PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

Bingxuan Li; Cheng Qian; Eitan Anzenberg; Heng Ji; Jeonghwan Kim; Niran Kundapur; Xiusi Chen

arxiv: 2601.11957 · v3 · submitted 2026-01-17 · 💻 cs.CL

PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

Bingxuan Li , Jeonghwan Kim , Cheng Qian , Xiusi Chen , Eitan Anzenberg , Niran Kundapur , Heng Ji This is my paper

Pith reviewed 2026-05-16 13:06 UTC · model grok-4.3

classification 💻 cs.CL

keywords calendar conflict resolutionreinforcement learninglanguage agentspreference memorytime managementbenchmarkerror reduction

0 comments

The pith

PEARL uses reinforcement learning and an external memory of inferred preferences to cut errors in resolving calendar conflicts by 55 percent.

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

The paper sets out to show that language agents can handle the repeated decisions required when calendar invitations overlap by learning user priorities over many rounds. It creates a year-long benchmark where agents must infer and adapt to preferences for attendees, topics, and timing without explicit instructions each time. The proposed method adds a memory store for those preferences and trains the agent with rewards given after every round for decision accuracy and sensible memory use. If the approach works, agents could automate time management at scale instead of requiring constant human oversight or failing on complex schedules.

Core claim

PEARL augments a language agent with an external preference memory that stores and updates strategies such as attendee priorities and topic importance, then optimizes the agent through round-wise rewards that directly supervise decision correctness, ranking quality, and memory usage across a full year of simulated conflicts, producing an error reduction rate of 0.76 and a 55 percent improvement in average error rate over the strongest baseline on CalConflictBench.

What carries the argument

An external preference memory paired with round-wise reinforcement learning rewards that let the agent accumulate and apply inferred user strategies across sequential conflict rounds.

If this is right

Agents can build and refine a running model of user priorities instead of treating each conflict in isolation.
Decision accuracy improves steadily as the memory accumulates evidence from prior rounds.
Ranking of options such as attend, reschedule, or decline becomes more consistent with the stored preferences.
Memory updates remain efficient because rewards penalize unnecessary or incorrect entries.

Where Pith is reading between the lines

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

The same memory-plus-reward structure could transfer to other sequential preference tasks such as email triage or project task ordering.
Long-horizon performance gains suggest external memory can mitigate context-window limits that currently hinder language agents on year-scale problems.
If the method scales, it points toward assistants that require less frequent human correction once initial preferences are observed.

Load-bearing premise

The synthetic conflicts and preference signals in the benchmark match the patterns real users would follow when choosing among overlapping meetings.

What would settle it

Testing the same agent on a set of real user calendars with logged choices and explicit preference feedback to check whether the error reduction holds outside the synthetic data.

Figures

Figures reproduced from arXiv: 2601.11957 by Bingxuan Li, Cheng Qian, Eitan Anzenberg, Heng Ji, Jeonghwan Kim, Niran Kundapur, Xiusi Chen.

**Figure 1.** Figure 1: Illustration of the proposed calendar conflict resolution task. At decision round t, the agent observes (i) the conflicting events Et, (ii) contextual information, and (iii) the current calendar state Ct. The agent selects exactly one event to accept (a i t = 1) and declines the rest (a i t = 0), producing the accepted event, declined events, a priority ranking, and rationale. lustrated in [PITH_FULL_IM… view at source ↗

**Figure 2.** Figure 2: Average Error Rate of Qwen3-8b under different numbers of conflicting events per round ( [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Average Optimal Rank Distance (ORD) over [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 4.** Figure 4: Overview of PEARL. Top-left: Agent action space. At each turn, the agent can take a decision action adecision (accept/decline an event ei) or a hub action ahub that queries (list) or updates (update) the external Strategy Hub. Top-right: Agent rollout. The policy model generates a multi-turn trajectory; when a decision action is emitted, the round terminates and the next conflict is presented. Bottom: Trai… view at source ↗

**Figure 5.** Figure 5: Error vs. decision rounds of PEARL and zero-shot baseline 8 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Conflict event generation process [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Case study: Responses from two models Model behaviors [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

read the original abstract

Overlapping calendar invitations force busy professionals to repeatedly decide which meetings to attend, reschedule, or decline. We refer to this preference-driven decision process as calendar conflict resolution. Automating this decision process is crucial yet challenging. Scheduling logistics can drain hours, and human delegation often fails at scale, which motivates us to ask: Can we trust large language models (LLMs) or language agents to manage time? To enable a systematic study of this question, we introduce CalConflictBench, a benchmark for long-horizon calendar conflict resolution. In CalConflictBench, conflicts are presented to agents round-by-round over a calendar year, requiring them to infer and adapt to user preferences progressively. Our experiments show that current LLM agents perform poorly with high error rates, e.g., Qwen-3-30B-Think has an average error rate of 35%. To address this gap, we propose PEARL, a reinforcement-learning framework that (i) augments the language agent with an external preference memory that stores and updates inferred strategies (e.g., attendee priorities, topic importance, time/location preferences), and (ii) optimizes the agent with round-wise rewards that directly supervise decision correctness, ranking quality, and memory usage across rounds. Experiments on CalConflictBench show that PEARL achieves an error reduction rate of 0.76 and a 55% improvement in average error rate compared to the strongest baseline.

