Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/beyond-the-assistant-turn-user-turn-generation-as-a-probe-of-interaction-awareness-in-language-models

stale

Proof freshness: stale
Proof status: unverified
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: 33%

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 beyond-the-assistant-turn-user-turn-generation-as-a-probe-of-interaction-awareness-in-language-models | Route /signal-canvas/beyond-the-assistant-turn-user-turn-generation-as-a-probe-of-interaction-awareness-in-language-models

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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: Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

PDF: https://arxiv.org/pdf/2604.02315v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-04-03T20:50:40.241Z

Signal Canvas receipt window

Watch and verify: Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

/buildability/beyond-the-assistant-turn-user-turn-generation-as-a-probe-of-interaction-awareness-in-language-models

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Subject: Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

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No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Standard LLM benchmarks evaluate the assistant turn: the model generates a response to an input, a verifier scores correctness, and the analysis ends. This paradigm leaves unmeasured whether the LLM encodes any awareness of what follows the assistant response.
Implicationpartial
Directly and explicitly stated in the abstract as the core motivation for the research
Verificationpartial
partial
Evidencepartial
We propose user-turn generation as a probe of this gap: given a conversation context of user query and assistant response, we let a model generate under the user role.
Implicationpartial
Explicitly proposed as the main method in the abstract with clear rationale
Verificationpartial
partial
Evidencepartial
Through experiments across $11$ open-weight LLMs and $5$ datasets, we show that interaction awareness is decoupled from task accuracy.
Implicationpartial
Directly stated in abstract with supporting evidence from Qwen3.5 family experiments
Verificationpartial
partial
Evidencepartial
In particular, within the Qwen3.5 family, GSM8K accuracy scales from $41\%$ ($0.8$B) to $96.8\%$ ($397$B-A$17$B), yet genuine follow-up rates under deterministic generation remain near zero.
Implicationpartial
Specific numeric evidence provided in abstract with clear comparison
Verificationpartial
partial
Evidencepartial
In contrast, higher temperature sampling reveals interaction awareness is latent with follow up rates reaching $22\%$.
Implicationpartial
Specific numeric result stated in abstract with clear experimental finding
Verificationpartial
partial
Evidencepartial
Controlled perturbations validate that the proposed probe measures a real property of the model
Implicationpartial
Directly stated in abstract as validation of the method
Verificationpartial
partial
Evidencepartial
and collaboration-oriented post-training on Qwen3.5-2B demonstrates an increase in follow-up rates.
Implicationpartial
Directly stated in abstract as experimental result
Verificationpartial
partial
Evidencepartial
Our results show that user-turn generation captures a dimension of LLM behavior, interaction awareness, that is unexplored and invisible with current assistant-only benchmarks.
Implicationpartial
Direct conclusion stated in abstract summarizing the paper's contribution
Verificationpartial
partial

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Beyond the Assistant Turn: User Turn Generation as a Probe of Interaction Awareness in Language Models

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