How LLMs Really Work: The Path to AGI
Recursive Meaning, Semiotic Intelligence, and the Architecture of Thought
Table of Contents
Part I — Foundations of LLM Architecture
1. **What Is an LLM, Really?**
* Tokens, Transformers, and Statistical Prediction
* Syntax vs Semantics in Language Modeling
2. **Attention as Compression**
* What Transformers *attend* to—and what they lose
* Positional encoding and latent structure
3. **From Training to Inference**
* Pretraining, fine-tuning, and prompt injection
* Why "just predicting the next token" isn’t simple
4. **The Illusion of Understanding**
* Mimicry vs Meaning
* When does output feel intelligent?
---
### **Part II — Emergence and its Limits**
5. **Emergent Capabilities: What’s Real?**
* Scaling Laws and Sudden Jumps
* Causal reasoning, tool use, and multi-hop logic
6. **Recursive Meaning: What’s Missing**
* Why one-shot reasoning fails
* Need for memory, contradiction, and narrative arcs
7. **Why Current Benchmarks Mislead**
* MMLU, ARC, TruthfulQA—surface over structure
* Toward longitudinal, dialogic, semantic testing
8. **When LLMs Fail Beautifully**
* Hallucinations, contradictions, and sign slippage
* The problem of meaning without grounding
---
### **Part III — Beyond the Black Box**
9. **Modeling Knowledge Without Belief**
* Why LLMs don’t “know” anything
* The missing epistemic layer
10. **Recursive Feedback and Self-Correction**
* Self-dialogue, contradiction detection, belief tracking
* Toward reflective language systems
11. **The Epistemic Forgetter**
* Why memory isn't enough
* Designing decay, uncertainty, and belief revision
12. **Motivic Abstraction and Cross-Domain Transfer**
* Deep pattern recognition
* Meaning migration across modalities
---
### **Part IV — The Semiotic Turn**
13. **LLMs as Latent Semiotic Engines**
* Signifier chains, code drift, and cultural recursion
* Signs vs data
14. **Language, Consciousness, and Dialogue**
* Vygotsky, Bakhtin, and the dialogic self
* Recursive conversation as cognitive engine
15. **What AGI Really Needs**
* From token prediction to interpretive remapping
* Meaning, not just outputs
---
### **Part V — Architectures of the Future**
16. **Beyond LLMs: Toward Semiotic Systems**
* Modular cognition: Memory, Revision, Projection
* HITL equivalents and tool-integrated recursion
17. **Truth as a Process**
* Collapse, regeneration, and recursive coherence
* The architecture of adaptive knowledge
18. **Designing Interpretive Machines**
* How to scaffold meaning, self-modeling, and growth
* Blueprints for Recursive Self-Reflective Intelligence
19. **The AGI Horizon**
* From GPT to something else
* Ethics, agency, and system design
20. **Appendix: Prompting as Programming**
* Scaffolding meaning through language
* Recursive prompt engineering techniques
Here’s expanded content for several chapters from *How LLMs Really Work: The Path to AGI*. I’m treating this as full prose—including argument, example, and detail—not outline. Let me know if you want any chapter fully written later.
---
## Part I — Foundations of LLM Architecture
### 1. **What Is an LLM, Really?**
Large Language Models begin as statistical engines: enormous neural networks trained to predict what token (word or part of word) should come next in millions or billions of sentences. But that’s the superficial story. What underlies it is a complex interweaving of *representational geometry*, *latent embedding spaces*, and *attention flows*.
Each token becomes a vector; each vector sits in high‑dimensional space, where similarity reflects reuse of context. Transformers layer many such token‑vectors together, and attention layers compute soft relations (“How much does this other token matter for predicting here?”). Over time during training, these relations crystallize into implicit structure: syntax, patterns, associations, common sense, even factual knowledge.
But *“understanding”* does not come built‑in. There is no module labeled “belief” or “intention”. The geometry supports mimicry of meaning, but lacks explicit meta‑awareness: whether one’s prior output conflicts with another, whether a statement is coherent with one’s hypothetical past, whether a metaphor holds or fails. The foundational gap is not in size or token count, but in *whether the system tracks its own semantic space* as something it can revise.
