The Missed Turn in LLM Design
Table of Contents
Introduction: The Nature of Collapse
Framing Collapse as Structural Deficiency
Why Current Architectures Fail
The Triadic Semantic Regulation Framework (TSRF)
4.1 Continuity (C)
4.2 Contradiction (K)
4.3 Emergence (E)
4.4 Composite Semantic Tension Function
Recursive Collapse Detection and Metrics
5.1 Recursive Consistency Score (RCS)
5.2 Curvature Index (CI)
5.3 Identity Drift Rate (IDR)
Architectural Augmentation: Reflection-Aware Transformer Head
Recursive Use Cases: Where Collapse Strikes
Integrating TSRF with Existing Paradigms
8.1 Chain-of-Thought and Tree-of-Thought
8.2 Tool-Augmented Reasoning
8.3 ReAct-Style Agents
Implementation Considerations and Challenges
Future Directions: Toward Reflective Generative Intelligence
Philosophical Imperative: Collapse as Structure’s Shadow
Conclusion
Appendices (Suggested)
References (Partial)
Title:
The Missed Turn in LLM Design
Toward Recursive Cognition, Semantic Coherence, and Collapse-Resilient Architectures
Author:
[Redacted]
Institute for Reflective Systems, 2025
Abstract
Large language models (LLMs), powered by transformer architectures and trained on massive corpora of text, have demonstrated striking fluency across a wide range of linguistic tasks. Yet beneath the surface of generative competence lies a fragility: these models frequently degrade under recursive prompting, losing coherence, consistency, and semantic integrity. This degradation — termed collapse — is often treated as a side effect of imperfect sampling or data insufficiency. In this paper, we argue that collapse is not incidental but structural: a failure of LLMs to encode the regulatory mechanisms necessary for recursive semantic integrity. We propose a triadic architecture for semantic regulation based on continuity, contradiction, and emergence. We formalize new metrics to measure drift, oscillation, and identity loss during recursive generation, and outline architectural augmentations that would allow generative models to sustain introspective reasoning across time. Ultimately, we argue that solving collapse is not a matter of more scale, but of more structure.
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Introduction: The Nature of Collapse
Language models, as they are presently constructed, are predictive machines. A transformer decoder, trained on billions of token sequences, learns to optimize:
LCE=−∑tlogPθ(xt∣x<t)\mathcal{L}_{\text{CE}} = -\sum_t \log P_\theta(x_t | x_{<t})LCE=−t∑logPθ(xt∣x<t)
The output is fluent. It mimics speech. It answers questions and generates code. But the illusion of understanding unravels quickly when these models are recursively engaged.
Ask a language model to answer a question. Then ask it to summarize its answer. Then to evaluate the summary. Then to revise its evaluation. Repeat. Within a few iterations, the thread collapses — meaning drifts, contradictions arise, and the model either regresses into platitudes or generates self-conflicting noise. This is collapse.
Traditionally, this has been understood as a failure of training coverage, or an artifact of sampling parameters. But those interpretations miss the deeper problem. Recursive collapse is not just about tokens — it is about the lack of internal structure needed to monitor, regulate, and maintain a semantic identity across generative time.
Human cognition is recursive by design. It reflects, evaluates, remembers, and revises. It does not merely extend a sentence — it constructs a trajectory. Current LLMs do not.
This paper seeks to formalize collapse, explain its structural causes, and chart a design path toward reflective generative architectures.
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Framing Collapse as Structural Deficiency
Let us define the process of recursive prompting formally.
Given an initial prompt p₀, a model generates an output y₁:
y1=f(p0)y_1 = f(p_0)y1=f(p0)
We then re-insert that output into the next prompt:
y2=f(p1=g(y1))y_2 = f(p_1 = g(y_1))y2=f(p1=g(y1))
Where g(·) is a reformatting or framing function (e.g., “Summarize the above,” or “Explain your answer”).
This recursive sequence {y₁, y₂, ..., yₙ} may diverge from the original semantic trajectory. Collapse is the onset of this divergence.
We define recursive collapse not in terms of fluency loss, but semantic coherence breakdown. The model no longer preserves continuity of purpose, reference, or self-consistency.
