Interference Aging: Retrieval & Decoding Challenges

• Interference aging is the accumulation of confusable items that leads to increased retrieval or decoding errors in long-lived systems despite intact underlying data.
• In AI agents, interference aging is quantified using interference resistance metrics and counterfactual probe methodologies to diagnose retrieval ambiguity.
• In wireless communications, interference aging manifests as channel decorrelation or adversarial attacks that increase inter-user interference and degrade system performance.

Interference aging refers to the time-dependent escalation of retrieval or decoding errors in long-lived systems—artificial agents or wireless communication systems—caused by the proliferation of confusable or interfering items in storage or channel states. The phenomenon is distinct from loss of information at write time, explicit revision, or sudden maintenance-induced regressions; instead, it emerges as the retrieval or decoding process becomes increasingly confounded by an accumulation of similar alternatives or time-evolving mismatches, even when the underlying facts or channels remain intact. This concept has been independently formalized in cognitive agent systems, where it describes memory retrieval degradation, and in wireless communications, where it manifests as active channel aging due to adversarial or environmental effects.

1. Conceptual Taxonomy and Contexts

In artificial agent systems, interference aging arises when an agent’s memory store accumulates an increasing number of similar entries (“interference pairs”), so that retrieval accuracy degrades over system lifetime despite no loss or corruption of the original facts (Zhu et al., 25 May 2026). In wireless networks, the related concept of “active channel aging” (often also called interference aging) refers to the loss of channel orthogonality or CSI validity due to either adversarial actions (e.g., illegal intelligent reflecting surfaces randomly reconfiguring reflection phases) or natural mobility-induced fading, resulting in increased inter-user interference (Huang et al., 2023, Huang et al., 2023, Chattopadhyay et al., 2023, Qian et al., 2024).

Interference aging stands fundamentally apart from three other memory/channels aging mechanisms:

• Compression aging: Information loss/overwrite at write time (e.g., fact summarization omits key detail, or channel estimate is undersampled).
• Revision aging: Missed propagation of legitimate fact or rule changes (e.g., failure to update upon retraction).
• Maintenance aging: Abrupt regressions after lifecycle events such as memory/prompt flush or system compaction.

The sole driver of interference aging is the accumulation of similar, confusable entries or the progressive decorrelation of stored and current channel states, leading to retrieval or decoding ambiguity (Zhu et al., 25 May 2026).

2. Formalization and Measurement in Agent Systems

In the agent context, interference aging is rigorously defined using a temporal dependency directed acyclic graph (DAG) $G = (\mathcal{F}, \mathcal{E}, \mathcal{I})$, where nodes are facts, edges reflect version/dependency structure, and $\mathcal{I}$ denotes the set of interference pairs—pairs of facts differing only by crucial distinguishing features. The key measurement is the interference resistance metric at session $t$:

$$\mathrm{interference\_resistance}(t) = \frac{1}{|P_{t}|}\sum_{p\in P_{t}} \mathbf{1}[\text{retrieved}(p)=\text{gold}(p)]$$

where $P_t$ are interference probes at session $t$ and a probe is scored correct only if it uniquely retrieves the correct fact among its confusable siblings (Zhu et al., 25 May 2026). As session count and memory store size grow, this metric typically decays from near 1 to near 0, independently of the integrity or staleness of the items themselves.

Error attribution is achieved via a triple-probe methodology:

• P1 (baseline): Full agent harness; measures overall performance.
• P2 (oracle retrieval): Replaces retrieval with a gold-fact oracle; exposes write-time omissions.
• P3 (oracle context): Injects factual gold into context, measuring pure utilization/reasoning failure.

Interference aging is diagnosed specifically by the growth of the read/retrieval error share $\mathrm{Acc}_{P2} - \mathrm{Acc}_{P1}$, as P2 recovers accuracy lost to retrieval ambiguity, while write and utilization error shares remain stable (cf. Table below).

Probe	What is measured	Role in diagnosing interference aging
P1	Baseline (W+R+U)	Aggregate observed error
P2	Oracle retrieval	Error due to W+U only (removes retrieval bias)
P3	Oracle context	Error due solely to utilization (U)

A large gap between P1 and P2 is the signature of interference aging.

3. Mechanisms in Wireless Communications

In multi-user MIMO and RIS-assisted systems, interference aging principally denotes the rise of inter-user interference as a consequence of channel aging or adversarial “active channel aging.” In legitimate aging, user mobility causes time-dependent correlation decay between estimated and true channels (modeled via Bessel functions $\rho_k[n]=J_0(2\pi f_{D,k} T_s n)$ with Doppler shift $f_{D,k}$), producing both reduced desired-signal power ($\propto \rho^2$) and increased interference/uncertainty ($\mathcal{I}$0) (Chattopadhyay et al., 2023, Qian et al., 2024).