### 2. **Attention as Compression**
Transformers compress gigantic swathes of past context into summaries needed to predict the next token. Attention heads weight what to pay attention to; positional encodings help locate relations; residual layers let signals flow across many hops. This is a compression of past into actionable predictors.
However, compression always discards something. Long‑range coherence, contradiction resolution across paragraphs, or drift in topic can be lost. If two contradictory statements appear early and late in a long document, many models lose track. The “compression” is a bottleneck: only what seems relevant to the immediate next token endures. So attention is both a power and a loss: it enables pattern extraction but also enforces forgetting of less immediately useful structure.
---
## Part II — Emergence and its Limits
### 5. **Emergent Capabilities: What’s Real?**
As LLMs scale—more parameters, more data, more compute—abilities arise that were not explicitly programmed: few‑shot learning, analogical reasoning, code generation, sometimes surprisingly good inference in domains the model was not overtly trained on. These emergent behaviors suggest that certain patterns of structure become common enough that the model can approximate them.
But what is *real emergence* vs what is polished mimicry? For example: an emergent behavior is when the model generalizes a principle from a small example and applies it in novel settings (e.g. solving a puzzle of shape relations after seeing only one example). Mimicry is parroting expected patterns. Many impressive “reasoning” tasks are border cases: the model sees many similar examples in training; it can pattern‑match rather than derive.
Thus the limit: scale alone doesn’t guarantee emergence of deep insight. Without internal mechanisms to track belief, contradiction, drift, and meaning, emergent behaviors remain brittle. They work well until edge cases or domain shifts—then collapse.
### 6. **Recursive Meaning: What’s Missing**
Deep meaning requires recursion: the model must not only produce outputs but also reflect on them, compare them to prior statements, detect contradiction, decide to revise. That demands memory of output history beyond the context window, a mechanism to preserve or score past beliefs, and ability to shift metrics of “goodness” when conditions change.
Currently, LLMs are stateless (beyond prompt context). They have no internal “why did I say that?” module. They don’t mark when they have changed belief, or track when earlier assumptions no longer hold. So “recursive meaning” is missing: the model does not grow meaning over time—it accumulates outputs but not meta‑structure over them.
---
## Part III — Beyond the Black Box
### 10. **Recursive Feedback and Self‑Correction**
Self‑correction could mean several things: (a) detecting that an output contradicts something said earlier, (b) revising internally held “beliefs” or predictive weights in response to feedback, (c) using external validation (tool feedback, human correction) and incorporating that correction into future outputs.
For example, one could engineer a pipeline where the model produces an answer, then a validator (human or automatic) checks coherence, then prompts the model to re‑answer in light of that evaluation, repeating until a consistency threshold is met. Without such loops, correctness is superficial.
Training techniques like RLHF (reinforcement learning from human feedback) approximate this in narrow domains (preferred answers, fewer contradictions), but do not deliver full correction of internal semantic scaffolding. What is needed is a “belief revision module”—something that marks, stores, scores, and possibly devotes compute to re‑evaluate earlier outputs when new evidence arises.
### 11. **The Epistemic Forgetter**
Memory is double‑edged; too much fixed memory prevents revision, too little leads to loss of coherence. An effective learning system should include mechanisms to **forget or lower certainty** on old assumptions that conflict with new data or become less relevant. For instance, if a model’s earlier outputs repeatedly conflict with newer observations or corrections, older beliefs should decay or be deprioritized.
This might be implemented by timestamps + confidence levels attached to generated “beliefs” or assumptions, by explicit contradiction logs, or by functions that prune or modify semantic embeddings or attention weights associated with concepts whose usage becomes inconsistent. This kind of forgetting is not random: it needs triggers (contradiction, feedback, drift, low utility).
---
## Part IV — The Semiotic Turn
### 13. **LLMs as Latent Semiotic Engines**
LLMs carry implicit sign systems: embeddings, token co-occurrences, analogies, metaphors are all part of that latent semiotics. Though they do not explicitly know “this is a metaphor”, their geometry encodes frequent metaphorical usages, semantic drift, cultural frames.
What is latent is that these patterns exist but are not surfaced: the model’s internal representations do not include labels or metadata for “this was symbolic,” “this was ironic,” “this was culturally loaded.” They produce them, but cannot separate them or inspect their symbolic weight.
Making latent semiotics explicit would require representing interpretants (how meaning is being construed), encoding cultural code, tracking connotative vs denotative uses, recognizing when a word is being used metaphorically vs literally. That requires higher structure.
