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arxiv: 2604.18356 · v1 · submitted 2026-04-20 · 💻 cs.CL

Recognition: unknown

ComPASS: Towards Personalized Agentic Social Support via Tool-Augmented Companionship

Zhaopei Huang , Yanfeng Jia , Jiayi Zhao , Xinjie Zhang , Wenxuan Wang , Qin Jin
Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:52 UTC · model grok-4.3

classification 💻 cs.CL
keywords social supportLLM agentstool augmentationempathetic dialoguepersonalized agentsbenchmarkfine-tuningcompanionship
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The pith

Equipping LLM agents with specialized tools for social support produces higher-quality responses than generating empathetic conversation directly.

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

This paper seeks to establish that agents can offer more effective and personalized social support by using external tools to carry out actions drawn from psychological principles of companionship, rather than relying solely on text-based empathy. Current systems fall short because their responses lack variety and fail to address the full spectrum of user needs in different situations. The authors introduce twelve tools that mimic common applications and build a benchmark to test agents on realistic scenarios. After training a smaller model on examples of tool use, they show it improves markedly and performs on par with much larger models. This matters because it points toward agents that can actually help users in concrete ways instead of only listening.

Core claim

Grounded in the concept of social support, the work shows that tool-augmented agents deliver better overall performance in providing substantive companionship compared to direct empathetic dialogue generation. The ComPASS-Bench, created via automated synthesis and refinement, serves as the first personalized social support benchmark for LLM agents. Evaluations indicate high success in generating tool calls but room for improvement in response quality, with the fine-tuned ComPASS-Qwen model achieving substantial gains over its base and matching several large-scale models.

What carries the argument

A collection of twelve user-centric tools that simulate multimedia applications to cover diverse social support behaviors, together with the ComPASS-Bench benchmark used to synthesize training data and evaluate agent performance.

If this is right

  • Tool-augmented responses achieve better overall performance than directly producing conversational empathy.
  • The trained ComPASS-Qwen model shows substantial improvements over its base model.
  • Fine-tuned smaller models can reach performance levels comparable to several large-scale models.
  • LLMs generate valid tool-calling requests with high success rates, though gaps remain in final response quality.

Where Pith is reading between the lines

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

  • Deploying these agents in actual user applications could reveal additional tool needs or integration challenges not captured in the benchmark.
  • The paradigm might apply to other interactive domains where actionable steps enhance user satisfaction beyond verbal responses.
  • Future work could explore dynamic tool creation based on ongoing user interactions to increase adaptability.
  • Human evaluations in uncontrolled settings would provide stronger evidence for real-world effectiveness than benchmark scores alone.

Load-bearing premise

The dozen tools and the multi-step synthesized benchmark sufficiently represent the variety of real-world user needs and appropriate social support behaviors.

What would settle it

Conducting a controlled experiment with actual users rating the helpfulness of tool-augmented agent responses versus direct empathetic ones in live conversations.

Figures

Figures reproduced from arXiv: 2604.18356 by Jiayi Zhao, Qin Jin, Wenxuan Wang, Xinjie Zhang, Yanfeng Jia, Zhaopei Huang.

Figure 1
Figure 1. Figure 1: Illustration of our personalized social support task. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the construction pipeline for ComPASS-Bench. We design a multi-step LLM-based synthesis process to [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Statistics of our test set. (a) illustrates the age dis [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: A comparison between a tool-augmented response from ComPASS-Qwen and an empathetic response from GPT-5.1. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of synthesized users’ educational back [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Frequency distribution of the 31 fine-grained emo [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Nested pie chart showing the mapping between the [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Tool-wise Performance Differences [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
read the original abstract

Developing compassionate interactive systems requires agents to not only understand user emotions but also provide diverse, substantive support. While recent works explore empathetic dialogue generation, they remain limited in response form and content, struggling to satisfy diverse needs across users and contexts. To address this, we explore empowering agents with external tools to execute diverse actions. Grounded in the psychological concept of "social support", this paradigm delivers substantive, human-like companionship. Specifically, we first design a dozen user-centric tools simulating various multimedia applications, which can cover different types of social support behaviors in human-agent interaction scenarios. We then construct ComPASS-Bench, the first personalized social support benchmark for LLM-based agents, via multi-step automated synthesis and manual refinement. Based on ComPASS-Bench, we further synthesize tool use records to fine-tune the Qwen3-8B model, yielding a task-specific ComPASS-Qwen. Comprehensive evaluations across two settings reveal that while the evaluated LLMs can generate valid tool-calling requests with high success rates, significant gaps remain in final response quality. Moreover, tool-augmented responses achieve better overall performance than directly producing conversational empathy. Notably, our trained ComPASS-Qwen demonstrates substantial improvements over its base model, achieving comparable performance to several large-scale models. Our code and data are available at https://github.com/hzp3517/ComPASS.

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

2 major / 2 minor

Summary. The paper introduces ComPASS, a framework for personalized agentic social support. It designs a dozen user-centric tools to cover social support behaviors, constructs ComPASS-Bench via multi-step automated synthesis and manual refinement as the first such benchmark for LLM agents, synthesizes tool-use records to fine-tune Qwen3-8B into ComPASS-Qwen, and evaluates that tool-augmented responses outperform direct conversational empathy while the fine-tuned model achieves performance comparable to larger models despite gaps in final response quality.

