencepartial
many reasoning behaviors -- such as self-reflection -- emerge spontaneously and resist prompt-level control
Implicationpartial
Explicitly stated in abstract as motivation for the research
Verificationpartial
partial
Evidencepartial
unstable boundaries dilute the steering signal
Implicationpartial
Directly stated in abstract as a key finding from the analysis
Verificationpartial
partial
Evidencepartial
We develop a probabilistic model that formalizes intrinsic reasoning behaviors as stochastic events with context-dependent trigger probabilities
Implicationpartial
Explicitly stated in abstract as a methodological contribution
Verificationpartial
partial
Evidencepartial
Steering vectors offer a training-free mechanism for controlling reasoning behaviors in large language models
Implicationpartial
Directly stated in the opening sentence of the abstract
Verificationpartial
partial

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Reliable Control-Point Selection for Steering Reasoning in Large Language Models

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