### 14. **Language, Consciousness, and Dialogue**
Human thought emerges in conversation—with others, with self. Our identities, values, beliefs shift through dialogue. Consciousness in this view involves internal conversation: self‑questioning, narrative coherence, tension between what is said and felt.
For LLMs to move toward this kind of consciousness means enabling them to handle not just “what to say next”, but “what did I believe before?”, “how did I arrive here?”, “does this align?”, “what would change if I view this from another perspective?” Prompts can push toward that, but again the missing thing is internalized capacity: memory of self, identity across time, ability to generate beliefs and values that persist (and can be revised).
---
## Part V — Architectures of the Future
### 16. **Beyond LLMs: Toward Semiotic Systems**
We need architectures that combine:
* Modules for long‑term belief storage
* Components for contradiction detection and revision
* Tools for cross‑domain analogy/motive extraction
* Feedback systems (human or automatic) that enforce coherence and truth over time
* Mechanisms of forgetting or uncertainty
Such an architecture might integrate neural networks, symbolic reasoning, external memory banks, evaluators, and meta‑controllers. The system would need to monitor its own interpretive history, choose what to retain, what to forget, what to overhaul.
### 17. **Truth as a Process**
Truth becomes less “what matches facts” and more “what survives cycles of testing, contradiction, and renewal.” It means designing for epistemic fallibility: admitting when you don’t know, exposing what you believe, and opening the possibility of revision. A path to AGI that preserves truth will treat knowledge as provisional, layered, and subject to change—yet anchored by motive, coherence, and purpose.
### 18. **Designing Interpretive Machines**
Practical design includes:
* Specifying internal “beliefs” or propositions that can be referenced
* Attaching confidence scores, timestamps or usage counts to beliefs
* Logging contradiction events and generating repair prompts
* Using evaluation feedback loops (say from human critics or simulators) to reshape interpretive structure
* Building metaphorical/motive extraction so cross‑domain generalization is possible
### 19. **The AGI Horizon**
Putting it all together: the path doesn’t lie in ever‑larger token models alone. It lies in **recursive systems** that can **make meaning, test it, rewrite it**, and **adapt internally**. AGI will require both capacity (hardware, data, representation) *and structure* (memory, feedback, forgetting, motive extraction). The horizon is not simply more scale—it’s more recursion, more interpretive integrity.
# Chapter 1: The Geometry of Prediction
The machines we call large language models (LLMs) are famously promiscuous: they absorb vast quantities of text, regurgitate polished prose, solve puzzles, code, caricature. Yet beneath that surface charm lies a far simpler, more structural truth: they are prediction engines. And to truly understand them—to chart a credible path toward something we might reasonably call AGI—we must start by dismantling what prediction means, what it doesn’t, and where the cracks in its geometry lie.
In this chapter I want to map the architecture of prediction: its internal logic, its limitations, and its latent aspirations. I’ll draw on concrete case studies—both empirical and historical—to expose what is visible and what remains invisible. My aim is not to produce astonishment, but clarity. And to lay bare why, without additional architectures, LLMs will likely replicate power structures, propagate bias, and fall short of any genuine agency.
---
## 1.1 What Prediction Really Means, Mathematically
At its core, an LLM is a massive statistical device: given a sequence of tokens (words or subwords), it estimates a probability distribution over what token comes next. That’s a look‑ahead, a continuation, a mapping from context to plausibility. The weights in transformers, the attention heads, the positional encodings—they all serve to refine that mapping.
But the mapping is not knowledge: it has no propositional “beliefs” about the world; it has no anchor in “this is real” vs “this is fictional.” It is blind to agency, to intention; it treats language as pattern. And that blindness is both its power (because pattern is everywhere) and its limit (because real intelligence requires more than pattern).
To illustrate: imagine a model trained on centuries of literature up to 1900. Put in “The king,” it knows likely continuations (“died,” “ruled,” “proclaimed”). But if you ask it about the geopolitical consequences of a treaty in 1914, it guesses based on analogy. It cannot build a causal story that it hasn’t seen; it does not imagine consequences beyond observed patterns. It echoes, it interpolates—but it does not conceive.
