arxiv: 2604.17886 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.AI

Recognition: unknown

Latent Preference Modeling for Cross-Session Personalized Tool Calling

Yejin Yoon , Minseo Kim , Taeuk Kim

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:08 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords personalized tool callingpreference modelingmulti-session dialoguesmemory-augmented agentsgenerate-verify-refineLLM agentsMPT benchmarktool use

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The pith

PRefine extracts reusable constraints from history via a generate-verify-refine loop to raise tool-calling accuracy while using only 1.24% of the tokens required by full-history prompting.

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

Users frequently leave preferences unstated when asking LLM agents to use tools, so agents receive under-specified inputs that cannot execute correctly. The paper introduces the MPT benchmark of 265 multi-session dialogues that isolate three concrete difficulties: recalling earlier preferences, inducing preferences from partial evidence, and transferring them to new tasks. PRefine treats preferences as evolving hypotheses that an LLM generates, verifies against history, and refines into reusable constraints. This loop produces higher tool-calling accuracy than feeding the entire dialogue history while consuming only 1.24 percent of the tokens. The result implies that personalization succeeds when memory records the reasons for past choices rather than the choices alone.

Core claim

PRefine improves tool-calling accuracy by maintaining user preferences as evolving hypotheses that are generated, verified, and refined from multi-session history, extracting reusable constraints that generalize across preference recall, induction, and transfer while requiring only 1.24% of the tokens used by full-history prompting on the MPT benchmark.

What carries the argument

The generate-verify-refine loop that represents preferences as updatable hypotheses and extracts reusable constraints from dialogue history.

If this is right

Agents can sustain cross-session personalization without exhausting context windows.
Memory designs should store extracted constraints rather than raw conversation turns.
Performance on under-specified tool requests rises when reasons for choices are modeled explicitly.
Token-efficient personalization becomes feasible for longer or more frequent interactions.

Where Pith is reading between the lines

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

The same loop might reduce context costs in other agent tasks such as multi-turn planning or recommendation.
Exposing the refined hypotheses could make agent decisions more interpretable to users.
The method may need additional safeguards when user preferences change rapidly or conflict within a session.

Load-bearing premise

An LLM can reliably generate, verify, and refine hypotheses that capture user preferences accurately enough to improve tool calls and generalize across recall, induction, and transfer.

What would settle it

Running PRefine on the MPT benchmark and finding either no accuracy improvement over full-history prompting or token consumption above 1.24% of that baseline would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2604.17886 by Minseo Kim, Taeuk Kim, Yejin Yoon.

**Figure 2.** Figure 2: Overview of MPT construction. Individual SGD sessions are grouped into a multi-session interaction history (S, A≤T), from which cross-domain preference evidence is annotated as shared behavioral constraints. Target queries are constructed by intentionally under-specifying preferencesensitive arguments. We validate the grouping annotations through a human study with 19 annotators (Appendix A.6), finding s… view at source ↗

**Figure 3.** Figure 3: PREFINE’s generate-verify-refine loop. At each session T+1 (e.g., Session 7), candidate preference hypotheses h (i) are generated from the current dialogue sT+1, tool call aT+1, and prior memory MT (e.g., M6). Here, MT denotes the single preference hypothesis accepted at session T and is updated to MT+1 upon acceptance of a new hypothesis. The updated memory is then used to constrain tool-call decisions a … view at source ↗

**Figure 4.** Figure 4: Average number of predicted API arguments per model under Base prompting and [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Memory footprint comparison across methods. (a) Average number of retrieved tokens at [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 6.** Figure 6: Domain-wise distribution of preference groups per example (left) and API call frequency [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Illustration of the three preference modeling types in MPT. Given the same context—a user [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Example of multi-session preference aggregation in MPT. Session-level dialogues are omitted for brevity [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

**Figure 9.** Figure 9: Prompt templates for the PREFINE generator and verifier. The generator proposes latent preference hypotheses as abstract, decision-level constraints from accumulated interaction history. The verifier evaluates each candidate against four validity conditions and provides structured feedback for refinement. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗

**Figure 10.** Figure 10: Inference prompts used in our experiments. The base prompting template (left) instructs [PITH_FULL_IMAGE:figures/full_fig_p025_10.png] view at source ↗

read the original abstract

Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental challenge for tool-augmented agents, as API execution typically requires complete arguments, highlighting the need for personalized tool calling. To study this problem, we introduce MPT, a benchmark comprising 265 multi-session dialogues that cover three challenges: Preference Recall, Preference Induction, and Preference Transfer. We also propose PRefine, a test-time memory-augmented method that represents user preferences as evolving hypotheses. Through a generate--verify--refine loop, it extracts reusable constraints from history and improves tool-calling accuracy while using only 1.24% of the tokens required by full-history prompting. These results indicate that robust personalization in agentic systems depends on memory that captures the reasons behind user choices, not just the choices themselves.