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

PEARL adds a concrete new benchmark for calendar conflicts and an RL agent with preference memory that cuts errors, but the synthetic data leaves transfer to real users unproven.

read the letter

The main takeaway is a new benchmark, CalConflictBench, that turns calendar conflict resolution into a long-horizon task where agents see conflicts round by round across a year and must infer and adapt to preferences. They pair it with PEARL, which adds an external memory to store inferred rules like attendee priorities and uses round-wise RL rewards on decision correctness and memory use. Standard LLM agents hit around 35% error on it, while PEARL shows a 0.76 error reduction and 55% better average error rate than the strongest baseline. That setup is a straightforward application of memory-augmented RL to a practical scheduling problem, and the numbers are reported clearly enough to see the claimed lift. The approach is new enough as a packaged benchmark plus agent for this domain. The soft spot is the fully synthetic nature of the conflicts and preference signals. Nothing in the work checks whether the generation rules match how real professionals weigh meetings, topics, or context, so the gains could be tied to benchmark-specific patterns rather than general time-management skill. Round-wise rewards are also defined against the same internal ground truth used for evaluation, which creates the usual circularity risk. The abstract gives no benchmark construction details, statistical tests, or error bars, so robustness is hard to judge from what's here. This is for people building LLM agents for everyday productivity tasks. A reader working on memory or RL supervision for agents would find the concrete architecture useful to look at. It deserves peer review because the benchmark is fresh and the framework is specific enough that referees can check the implementation and ask for real-user validation.

Referee Report

3 major / 2 minor

Summary. The paper introduces CalConflictBench, a benchmark for long-horizon calendar conflict resolution in which agents resolve overlapping invitations round-by-round over a simulated year by progressively inferring user preferences. It proposes PEARL, an RL framework that augments an LLM agent with an external preference memory storing inferred strategies (attendee priorities, topic importance, etc.) and optimizes the agent via round-wise rewards on decision correctness, ranking quality, and memory usage. Experiments report that PEARL achieves a 0.76 error reduction rate and 55% improvement in average error rate over the strongest baseline (e.g., Qwen-3-30B-Think at 35% error).

Significance. If the benchmark faithfully captures real scheduling behavior and the RL updates generalize, the work would provide a concrete, reproducible path toward reliable LLM-based time-management agents, a practical capability with clear user impact. The explicit external memory plus round-wise supervision is a clean architectural contribution that could be adapted to other long-horizon preference-learning settings.

major comments (3)

[Benchmark construction] Benchmark construction (CalConflictBench section): the paper provides no explicit description of the generative rules for synthetic conflicts, preference signals, or priority weighting, so it is impossible to judge whether the reported 0.76 error reduction and 55% improvement reflect genuine time-management capability or benchmark-specific artifacts.
[Experiments] Reward definition and evaluation (Experiments section): round-wise rewards are defined directly against the same benchmark-internal ground truth used for final metrics, creating a circularity risk that optimization success may be tied to the reward formulation rather than independent external validation.
[Results] Experimental reporting (Results subsection): aggregate numbers (0.76 reduction, 55% improvement) are given without statistical tests, error bars, number of runs, or controls for overfitting in the RL loop, making it impossible to assess the reliability of the headline gains.

minor comments (2)

[Method] Notation for the external preference memory is introduced without a formal update equation or pseudocode, which would help readers replicate the self-evolution mechanism.
[Abstract] The abstract cites Qwen-3-30B-Think; the main text should clarify whether this is an off-the-shelf model or a fine-tuned variant.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript to improve clarity and rigor.

read point-by-point responses

Referee: [Benchmark construction] Benchmark construction (CalConflictBench section): the paper provides no explicit description of the generative rules for synthetic conflicts, preference signals, or priority weighting, so it is impossible to judge whether the reported 0.76 error reduction and 55% improvement reflect genuine time-management capability or benchmark-specific artifacts.

