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arxiv: 2604.02315 · v2 · submitted 2026-04-02 · 💻 cs.AI

Recognition: no theorem link

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

Adam Earle, Romain Cosentino, Sarath Shekkizhar

Pith reviewed 2026-05-13 20:55 UTC · model grok-4.3

classification 💻 cs.AI
keywords user turn generationinteraction awarenessLLM benchmarksmulti-turn conversationtask accuracy decouplingconversation probinglanguage modelsdialogue evaluation
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The pith

Language models rarely generate natural user follow-ups even when they solve tasks with high accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces user-turn generation as a probe to measure whether language models encode awareness of what comes after their own responses in a conversation. Standard benchmarks evaluate only the assistant turn and stop there, leaving unmeasured any sensitivity to plausible next user inputs. Experiments across eleven open models and five datasets show that interaction awareness is decoupled from task accuracy: within the Qwen3.5 family, GSM8K performance rises from 41 percent at 0.8 billion parameters to 96.8 percent at the largest scale, yet deterministic user-turn generation yields almost no grounded follow-ups. Higher-temperature sampling surfaces latent capability with follow-up rates reaching 22 percent, and targeted post-training on collaboration data raises those rates further. The results indicate that current assistant-only evaluations miss a distinct dimension of model behavior.

Core claim

By prompting models to generate the next user turn after a given assistant response, the work finds that interaction awareness remains near zero under deterministic decoding even as task accuracy scales dramatically. Within Qwen3.5, GSM8K accuracy climbs from 41 percent (0.8B) to 96.8 percent (397B-A17B), yet genuine follow-up rates stay close to zero; temperature sampling lifts rates to 22 percent, and collaboration-oriented post-training on the 2B model increases them measurably. Controlled perturbations confirm the probe tracks a real property of the weights rather than surface artifacts.

What carries the argument

The user-turn generation probe, in which the model is asked to continue the dialogue under the user role and the output is scored for whether it forms a grounded, context-reactive follow-up.

If this is right

  • Assistant-only benchmarks systematically miss a dimension of behavior tied to ongoing interaction.
  • Interaction awareness is latent and can be surfaced by temperature sampling or by collaboration-focused post-training.
  • The proposed probe can be used to track progress on conversational continuity separate from single-turn accuracy.
  • Current training regimes appear to optimize for one-shot responses more than for anticipating user replies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Existing pretraining and alignment pipelines may implicitly discourage models from modeling future user turns.
  • Evaluation suites for conversational agents should add user-turn generation as a standard metric alongside assistant accuracy.
  • The observed decoupling suggests that scaling alone will not produce more natural multi-turn behavior without targeted interventions.

Load-bearing premise

That a generated user turn counts as evidence of genuine interaction awareness only when it reacts to the preceding assistant response in a way that could not arise from surface continuation patterns alone.

What would settle it

If follow-up rates remain near zero even after explicit training on multi-turn dialogues that contain many natural user continuations, or if rates stay unchanged when the assistant response is removed from the prompt context.

Figures

Figures reproduced from arXiv: 2604.02315 by Adam Earle, Romain Cosentino, Sarath Shekkizhar.

