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arxiv: 2602.03429 · v2 · pith:3FVYZ57Rnew · submitted 2026-02-03 · 💻 cs.AI · cs.CL· cs.HC· cs.LG

DiscoverLLM: From Executing Intents to Discovering Them

Tae Soo Kim , Yoonjoo Lee , Jaesang Yu , John Joon Young Chung , Juho Kim This is my paper

Pith reviewed 2026-05-16 07:59 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.HCcs.LG
keywords intent discoveryuser simulatorLLM trainingadaptive interactionconcretization rewardinteractive benchmarksconversation efficiencycreative and technical tasks
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The pith

DiscoverLLM trains LLMs to help users form and discover intents they have not yet formed by modeling how those intents concretize during interaction.

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

The paper establishes a framework that shifts LLMs from simply executing stated user requests to actively collaborating in intent discovery for ambiguous or open-ended tasks. It does so by introducing a user simulator that represents cognitive state as a hierarchy of intents, where the degree to which an intent has become concrete serves as a training reward. A sympathetic reader would care because many real-world requests in creative writing, technical work, or design remain vague precisely because the user has not yet observed enough options to decide what they want. If successful, the approach yields models that diverge to surface possibilities when intent is unclear and converge to implement once it solidifies, producing both higher task success and shorter conversations.

Core claim

DiscoverLLM trains language models using a user simulator that maintains a hierarchy of intents whose progressive concretization supplies a reward signal; the resulting models learn to explore options adaptively when intents remain abstract and to refine and execute once intents concretize, yielding over 10 percent higher performance and up to 40 percent shorter conversations on interactive benchmarks in creative writing, technical writing, and SVG drawing, together with higher satisfaction ratings from 75 human participants.

What carries the argument

The user simulator that models cognitive state with a hierarchy of intents whose degree of concretization serves as the reward signal for training.

If this is right

  • Trained models adaptively diverge to surface options when intent is unclear and converge once it concretizes.
  • Task performance rises by more than 10 percent across creative writing, technical writing, and SVG drawing benchmarks.
  • Average conversation length drops by up to 40 percent while user-reported satisfaction increases.
  • The same training signal supports generalization across multiple open-ended interaction domains.

Where Pith is reading between the lines

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

  • The concretization reward could be combined with direct human feedback to create hybrid training loops that require less simulator fidelity.
  • Similar hierarchy-based simulators might apply to domains such as code generation or product design where users also discover requirements through iteration.
  • If the hierarchy proves robust, future models could internalize the same divergence-convergence policy without needing an external simulator at inference time.

Load-bearing premise

The user simulator accurately captures how human users form and concretize their intents so that the simulated degree of concretization supplies a reliable training reward.

What would settle it

A controlled comparison in which real users interacting with the trained model show no measurable gain in task success, intent clarity, or satisfaction relative to a baseline that simply asks clarification questions.

Figures

Figures reproduced from arXiv: 2602.03429 by Jaesang Yu, John Joon Young Chung, Juho Kim, Tae Soo Kim, Yoonjoo Lee.

Figure 1
Figure 1. Figure 1: DISCOVERLLM Framework: A simulated user with a latent intent hierarchy (1) interacts with a model. The user can only articulate discovered intents (2), and model responses (3) that successfully probe or satisfy undiscovered intents trigger state updates (4). The framework computes rewards based on discovery progress (5), which are used for fine-tuning of the model (6). Abstract To handle ambiguous and open… view at source ↗
Figure 2
Figure 2. Figure 2: (A) Intent tree construction: (1) Given an artifact type and its content, we generate a specific intents list, (2) iteratively abstract them across levels, and (3) organize all resulting intents into a tree hierarchy. (B) Simulation Example: The user simulator begins (t = 0) with only a few abstract intents discovered and provides an initial request based on these. At t = 1, the model’s response fails to p… view at source ↗
Figure 3
Figure 3. Figure 3: Behavioral patterns across Qwen3-8B variants in Cre￾ative Writing. Turns are classified as divergent (D) or convergent (C) and analyzed as trigrams. Base is almost entirely convergent (91% CCC), SFT overfits toward divergence (43% DDD), and DISCOVERLLM variants show more balanced patterns. divergent (DDD), 2-consecutive (e.g., CCD), and alternating (i.e., CDC, DCD) [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Case study on Creative Writing shows how Qwen3-8B (left) fails to move the conversation forward as it only continues to revise the same story, while DISCOVERLLM (right) notices ambiguity and diverges, providing options that help the user discover new intents. Llama-3.1-8B-Instruct Discover↑ #Tok(k)↓ Base 47.8 3.64 Prompted Base 48.8 3.19 COLLABLLM 45.3 3.13 SFT 35.0 1.81 DPO 54.6 3.37 SFT+DPO 51.8 3.16 Rel… view at source ↗
Figure 5
Figure 5. Figure 5: User study results. Participants rated interaction satisfaction (a) and final writing satisfaction (b) higher with DISCOVERLLM, while spending less time (c). Interaction ratings every three turns (d) show DISCOVERLLM achieves higher satisfaction in early turns. fluent sections.” Interestingly, some noted that it seemed to understand their latent preferences: “anticipated what I was thinking” and “knew my n… view at source ↗
Figure 6
Figure 6. Figure 6: Example intent hierarchy for a story artifact presents a structure consisting of three intent trees. (b) Recursive Intent Evaluation. Starting from the provided root node, the evaluator assesses each intent by cascading through the tree structure. For each intent node, the evaluator provides reasoning and a binary judgment of whether the response probed or satisfied. If the response probed or satisfied the… view at source ↗
Figure 7
Figure 7. Figure 7: Turn-by-turn intent discovery scores for baselines and the best DISCOVERLLM variant for each task and base model. Then, we applied our framework to initialize user simulators with these artifacts and evaluated the models on simulated conversations against these user simulators. Similar to the main experiments, for each artifact, we conducted three conversations for each evaluated model and averaged the res… view at source ↗
Figure 8
Figure 8. Figure 8: Example evaluation conversation (3 turns) of Prompted Base (Qwen3-8B) against DISCOVERLLM (SFT+DPO+GRPO) in a Technical Writing task. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Interface used during the user study: (1) Initial page explaining the task and providing disclaimers to participants. (2) Page showing the assigned task to the participant and presenting them with possible topics or intents for the writing task. (3) Multi-turn chat interface. (4) Prompt requesting participants to provide interaction ratings every three turns. (5) Prompt indicating to participants that they… view at source ↗
read the original abstract

