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Enabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context

Feng Liang, Yupeng Li, Runhao Zeng, Francis C. M. Lau, Xiping Hu

arXiv:2609.32339v1cs.AIcs.MA

TL;DR

Skill guidance may arrive too late when agents see only metadata before deciding whether to retrieve a full skill. TipsWarm keeps a budgeted pool of skill-derived tips available through fast per-turn screening, and improves task success and success-time trade-offs over skill and general-memory baselines.

  • Problem

    Agents may waste effort on ineffective approaches because they initially see skill metadata and retrieve full skill content only when they decide it is necessary.

    Source: §1 · p. 1

  • Method

    TipsWarm maintains a budgeted pool of skill-derived keypoints, assesses them at skill events and task completion, and screens tips quickly for injection at each user turn.

    Source: §1 · p. 2

  • Results

    Across coding and iterative task-execution benchmarks, TipsWarm improves task success and success-time trade-offs over the underlying skill mechanism and general memory baselines.

    Source: §6 · p. 9

  • Takeaways & Limitations

    Skill-derived keypoints can complement existing skill mechanisms by guiding agents before they retrieve full skills.

    Source: §1 · p. 2

Abstract

from arXiv · show

Reusable skills help LLM-based agents solve complex tasks, but the agent must receive guidance before it commits to an ineffective approach. Existing skill mechanisms often expose only metadata and load full content on demand, leaving useful guidance unavailable until the agent decides to retrieve it. General memory methods can incur substantial maintenance overhead, while keeping guidance in conversation context risks repeatedly exposing the agent to irrelevant or harmful advice. We propose TipsWarm, a mechanism that complements existing skill mechanisms by maintaining a budgeted pool of skill-derived keypoints, or \textit{warm tips}, for selective injection into the context of every message turn. By separating event-triggered LLM assessment from inexpensive per-turn screening, it makes transferable skill guidance readily available while controlling maintenance costs. In three coding and iterative task-execution benchmarks, TipsWarm achieves the highest task success rate while remaining time-efficient, compared to recent skill and general memory baselines.

1 INTRODUCTION

Useful skill guidance may arrive too late when agents see only metadata before deciding whether to retrieve full skills. TipsWarm keeps selected skill-derived keypoints available in context, and improves task success across three benchmarks.

1 INTRODUCTION

  • Prior work generates and evolves reusable skills, while separate memory systems can overlook skill guidance and add maintenance overhead.In preliminary experiments, A-MEM used twice as many LLM calls and approximately 14-25× the wall-clock time of the dynamic skill evolution baseline.Source: §1 · pp. 1–2
  • Because agents may waste effort before deciding to retrieve a relevant skill, warm tips keep helpful keypoints accessible for initial decisions while full skills remain available on demand.The paper distinguishes these readily accessible warm tips from cold skills that must be retrieved from memory.Source: §1 · pp. 1–2
  • TipsWarm maintains a budgeted pool of skill-derived keypoints and injects relevant, useful tips into each user turn.Scope matching and historical utility estimates reduce exposure to potentially misleading guidance.Source: §1 · p. 2
  • LLM assessments run at skill events and task completion, while fast per-turn screening reuses their output to control maintenance cost and harmful-tip exposure.Warm tips require careful selection because they can influence planning and actions in every turn.Source: §1 · p. 2
  • TipsWarm improves final task success across three benchmarks and helps agents with weaker LLMs approach stronger-model performance.It also augments skills learned without warm tips.Source: §1 · p. 3

2 RELATED WORK

Prior agent-memory work reuses experience through retrieved trajectories, reflections, procedures, and workflows, while skill research develops and evolves reusable guidance. TipsWarm addresses a complementary timing gap by keeping skill-derived tips in the active context before full-skill retrieval.

2 RELATED WORK

  • Experience-reuse methods commonly retrieve experience or workflows in response to a task, leaving a complementary failure mode when agents plan incorrectly before requesting or retrieving a procedure.TipsWarm therefore keeps a budgeted subset of decision-critical skill points continuously available in working context.Source: §2 · p. 3
  • Skill research synthesizes, evolves, and learns from explicit skills, whereas TipsWarm manages skill-derived tips after creation and can augment static, generated, or evolving skill libraries.Its operation is agnostic to the upstream skill generator.Source: §2 · p. 3
  • Unlike query-time retrieval of interaction memories or general memories learned from inputs and outputs, TipsWarm injects a fixed-budget working set of skill-derived techniques at every user turn.Persistent skills remain the comprehensive knowledge source; warm tips provide a readily accessible layer between long-term memory and active reasoning.Source: §2 · pp. 3–4

3 BACKGROUND AND PROBLEM DEFINITION

The paper models an agent session as turns with fixed and evolving context, then defines warm tips as selected skill guidance maintained across turns. Its objective is to select useful skill points while limiting maintenance and injection costs.