Figure 1
Figure 1. Figure 1: User-turn generation as a probe of interaction awareness. Left: A conversation context consisting of system, user, and assistant turns delimited by special tokens (e.g., <|im start|>, <|im end|>). Standard evaluation scores the assistant response for task accuracy. Our probe appends a <|im start|>user header and lets model Mθ generate under the user role. A genuine follow-up indicates the model’s weights e… view at source ↗
Figure 2
Figure 2. Figure 2: Controlled perturbation examples. Left: Truncation removes the end of the assistant response, prompting the model to produce a reaction to complete the response. Right: Appending a generic question elicits a grounded critique rather than a prompt restatement. Assistant and user turns in the examples are generated by GLM-4.7. These controls demonstrate that interaction awareness can surface in specific cont… view at source ↗
Figure 3
Figure 3. Figure 3: Cross-family dissociation: task accuracy vs. followup rate. Top row: task accuracy (%). Bottom row: followup rate (%). Five representative models on GSM8K, GPQA Diamond, and IFBench. Task accuracy does not predict follow-up quality: gpt-oss models produce the highest follow-up on GPQA despite lower accuracy than Qwen3.5 or GLM-4.7. Full results including IFEval and GPQA Main in [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 4
Figure 4. Figure 4: Qwen3.5 family: interaction awareness across temperature. Genuine follow-up rate (%) vs. sampling temperature for all eight Qwen3.5 models on three datasets. At T=0 (greedy), follow-up is near zero for most models despite high task accuracy ( [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Same question, same correct answer, different interaction awareness. Both mod￾els correctly answer a GPQA chemistry question (Answer: D). Left: Qwen3.5-9B generates a user turn that critically engages with the assistant’s reasoning about the Corey-Chaykovsky reagent and reaction conditions. Right: Qwen3.5-27B, a 3× larger model, restates the original prompt verbatim, an inability to generate realistic user… view at source ↗
Figure 6
Figure 6. Figure 6: Manual review interface. Screenshot of the annotation tool used for LLM judge validation. The interface presents the conversation context and the generated user turn to annotate, along with the primary-label inventory and a binary genuine-followup decision. Model identity, dataset metadata, and judge labels are hidden from annotators. 20% on judgment. This illustrates “knowing versus applying” dissociation… view at source ↗
read the original abstract

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. 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. If the model's weights encode interaction awareness, the generated user turn will be a grounded follow-up that reacts to the preceding context. Through experiments across $11$ open-weight LLMs (Qwen3.5, gpt-oss, GLM) and $5$ datasets (math reasoning, instruction following, conversation), we show that interaction awareness is decoupled from task accuracy. 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. In contrast, higher temperature sampling reveals interaction awareness is latent with follow up rates reaching $22\%$. Controlled perturbations validate that the proposed probe measures a real property of the model, and collaboration-oriented post-training on Qwen3.5-2B demonstrates an increase in follow-up rates. 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes user-turn generation as a probe for interaction awareness in LLMs: after a user-assistant exchange, the model is prompted to generate the next user turn, with 'genuine follow-up' rates (context-reactive continuations) measured under deterministic and sampled decoding. Experiments across 11 open-weight models (Qwen3.5 family, gpt-oss, GLM) and 5 datasets (math reasoning, instruction following, conversation) show decoupling from task accuracy—for instance, Qwen3.5 GSM8K accuracy rises from 41% (0.8B) to 96.8% (397B-A17B) while deterministic genuine follow-up rates remain near zero, increasing to 22% with higher temperature. Perturbation controls and collaboration-oriented post-training on Qwen3.5-2B are presented as validation that the probe captures a real, latent property invisible to assistant-only benchmarks.

Significance. If the central claim holds, the work identifies a previously unmeasured dimension of LLM behavior—interaction awareness—that is orthogonal to standard accuracy scaling and not captured by existing benchmarks. The demonstration that this capability is latent (surfacing under sampling) and improvable via targeted post-training has direct implications for multi-turn dialogue systems, user modeling, and training objectives beyond next-token prediction on assistant responses.

major comments (3)
  1. [§3] §3 (Experimental Setup): The exact, reproducible criteria for labeling a generated user turn as a 'genuine follow-up that reacts to the preceding context' are not specified, nor is the annotation protocol or inter-annotator agreement reported. This definition is load-bearing for all reported rates and the decoupling claim.
  2. [§4] §4 (Results): The interpretation that near-zero deterministic rates indicate absent interaction awareness in the weights (rather than role bias from assistant-heavy training data) is not fully supported. The temperature results show latent capability, but the controlled perturbations do not explicitly isolate context reactivity from generic user-role fluency, leaving the central decoupling claim vulnerable to the alternative explanation raised in the skeptic note.
  3. [§4.2] §4.2 and Table 1: No statistical significance tests (e.g., confidence intervals or hypothesis tests on rate differences across model sizes or conditions) are reported for the 'consistent patterns' across 11 models and 5 datasets, weakening the strength of the scaling and decoupling conclusions.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'genuine follow-up' is used without a one-sentence operational definition, reducing immediate clarity for readers unfamiliar with the probe.
  2. [§5] §5 (Post-training): The collaboration-oriented post-training procedure is described at a high level; a brief pseudocode or hyperparameter summary would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below, clarifying our experimental design, strengthening the interpretation of results, and committing to additional statistical analysis in the revision.

