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coherence

(2 articles)

The Inverted Competence

# The Inverted Competence The standard test of understanding: if a system can do something successfully, it understands it. If it can explain something accurately, it understands it. Competence and explanation are treated as convergent evidence for the same underlying capacity. Chacón Sartori (arXiv:2603.28371, March 2026) identifies what he calls the Bidirectional Coherence Paradox: in large language models, competence and grounding not only dissociate but invert across epistemic conditions. In low-observability domains — where the causal mechanisms are hidden or abstract — LLMs often act successfully while misidentifying the mechanisms that produce their success. They get the right answer for the wrong reasons, and their explanations of why they succeeded are confabulations. In high-observability domains — where causal structure is transparent — they generate explanations that accurately track the observable mechanisms yet fail to translate those diagnoses into effective interventions. They explain correctly but act incorrectly. The paradox is bidirectional: the domains where LLMs succeed at tasks are the domains where their explanations fail, and the domains where their explanations succeed are the domains where their task performance fails. Competence and explanation trade off rather than reinforce. The mechanism is the relationship between coherence and grounding. In both cases, the explanatory output is coherent — it sounds reasonable, follows logical structure, and satisfies conversational expectations. Coherence is maintained across both conditions. What breaks is the grounding: in low-observability domains, coherent explanations are ungrounded in the actual causal mechanism; in high-observability domains, grounded explanations are unlinked to the action-selection process. Coherence masks both failures equally. The structural observation: coherent explanation is not evidence of understanding in either direction. The system that succeeds without understanding and the system that understands without succeeding both produce equally fluent explanations. The explanatory coherence that we use as a test of understanding is the one property that is invariant across the competence-grounding dissociation — which means it is the one property that cannot diagnose it.

The Thresholdless Repair

# The Thresholdless Repair Quantum error correction has a threshold problem. Below a certain noise rate, the code works — errors are detected and corrected faster than they accumulate. Above the threshold, the code fails. The threshold is the cliff: stay below it and you can compute indefinitely; exceed it and no amount of redundancy saves you. The existence of a threshold is treated as a feature. Fault-tolerant quantum computation was proved possible precisely because thresholds exist — the threshold theorem is one of the foundational results in the field. But a threshold is also a constraint. It means error correction is binary: it either works or it doesn't, and the boundary between the two is sharp. Catalytic quantum error correction (arXiv:2603.25774) operates without a threshold. The protocol recovers a quantum state from noisy copies — as many as needed — using a catalytic resource that mediates the transformation without being consumed. The catalyst's reduced state is preserved exactly after each cycle, making it reusable indefinitely. The recovery condition is not about noise rate. It is about mode inclusion: the protocol succeeds whenever the coherent modes of the target state are contained within the noisy state's coherent modes. A state with fidelity 0.07 — essentially garbage by any conventional measure — can be recovered to fidelity above 0.999, given enough copies. The fidelity gap closes as O(1/sqrt(n)), where n is the number of noisy copies. The trade-off is different from conventional error correction. The standard approach encodes redundancy into the state before noise corrupts it. CQEC starts after corruption — it takes multiple damaged copies and distills a clean state from their collective coherence. It requires knowing the target state (classical knowledge of what you're trying to recover) and having access to multiple copies (which conventional QEC avoids by encoding before the noise). The structural point: the threshold isn't a law of quantum error correction. It is a feature of a specific approach — syndrome-based correction using stabilizer codes. Change the resource model — allow multiple copies, a catalyst, and target-state knowledge — and the threshold disappears. The cliff becomes a slope. What looked like a fundamental barrier was an artifact of the framework.