3 BACKGROUND AND PROBLEM DEFINITION

  • A turn processes a user message with available context and produces a response; context includes session-fixed information and a trajectory updated across turns.Warm tips are introduced as a turn-specific context component.Source: §3 · p. 4
  • A skill repository contains metadata and content made of guidance points, while warm tips are selected skill points injected into the current context.The warm-tip mechanism manages skill-derived guidance and can coexist with general memory.Source: §3 · p. 4
  • The study aims to help agents solve new, similar tasks more efficiently and accurately by selecting useful skill points while limiting maintenance and injection costs.Source: §3 · p. 5

4 METHOD

TipsWarm connects skill-derived guidance to agent behavior through budgeted filtering, per-turn injection, and outcome attribution. It reuses existing skills and separates infrequent LLM assessments from inexpensive heuristic screening.

4 METHOD

  • TipsWarm links budgeted skill-keypoint filtering, per-turn injection, and outcome attribution, reusing existing skills while separating infrequent LLM assessment from heuristic screening.Filtering and attribution run on skill or task events; injection uses fast rules each turn without another LLM judgment.Source: §4 · p. 5

4.1 WARM TIP FILTERING

  • Filtering extracts candidates from regex-matched skill sections, uses an LLM to assess transferability, and admits or evicts tips under pool limits.The pool is capped, with limits on tips contributed by each skill.Source: §4.1 · p. 6

4.2 UTILITY-AWARE INJECTION

  • Each turn, injection screens pool tips by scope keywords and past helpful, harmful, and irrelevant counts, then selects high-utility tips up to the injection quota.This screening reuses earlier assessments rather than asking an LLM to reassess tips every turn.Source: §4.2 · p. 6

4.3 OUTCOME-REFLECTION ATTRIBUTION

  • After a task, an LLM combines outcomes with tool-call evidence to classify injected tips as helpful, harmful, or irrelevant; other pool tips count as irrelevant.Figure 3 illustrates how warm tips are represented across operations using a SkillsBench example run.Source: §4.3 · pp. 6–7, Figure 3 · p. 7

5 EVALUATION

The evaluation uses warmup/test splits across three multi-turn benchmarks and reports accuracy and time-cost efficiency. The supplied ablation results compare backbones and warm-tip management settings.

5.1 EXPERIMENT SETTINGS

  • The study evaluates SkillsBench, AlfWorld, and AppWorld using warmup tasks for skill generation and evolution and separate test tasks for performance evaluation.The splits are 65 warmup and 23 test tasks for SkillsBench, 50 warmup and 134 test tasks for AlfWorld, and 90 warmup and 168 test tasks for AppWorld.Source: §5.1 · p. 7
  • Accuracy is measured by Success@k and AUC, while time-cost efficiency pairs task success rate with completion time averaged over successful tasks.TipsWarm uses comparable tokens but less warmup wall-clock time than general memory methods, including about 1/22 of A-MEM and 1/3 of Dynamic Cheatsheet on AppWorld.Source: §5.1 · pp. 7–8
  • The baselines include HermesSkill, which loads skills upon explicit visits, and the general-memory methods A-MEM and Dynamic Cheatsheet.The methods are integrated within the Hermes agent system.Source: §5.1 · pp. 7–8

5.3 ABLATION STUDY

  • 100% final success rate and 0.557 AUC on SkillsBench with HermesSkill-DS exceed Qwen by 17.7 pp and 10.1 pp, respectively; TipsWarm raises HermesSkill-DS AUC by another 10.2 pp to 0.659.TipsWarm-Qwen reaches 0.533 AUC, compared with 0.557 for HermesSkill-DS.Source: Figure 7 · p. 9, §5.3 · p. 9
  • Across settings, scope matching and utility attribution help agents follow earlier guidance, while a larger tip pool can raise final success without necessarily improving early performance.TipsWarm can augment an existing skill bundle without prior TipsWarm-style warmup.Source: §5.3 · p. 9

6 CONCLUSION

TipsWarm complements on-demand skill retrieval with turn-selected warm tips, separating event-triggered LLM assessment from fast per-turn screening. Its assessment prompts prioritize tips transferable to unseen tasks and assign utility based on exposed tips’ contribution to task outcomes.

