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arxiv: 2604.17283 · v1 · submitted 2026-04-19 · 💻 cs.CL · cs.AI

Recognition: unknown

HorizonBench: Long-Horizon Personalization with Evolving Preferences

Shuyue Stella Li , Bhargavi Paranjape , Kerem Oktar , Zhongyao Ma , Gelin Zhou , Lin Guan , Na Zhang , Sem Park
show 4 more authors
Lin Chen Diyi Yang Yulia Tsvetkov Asli Celikyilmaz
Authors on Pith no claims yet

Pith reviewed 2026-05-10 05:48 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords long-horizon personalizationpreference evolutionstate trackingAI benchmarksuser modelinglanguage modelsbelief updating
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The pith

Frontier models fail to track evolving user preferences because they do not update beliefs on new life events.

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

This paper creates HorizonBench to evaluate how well AI systems handle personalization when user preferences shift over six months of interactions due to life events. It generates realistic conversation histories using a mental state graph that tracks every preference change with ground truth. Testing shows that even advanced models achieve at most 52.8 percent accuracy, with many performing no better than chance, often because they ignore updates and stick to initial statements. The work highlights that state-tracking ability, rather than context length, is the key obstacle to effective long-term user modeling.

Core claim

We define long-horizon personalization as the task of inferring preference changes from life events in extended dialogues. Using a data generator from a structured mental state graph, we build HorizonBench with 4,245 test items across 360 users, each with average 4,300-turn histories. Across 25 frontier models, performance tops out at 52.8 percent, and over one third of errors on evolved preferences involve selecting the original unupdated value. This failure holds regardless of context length or how explicitly the change is stated, pinpointing state-tracking as the central limitation.

What carries the argument

The structured mental state graph used to generate conversations, providing explicit provenance for each preference change over simulated 6-month periods.

If this is right

  • Improving long-horizon personalization will require specific advances in dynamic belief updating rather than relying on larger context windows alone.
  • Models that do not track state changes will give incorrect responses based on outdated preferences in ongoing user interactions.
  • Evaluation benchmarks for user modeling must include ground-truth evolution to properly diagnose tracking failures.
  • Architectures designed for theory-of-mind reasoning need to incorporate mechanisms for handling preference shifts explicitly.

Where Pith is reading between the lines

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

  • Systems could maintain a separate, updatable record of user preferences outside the model's context to avoid tracking errors.
  • This approach to benchmarking could be adapted to other domains involving long-term state changes, such as multi-session planning or personalized recommendations.
  • Real deployments might need explicit confirmation steps when models detect potential preference updates to ensure accuracy.

Load-bearing premise

The simulated dialogues generated from the mental state graph reflect the way real users naturally evolve their preferences in response to life events.

What would settle it

Running the same models on a collection of real human multi-month conversation logs with independently verified preference changes would reveal whether the observed state-tracking failures occur at similar rates.

Figures

Figures reproduced from arXiv: 2604.17283 by Asli Celikyilmaz, Bhargavi Paranjape, Diyi Yang, Gelin Zhou, Kerem Oktar, Lin Chen, Lin Guan, Na Zhang, Sem Park, Shuyue Stella Li, Yulia Tsvetkov, Zhongyao Ma.