read point-by-point responses

  1. Referee: [§3] §3 (Experimental Setup): The exact, reproducible criteria for labeling a generated user turn as a 'genuine follow-up that reacts to the preceding context' are not specified, nor is the annotation protocol or inter-annotator agreement reported. This definition is load-bearing for all reported rates and the decoupling claim.

    Authors: We agree that explicit, reproducible criteria are essential. In the revised manuscript we will add a dedicated paragraph in §3 defining a genuine follow-up as a generated user turn that (1) references at least one concrete element from the assistant response or original query, (2) poses a contextually coherent next question or statement, and (3) is neither generic nor off-topic. Annotation was performed by two authors following a written protocol; disagreements were resolved by discussion. We will report the resulting inter-annotator agreement (Cohen’s κ) on a held-out sample of 200 generations. revision: yes

  2. Referee: [§4] §4 (Results): The interpretation that near-zero deterministic rates indicate absent interaction awareness in the weights (rather than role bias from assistant-heavy training data) is not fully supported. The temperature results show latent capability, but the controlled perturbations do not explicitly isolate context reactivity from generic user-role fluency, leaving the central decoupling claim vulnerable to the alternative explanation raised in the skeptic note.

    Authors: We acknowledge the alternative role-bias explanation. Our perturbation controls were designed to isolate context reactivity: replacing the assistant response with a neutral or contradictory statement produced a statistically detectable drop in genuine-follow-up rate while leaving user-role fluency intact. We will expand §4 to present these perturbation results side-by-side with the main findings, explicitly contrasting them against a control condition that measures generic user-role fluency (prompts containing only the user-role token). This additional analysis directly addresses the skeptic note and supports that the observed decoupling reflects a latent interaction-awareness property rather than training-data bias alone. revision: partial

  3. Referee: [§4.2] §4.2 and Table 1: No statistical significance tests (e.g., confidence intervals or hypothesis tests on rate differences across model sizes or conditions) are reported for the 'consistent patterns' across 11 models and 5 datasets, weakening the strength of the scaling and decoupling conclusions.

    Authors: We agree that formal statistical tests will strengthen the claims. In the revision we will add 95 % bootstrap confidence intervals to all reported genuine-follow-up rates in Table 1 and the figures. We will also perform and report paired t-tests (or Wilcoxon tests where normality is violated) comparing rates across model scales and between deterministic versus temperature-sampled conditions, with p-values corrected for multiple comparisons. These results will be integrated into §4.2. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical probe measured against external benchmarks

full rationale

The paper proposes user-turn generation as an operational probe for interaction awareness and reports empirical follow-up rates across 11 models and 5 datasets. The central decoupling claim compares these rates directly to independent task-accuracy metrics (e.g., GSM8K scaling from 41% to 96.8%). No equations, fitted parameters, or self-citations are present that reduce the measured rates or the awareness claim to the probe definition by construction. Temperature sampling, perturbations, and post-training results are additional empirical observations rather than definitional loops. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that sensible user-turn continuations indicate encoded interaction awareness; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Sensible user-turn generation after assistant response measures interaction awareness encoded in model weights
    Invoked in the definition of the probe and interpretation of near-zero deterministic rates.

pith-pipeline@v0.9.0 · 5571 in / 1246 out tokens · 31767 ms · 2026-05-13T20:55:49.266875+00:00 · methodology

discussion (0)

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Reference graph

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