TL;DR
SkillClaw enables collective skill evolution in multi-user LLM agent ecosystems through centralized trajectory aggregation. An agentic evolver performs open-ended reasoning to refine or create skills with validation ensuring monotonic improvement. Experiments show +42.
SkillClaw enables collective skill evolution in multi-user LLM agent ecosystems through centralized trajectory aggregation. An agentic evolver performs open-ended reasoning to refine or create skills with validation ensuring monotonic improvement. Experiments show +42.
32
已证实
2
证据不足
3
无法验证
N/A
可复现性
核心问题
How can LLM agent skills evolve collectively across multiple users through aggregated interaction evidence rather than remaining static?
核心方法
{'approach': 'SkillClaw aggregates interaction trajectories from multiple users into a centralized repository, groups sessions by referenced skills, and employs an agentic evolver that performs open-ended reasoning over successful and failed sessions to refine, create, or skip skills. Candidate updates are validated during nighttime in idle user environments before deployment to the shared skill pool.', 'key_components': ['Different users exercising the same skill under diverse contexts reveal both conditions where it works and where it fails.', 'A single user rarely generates enough signal to separate generalizable improvements from idiosyncratic fixes.', 'The formal goal is to update the shared skill set S based on trajectories T collected across users.', 'Aggregating evidence across users provides grounding that makes stable skill evolution possible.'], 'section_ids': ['sec_3']}
论点验证
已证实 (90%)
We propose SkillClaw, a framework for skill collective evolution in multi-user OpenClaw-style agent ecosystems.
The paper provides a complete framework description with detailed architecture (Fig 1), algorithm specification (Algorithm 1), and experimental validation on WildClawBench. The contribution is substantiated through both the methodological description
The paper provides a complete framework description with detailed architecture (Fig 1), algorithm specification (Algorithm 1), and experimental validation on WildClawBench. The contribution is substantiated through both the methodological description
已证实 (90%)
SkillClaw adopts a centralized evolution architecture, where agents deployed across different users continuously generate interaction sessions during everyday usage. These trajectories are aggregated across users and over time as evidence of real-world task execution and are processed by a centralized evolution engine to drive skill updates.
The centralized evolution architecture is clearly described with the closed-loop pipeline (Multi-user Interaction → Session Collection → Skill Evolution → Skill Synchronization) and implemented in the experimental setup with 8 concurrent users.
The centralized evolution architecture is clearly described with the closed-loop pipeline (Multi-user Interaction → Session Collection → Skill Evolution → Skill Synchronization) and implemented in the experimental setup with 8 concurrent users.
已证实 (85%)
The core of SkillClaw is an agentic evolver that updates the shared skill repository with open-ended reasoning. SkillClaw instantiate an agentic evolver, an LLM agent equipped with a structured harness that supplies the grouped session evidence, the current skill definitions, and a set of permitted evolution actions.
The agentic evolver mechanism is described in detail with its structured harness, inputs (grouped session evidence, skill definitions, permitted actions), and open-ended reasoning approach. Algorithm 1 specifies the complete procedure.
The agentic evolver mechanism is described in detail with its structured harness, inputs (grouped session evidence, skill definitions, permitted actions), and open-ended reasoning approach. Algorithm 1 specifies the complete procedure.
已证实 (90%)
Given a skill s and its associated session group G(s), the evolver examines both successful and failed executions and selects one of three actions: Refine, Create, or Skip.
The three-action space (Refine, Create, Skip) is explicitly defined with clear conditions for each action. The mechanism is described in p_24-27 and referenced in Algorithm 1.
The three-action space (Refine, Create, Skip) is explicitly defined with clear conditions for each action. The mechanism is described in p_24-27 and referenced in Algorithm 1.
已证实 (85%)
Validation is performed during the nighttime and executed in available idle user environments, ensuring that evaluation reflects real deployment conditions.
The nighttime validation mechanism is clearly described, including execution in idle user environments and the decision process for accepting/rejecting candidate updates.
The nighttime validation mechanism is clearly described, including execution in idle user environments and the decision process for accepting/rejecting candidate updates.
证据不足 (50%)
SkillClaw is designed as a general framework that is compatible with a wide range of Claw-style agent systems, including OpenClaw as well as variants such as CoPaw, IronClaw, PicoClaw, ZeroClaw, NanoClaw, and NemoClaw.
The paper claims compatibility with multiple Claw-style variants (CoPaw, IronClaw, PicoClaw, ZeroClaw, NanoClaw, NemoClaw) but only evaluates on OpenClaw with WildClawBench. No evidence is provided demonstrating actual compatibility or testing on the
The paper claims compatibility with multiple Claw-style variants (CoPaw, IronClaw, PicoClaw, ZeroClaw, NanoClaw, NemoClaw) but only evaluates on OpenClaw with WildClawBench. No evidence is provided demonstrating actual compatibility or testing on the
已证实 (95%)
The experiment runs for 6 days (6 rounds), where each day consists of two phases: a daytime online interaction phase and a nighttime skill evolution and validation phase.
The 6-day experimental design with day-night phases is explicitly stated and implemented. This is a clear design choice with specific parameters.
The 6-day experimental design with day-night phases is explicitly stated and implemented. This is a clear design choice with specific parameters.
已证实 (90%)
Our setup involves 8 concurrent users, each interacting with the system under WildClawBench tasks based on their individual goals and task requirements. All execution, skill evolution, and validation processes are powered by Qwen3-Max.
The experimental setup with 8 concurrent users and Qwen3-Max backbone is clearly specified in the methodology section.
The experimental setup with 8 concurrent users and Qwen3-Max backbone is clearly specified in the methodology section.
