LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

Adam S. Jovine; David B. Shmoys; Francis Bahk; Jingjing Wang; Matthew Ford; Peter I. Frazier; Tinghan Ye

arxiv: 2510.25799 · v2 · pith:MS6EBGATnew · submitted 2025-10-29 · 💻 cs.CL

LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection

Adam S. Jovine , Tinghan Ye , Francis Bahk , Jingjing Wang , Matthew Ford , David B. Shmoys , Peter I. Frazier This is my paper

Pith reviewed 2026-05-21 20:24 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLMmulti-objective selectionpreference elicitationdecision-making agentiterative refinementutility functiontournament selectionnatural language

0 comments

The pith

LLM agents iteratively refine internal preference models to select from options with competing objectives.

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

The paper introduces LISTEN, a framework that positions large language models as decision-making agents for choosing among many candidates that involve multiple conflicting goals. Humans often find it hard to state their preferences explicitly for tasks such as flight booking or exam scheduling, so the method lets users interact through ordinary language while the model updates its own understanding step by step. Two concrete algorithms stay within LLM limits: LISTEN-U updates a parametric utility function, and LISTEN-T runs tournament-style comparisons on small batches of candidates. Experiments on flight booking, shopping, and scheduling show that LISTEN-U performs best when preferences fit a parametric form, a property tracked by a new concordance metric, whereas LISTEN-T gives steadier results in general. The work therefore points toward steering complex choices directly with natural language rather than formal preference models.

Core claim

LISTEN treats the LLM as an agent that iteratively refines its internal preference model and takes actions such as proposing utilities or selecting candidates to maximize alignment with a user's implicit goals. LISTEN-U refines a parametric utility function while LISTEN-T performs tournament-style selections over small batches. On tasks including flight booking, shopping, and exam scheduling, LISTEN-U excels when preferences are parametrically aligned according to a novel concordance metric, while LISTEN-T delivers more robust performance overall.

What carries the argument

The agentic iterative loop in which the LLM proposes utilities or selects candidates while updating its preference model from natural-language input.

If this is right

LISTEN-U achieves stronger results precisely when the concordance metric shows that preferences follow a parametric form.
LISTEN-T maintains reliable performance even when preferences lack a clear parametric structure.
Natural-language interaction can substitute for explicit formalization of complex preferences in practical selection problems.
The framework scales to realistic tasks by keeping each LLM call within context and cost limits through batch or utility refinement.

Where Pith is reading between the lines

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

The same iterative loop could be paired with external solvers to handle option sets larger than current LLM context windows allow.
Conversational versions of LISTEN might support ongoing preference updates as user priorities shift over time.
Real-user trials could test whether the selections increase satisfaction compared with conventional recommendation interfaces.

Load-bearing premise

The LLM can accurately interpret natural-language descriptions of preferences and use successive interactions to improve its own selections toward the user's implicit goals.

What would settle it

An experiment on the flight-booking or exam-scheduling task in which LISTEN selections fail to produce higher alignment with stated user trade-offs than a non-iterative LLM baseline or a standard multi-objective solver.

read the original abstract

Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex, implicit preferences. To address this, we introduce LISTEN (LLM-based Iterative Selection with Trade-off Evaluation from Natural-language), an agentic LLM-based framework that treats the LLM as a decision-making agent capable of iteratively refining its internal preference model and taking actions (e.g., proposing utilities or selecting candidates) to maximize alignment with a user's implicit goals. To operate within LLM constraints like context windows and inference costs, we propose two iterative algorithms: LISTEN-U, which uses the LLM to refine a parametric utility function, and LISTEN-T, a non-parametric method that performs tournament-style selections over small batches of solutions. Evaluated on diverse tasks including flight booking, shopping, and exam scheduling, our results show LISTEN-U excels when preferences are parametrically aligned (a property we measure with a novel concordance metric), while LISTEN-T offers more robust performance overall. This work explores a promising direction for steering complex multi-objective decisions directly with natural language, reducing the cognitive burden of traditional preference elicitation. Code is available at https://github.com/AdamJovine/LISTEN.

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.

Desk Editor's Note private letter to a colleague

LISTEN adds two iterative LLM algorithms and a concordance metric for natural-language multi-objective selection, but the abstract gives almost no numbers or controls to back the performance claims.

read the letter

The main point is that this paper puts forward LISTEN, a framework that turns an LLM into an iterative agent for picking from options with several competing goals, all driven by natural language instead of explicit utility functions. They split it into LISTEN-U, which has the model refine a parametric utility over rounds, and LISTEN-T, which runs small-batch tournaments to stay inside context limits, plus a new concordance score to measure how well user preferences fit a parametric model. The tests on flight booking, shopping, and exam scheduling are reasonable real-world proxies, and shipping the code helps anyone who wants to inspect or extend it. That combination of concrete algorithms and practical tasks is the clearest addition here. The soft spots sit mostly in the evidence. The abstract states that LISTEN-U does better under parametric alignment while LISTEN-T is more robust overall, yet it supplies no quantitative scores, no single-shot LLM baselines, no iteration ablations, and no checks on prompt or temperature sensitivity. Without those, the gains could just reflect the base model rather than the proposed loops. The concordance metric is new but its reliability is not shown in any detail from the summary. This is the kind of work that could interest people building decision-support tools or studying agentic LLMs for preference elicitation. A reader already thinking about human-AI interaction or recommendation systems might pick up usable ideas, though anyone expecting strong empirical backing will need the full experiments. I would send it to peer review so referees can examine the actual runs and see whether the iterative steps deliver measurable improvement once the controls are in place.