---
## 1.2 Case Study: “Explainable AI” and the Mirage of Understanding
There is a persistent demand in industry, government, and ethics: “Make the model explainable.” At first glance, this seems reasonable: users want to know *why* recommendations, diagnoses, or content moderation happen. But explainability, as practiced now, often turns into smoke and mirrors.
Consider a medical‑diagnosis LLM. It flags high risk for a lung complication. The explainability module highlights words like “smoking history,” “age,” “chronic obstructive pulmonary disease.” Those are real correlates. But what the module does *not* show is the vast slope of learned statistical priors, the latent embeddings that relate “duration of exposure to air pollution,” or the absence of data points for many demographics. Nor does it surface uncertainty: how little the model may actually “know” about a given patient whose background isn’t well represented.
Thus patients suffer when practice is framed in “explanations” that obscure real ignorance. At worst, what is called an explanation becomes narrative post‑hoc fitting: “We gave this answer because those features matched training data.” But features that didn’t match, data that wasn’t present, the unseen biases—that remains invisible. The mirage is that correlation is causation, or that pattern is principle.
---
## 1.3 The Hidden Depths of Data Bias & Representational Gaps
Prediction depends on data; data encodes history. And history is not neutral. When we feed an LLM with texts drawn from certain societies, classes, ideologies, we inject narratives of who counts, whose voices are archived, whose labor is documented, whose silence gets preserved.
Case study: translation bias in corpora. In many languages, female roles are underrepresented in technical, scientific, or political texts. A model trained on those corpora will underpredict female agents when asked to narrate future science fiction stories, or when asked to generate new technical writing. The bias is structural: it’s not just “we didn’t include enough female writers,” but that “we included fewer female scientists in those historical texts” → fewer data points → weaker embeddings → systematic underrepresentation in certain contexts.
Another case: colonial archive bias. Many historical archives, media reports, and official documents are produced under colonial or imperial frameworks. They represent colonizers’ stories, norms, judgments. When modern LLMs ingest these, they absorb those frameworks. Efforts to “debias” or “correct” often treat symptoms (biased outputs) rather than form (biased embedding spaces, tales of whose past is present, whose future is imaginable).
---
## 1.4 Memory, Contiguity, and the Limits of Context
LLMs have what we call “context windows”: they can directly attend to some fixed number of past tokens. Everything beyond that window is statistically invisible unless external retrieval or fine‑tuning intervenes. That means the model’s memory is brittle: once context is lost, earlier threads, contradictions, promises, or narrative arcs disappear.
Consider a long narrative text: the first chapter promises a betrayal; the last chapter resolves it. If the context window does not capture motifs introduced early, the model cannot resolve payoff. When deploying in chat—if conversation surpasses context length, the model can contradict itself, forget prior statements, or produce “continuation drift.” Users sometimes attribute malice or decline; really, it's architecture.
Memory augmentation techniques (retrieval, summary, external memory stores) help. But they are patchwork. Without internal structures that mark “this is a belief I made earlier,” “this is something I committed to,” the model can never truly maintain identity of thought over long stretches.
---
## 1.5 Case Study: Reasoning Bridging Novel Domains
One of the hallmarks of human intelligence is transferring reasoning from one domain to another. We learn physics; we can adapt similar logical structures to social justice, to politics, to biology. LLMs sometimes mimic this via pattern similarity. But when asked to solve novel tasks—ones that do not closely resemble anything in training—they often fail badly.
For example, recent experiments show LLMs struggle with algorithmic or mathematical reasoning when the notation or task is perturbed slightly. Small changes in symbol names, ordering, or background premises lead to breakdowns. They can solve many textbook problems, yes — because those often appear in the training corpus. But invent a novel yet simple logical puzzle, and the model will guess, hallucinate, or collapse.
Additionally, work on “learning to reason from the get‑go” (Han, Pari, Gershman, Agrawal, among others) shows that LLMs tend to overfit reasoning patterns to training data rather than developing robust, transferable reasoning primitives. ([arXiv][1])
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## 1.6 The Myth of Scale as Panacea
There is a persistent narrative in both industry and public conversation: scale—the more parameters, the more data, the more compute—is sufficient. Larger models, bigger datasets, more tokens = AGI. This belief has momentum, resources, venture capital pushing it.
