提出可保留冲突的多智能体记忆系统,让矛盾可见可纠。
StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
- 基于OpSet/CRDT设计不可变历史与显式冲突对象
- 在282题测试中保持矛盾可见,而传统方法掩盖冲突
- 适合需要安全拒绝与可审计修正的场景
智能体系统在分支、重试和副本间积累冲突观测,但多数内存层仍通过难以检查或修正的覆盖规则合并数据。我们提出StateFuse,一种基于标准OpSet/CRDT合并的冲突感知复制内存协议。它不引入新连接代数,而是定义面向智能体的语义层:包含不可变历史、显式冲突对象、精确且语义化的修正处理机制(claim_id / claim_ref)、确定性谓词合约,以及投影时解析且不可重写复制状态的机制。在匹配解析器与验证策略下,对比平坦多值、原始日志、溯源风格及折叠基线,在282题官方含冲突的MemoryAgentBench子集上,各方法答案准确率相当,但保留冲突的表面能持续显示矛盾,而折叠表面则隐藏矛盾。在统一验证的受控智能体循环中,保留模糊性支持更安全的拒绝与修正,优于早期折叠。修正句柄消融实验进一步表明,当精确先验标识符不可用时,语义化处理机制至关重要。结论为:StateFuse最适合作为公开记忆契约,用于揭示矛盾、安全拒答与可审计修正,而非普遍提升准确率。
原文摘要 · Abstract (English)
Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join algebra; it defines an agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.
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