Figure 1
Figure 1. Figure 1: HORIZONBENCH pipeline overview. (1) The generator produces conversations from a structured mental state graph, so every conversation turn is traceable to the otherwise unobservable mental state that produced it. (2) Preferences are established in conversations, then (3) implicitly shifted by evolution events without being restated. (4) Conversation episodes are generated from the updated mental state insta… view at source ↗
Figure 2
Figure 2. Figure 2: An end-to-end HORIZONBENCH item. The user’s emotional-support prefer￾ence for response structure evolves from narrative storytelling (established Sep) to step by step action plan (Nov) through causally grounded life events. The test question is embedded in a ∼370K-token conversational history; answering correctly requires tracking the preference evolution. The pre-evolution distractor (A) uses the user’s o… view at source ↗
Figure 3
Figure 3. Figure 3: Per-model accuracy on HORIZONBENCH (4,245 items). Bars are colored by model family with bootstrap 95% CIs (B=10,000). Dashed line marks the 20% chance baseline. The best frontier model achieves only 52.8%, and most models score at or below chance. Qualitative Example [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Pre-evolution distractor selection rate. Among wrong answers on evolved items, we plot the fraction that selected the outdated value. All 25 models exceed the 25% uniform-error baseline (1 of 4 wrong op￾tions, dashed line, p < 0.001, one-sided bino￾mial test), indicating consistent anchoring on the originally stated preference. Claude-opus-4.5 Claude-3.7-sonnet Claude-opus-4 Claude-sonnet-4.5 Claude-3.5-ha… view at source ↗
read the original abstract

User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this problem as long-horizon personalization and observe that progress on it is limited by data availability and measurement, with no existing resource providing both naturalistic long-horizon interactions and the ground-truth provenance needed to diagnose why models fail. We introduce a data generator that produces conversations from a structured mental state graph, yielding ground-truth provenance for every preference change across 6-month timelines, and from it construct HorizonBench, a benchmark of 4,245 items from 360 simulated users with 6-month conversation histories averaging ~4,300 turns and ~163K tokens. HorizonBench provides a testbed for long-context modeling, memory-augmented architectures, theory-of-mind reasoning, and user modeling. Across 25 frontier models, the best model reaches 52.8% and most score at or below the 20% chance baseline. When these models err on evolved preferences, over a third of the time they select the user's originally stated value without tracking the updated user state. This belief-update failure persists across context lengths and expression explicitness levels, identifying state-tracking capability as the primary bottleneck for long-horizon personalization.

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 HorizonBench, a benchmark for long-horizon personalization where user preferences evolve over 6-month simulated timelines. It constructs 4,245 test items from 360 users via a data generator based on a structured mental state graph that provides ground-truth provenance for every preference change. Experiments across 25 frontier models show the best performer at 52.8% accuracy with most at or below the 20% chance baseline; error analysis indicates that models frequently revert to originally stated preferences (>1/3 of errors on evolved items) rather than tracking updates, and this pattern holds across context lengths and expression explicitness, positioning state-tracking as the primary bottleneck.

Significance. If the synthetic generator produces representative preference dynamics, the benchmark would provide a valuable diagnostic tool for long-context modeling, memory architectures, and theory-of-mind capabilities in user modeling, with the explicit ground-truth provenance enabling precise failure-mode analysis that existing resources lack. The scale (long histories averaging 4,300 turns) and multi-model evaluation are strengths for identifying systematic limitations.

major comments (3)
  1. [§3] §3 (Generator and Mental State Graph): The central claim that state-tracking is the primary bottleneck rests on the assumption that the structured mental state graph produces preference evolutions comparable to real users. The manuscript provides no validation, ablation, or comparison against naturalistic data (e.g., gradual drift, conflicting signals, or unstated context), which is load-bearing because the observed reversion errors could be artifacts of the graph's explicit, discrete life-event updates rather than a general architectural limitation.
  2. [§5] §5 (Experiments): The error analysis reports reversion to original preferences in over one-third of evolved-item errors and claims persistence across context lengths and explicitness levels, but lacks statistical tests for the pattern's significance, per-model breakdowns, or controls for generator-specific artifacts (such as how life events are verbalized). This weakens the diagnosis that state-tracking, rather than other factors like prompt sensitivity, is the dominant issue.
  3. [Abstract and §4] Abstract and §4 (Benchmark Items): The 20% chance baseline is invoked without specifying the number of options per multiple-choice item, the construction of distractors, or how the baseline accounts for the distribution of preference values in the mental state graph. This detail is needed to confirm the baseline is fair and that the reported sub-chance performance is not an artifact of the evaluation setup.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly discuss the limitations of synthetic data for generalizing claims about real-user personalization.
  2. [Results figures] Figure captions and axis labels in the results section should clarify how context length is operationalized across models with varying window sizes.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive review. We address each major comment below and describe the revisions we will make to improve the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Generator and Mental State Graph): The central claim that state-tracking is the primary bottleneck rests on the assumption that the structured mental state graph produces preference evolutions comparable to real users. The manuscript provides no validation, ablation, or comparison against naturalistic data (e.g., gradual drift, conflicting signals, or unstated context), which is load-bearing because the observed reversion errors could be artifacts of the graph's explicit, discrete life-event updates rather than a general architectural limitation.