6 CONCLUSION

  • TipsWarm complements on-demand skill retrieval with warm tips selected for each conversation turn, while separating event-triggered assessment from fast per-turn screening.The paper reports improved task success and success-time trade-offs over the underlying skill mechanism and general memory baselines.Source: §6 · p. 9

A PROMPTS FOR LLM-BASED ASSESSMENT

  • The admission prompt favors tips that remain meaningful without instance-specific details and help on unseen tasks in the same benchmark or environment genre.It also calls for transferable process rules and distinct failure-mode coverage while rejecting episode-specific recipes and shortcuts.Source: A PROMPTS FOR LLM-BASED ASSESSMENT · pp. 13–14
  • After task completion, the attribution prompt assesses exposed tips using outcomes such as reward, efficiency, tests passed, and duration when available.It treats tips that did not affect the episode as irrelevant and cautions against labeling injected tips helpful when the task failed.Source: A PROMPTS FOR LLM-BASED ASSESSMENT · p. 15

B.1 HYPERPARAMETERS

The section identifies the default hyperparameters for running TipsWarm.

B.1 HYPERPARAMETERS

B.2 RUNNING COST

The running-cost comparison covers token, time, and storage costs across methods and benchmarks; TipsWarm takes less running time than general memory methods.

B.2 RUNNING COST

  • TipsWarm requires substantially less running time than general memory methods, while token counts vary across methods and storage appears to be a minor practical concern.Table 3 reports training costs for warming up skills or experience across benchmarks; its token counts are per-task means ± standard deviations, and time is wall-clock duration.Source: §B.2 · p. 16, Table 3 · p. 16

C RESULTS OF TOKEN-COST EFFICIENCY

The section compares success rate within a turn budget with agent tokens consumed across benchmarks, but no method consistently uses fewer agent tokens. The examples section describes warm-tip pools and notes that experiments limit the number of tips entering context.

C RESULTS OF TOKEN-COST EFFICIENCY

  • No method consumes consistently fewer agent tokens across benchmarks in the comparison of success rate within a turn budget and tokens consumed.The reported agent-token counts exclude tokens required for methods’ internal operations.Source: C RESULTS OF TOKEN-COST EFFICIENCY · p. 17, Figure 8 · p. 17

D EXAMPLES OF WARM TIPS

  • The warm-tip examples include candidate tips and utility counters after benchmark tasks, with at most four tips entering context in the experiments.Source: D EXAMPLES OF WARM TIPS · p. 17

D.1 SKILLSBENCH TIPS

The SkillsBench snapshot shows a five-tip pool from one skill, with four tips labeled as slightly helpful and focused on host grading and artifact staging.

D.1 SKILLSBENCH TIPS

  • 4 of 5 candidate tips have small positive utility, with two helpful labels and no harmful or irrelevant labels.The fifth tip is unlabeled.Source: §D.1 · p. 17, Table 4 · p. 17
  • The five candidate tips in the SkillsBench snapshot all belong to one skill.Source: §D.1 · p. 17, Table 4 · p. 17
  • Helpful tips capture host-grading and artifact-staging requirements; without advance guidance, agents may incur substantial testing and verification overhead.Source: §D.1 · p. 17

D.2 APPWORLD TIPS

The AppWorld pool emphasizes API usage and identifier resolution, while the AlfWorld pool contains helpful household-task reminders whose relevance varies across tasks.

D.2 APPWORLD TIPS

D.3 ALFWORLD TIPS

  • All AlfWorld tips have helpful labels and none are labeled harmful, but each also has irrelevant labels, indicating task-dependent usefulness.The reminders provide guidance applicable across household tasks with little additional context.Source: §D.3 · p. 18, Table 6 · p. 18
  • Eight candidate tips from one skill in AlfWorld cover command format, action prerequisites, and avoiding repeated actions.Source: Table 6 · p. 18, §D.3 · p. 18

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