Coalition-Aware Skill Reliability for Self-Evolving Agents

Qiyan Zhao; Xiaofeng Zhang; Bo Liu; Minda Chen; Wei Xiong; Jingyang Chen; Guanting Ye; Wenhao Yu

cs.AIAug 23, 2026

Coalition-Aware Skill Reliability for Self-Evolving Agents

Authors: Qiyan Zhao, Xiaofeng Zhang, Bo Liu, Minda Chen, Wei Xiong, Jingyang Chen, Guanting Ye, Wenhao Yu,

Organizations: CASIA · LongShine AI Lab · SJTU · USTC ·

Abstract

Agent skills, structured artifacts distilled from interaction trajectories and dynamically reused from skill banks, have become a central mechanism for enabling large language model (LLM)-based self-evolving agents to learn from past experience. Yet existing work has largely focused on the operational aspects of skills, such as acquisition, evolution, and retrieval, while leaving a more fundamental reliability question unresolved: Do accumulated skills in an agent's skill bank actually make positive mechanistic contributions? We investigate this question through systematic skill-bank audits across alternative bank compositions and deployment domains, measuring the resulting changes in agent behavior. These audits reveal two recurring reliability failures: coalition pollution, where bank-level gains conceal negative coalition-level skill contributions, and cross-domain utility reversal, where source-beneficial skills reverse their effects after transfer. These findings motivate two reliability interventions: coalition-aware skill selection during skill accumulation and label-free skill masking after transfer. Coalition-Aware Skill Selection (CASS) selects more reliable candidate skills for the current bank using sampled Shapley marginals. Unsupervised Skill-Masked Coalition Optimizer (u-SMCO) masks transferred skills whose exclusion improves retrieval quality on unlabeled target-domain data. Agentic experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld show that CASS and u-SMCO consistently improve task performance and cross-domain generalization over strong skill-based self-evolving agent baselines. Beyond accuracy, coalition-conditioned reliability modeling reduces sensitivity to noisy outcome-reward fluctuations during reinforcement learning and exposes the limits of isolation-based skill evaluation.

Figures & tables

Figure 1: Motivation. (a) Existing self-evolving agents overlook coalition pollution during skill evolution, admitting unreliable skills with negative coalition-level contributions. (b) During cross-domain transfer, prior methods ignore cross-domain utility reversal, allowing skills that become unreliable in the target domain to influence subsequent decisions. These two mechanistic failure modes motivate our work.

Figure 2: Skill reliability is not intrinsic. We audit the same skills across bank compositions and deployment domains (skills relabeled Skill_XX ). (a) Acceptance is not contribution: the gate admits Skill_AP (purple star), yet it contributes ${\approx}0$ while the incumbent Skill_IN carries the bank. (b) Contribution depends on partners: the interaction matrix reveals synergies and antagonisms, strongest between Skill_IN and Skill_CR (boxed). (c) Same bank, different verdict: Skill_IN helps at the source (LoCoMo) and reverses at the target (HotpotQA-50).

Figure 3: Reliability failures revealed by SMRA. (a) Decisions at the noise floor: accept/reject margins are commensurate with the

\pm 1

SE error bars; the shaded band marks the

\pm 0.04

single-skill audit resolution, an order of magnitude wider. (b) Partner removed, marginal drops: insert falls from

+0.084

+0.018

once capture_contextual_relationships is removed. (c) Source benefit, target reversal: insert helps on the source (LoCoMo), reverses on the target (HP-50).

Skill	$\Delta_{s}(\mathcal{B}_{3})$	$\Delta_{s}(\mathcal{B}_{4})$	Full name
Skill_IN	$+0.061^{*}$	$+0.084^{*}$	insert
Skill_DE	$+0.035$	$-0.011$	delete
Skill_UP	$+0.013$	$+0.019$	update
Skill_NP	$-0.002$	$+0.011$	noop
Skill_TD	$-0.010$	$-0.002$	capture_temporal_details
Skill_CR	$+0.014$	$+0.029$	capture_contextual_relationships

Table 1: Per-skill SMRA contributions before (

\mathcal{B}_{3}

) and after (

\mathcal{B}_{4}

) the gate’s acceptance (LoCoMo). ∗ :

95\%

CI excludes zero; † : newly accepted.