Authors: We agree that the original manuscript lacked sufficient detail on benchmark construction. In the revised version, we will add a new subsection under CalConflictBench that explicitly describes the generative rules for synthetic conflicts, the sampling of preference signals (including how they are revealed progressively across rounds), and the priority weighting scheme for attendees, topics, and time preferences. This will allow readers to assess the benchmark's fidelity and the generalizability of our results. revision: yes
Referee: [Experiments] Reward definition and evaluation (Experiments section): round-wise rewards are defined directly against the same benchmark-internal ground truth used for final metrics, creating a circularity risk that optimization success may be tied to the reward formulation rather than independent external validation.

Authors: We acknowledge the referee's concern regarding potential circularity. The round-wise rewards focus on immediate per-round outcomes (decision correctness and ranking quality), whereas the primary evaluation metrics measure long-horizon error reduction and preference inference across the full simulated year. To strengthen the paper, we will add a clarifying paragraph distinguishing these aspects and include an ablation study using proxy-based rewards (e.g., based on simulated user feedback rather than direct ground truth) to demonstrate that performance gains are not solely due to the reward formulation. revision: partial
Referee: [Results] Experimental reporting (Results subsection): aggregate numbers (0.76 reduction, 55% improvement) are given without statistical tests, error bars, number of runs, or controls for overfitting in the RL loop, making it impossible to assess the reliability of the headline gains.

Authors: We agree that the experimental reporting requires greater statistical rigor. In the revised manuscript, we will update the Results section to report all metrics as means over 5 independent runs with standard deviations, include error bars in figures, perform paired statistical significance tests against baselines, and add details on the RL training procedure (including validation splits and early stopping criteria) to address potential overfitting concerns. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical evaluation chain

full rationale

The paper introduces CalConflictBench and defines round-wise rewards to supervise decision correctness, ranking quality, and memory usage, then reports empirical error reduction (0.76) and improvement (55%) versus baselines after RL optimization. No equations, self-definitional reductions, fitted parameters renamed as predictions, or load-bearing self-citations are present that would make the performance result equivalent to the inputs by construction. The derivation consists of a proposed framework and comparative experiments on the introduced benchmark rather than a tautological identity or forced uniqueness theorem. This is a standard empirical setup and scores as self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unstated premise that user preferences can be reliably inferred and stored in an external memory from round-by-round interactions and that the defined rewards accurately reflect true decision quality without circular dependence on the benchmark itself.

axioms (1)

domain assumption User preferences are stable enough to be captured by a memory that updates across rounds without external validation.
Invoked implicitly when the framework claims to infer and store strategies such as attendee priorities and topic importance.

invented entities (1)

external preference memory no independent evidence
purpose: Stores and updates inferred strategies for attendee priorities, topic importance, and time/location preferences.
New component added to the language agent to enable progressive adaptation.

pith-pipeline@v0.9.0 · 5571 in / 1321 out tokens · 25369 ms · 2026-05-16T13:06:01.068261+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages · 1 internal anchor

[1]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Hui Yang, Sifu Yue, and Yunzhong He. 2023. Autogpt for online decision making: Benchmarks and addi- tional opinions.arXiv preprint arXiv:2306.02224. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023a. Tree of thoughts: Deliberate problem solving w...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[2]

verbose database queries correlate with null results

Llamafactory: Unified efficient fine-tuning of 100+ language models. InProceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand. Association for Computa- tional Linguistics. Kunlun Zhu, Zijia Liu, Bingxuan Li, Muxin Tian, Yingxuan Yang, Jiaxun Zhang, Pengrui Han, Qipen...

work page arXiv 2025
[3]

weekly group meeting

Regular meeting schemas M(r): templates for commonly recurring events, including (i) canonical topics (e.g., “weekly group meeting”, “1:1 mentoring”, “sponsor sync”), (ii) typical cadence (weekly/biweekly/monthly), (iii) de- fault duration distributions, (iv) attendee pat- terns (direct reports, cross-team stakeholders, external partners), and (v) common ...

work page
[4]

Priority principles P(r) : a small set of explicit, interpretable principles governing decisions un- der conflict, such as leadership/oversight obliga- tions, deadline sensitivity, people management duties, and external relationship maintenance

work page
[5]

in-person required

Conflict reasons C(r): common causes of de- cline/postpone for that role, such as deadline clashes, hierarchical obligations, travel con- straints, task urgency spikes, teaching/commit- tee constraints, or sponsor milestone collisions. Each conflict reason c∈C(r) defines a trans- formation over event metadata (e.g., inserting a deadline marker, adding a s...

work page
[6]

Role realism:Are the event topics and ca- dences plausible for this role?

work page
[7]

Org-chart consistency:Do attendees reflect correct reporting lines and stakeholder relation- ships?

work page
[8]

Conflict coherence:Do the competing events genuinely overlap and create a meaningful trade- off?

work page
[9]

Principle alignment:Is the accepted event jus- tified by P(r) under the provided context sig- nals?

work page
[10]

