Exploring Expert Failures in AI Systems

• Expert Failures (EEF) are systematic breakdowns in decision-making by human or AI specialists that expose hidden vulnerabilities in complex systems.
• Rigorous taxonomies and formal models delineate EEF across phases like Localization, Repair, and Iteration, enabling precise diagnostic evaluation.
• Dynamic mitigation strategies and advanced architectures harness failure trajectories to optimize multi-agent systems and enhance fault tolerance.

Expert Failures (EEF) encompass systematic errors and breakdowns in decision-making, reasoning, or performance by human or AI-based specialists. The phenomenon is crucial for modern agentic systems, automated code repair, cognitive diagnosis, and multi-agent orchestration, as expert failures often conceal or propagate underlying system vulnerabilities that simple performance aggregates cannot detect. Recent work has established rigorous taxonomies, theoretical models, and intervention strategies, demonstrating that expert failures arise from cognitive, architectural, and systemic factors and revealing actionable pathways for diagnostic evaluation and robust system redesign (Liu et al., 17 Sep 2025, Mars, 7 Jan 2026, Lan et al., 17 Apr 2025, Sorstkins et al., 18 Sep 2025, Sagar et al., 2024, Wu et al., 2024, Whitehead et al., 2022).

1. Taxonomies and Formalizations of Expert Failures

Efforts to codify expert failures derive from systematic trace analysis of expert or agentic systems. In automated issue solving, a comprehensive failure taxonomy has been proposed, partitioning the lifecycle into three phases: Localization, Repair, and Iteration & Validation. Each phase is subdivided into categories and fine-grained subcategories, formalized to distinguish distinct mechanisms leading to failure (Liu et al., 17 Sep 2025).

Three-phase taxonomy (SWE-Bench context):

Phase	Main Categories	Sample Subcategories
Localization	Issue Misleading, Superficial Info	Keywords, Referred Code
Repair	Fix Strategy, Implementation, Incomplete Repair, Issue Interference	Logic Errors, Data Errors, Ignoring Dependencies
Iteration/Validation	Reproduction/Verification, Iteration Anomalies, Context Amnesia	Run Setup Failure, Blind Strategy Switching, Non-Progressive Iteration

Formal examples:

• Localization error: The agent selects region $\hat{L}$ by maximizing similarity with description, yet $\hat{L} \neq L^*$, the true faulty region.
• Iteration anomaly: Persistent non-progressive loops or blind switching in agentic architectures (e.g. validation retreat, cognitive deadlock).

In the cognitive domain, expert suboptimality is defined as

$$\beta_i(T) = \hat{y}_i(T) - \theta(T) = b_i(T) + \varepsilon_i(T)$$

where $b_i(T)$ encodes systematic bias, and $\varepsilon_i(T)$ is random error (Whitehead et al., 2022). Anchoring, availability, representation, confirmation, and overconfidence dominate the landscape.

2. Mechanisms and Conditions Underlying Expert Failures

Recent theoretical models classify "Transitive Expert Error" (TEE) as a distinctive failure mode where calibrated experts in domain A become less accurate than novices when faced with inputs from domain B. Key mechanisms are:

• Structural similarity bias: Overweighting surface features at the expense of causal fit, causing pattern recognition systems (human or AI) to misfire at competence boundaries.
• Authority persistence: High subjective confidence is maintained by social reinforcement and absent metacognitive cues, masking uncertainty.

Expressed mathematically: $$\text{TEE}:\quad \Delta\epsilon = \epsilon_E(B) - \epsilon_N(B) > 0$$ with model-specific instantiations of surface similarity and confidence propagation vectorization (Mars, 7 Jan 2026).

Three conditions intensify TEE:

• Shared vocabulary across boundaries (false friends).
• Social pressure (crisis, immediacy).
• Delayed feedback (no opportunity for self-correction).

3. Architectures, Routing, and Failure Propagation

In multi-agent and modular expert systems, routing-induced and coverage-induced failures are exacerbated by architectural design. For sparse Mixture-of-Experts (MoE) architectures and agentic systems, gates or routers may select wrong specialists or force assignment where none is competent. This results in "hallucination" phenotypes: outputs that are coherent, structurally plausible, and confidently rendered but causally erroneous.

Routing failures are captured as: $$P_{\mathrm{route\_error}}(x) = \Pr\left( \arg\max_i g_i(x) \neq i^* \right )$$

$$P_{\mathrm{coverage\_error}}(x) = \Pr(\text{forced routing} \wedge C_\mathrm{gap}(x))$$

where $C_\mathrm{gap}(x)$ is a coverage gap event and $g_i(x)$ is gating for expert $i$ (Mars, 7 Jan 2026, Wu et al., 2024).