Figure 4: One CASS gate decision. Each sampled coalition is knocked out and scored against the full bank on one fixed probe, so every margin is a paired difference from

V(\mathcal{B})

; green marks removal that costs value, red that gains it. Algorithm A1 gives the full procedure.

	Conversational Benchmarks						Embodied Interactive Tasks
	LoCoMo		LongMemEval		Avg.		ALF-Seen		ALF-Unseen		Avg.
Method	F1	L-J	F1	L-J	F1	L-J	SR	#Stps $\downarrow$	SR	#Stps $\downarrow$	SR
CoN	$30.86$	$41.72$	$30.78$	$56.44$	$30.82$	$49.08$	$75.00$	$19.15$	$80.60$	$17.38$	$77.80$
ReadAgent	$28.63$	$38.25$	$24.48$	$42.62$	$26.56$	$40.44$	$62.86$	$26.14$	$71.64$	$22.88$	$67.25$
MemoryBank	$36.80$	$44.43$	$30.56$	$41.96$	$33.68$	$43.20$	$60.71$	$28.23$	$66.42$	$24.64$	$63.57$
A-MEM	$39.39$	$49.71$	$25.83$	$38.04$	$32.61$	$43.88$	$62.86$	$27.53$	$70.15$	$23.79$	$66.51$

Table 2: Main results (%). L-J is the LLM-as-Judge score; #Stps is the mean number of environment interactions (lower is better). † our reproduction from MemSkill ’s released project.

Panel of Figure 5 — Figure 5: Gate decisions at the four outer epochs, and the resulting bank size. CASS accepts $1/12$ , MemSkill $6/12$ .

Benchmark	MemSkill $e_{12}$	MemSkill final	$\Delta$
LoCoMo	0.5653	0.5303	$-3.50$
LME	0.5000	0.5248	$+2.48$
HP-50	0.3008	0.2695	$-3.13$
Avg.	0.4554	0.4415	$-1.38$

Table 3:

K

-controlled ablation of the gate’s additions.

Panel of Figure 6 — Figure 6: Case study. A coalition-polluting skill accepted by the baseline gate, a coalition-aware skill accepted by CASS , and a utility-reversing skill masked by u -SMCO .

Bank	$K$	Masked skills	$\Delta$ HP-50 (pp)
CASS s1	10 $\to$ 9	AP	$2.13$
CASS s2	11 $\to$ 9	IN , UP	$4.09$
CASS s3	10 $\to$ 8	ES , IN	$4.55$
MemSkill s1	13 $\to$ 9	ES , IN , TD , AP	$5.01$
MemSkill s2	12 $\to$ 10	IN , ES	$8.58$
MemSkill s3	11 $\to$ 9	TD , IN	$7.92$

Table 4: u -SMCO on the six trained banks with an unlabeled 20-query target probe;

\Delta

HP-50 is the LLM-Judge change from masking. Skill codes follow Table 1 .

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Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge. However, existing methods often consolidate experience based on semantic similarity or LLM judgments, which may merge superficially related but behaviorally incompatible strategies and thereby degrade performance. To address the issue, we propose SkillCommit, an online skill evolution framework that continuously transforms experience into a hierarchical library of reusable skills. Rather than generalizing from individual experiences immediately, SkillCommit first preserves each experience as an instance-specific patch, retaining its locally validated behavior. As patches accumulate, SkillCommit abstracts those sharing a common behavioral mechanism into higher-level skills. Specifically, for each incoming skill, embedding-based retrieval first identifies candidate related skills. Cross-instance replay and an LLM-based mechanism check determine whether these skills transfer across cases and share a common underlying mechanism. Candidates that pass both checks are abstracted into a higher-level skill and committed only if it preserves the validated behavior of all constituent skills. Experiments on RuleArena, OpenExempt and KOR-Bench demonstrate that SkillCommit consistently improves agent performance across diverse domains. Moreover, the learned skills transfer across model scales and families, enabling cross-model experience transfer.

Abstract

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