Experiment planning and daily priorities sync

Metadata quality:Are titles, locations, and constraints natural (no duplicates, no contradic- tions)? Edits and rejection.Annotators can (i) edit event titles/attributes, (ii) swap the accepted label if inconsistent with principles, (iii) rewrite the con- flict reason/context for coherence, or (iv) reject the datapoint if it cannot be repaired cheaply. An...

work page 2025
[11]

Evaluate all conflict events considering : - The principles and reasoning provided for each event - The organizational hierarchy and relationships - The urgency and importance of each event - Historical patterns from similar past decisions - The impact on stakeholders and organizational goals - Time constraints and scheduling flexibility

work page
[12]

Rank all conflict events ( including the regular event ) in order of priority

work page
[13]

Select the single event that should be accepted

work page
[14]

prio rity_ran king ( total { M } events )

Respone in the required format . # Inputs : # # History Conflict Calendar Events and User Decisions : { h i s t o r y _ c a l e n d a r _ e v e n t s } # # Organization Chart : { org_chart } # # Conflict Calendar Event to Solve : { c o n f l i c t _ c a l e n d a r _ e v e n t } # Output Format : Provide your response in the following structured format : ...

work page 2024

[1] [1]

Qwen3 Technical Report

Qwen3 technical report.arXiv preprint arXiv:2505.09388. Hui Yang, Sifu Yue, and Yunzhong He. 2023. Autogpt for online decision making: Benchmarks and addi- tional opinions.arXiv preprint arXiv:2306.02224. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023a. Tree of thoughts: Deliberate problem solving w...

work page internal anchor Pith review Pith/arXiv arXiv 2023

[2] [2]

verbose database queries correlate with null results

Llamafactory: Unified efficient fine-tuning of 100+ language models. InProceedings of the 62nd Annual Meeting of the Association for Compu- tational Linguistics (Volume 3: System Demonstra- tions), Bangkok, Thailand. Association for Computa- tional Linguistics. Kunlun Zhu, Zijia Liu, Bingxuan Li, Muxin Tian, Yingxuan Yang, Jiaxun Zhang, Pengrui Han, Qipen...

work page arXiv 2025

[3] [3]

weekly group meeting

Regular meeting schemas M(r): templates for commonly recurring events, including (i) canonical topics (e.g., “weekly group meeting”, “1:1 mentoring”, “sponsor sync”), (ii) typical cadence (weekly/biweekly/monthly), (iii) de- fault duration distributions, (iv) attendee pat- terns (direct reports, cross-team stakeholders, external partners), and (v) common ...

work page

[4] [4]

Priority principles P(r) : a small set of explicit, interpretable principles governing decisions un- der conflict, such as leadership/oversight obliga- tions, deadline sensitivity, people management duties, and external relationship maintenance

work page

[5] [5]

in-person required

Conflict reasons C(r): common causes of de- cline/postpone for that role, such as deadline clashes, hierarchical obligations, travel con- straints, task urgency spikes, teaching/commit- tee constraints, or sponsor milestone collisions. Each conflict reason c∈C(r) defines a trans- formation over event metadata (e.g., inserting a deadline marker, adding a s...

work page

[6] [6]

Role realism:Are the event topics and ca- dences plausible for this role?

work page

[7] [7]

Org-chart consistency:Do attendees reflect correct reporting lines and stakeholder relation- ships?

work page

[8] [8]

Conflict coherence:Do the competing events genuinely overlap and create a meaningful trade- off?

work page

[9] [9]

Principle alignment:Is the accepted event jus- tified by P(r) under the provided context sig- nals?

work page

[10] [10]

Experiment planning and daily priorities sync

Metadata quality:Are titles, locations, and constraints natural (no duplicates, no contradic- tions)? Edits and rejection.Annotators can (i) edit event titles/attributes, (ii) swap the accepted label if inconsistent with principles, (iii) rewrite the con- flict reason/context for coherence, or (iv) reject the datapoint if it cannot be repaired cheaply. An...

work page 2025

[11] [11]

Evaluate all conflict events considering : - The principles and reasoning provided for each event - The organizational hierarchy and relationships - The urgency and importance of each event - Historical patterns from similar past decisions - The impact on stakeholders and organizational goals - Time constraints and scheduling flexibility

work page

[12] [12]

Rank all conflict events ( including the regular event ) in order of priority

work page

[13] [13]

Select the single event that should be accepted

work page

[14] [14]

prio rity_ran king ( total { M } events )

Respone in the required format . # Inputs : # # History Conflict Calendar Events and User Decisions : { h i s t o r y _ c a l e n d a r _ e v e n t s } # # Organization Chart : { org_chart } # # Conflict Calendar Event to Solve : { c o n f l i c t _ c a l e n d a r _ e v e n t } # Output Format : Provide your response in the following structured format : ...

work page 2024