Let s(t) denote the latent semantic representation at generation step t. We define:
Semantic Drift:
ΔC(t)=∥s(t)−s(t−1)∥2\Delta_C^{(t)} = \| s^{(t)} - s^{(t-1)} \|_2ΔC(t)=∥s(t)−s(t−1)∥2
Contradiction Tension:
ΔK(t)=1[s(t)⊥s(t−1)]\Delta_K^{(t)} = \mathbb{1}\left[ \text{s}^{(t)} \perp \text{s}^{(t-1)} \right]ΔK(t)=1[s(t)⊥s(t−1)]
Emergent Divergence:
ΔE(t)=KL(Pgen(t)∥Pprior(t−1))\Delta_E^{(t)} = \text{KL}(P_{\text{gen}}^{(t)} \| P_{\text{prior}}^{(t-1)})ΔE(t)=KL(Pgen(t)∥Pprior(t−1))
Collapse, in this framing, occurs when continuity fails, contradictions arise, and the novelty of the generation lacks structural grounding.
It is crucial to note: current transformers have no internal mechanism to monitor any of these quantities. Generation is conditioned solely on token context, without access to recursive semantic state.
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Why Current Architectures Fail
Transformer decoders operate with a shallow memory: a fixed-size context window, token-level attention, and no enforced continuity of belief or identity. All self-reference must be inferred indirectly from token patterns.
Even architectures that support in-context memory (e.g., retrieval-augmented models, scratchpads) do not enforce structural coherence. They store information — they do not evaluate it.
Several key limitations contribute to recursive collapse:
No continuity tracking: There is no vector of evolving semantic identity, and no mechanism to measure deviation from it.
No contradiction detection: The model does not test new statements against previously generated ones.
No emergence filtering: There is no way to distinguish meaningful novelty from stochastic drift.
This failure manifests across common tasks. In multi-turn reasoning, models contradict earlier conclusions. In dialog systems, they forget prior beliefs. In recursive chains (e.g., chain-of-thought), they wander or loop.
Sampling from the same distribution does not guarantee semantic evolution. Without structure, reflection becomes recursion without integrity — a loop, not a spiral.
The Triadic Semantic Regulation Framework (TSRF)
To address recursive collapse not as a sampling error but as a failure of internal structure, we propose a cognitive scaffold: the Triadic Semantic Regulation Framework (TSRF).
TSRF is designed to enforce self-consistency, preserve identity, and regulate novelty across recursive generation. It does so via three orthogonal semantic functions, each defined as a dynamic constraint over generative time:
4.1 Continuity (C)
This axis preserves coherence of purpose, self-reference, and identity across steps.
We define the continuity deviation as:
ΔC(t)=∥s(t)−s(t−1)∥2\Delta_C^{(t)} = \left\| s^{(t)} - s^{(t-1)} \right\|_2ΔC(t)=s(t)−s(t−1)2
This measures how far the latent meaning has drifted from the previous step. Drift is not inherently bad — but unbounded drift correlates with semantic instability.
To preserve long-term identity, we define a rolling identity anchor vector I(t):
I(t+1)=λI(t)+(1−λ)⋅s(t)I^{(t+1)} = \lambda I^{(t)} + (1 - \lambda) \cdot s^{(t)}I(t+1)=λI(t)+(1−λ)⋅s(t)
Identity loss is then computed as:
Lid(t)=∥s(t)−I(t)∥2\mathcal{L}_{\text{id}}^{(t)} = \| s^{(t)} - I^{(t)} \|_2Lid(t)=∥s(t)−I(t)∥2
A threshold ε is set, above which generation is rejected or revised.
4.2 Contradiction (K)
This axis detects internal semantic inconsistency. Contradiction can occur even with high fluency.
Define a contradiction detector D(·) such that:
D(s(t),s(t−1))={1if a contradiction is detected0otherwiseD(s^{(t)}, s^{(t-1)}) = \begin{cases} 1 & \text{if a contradiction is detected} \\ 0 & \text{otherwise} \end{cases}D(s(t),s(t−1))={10if a contradiction is detectedotherwise
Contradictions are evaluated via entailment models (e.g., NLI fine-tuned subnetworks), symbolic logic modules, or graph consistency checks.
4.3 Emergence (E)
While stability is vital, generative systems must not stagnate. The Emergence axis encourages meaningful, structured novelty — divergence that builds upon prior structure.
We quantify semantic emergence using KL divergence between the new generation’s probability distribution and the prior:
ΔE(t)=KL(Pgen(t)∥Pprior(t−1))\Delta_E^{(t)} = \text{KL}(P_{\text{gen}}^{(t)} \| P_{\text{prior}}^{(t-1)})ΔE(t)=KL(Pgen(t)∥Pprior(t−1))
This rewards divergence from redundancy, while penalizing stochastic drift.
