Do Large Language Models Mentalize When They Teach? | Signal Canvas | ScienceToStartup

Do Large Language Models Mentalize When They Teach? | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/do-large-language-models-mentalize-when-they-teach

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-03
Score updated: 2026-04-03
Score fresh until: 2026-05-03
References: 0
Source count: 0
Coverage: 33%

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

Agent Handoff

Do Large Language Models Mentalize When They Teach?

Canonical ID do-large-language-models-mentalize-when-they-teach | Route /signal-canvas/do-large-language-models-mentalize-when-they-teach

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/do-large-language-models-mentalize-when-they-teach

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Do Large Language Models Mentalize When They Teach?

PDF: https://arxiv.org/pdf/2604.01594v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:41.059Z

Signal Canvas receipt window

Not build-ready: Do Large Language Models Mentalize When They Teach?

/buildability/do-large-language-models-mentalize-when-they-teach

Ignoreblocked

Subject: Do Large Language Models Mentalize When They Teach?

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
prompt compliance does not guarantee better teaching decisions
Implicationpartial
Explicitly stated as conclusion in abstract
Verificationpartial
partial
Evidencepartial
most LLMs perform well, show little change in strategy over trials, and their graph-by-graph performance is similar to that of humans
Implicationpartial
Directly stated in abstract with clear comparison to human performance
Verificationpartial
partial
Evidencepartial
Model comparison (BIC) shows that Bayes-Optimal teaching best explains most models' choices
Implicationpartial
Explicitly stated in abstract with specific methodology (BIC)
Verificationpartial
partial
Evidencepartial
show little change in strategy over trials
Implicationpartial
Directly stated in abstract as an observed result
Verificationpartial
partial
Evidencepartial
these scaffolds do not reliably improve later teaching on heuristic-incongruent test graphs
Implicationpartial
Directly stated in abstract with specific condition (heuristic-incongruent test graphs)
Verificationpartial
partial
Evidencepartial
can sometimes reduce performance
Implicationpartial
Directly stated in abstract but qualified with 'can sometimes'
Verificationpartial
partial
Evidencepartial
models follow auxiliary inference- or reward-focused prompts
Implicationpartial
Directly stated in abstract as observed behavior
Verificationpartial
partial
Evidencepartial
cognitive model fits provide insight into LLM tutoring policies
Implicationpartial
Directly stated as conclusion in abstract
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.