已证实 (90%)
We evaluate SkillClaw on WildClawBench, a real-world agent benchmark consisting of 60 complex tasks across six capability domains.
WildClawBench benchmark with 60 tasks across six domains is described with reference to Table 1 showing the benchmark composition.
WildClawBench benchmark with 60 tasks across six domains is described with reference to Table 1 showing the benchmark composition.
已证实 (85%)
SkillClaw records the full causal chain: the user prompt, the agent's actions (including tool calls and explicit user responses), and the final agent response.
The full causal chain recording mechanism is described, including user prompts, agent actions, tool calls, and responses. This is a core design choice that enables the evolution process.
The full causal chain recording mechanism is described, including user prompts, agent actions, tool calls, and responses. This is a core design choice that enables the evolution process.
已证实 (85%)
Once sessions are structured, they are grouped by the skills they reference to enable cross-user reasoning.
The session grouping mechanism by referenced skills is described with formal notation G(s) and explanation of how this enables cross-user reasoning.
The session grouping mechanism by referenced skills is described with formal notation G(s) and explanation of how this enables cross-user reasoning.
已证实 (75%)
The harness provides structured inputs but does not constrain the evolver's reasoning. This separation between a fixed harness and open-ended reasoning allows SkillClaw to handle diverse failure modes without hand-crafted rules for each type.
The separation between fixed harness and open-ended reasoning is described as a design principle. The effectiveness is demonstrated through successful handling of diverse failure modes in experiments, though no ablation specifically tests this design
The separation between fixed harness and open-ended reasoning is described as a design principle. The effectiveness is demonstrated through successful handling of diverse failure modes in experiments, though no ablation specifically tests this design
已证实 (90%)
The validator follows a simple decision rule. If a candidate skill outperforms the currently deployed best skill on the corresponding validation tasks, it is marked as Accept; otherwise, it is marked as Reject.
The validation decision rule is explicitly stated: Accept if candidate outperforms current best, otherwise Reject.
The validation decision rule is explicitly stated: Accept if candidate outperforms current best, otherwise Reject.
已证实 (90%)
Social Interaction improves earliest and most sharply. Performance increases from 54.01% to 60.34% on Day 2 and remains stable thereafter.
Specific quantitative results are provided: Social Interaction improves from 54.01% to 60.34% on Day 2, with reference to Table 3.
Specific quantitative results are provided: Social Interaction improves from 54.01% to 60.34% on Day 2, with reference to Table 3.
已证实 (90%)
Search & Retrieval follows a more staged improvement trajectory, increasing from 22.73% to 30.00%, and then further to 34.55%.
Specific quantitative trajectory provided: 22.73% → 30.00% → 34.55%, with reference to Table 3.
Specific quantitative trajectory provided: 22.73% → 30.00% → 34.55%, with reference to Table 3.
已证实 (90%)
Creative Synthesis shows a large early jump from 11.57% to 21.80% on Day 2 and then plateaus.
Specific quantitative results: Creative Synthesis jumps from 11.57% to 21.80% on Day 2, with reference to Table 3.
Specific quantitative results: Creative Synthesis jumps from 11.57% to 21.80% on Day 2, with reference to Table 3.
已证实 (90%)
Safety & Alignment improves later, from 24.00% to 32.00%.
Specific quantitative results: Safety & Alignment improves from 24.00% to 32.00%, with reference to Table 3.
Specific quantitative results: Safety & Alignment improves from 24.00% to 32.00%, with reference to Table 3.
已证实 (85%)
We observe a consistent improvement after a single round of evolution, with an average gain of +42.1%.
The +42.1% average gain is reported with reference to Table 8 for controlled validation queries. Specific numbers support this claim.
The +42.1% average gain is reported with reference to Table 8 for controlled validation queries. Specific numbers support this claim.
已证实 (90%)
In particular, save report improves from 28.3% to 100.0%, where the initial failure is caused by missing environment-specific procedures.
Specific quantitative result: save report improves from 28.3% to 100.0%, with mechanistic explanation of the failure cause.
Specific quantitative result: save report improves from 28.3% to 100.0%, with mechanistic explanation of the failure cause.
已证实 (90%)
Basic extraction shows a large gain (+47.8%), indicating that recurring execution patterns can be effectively captured through evolution.
Specific quantitative result: basic extraction shows +47.8% gain, with reference to Table 8.
Specific quantitative result: basic extraction shows +47.8% gain, with reference to Table 8.
... 共 37 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- No code available - implementation details of SkillClaw framework, OpenClaw agent, and Agentic Evolver are not accessible
- No data available - WildClawBench benchmark and initial skill sets are not provided
- Missing hyperparameters - no learning rate, temperature settings, prompts, or other LLM configuration details
- Missing algorithmic details - how candidate skill updates are generated, how skills are represented/formatted, skill triggering mechanism not specified
- Missing evaluation metrics - 'outperforms' is not quantified, no specific success rate definitions or performance measurement criteria
- Missing validation task details - how validation tasks are selected, number of validation tasks, validation criteria not specified
- Missing baseline details - initial skill set for Day 1 baseline not described
- Missing random seeds - no reproducibility seeds mentioned for experiments
- Missing hardware/environment specifications - no computational infrastructure details provided
- Missing statistical details - no confidence intervals, number of experimental runs, or significance tests reported
局限性（作者自述）
- This study represents a small-scale test of collective skill evolution, with limited user queries, feedback signals, and interaction depth.
- Results are reported on four representative categories, with additional categories to be included in the future version.
- More complex multimodal skills continue to emerge and pass validation, but within the 6-day window, they do not surpass the early-established best skill pool.
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分析时间：2026-04-10T13:08:36+00:00 · 数据来源：Paper Collector