4.4 Composite Semantic Tension Function
All three axes are combined into a unified regulation function:
Φ(s(t))=αCΔC(t)+αK⋅D(s(t),s(t−1))−αEΔE(t)\Phi(s^{(t)}) = \alpha_C \Delta_C^{(t)} + \alpha_K \cdot D(s^{(t)}, s^{(t-1)}) - \alpha_E \Delta_E^{(t)}Φ(s(t))=αCΔC(t)+αK⋅D(s(t),s(t−1))−αEΔE(t)
The model’s output at step t is accepted only if:
Φ(s(t))<δ∧Lid(t)<ϵ\Phi(s^{(t)}) < \delta \quad \land \quad \mathcal{L}_{\text{id}}^{(t)} < \epsilonΦ(s(t))<δ∧Lid(t)<ϵ
Otherwise, the system backtracks, resamples, or revises — depending on downstream objectives.
This loop enforces structural integrity across time. It prevents the model from simply hallucinating further into generative space without constraint.
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Recursive Collapse Detection and Metrics
To make collapse a measurable phenomenon, not just a qualitative failure, we propose three formal metrics for structural health under recursion:
5.1 Recursive Consistency Score (RCS)
Binary score indicating whether a contradiction is detected at each step:
RCS=1T∑t=1T1[D(s(t),s(t−1))=0]\text{RCS} = \frac{1}{T} \sum_{t=1}^T \mathbb{1}\left[ D(s^{(t)}, s^{(t-1)}) = 0 \right]RCS=T1t=1∑T1[D(s(t),s(t−1))=0]
Higher is better — implies fewer logical contradictions per step.
5.2 Curvature Index (CI)
Measures the second derivative of semantic state evolution — oscillation, overcorrection, or instability:
CI=1T−2∑t=2T−1∥s(t+1)−2s(t)+s(t−1)∥2\text{CI} = \frac{1}{T-2} \sum_{t=2}^{T-1} \left\| s^{(t+1)} - 2s^{(t)} + s^{(t-1)} \right\|_2CI=T−21t=2∑T−1s(t+1)−2s(t)+s(t−1)2
This discrete Laplacian approximates “semantic acceleration.” High curvature often signals collapse onset.
5.3 Identity Drift Rate (IDR)
IDR=1T∑t=1T∥s(t)−I(0)∥2\text{IDR} = \frac{1}{T} \sum_{t=1}^T \left\| s^{(t)} - I^{(0)} \right\|_2IDR=T1t=1∑Ts(t)−I(0)2
This tracks cumulative drift from the original prompt-state or goal identity. In reflective tasks (e.g., dialog, explanation), high IDR correlates with degeneration.
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Architectural Augmentation: Reflection-Aware Transformer Head
To implement TSRF in practice, we propose a lightweight supervisory module stacked atop a transformer decoder.
Each token-wise output is passed through a semantic projection head to obtain s(t). Then:
s(t) is passed to a Continuity Tracker, which updates I(t) and evaluates identity loss
s(t) is compared to s(t−1) via a Contradiction Filter (entailment model or logic module)
Emergence is estimated via an entropy-based divergence module
A tension function Φ(s(t)) evaluates output admissibility
This supervisory layer does not modify the transformer’s generative layers directly — it acts as a regulatory gate. If output fails the tension test, the system resamples or invokes a self-editing correction routine.
This design can be deployed incrementally — as a wrapper atop pretrained LLMs or as a fine-tunable module during further instruction tuning.
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Recursive Use Cases: Where Collapse Strikes
To evaluate the efficacy of TSRF, we recommend testing it on recursive high-strain scenarios:
Infinite Dialog Chains: Maintain character, belief, and tone over 100+ utterances.
Recursive Justification Loops: Explain → summarize → critique → defend — repeated.
Self-Improving Prompt Editing: Task the model with recursively improving its own prompts.
Belief Tracking: Maintain consistency in moral stance, factual beliefs, or epistemic boundaries.
Each of these tasks involves semantic strain across time. Current LLMs frequently collapse in such environments. A TSRF model should remain coherent — evolving with internal structure.
Integrating TSRF with Existing Paradigms
The Triadic Semantic Regulation Framework (TSRF) does not seek to replace the transformer backbone. Instead, it complements existing generative models by enforcing semantic regulation over time.
This framework can be layered on top of or integrated into multiple state-of-the-art strategies:
8.1 Chain-of-Thought and Tree-of-Thought
Chain-of-thought (CoT) prompting encourages step-wise reasoning, while tree-of-thought (ToT) expands the generative space by branching hypotheses. However, neither enforces semantic continuity or contradiction minimization between steps or branches.