The Lazarus architecture demonstrates robust expert placement and fault-tolerant MoE training, employing Minimum Rank Overlap (MRO) optimization, dynamic expert replication, and flexible token dispatch, yielding up to $5.7\times$ speedup under frequent failures (Wu et al., 2024).

4. Diagnostic and Mitigation Methodologies

Diagnostic protocols for expert failures combine static benchmarking, dynamic evaluation (instrumenting reasoning/intermediate states), and cognitive failure mapping. Examples include:

• Manual trace analysis: Dual-coder review and detailed codebook for 25 failure subcategories in issue repair (Liu et al., 17 Sep 2025).
• Dynamic evaluation protocols: Four-stage loop—Golden curation, Silver mutation, LLM-based Agent Judge, Vectorized recommendation mapping—enables identification and remediation of cognitive failures (e.g., extraction drift, biased phrasing, tool misrouting) (Sorstkins et al., 18 Sep 2025).

Extraction and behavior diagnostics utilize precision/recall/F1 on extracted entities and rubric-driven scoring for stylistic/semantic alignment, respectively.

Mitigation strategies:

• Multi-expert activation with disagreement detection: Activate top-$k$ specialists, measure output divergence, and escalate upon significant disagreement (Mars, 7 Jan 2026).
• Boundary-aware calibration: Entropy regularization flattens output distributions near competence boundaries, enabling "I don't know" signaling.
• Coverage gap detection by meta-expert: Classifier distinguishes in-domain, boundary, and gap cases, facilitating abstention or fallback to generalists.

Modular interfaces for cognitive bias management add action modules (pre-mortem, forced anonymity, risk profiling, deconstruction), each interrupting workflow to reduce bias and herding (Whitehead et al., 2022).

5. Learning from Expert Failures and Repurposing Beneficial Actions

EEF leverages failed expert trajectories instead of discarding them. Beneficial segments—navigation/recovery actions, local plans—are identified, while harmful segments are strictly excluded. In agentic LLMs, this approach counteracts training simplicity bias and enhances exploration for out-of-distribution subtasks (Lan et al., 17 Apr 2025).

Algorithmic implementation:

• Simulate from $M$ intermediate failure states per trajectory.
• Mine positive rollouts from these states.
• Mask loss such that only beneficial suffix actions propagate gradient.

Quantitative results demonstrate EEF's ability to raise win rates (WebShop $62$\%, ScienceWorld $81$) over previous SOTA (RFT, GPT-4). Ablations reveal increased navigation skill acquisition and robustness to expert quality variations.

Recommendations for broader adoption include integrating incremental preference learning, efficient beneficial-action search (binary, MCTS), and optimal expert demonstration budget allocation.

6. Benchmarks, Metrics, and Impact

Diagnostic benchmarks such as SWE-Bench-Verified provide per-task human difficulty labeling and Dockerized harnesses for reproducibility. Failure benchmarks go beyond aggregate pass rates and introduce rich taxonomies, failure fingerprints, and distributional analysis across architectures (Liu et al., 17 Sep 2025, Sorstkins et al., 18 Sep 2025).

Key metrics for expert failure characterization:

• Fractional failure rates per category/subcategory.
• Precision, recall, F1 for extraction; rubric scoring for behavioral alignment.
• Entropy of action-selection (failure entropy), Wasserstein landscape distances.
• Speedup ($2.8\times$–$5.7\times$) in fault-tolerant MoE training versus checkpoint/restart systems (Wu et al., 2024).

A plausible implication is that future research should prioritize explainable, context-sensitive failure diagnostics and taxonomy-exposing benchmarks rather than solely optimizing for task-level accuracy.

7. Future Directions and Recommendations

Current efforts recommend explicit self-audit and expert oversight layers to break cognitive deadlocks in agentic systems. For complex, multi-file issues, orchestrating a portfolio of pipeline and agentic tools is effective; lightweight pipelines suffice for localized bugs, while expert-mediated routines address broader contexts (Liu et al., 17 Sep 2025). Integration of richer simulators, domain-knowledge modules, and meta-reasoning halts unproductive loops and compensates boundary weaknesses.

For cognitive evaluation protocols, embedding prescriptions in vectorized recommendation maps enables scalable, reproducible expert behavior transfer and refinement (Sorstkins et al., 18 Sep 2025).

It is suggested that cross-domain generalization of bias-management modules (modular interfaces, action packs) is feasible, though facilitator dependence and time cost remain challenges (Whitehead et al., 2022). Automated monitoring and AI-driven module selection offer promising paths to scalable expert failure mitigation across domains.

In sum, systematizing and repurposing expert failures—via rigorous taxonomies, adaptive architectures, and dynamic evaluation—constitutes a foundational advance for robust, interpretable, and collaborative AI expert systems.