In the adversarial setting, a fully passive jammer—typically an illegal IRS—deliberately changes its reflection phases between pilot (estimation) and data (transmission) phases, causing the base station to apply out-of-date beamforming and breaking user-channel orthogonality. This results in persistent, power- and bit-depth-independent degradation of system SINR and sum rate (Huang et al., 2023, Huang et al., 2023). Critically, such attacks cannot be mitigated by increasing transmit power, using frequency hopping, or increasing phase quantization of IRS elements.

4. Experimental Highlights and System Impact

Empirical studies across agent systems demonstrate that interference aging emerges whenever scenarios inject confusable facts, as evidenced by sharp monotonic decay of interference resistance without corresponding compression or revision instability (Zhu et al., 25 May 2026). Model-specific results illustrate wide performance variation under identical interference pressure (e.g., Llama-3.1-8B falls to 0.20, GPT-4o to 0.10, and Qwen-8B with enhanced compression prompt achieves 0.46).

In MU-MISO and cell-free mMIMO systems, both simulation and theory show that interference power due to active channel aging increases with the number of IRS elements or the Doppler velocity, and is insensitive to IRS phase quantization depth—performance saturates regardless of increased AP power or sophisticated phase control, and sum rates can decrease to near zero for large-scale deployments (Huang et al., 2023, Huang et al., 2023, Qian et al., 2024).

Spectral efficiency loss under channel aging in mmWave systems can exceed 2–3 bps/Hz under fast fading unless countermeasures such as LSFD are deployed (Chattopadhyay et al., 2023). In RIS-assisted architectures, a two-phase channel estimation and optimized pilot assignment provide up to 10–15% SE gain relative to baseline estimators in the presence of channel aging and EMI (Qian et al., 2024).

5. Diagnostic Approaches and Attribution Frameworks

In cognitive agent systems, a stage-targeted diagnostic methodology is pivotal: use of counterfactual paired probes (P1/P2/P3) in conjunction with temporal DAGs provides a basis for mechanism-level error attribution and enables algorithmic targeting of repairs (Zhu et al., 25 May 2026). For wireless systems, analogues exist in the separation of desired, beamforming-uncertainty, and aging-induced interference terms in spectral efficiency formulas; these terms are analytically tractable and enable rigorous performance attribution and mitigation strategy optimization (Chattopadhyay et al., 2023, Qian et al., 2024).

For both domains, the unifying principle is that interference aging is uniquely localized to retrieval or decoding, enabling separable diagnosis from write, update, or utilization-induced degradations.

6. Mitigation Methodologies and System Remedies

Mitigation targeted at the retrieval/decoding layer is most effective:

• Agent systems:
  • Increase retrieval-top-k or candidate pool size within vector-indexed memories.
  • Use more discriminative retrieval algorithms (contrastive reranking, fine-grained quantized search).
  • Enforce explicit memory rereads in the planning or query pipeline (e.g., forced double tool invocation raises recall from 0.68 to 0.91 in Opus-4.7 (Zhu et al., 25 May 2026)).
  • Deduplicate or cluster similar entries at write time, focusing on interference pairs.
• Wireless systems:
  • Deploy two-layer LSFD in cell-free mMIMO to optimally exploit large-scale statistics and suppress aging-induced IUI, yielding up to 5 bps/Hz gain for slowly moving UEs even with hardware impairment floors (Chattopadhyay et al., 2023).
  • In RIS-assisted systems, employ two-phase channel estimation and large-scale-fading pilot assignment for robust pilot decontamination and improved estimation MSE (Qian et al., 2024).
  • No practical remedy exists against adversarial active channel aging (e.g., from disco IRS jams) aside from rigorous environment monitoring, as classical anti-jamming techniques are ineffective and attack impact scales with IRS size.

7. Comparative Summary Across Domains

While the agent and wireless communication manifestations of interference aging are domain-specific, both exhibit the same core: retrieval or decoding error rates rise due to increasingly crowded or decorrelated state, independent of information loss at write, revision, or catastrophic system events. Both expose fundamental limitations in static evaluation and motivate the need for lifespan-aware benchmarking, counterfactual diagnostics, and pipeline-stage targeted repair (Zhu et al., 25 May 2026, Huang et al., 2023, Huang et al., 2023, Chattopadhyay et al., 2023, Qian et al., 2024).

A synthesized comparative view is shown below.

Domain	Source of Aging	Key Metric / Signature	Primary Remediation
AI agents	Memory crowding	Decay of interference_resistance	Improved retrieval or explicit reread
Wireless systems	Channel decorrelation/adversarial IRS	Growth of IUI, SE drop	LSFD, 2-phase estimation, none (if adversarial IRS)

Each domain demonstrates that interference aging is fundamentally a system-lifetime property, not detectable in day-one or static scenarios. Understanding and countering it requires mechanism-specific metrics, longitudinal evaluation, and intervention at the retrieval/decoding interface.