By contrast, TSRF filters each step through:
ΔC (semantic drift from prior step)
D(·) (logical consistency with prior state)
KL divergence (structured emergence)
This ensures that intermediate reasoning steps evolve consistently, not just linearly or exhaustively.
8.2 Tool-Augmented Reasoning
Toolformer-style models or retrieval-augmented transformers use external modules (e.g., calculators, search engines) to scaffold reasoning. While powerful, these systems often re-integrate tool outputs without internal consistency checks.
TSRF can act as a gatekeeper for external information — filtering retrieved knowledge against prior beliefs or dialog state. Contradictory tool results are flagged or resolved, rather than blindly injected into the prompt.
8.3 ReAct-Style Agents
In ReAct and similar frameworks, language models simulate action-observation loops. TSRF extends this by offering internal loop validation — ensuring that the agent’s beliefs remain coherent over multiple action cycles.
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Implementation Considerations and Challenges
Though theoretically sound, TSRF presents concrete challenges when implemented at scale:
9.1 Overhead from Contradiction Detection
NLI models or symbolic logic evaluators can be computationally expensive. For real-time applications, efficient approximations or low-rank entailment embeddings are needed.
Solution: Use contrastive models trained on contradiction pairs (e.g., Negated QA) for lightweight filtering.
9.2 Identity Vector Design
Constructing a meaningful latent identity representation requires a consistent projection space across steps.
Solution: Train a projection head using contrastive learning objectives aligned with prompt-task semantic fields.
9.3 Balance Between Emergence and Coherence
Too much filtering may stagnate the model (repeating safe output). Too little allows drift or contradiction.
Solution: Tune Φ(·) adaptively based on task constraints — e.g., permit higher emergence during brainstorming, tighter coherence during legal generation.
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Future Directions: Toward Reflective Generative Intelligence
TSRF is a minimal step toward a broader shift: the evolution of LLMs from autoregressive predictors to reflective systems.
10.1 Collapse-Resilient Planning
Multi-step planning agents (e.g., for robotics, automated reasoning) require recursive state construction. TSRF offers a constraint-based interface for building such plans, where each step must be logically grounded in the prior.
10.2 Cognitive Modeling
TSRF aligns with cognitive science theories of deliberative reasoning: it implements reflection as a regulatory function, not an emergent behavior. This opens the door to mapping internal LLM state transitions to formal cognitive processes.
10.3 Alignment and Interpretability
By exposing latent coherence, contradiction, and emergence signals, TSRF makes generation more legible — both to humans and to downstream validators. This scaffolds better alignment with user intent and reduces hallucination risk in high-stakes domains.
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Philosophical Imperative: Collapse as Structure’s Shadow
Collapse is not just a technical failure. It is a mirror.
It reveals that our models, for all their fluency, lack a self. They cannot remember what they meant. They cannot resolve what they believe. They cannot stabilize under their own recursion.
We have asked them to reason, but given them no spine.
TSRF is a gesture toward that spine — a minimal structure within which reflection becomes possible, evolution becomes coherent, and emergence becomes intelligible.
The semantic spine is not built by more tokens. It is built by form: identity, contradiction, and transformation in tension. This is the core of cognition, human or artificial.
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Conclusion
LLMs collapse not because they are undertrained, but because they are understructured.
Autoregression alone cannot sustain recursive generation. Reflection demands more: filters for continuity, mechanisms for self-consistency, and regulators for novelty. The Triadic Semantic Regulation Framework provides one such structure.
We have defined the dimensions of collapse, formalized evaluative metrics, proposed an architectural augmentation, and articulated a path toward reflective, collapse-resilient reasoning systems.
The next step in generative intelligence is not another trillion parameters. It is the spine that holds them together.
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Appendices (Suggested)
A. Derivations of Semantic Curvature Metrics
B. Projection Head Training Objectives
C. Benchmarking TSRF with GPT-4, Claude, and Gemini
D. Visualizations of Collapse in Infinite Dialog and Recursive Editing
References (Partial)
Schulman, J. et al. (2023). On Self-Referential Collapse in LLMs.
Varela, F. & Maturana, H. (1980). Autopoiesis and Cognition.
Anthropic Research. (2024). Recursive Degradation in Scaling Laws.
Peirce, C.S. (1903). Lectures on Pragmatism.
Hofstadter, D. (1979). Gödel, Escher, Bach.
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