可压缩性不等于反馈：因果语义修复中的随机访问缺口
Compressibility is not Feedback: A Random-access Gap in Causal Semantic Repair
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中文总结 AI 辅助
该研究区分直接与接收端约束的语义修复问题，证明随机访问分离定理，并实验表明可压缩性仅提供修复上界，而非可部署反馈的证据。
中文摘要 AI 辅助
语义反馈可以在不重传整个消息的情况下修复有噪声的首次解码，但当前的端到端评估掩盖了反馈设计在何处失效。一个同时看到源端状态和接收端观测的预言机可能用少量比特描述有用的修正，而仅看到自身观测的接收端可能不知道应请求哪种修正。我们将这些分别表述为直接编码问题和接收端约束编码问题，将修复失真与保留的局部效用联系起来，并证明一个随机访问分离定理：一个直接比特是精确的，而接收端效用随源条件替代方案的数量而消失。一个合成阶梯验证了精确定律。在八对Reuters文本-JSCC编解码器的配对群体中，训练好的循环群体保留的直接16比特修复价值是训练好的DeepSC群体的三倍以上，尽管完全修复机会相当。然而，对所有八个循环检查点进行熵有效的接收端审计，并未针对重复、仅源端或打乱索引的对照得出明确结论。结果表明，可压缩修复是一个上界，而非可部署反馈的证据，并促使在匹配资源下同时报告两条速率曲线。
英文摘要
Semantic feedback can repair a noisy first decode without retransmitting the whole message, but current end-to-end evaluations hide where a feedback design fails. An oracle that sees both the source-side state and receiver observation may describe a useful correction with few bits, while the receiver, which sees only its own observation, may not know which correction to request. We formulate these as distinct direct and receiver-constrained coding problems, connect repair distortion to retained local utility and prove a random-access separation: one direct bit is exact although receiver utility vanishes with the number of source-conditioned alternatives. A synthetic ladder verifies the exact law. In paired populations of eight Reuters text-JSCC codecs, the trained recurrent population retains over three times more direct 16-bit repair value than the trained DeepSC population despite comparable full-repair opportunity. Nevertheless, an entropy-valid receiver audit across all eight recurrent checkpoints does not resolve against repetition, source-only or shuffled-index controls. The results show that compressible repair is an upper bound, not evidence of deployable feedback, and motivate reporting both rate curves under matched resources.