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

The paper introduces a new MPT benchmark for cross-session tool personalization and a PRefine method that claims big token savings via a generate-verify-refine loop, but the abstract leaves the evaluation too thin to judge if the accuracy gains are real.

read the letter

The main thing here is a new benchmark MPT with 265 multi-session dialogues covering preference recall, induction, and transfer, plus PRefine which models user preferences as evolving hypotheses and extracts reusable constraints through a generate-verify-refine loop. It reports tool-calling accuracy gains while using only 1.24% of the tokens needed for full-history prompting. That efficiency number is the part that stands out for anyone running agents with limited context. The framing is also useful: it correctly points out that just remembering past choices is not enough when users give under-specified requests, and that capturing the reasons behind choices could help with transfer to new sessions. The three challenges in the benchmark line up with real deployment issues. The idea of a test-time loop that turns history into constraints rather than dumping everything into the prompt is straightforward and worth testing. The soft spot is the missing experimental detail. The abstract states accuracy and token improvements but gives no baselines, no per-challenge breakdown, no statistical tests, and no check on whether the hypotheses produced by the loop are actually correct or just plausible-sounding. If the verify step accepts spurious constraints, especially in the induction or transfer cases, the accuracy claim would not hold and the token savings would not deliver net gains. The stress-test concern about the loop's reliability is on target from what is shown. This work is for people building tool-augmented agents or long-term memory systems who need to handle incomplete inputs over multiple sessions. A reader in that space could use the benchmark for their own experiments and the loop as a baseline idea. It deserves peer review because the problem is practical and the method is simple enough to reproduce, but the authors will need to add full results, ablations, and hypothesis-quality metrics before the claims are convincing.

Referee Report

2 major / 2 minor

Summary. The paper introduces the MPT benchmark of 265 multi-session dialogues spanning Preference Recall, Preference Induction, and Preference Transfer. It proposes PRefine, a test-time method representing user preferences as evolving hypotheses extracted via a generate-verify-refine loop from history, claiming improved tool-calling accuracy at 1.24% of the token cost of full-history prompting.

Significance. If the empirical results hold with proper controls, the work would advance memory-efficient personalization in tool-augmented agents by prioritizing latent reasons over raw choices. The MPT benchmark itself is a useful contribution for evaluating cross-session generalization. The reported token reduction is potentially impactful if accuracy gains prove robust rather than benchmark-specific.

major comments (2)

Abstract: the central claim of accuracy improvement and token savings (1.24% of full-history) is stated without any mention of baselines, statistical significance, error analysis, or per-challenge results, leaving the support for the headline result unassessable from the provided text.
Method section (generate-verify-refine loop): the assumption that the LLM-driven loop reliably infers, verifies, and refines generalizable preference hypotheses is load-bearing for the accuracy claim, yet no hypothesis-quality metric, per-challenge breakdown (especially for Induction/Transfer), or failure-case analysis is referenced to confirm the loop does not accept spurious constraints.

minor comments (2)

Clarify the exact representation and update rule for 'evolving hypotheses' (e.g., how constraints are stored and retrieved at test time).
Provide construction details and validation procedure for the 265 MPT dialogues to allow reproducibility of the three challenges.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our work. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: Abstract: the central claim of accuracy improvement and token savings (1.24% of full-history) is stated without any mention of baselines, statistical significance, error analysis, or per-challenge results, leaving the support for the headline result unassessable from the provided text.

Authors: We agree that the abstract would benefit from additional context to allow readers to better assess the claims. We will revise the abstract to briefly reference the primary baselines (including full-history prompting), note that improvements are statistically significant, and direct readers to the experimental section for per-challenge results and error analysis. revision: yes
Referee: Method section (generate-verify-refine loop): the assumption that the LLM-driven loop reliably infers, verifies, and refines generalizable preference hypotheses is load-bearing for the accuracy claim, yet no hypothesis-quality metric, per-challenge breakdown (especially for Induction/Transfer), or failure-case analysis is referenced to confirm the loop does not accept spurious constraints.

Authors: Per-challenge results for Induction and Transfer are reported in the experiments section and support the loop's contribution to generalization. We acknowledge, however, that the manuscript does not include a direct hypothesis-quality metric or dedicated failure-case analysis. We will add a subsection providing a manual evaluation of hypothesis quality on sampled cases and explicit failure examples showing how the verify step filters spurious constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method is self-contained

full rationale

The paper introduces the MPT benchmark and PRefine method as an empirical generate-verify-refine loop for extracting preferences from multi-session dialogues, with results reported on tool-calling accuracy and token usage. No equations, derivations, fitted parameters renamed as predictions, or self-citations appear in the provided text to create any reduction of outputs to inputs by construction. The central claims rest on benchmark evaluation rather than theoretical self-definition or imported uniqueness theorems, making the approach independent and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the modeling choice of representing preferences as evolving hypotheses and the assumption that an LLM can perform reliable generate-verify-refine extraction; no numerical free parameters are mentioned.

axioms (1)

domain assumption LLM-based agents can extract reusable preference constraints from dialogue history via generate-verify-refine
Invoked in the description of PRefine as the core mechanism for memory augmentation.

invented entities (1)

evolving hypotheses for user preferences no independent evidence
purpose: Compact representation of preferences that can be refined over sessions
New modeling construct introduced to enable the memory-augmented method without full history.

pith-pipeline@v0.9.0 · 5441 in / 1316 out tokens · 25633 ms · 2026-05-10T04:08:40.617760+00:00 · methodology

discussion (0)

Reference graph

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