Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Compared to this week’s papers
Verification pending
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 7/10
- Last proof check
- 2026-04-22
- Score updated
- 2026-04-22
- Score fresh until
- 2026-05-22
- References
- 94
- Source count
- 7
- Coverage
- 100%
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Canonical ID local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control | Route /signal-canvas/local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-controlMCP example
{
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"query_text": "Summarize Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control"
}
}source_context
{
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"query": "Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control",
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"topic_slug": null,
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}Evidence Receipt
Route status: buildingClaims: 1
References: 94
Proof: Verification pending
Freshness state: computing
Source paper: Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
PDF: https://arxiv.org/pdf/2604.19018v1
Repository: https://github.com/trustworthyrobotics/lqr-activation-steering
Source count: 7
Coverage: 100%
Last proof check: 2026-04-22T20:32:45.398Z
Signal Canvas receipt window
Ready for execution: Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
/buildability/local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control
Subject: Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Compute envelope
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Evidence ids
Receipt path
/buildability/local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control
Paper ref
local-linearity-of-llms-enables-activation-steering-via-model-based-linear-optimal-control
arXiv id
2604.19018
Freshness
Generated at
2026-04-22T20:32:45.398Z
Evidence freshness
stale
Last verification
2026-04-22T20:32:45.398Z
Sources
7
References
94
Coverage
100%
Hash state
Lineage hash
a958adce156ff971f12656d3c75f634d603b671ebee20f5542cce5cb4361298f
Canonical opportunity-kernel lineage hash.
Signature state
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Blockers
- No explicit blockers are present in this receipt window.
94 refs / 7 sources / Verification pending
Paper Conversation
Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.
Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Canonical Paper Receipt
Last verification: 2026-04-22T20:32:45.398ZFreshness: stale
Proof: partial
Repo: active
References: 94
Sources: 7
Coverage: 100%
No missing fields recorded.
No unresolved unknowns recorded.
Dimensions overall score 7.0
GitHub Code Pulse
Key claims
Startup potential card
Related Resources
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
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