To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask clarification questions). However, users are often ambiguous because they have not yet formed their intents: they must observe and explore outcomes to discover what they want. Simply asking "what kind of tone do you want?" fails when users themselves do not know. We introduce DiscoverLLM, a novel and generalizable framework that trains LLMs to help users form and discover their intents. Central to our approach is a novel user simulator that models cognitive state with a hierarchy of intents that progressively concretize as the model surfaces relevant options -- where the degree of concretization serves as a reward signal that models can be trained to optimize. Resulting models learn to collaborate with users by adaptively diverging (i.e., explore options) when intents are unclear, and converging (i.e., refine and implement) when intents concretize. Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines.

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 paper introduces DiscoverLLM, a framework for training LLMs to assist users in discovering and forming intents during open-ended interactions rather than merely executing pre-specified ones. It proposes a user simulator that represents cognitive state as a hierarchy of intents that progressively concretize, using the degree of concretization as a reward signal to train the model to adaptively diverge (explore) or converge (refine) in conversation. The approach is evaluated on new interactive benchmarks in creative writing, technical writing, and SVG drawing, claiming over 10% higher task performance and up to 40% shorter conversations than baselines, plus improved satisfaction and efficiency in a 75-participant user study.

Significance. If the central empirical claims hold after addressing missing details, the work could meaningfully advance interactive LLM training by shifting focus from intent execution to intent discovery, with potential applications in creative and collaborative tools. The introduction of intent-hierarchy simulators and new benchmarks represents a concrete contribution, and the inclusion of a human user study provides direct evidence beyond synthetic evaluation. The parameter-free nature of the core modeling assumption (no free parameters listed in the axiom ledger) is a strength if the hierarchy proves robust.

major comments (3)
  1. [Abstract] Abstract: the claim of 'over 10% higher task performance' and 'up to 40% shorter conversation length' is presented without naming the baselines, reporting statistical tests, or describing data exclusion rules, which leaves the magnitude and reliability of the gains difficult to assess given the soundness score of 4.0.
  2. [User simulator description] User simulator description (implied in §3 or equivalent): the reward signal derived from intent concretization degree is load-bearing for the training loop, yet no explicit formulation, hyperparameter sensitivity analysis, or ablation replacing the strict top-down hierarchy with a flatter model is provided, making it impossible to determine whether gains are robust or simulator-specific.
  3. [User study section] User study section: while 75 participants are cited as supporting transfer from simulator to humans, the manuscript does not report any direct comparison of simulator-predicted intent trajectories versus observed human trajectories or an ablation that weakens the hierarchy, leaving the generalization claim untested as noted in the skeptic analysis.
minor comments (2)
  1. [Abstract] The abstract and results paragraphs would benefit from a brief table summarizing baseline names, exact metrics, and confidence intervals to improve readability of the performance claims.
  2. [Methods] Notation for the intent hierarchy levels and concretization metric should be defined once with a small diagram or equation early in the methods to avoid repeated informal descriptions.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We have revised the manuscript to improve clarity on the reported results, provide the explicit mathematical formulation of the simulator, and add supporting analyses for the user study. Each major comment is addressed below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of 'over 10% higher task performance' and 'up to 40% shorter conversation length' is presented without naming the baselines, reporting statistical tests, or describing data exclusion rules, which leaves the magnitude and reliability of the gains difficult to assess given the soundness score of 4.0.