    Authors: We agree that empirical validation against real-user data would strengthen the work. However, the absence of long-horizon conversational datasets with ground-truth provenance for preference changes is the central motivation for creating the structured mental state graph. The graph models evolution via sequences of life events that can produce both incremental drifts and conflicting signals. We will expand §3 with additional details on these modeling choices and add an explicit limitations subsection acknowledging the synthetic nature of the data and the value of future naturalistic validation. This clarifies rather than alters the diagnostic focus of the benchmark. revision: partial

  2. Referee: [§5] §5 (Experiments): The error analysis reports reversion to original preferences in over one-third of evolved-item errors and claims persistence across context lengths and explicitness levels, but lacks statistical tests for the pattern's significance, per-model breakdowns, or controls for generator-specific artifacts (such as how life events are verbalized). This weakens the diagnosis that state-tracking, rather than other factors like prompt sensitivity, is the dominant issue.

    Authors: We accept this critique and will strengthen the analysis. The revised §5 will include statistical tests (binomial and ANOVA) for the significance of the reversion pattern across conditions. We will add a per-model breakdown table in the appendix. We will also report a new ablation varying life-event verbalization styles to control for generator artifacts. These changes will provide stronger support for identifying state-tracking as the primary bottleneck. revision: yes

  3. Referee: [Abstract and §4] Abstract and §4 (Benchmark Items): The 20% chance baseline is invoked without specifying the number of options per multiple-choice item, the construction of distractors, or how the baseline accounts for the distribution of preference values in the mental state graph. This detail is needed to confirm the baseline is fair and that the reported sub-chance performance is not an artifact of the evaluation setup.

    Authors: We apologize for the omission. Each item is a 5-option multiple-choice question: the ground-truth updated preference, the original preference, and three distractors sampled from the user's other values in the mental state graph. The 20% baseline is the uniform random selection rate over these five options. We will update both the abstract and §4 to state this explicitly, including the distractor construction procedure. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark and evaluation are empirically independent

full rationale

The paper defines long-horizon personalization, introduces an independent structured mental state graph generator to create synthetic 6-month conversations with explicit ground-truth provenance for every preference change, builds HorizonBench from it (4,245 items across 360 users), and evaluates 25 external frontier models on accuracy and error patterns such as reverting to original preferences. All reported results (e.g., best model at 52.8%, over one-third of errors on evolved items) are direct empirical measurements against this generated ground-truth rather than quantities defined by the paper's own fitted parameters, self-citations, or ansatzes. The identification of state-tracking as the primary bottleneck follows from observed model behaviors across context lengths and explicitness levels on the held-out benchmark, with no load-bearing step reducing by construction to the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the assumption that the mental-state-graph generator produces representative data; without full text, the exact free parameters in the generator cannot be audited.

axioms (1)
  • domain assumption User preferences evolve in response to life events according to structured rules that can be encoded in a graph
    Invoked to justify the data generator producing ground-truth changes
invented entities (1)
  • structured mental state graph no independent evidence
    purpose: To generate conversations with explicit provenance for every preference change
    New construct introduced to create the benchmark data

pith-pipeline@v0.9.0 · 5566 in / 1286 out tokens · 43819 ms · 2026-05-10T05:48:21.198338+00:00 · methodology

discussion (0)

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Reference graph

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