Referee Report

2 major / 2 minor

Summary. The paper introduces LISTEN, an agentic LLM framework for multi-objective selection directly from natural-language preferences. It defines two iterative algorithms—LISTEN-U, which refines a parametric utility function, and LISTEN-T, which performs tournament-style selection over small batches—and evaluates them on tasks such as flight booking, shopping, and exam scheduling. The central claim is that LISTEN-U excels when user preferences are parametrically aligned (measured by a novel concordance metric) while LISTEN-T provides more robust performance overall.

Significance. If the empirical results and the iterative refinement mechanism hold under rigorous controls, the work could provide a practical method for reducing cognitive load in complex preference-based decisions without requiring explicit utility elicitation. The release of code supports reproducibility. However, the absence of visible quantitative metrics, baselines, and validation of the concordance metric in the current presentation limits assessment of whether the claims advance the state of agentic LLM decision-making.

major comments (2)

[Results section] Results section (and abstract): The performance claims for LISTEN-U excelling under parametrically aligned preferences and LISTEN-T being more robust are stated without any reported quantitative metrics, effect sizes, baseline comparisons (e.g., single-shot LLM selection), statistical significance tests, or error bars. This directly undermines evaluation of the central empirical claim.
[Section 3 / 4] Section describing the iterative algorithms and concordance metric: The manuscript asserts that LISTEN-U and LISTEN-T maximize alignment via iterative refinement of an internal preference model, yet provides no ablation on iteration count, no comparison to non-iterative baselines, and no validation or sensitivity analysis for the novel concordance metric. Without these, it is unclear whether observed differences arise from the proposed agentic loop or from base LLM capabilities.

minor comments (2)

[Abstract] Abstract: Consider including one sentence with the key quantitative findings or at least the number of tasks and items per task to give readers an immediate sense of scale.
[Method] Notation: Define the concordance metric formally (including its inputs and range) at first use rather than relying on the high-level description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights important areas for strengthening the empirical rigor of our work. We address each major comment below and will incorporate revisions to provide clearer quantitative evidence and additional analyses.

read point-by-point responses

Referee: [Results section] Results section (and abstract): The performance claims for LISTEN-U excelling under parametrically aligned preferences and LISTEN-T being more robust are stated without any reported quantitative metrics, effect sizes, baseline comparisons (e.g., single-shot LLM selection), statistical significance tests, or error bars. This directly undermines evaluation of the central empirical claim.

Authors: We agree that the current results presentation would be strengthened by explicit quantitative reporting. In the revised manuscript, we will add detailed tables with specific alignment or selection success metrics, effect sizes, direct comparisons to single-shot LLM baselines, error bars from multiple independent runs, and statistical significance tests such as paired t-tests or Wilcoxon tests. These additions will allow readers to better assess the performance differences between LISTEN-U and LISTEN-T. revision: yes
Referee: [Section 3 / 4] Section describing the iterative algorithms and concordance metric: The manuscript asserts that LISTEN-U and LISTEN-T maximize alignment via iterative refinement of an internal preference model, yet provides no ablation on iteration count, no comparison to non-iterative baselines, and no validation or sensitivity analysis for the novel concordance metric. Without these, it is unclear whether observed differences arise from the proposed agentic loop or from base LLM capabilities.

Authors: We acknowledge that ablations and validation are necessary to isolate the contribution of the iterative agentic loop. The revised version will include experiments ablating the number of iterations, comparisons against non-iterative (single-pass) variants of both LISTEN-U and LISTEN-T, and a dedicated validation subsection for the concordance metric that includes sensitivity analysis and correlation with human judgments on a subset of tasks. These changes will clarify whether gains stem from the refinement process rather than base model capabilities alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external task evaluations

full rationale

The paper introduces the LISTEN framework and two algorithms (LISTEN-U and LISTEN-T) as an agentic approach for multi-objective selection, then reports empirical performance on independent external tasks including flight booking, shopping, and exam scheduling. The key observation that LISTEN-U excels under parametrically aligned preferences is tied to a novel concordance metric applied to those tasks, without any reduction of results to quantities defined by the paper's own fitted parameters, self-referential equations, or load-bearing self-citations. No derivation chain is present that collapses by construction; the work is self-contained as standard empirical framework evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests primarily on the domain assumption that LLMs possess sufficient capability to model and iteratively refine implicit user preferences through natural language, which is tested empirically rather than derived.

axioms (1)

domain assumption LLMs can serve as decision-making agents that iteratively refine an internal preference model to align with implicit user goals
This premise underpins the agentic framework and both LISTEN-U and LISTEN-T algorithms as described in the abstract.

pith-pipeline@v0.9.0 · 5772 in / 1371 out tokens · 60409 ms · 2026-05-21T20:24:47.984678+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

LISTEN-U, which uses the LLM to refine a parametric utility function, and LISTEN-T, a non-parametric method that performs tournament-style selections over small batches of solutions.
IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

we introduce a novel concordance metric... fraction of random linear utilities whose optimum matches the human top-ranked item

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extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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