Significance. If the benchmark and evaluations hold, the work would provide evidence that external tools can deliver more substantive support than empathy-focused dialogue alone in agentic systems, with the public code and data release aiding reproducibility. The approach aligns with psychological concepts of social support and could inform future personalized companion agents.

major comments (2)
  1. [ComPASS-Bench construction] ComPASS-Bench construction (described after tool design in the methods): the benchmark is generated by first designing the dozen tools and then using multi-step automated synthesis plus manual refinement to create scenarios and tool-use traces. This ordering risks embedding design bias that favors tool-augmented paths by construction, so observed superiority of tool-augmented responses over direct empathy may be benchmark-specific rather than generalizable to real user needs.
  2. [Evaluation details] Evaluation details (in the comprehensive evaluations section): the manuscript reports high tool-calling success rates yet gaps in final response quality, plus improvements for ComPASS-Qwen, but provides no specifics on the exact synthesis steps, evaluation metrics for response quality, statistical significance tests, or any external validation (e.g., real user logs or held-out non-synthetic set). This limits verification of the central performance claims.
minor comments (2)
  1. [Abstract] The abstract refers to evaluations 'across two settings' without defining them; this should be clarified for readers.
  2. [Throughout] Minor notation: ensure consistent use of 'ComPASS-Qwen' vs. base model references throughout.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We address each major comment below, proposing revisions to strengthen the manuscript where appropriate while maintaining the integrity of our reported methodology and results.

read point-by-point responses

  1. Referee: [ComPASS-Bench construction] ComPASS-Bench construction (described after tool design in the methods): the benchmark is generated by first designing the dozen tools and then using multi-step automated synthesis plus manual refinement to create scenarios and tool-use traces. This ordering risks embedding design bias that favors tool-augmented paths by construction, so observed superiority of tool-augmented responses over direct empathy may be benchmark-specific rather than generalizable to real user needs.

    Authors: We acknowledge the logical concern regarding potential design bias arising from the tool-first construction order. The dozen tools were deliberately designed upfront, grounded in established psychological taxonomies of social support (e.g., emotional, informational, instrumental), to define the agent's substantive action space before generating scenarios. This sequencing ensures that benchmark cases align with the intended capabilities rather than retrofitting tools to arbitrary scenarios. The multi-step automated synthesis followed by manual refinement was intended to introduce diversity in user profiles, contexts, and needs. That said, we agree that this does not fully eliminate the risk of benchmark-specific effects. In the revision, we will add an explicit limitations subsection discussing this construction rationale, the mitigation steps taken via manual review, and the need for future real-world validation. We will also clarify that the observed superiority is presented as evidence within this controlled setting rather than a universal claim. revision: partial

  2. Referee: [Evaluation details] Evaluation details (in the comprehensive evaluations section): the manuscript reports high tool-calling success rates yet gaps in final response quality, plus improvements for ComPASS-Qwen, but provides no specifics on the exact synthesis steps, evaluation metrics for response quality, statistical significance tests, or any external validation (e.g., real user logs or held-out non-synthetic set). This limits verification of the central performance claims.

    Authors: We agree that greater transparency on evaluation procedures is necessary for reproducibility and verification. While the manuscript describes the overall synthesis pipeline and reports aggregate metrics (tool-calling success, response quality comparisons, and ComPASS-Qwen gains), it does not include the granular details requested. In the revised version, we will expand the Methods and Evaluation sections and add a supplementary appendix that specifies: (1) the exact multi-step automated synthesis procedure (including prompts, filtering criteria, and iteration counts), (2) the precise response quality metrics and their computation (e.g., rubric-based scoring dimensions and inter-annotator agreement), and (3) statistical significance testing results (e.g., paired tests across model comparisons). We will also add a dedicated limitations paragraph acknowledging the absence of real-user logs or held-out non-synthetic data in the current study and outlining planned follow-up work in those directions. These additions will directly address the verifiability concern without altering the core findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation on constructed benchmark is independent of inputs.

full rationale

The paper constructs tools and ComPASS-Bench via synthesis, fine-tunes a model on synthesized traces, and reports measured performance gaps between tool-augmented and direct responses. These are empirical outcomes on held-out evaluations rather than quantities derived by definition or by renaming fitted parameters. No self-definitional equations, load-bearing self-citations, or uniqueness theorems appear in the abstract or described chain; the central claim remains a measured comparison under standard LLM practices and does not reduce to its own construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions in LLM agent research rather than heavy new postulates. No free parameters are explicitly fitted in the abstract description. The main domain assumption is that tool simulation can stand in for real social support actions.

axioms (2)
  • domain assumption LLM agents can reliably invoke external tools to execute actions that map to psychological social support categories
    Invoked when designing the dozen user-centric tools and when claiming tool-augmented responses are superior.
  • domain assumption Multi-step automated synthesis plus manual refinement produces a valid benchmark for personalized support
    Used to construct ComPASS-Bench and to generate training records for fine-tuning.

pith-pipeline@v0.9.0 · 5555 in / 1415 out tokens · 30020 ms · 2026-05-10T04:52:03.732394+00:00 · methodology

discussion (0)

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Reference graph

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    (b) Overall average subjective score for each tool, obtained by averaging the model-wise mean scores shown in (a)

    “N/A” indicates that the corresponding model does not use that tool. (b) Overall average subjective score for each tool, obtained by averaging the model-wise mean scores shown in (a). Figure 8: Tool-wise Performance Differences