    Authors: We agree that the abstract should enable readers to assess the claims more readily. In the revised version, we have updated the abstract to explicitly name the baselines (standard intent-execution LLMs and clarification-question baselines) and to state that gains are statistically significant (p < 0.05, paired t-tests across 100 runs). Data exclusion followed pre-specified rules for incomplete sessions (fewer than three turns due to interface errors), with full criteria now referenced in Section 4.1. These changes preserve the original magnitude of the results while improving transparency. revision: yes

  2. Referee: [User simulator description] User simulator description (implied in §3 or equivalent): the reward signal derived from intent concretization degree is load-bearing for the training loop, yet no explicit formulation, hyperparameter sensitivity analysis, or ablation replacing the strict top-down hierarchy with a flatter model is provided, making it impossible to determine whether gains are robust or simulator-specific.

    Authors: We thank the referee for identifying this gap in explicitness. The reward is defined as r = (1/H) * Σ c_h, where H is hierarchy depth and c_h is the fraction of concretized slots at level h (0 to 1). This formulation has been added to Section 3.2 together with the complete axiom ledger. We have also inserted a sensitivity analysis (Appendix B) varying the reward weight from 0.5× to 2.0×, confirming stable gains. Finally, we added the requested ablation replacing the hierarchy with a flat intent model; the flat variant yields only 3–5 % improvement versus 11–13 % for the hierarchical version, demonstrating the hierarchy’s contribution. These results appear in the revised Section 4.3. revision: yes

  3. Referee: [User study section] User study section: while 75 participants are cited as supporting transfer from simulator to humans, the manuscript does not report any direct comparison of simulator-predicted intent trajectories versus observed human trajectories or an ablation that weakens the hierarchy, leaving the generalization claim untested as noted in the skeptic analysis.

    Authors: We agree that direct validation of simulator fidelity strengthens the generalization argument. In the revised manuscript we have added Section 5.2, which compares simulator-predicted intent-concretization trajectories against the 75 human sessions and reports a Pearson correlation of 0.79 in refinement rate. We also include an ablation that weakens the hierarchy (randomized level ordering) during training; this yields 6–9 % lower task performance and reduced satisfaction scores relative to the original model. Both analyses are now reported in Section 5.3 and support the transfer claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity: framework and results rest on independent benchmarks and user study

full rationale

The paper introduces DiscoverLLM as a training framework that uses a novel user simulator modeling cognitive state via an intent hierarchy whose concretization degree supplies a reward signal. Models are then trained to adaptively diverge or converge based on this signal. Performance is measured empirically on proposed interactive benchmarks (creative writing, technical writing, SVG drawing) showing >10% task gains and up to 40% shorter conversations, plus a separate user study with 75 participants reporting improved satisfaction and efficiency. No equations, derivations, or self-citations appear in the provided text. The central claims do not reduce by construction to fitted parameters defined from the same data, nor do they rely on load-bearing self-citations or imported uniqueness theorems. The simulator structure is presented as an explicit modeling choice whose validity is tested via external benchmarks and human evaluation rather than assumed tautologically.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities beyond the high-level description of the user simulator; full details would be needed for a complete ledger.

pith-pipeline@v0.9.0 · 5545 in / 1097 out tokens · 27327 ms · 2026-05-16T07:59:13.500390+00:00 · methodology

discussion (0)

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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Reference graph

Works this paper leans on

58 extracted references · 58 canonical work pages · cited by 4 Pith papers

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    **Minimize Effort**: IMPORTANT! Be brief by keeping message to around 20 words, maximum of 40 words

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    What's Working

    **Follow Your Internal Thoughts**: Base your message solely on your internal thinking of "What's Working" and "What to Try Next". You are forbidden from adding any new information that is not in your analysis

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    **Maintain Coherence**: Stay consistent with the chat history

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    **Plain Text**: Use simple, plain text with only minimal or no punctuation, special characters, or formatting (e.g., no ellipses, no emojis, no markdown, no em-dashes, etc.)

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    However, when you have pursuing_fuzzy or latent_goal items, you can only hint at the issue or aspect in an implicit, vague, and incomplete way

    **Modify Explicitness based on Awareness**: When you have pursuing_clear items, you can provide the issue in an explicit, clear, and complete manner in your message. However, when you have pursuing_fuzzy or latent_goal items, you can only hint at the issue or aspect in an implicit, vague, and incomplete way

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    not sure

    **Express Uncertainty**: When you have only have pursuing_fuzzy or latent_goal items, you should be vague while also expressing some level of uncertainty or hesitation. You can use diverse methods to express this. For example: 92- Explicitly mention uncertainty (e.g., "not sure", "maybe", "perhaps", etc.) 93- Use abstract or imprecise language (e.g., "fle...

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    evaluations

    A list of requirements or constraints that the artifact should satisfy 6 7Your task is to evaluate how well the artifact satisfies the given requirements or constraints. 8 9## Evaluation Guidelines 10 11- **Holistic Assessment**: Consider the artifact as a complete work 12- **Comprehensive Evaluation**: Evaluate the artifact on each of the requirements or...

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    **Describe Directions**: Concise descriptions of distinct approaches

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    What can I show that helps us discover the direction together?

    **Show Direction Samples**: Small illustrative examples of different approaches 59 60### Guidelines 61- Explore the **smallest meaningful component** (one level at a time: structure, then tone, then details) 62- Show multiple directions with **conceptual distinction**, not minor variations 63- Make differences tangible enough that user can feel which reso...

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