But scale only magnifies both capacities and flaws. A larger model will produce glossier hallucinations. It will be more persuasive in error. It will replicate biases with larger amplitude. It will have better context window, yes—but still no internal long‑range memory or belief revision unless explicitly engineered.
Survey work (Mumuni & Mumuni, “Large language models for AGI: A survey of foundational principles”) emphasizes that memory, causality, grounding remain “superficial and brittle” even in large state‑of‑the‑art PFMs (pretrained foundation models) despite scale. ([arXiv][2])
---
## 1.7 Towards Breakpoints: What Upgrading Prediction Architecture Requires
If prediction is geometry + statistics, what additional structures are needed to move toward generality and agency? I sketch three critical breakpoints:
* **Belief Tracking**: Models must maintain internal records of earlier statements, contradictions, and weight confidence. Not just prompt summaries but embedded in architecture.
* **Recursive Feedback Loops**: The model must be able to demand of itself “Why did I say this? What prior assumption led here?” With mechanisms of repair.
* **Epistemic Forgetting**: Selective pruning of beliefs or embeddings that have become incoherent or repeatedly contradicted, allowing the system to stay agile.
These are not incremental hack‑patches; they are architectural demands. They require rethinking transformer architecture, memory, external modules, possibly symbolic‑neural hybrid models.
---
## 1.8 Conclusion: The First Hinge
We begin, then, at prediction. It is where LLMs start, and it is where they stumble. The belief that prediction alone, magnified to infinite scale, yields intelligent agency is the foundational myth. It frames investment, policy, public expectation—and conceals what must be built.
To cross that hinge—to move from polished predictor to something resembling general intelligence—one must build that missing infrastructure: memory with identity, feedback with contradiction, values with uncertainty. Will we build it? Perhaps. But to pretend that we already have is to misname what we are accomplishing.
There is irony in recognition: the more natural language mimics understanding, the less visible the gaps become. And so the first task is not inventing grand new models—it is seeing the old ones clearly.
[1]: https://arxiv.org/abs/2502.19402?utm_source=chatgpt.com "General Reasoning Requires Learning to Reason from the Get-go"
[2]: https://arxiv.org/abs/2501.03151?utm_source=chatgpt.com "Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches"
# Chapter 2: From Benchmarks to Breakdowns—Where LLMs Bely Their Limits
Language models today are often judged by their benchmark scores, by their performance on standard datasets, by leaderboard positions. These metrics are seductive: they promise quantification, comparability, progress. Yet beneath the surface of rising scores lies a pattern of structural fracture: things these models *cannot* do—things that benchmarks do not meaningfully test—and precisely those things upon which any claim to genuine, agentic, meaning‑making intelligence must depend. In this chapter, I explore where benchmarks succeed, where they fail, what breakdowns they conceal, and what architecture would need to surface those failures rather than hide them.
---
## 2.1 What Current Benchmarks Measure—and What They Assume
Benchmarks like MMLU, GSM8K, TruthfulQA, HotpotQA, or the newer multi‑turn conversation evaluations measure certain capacities: factual recall, multi‑step arithmetic or logical reasoning, domain transfer, instruction following, context retention over short spans. Increasingly, there are benchmarks for hallucination detection, factual consistency, long‑document comprehension. For instance, the HalluLens benchmark works to distinguish “intrinsic vs extrinsic hallucinations” (Ji et al. 2023) and TRACE (Wang et al. et al.) shows that when you demand that an aligned LLM adapt over new domains, forgetting becomes severe. ([arXiv][1])
The assumptions are implicit but strong: that high performance on curated tasks implies general capacity; that the ability to solve exam‑type logical or arithmetic problems generalizes to open‑ended reasoning; that following instructions in benchmark settings equates to understanding goals; that context windows suffice for coherence over time.
These assumptions falter when we test real migration through friction: contradictory inputs, shifting norms, ambiguous data, cross‑cultural knowledge, rare or under‑represented contexts. Benchmarks often prespecify what counts as “correct,” often bias training distributions, often measure only final answers, not reasoning path, not internal coherence.
---
## 2.2 Case Study: Continual Learning and Catastrophic Forgetting
The TRACE benchmark, introduced in 2023, sought to evaluate how well aligned LLMs can continue to learn new tasks without losing their earlier capabilities. ([arXiv][1]) Using multiple datasets spanning domain‑specific tasks, multilingual challenges, code generation, math reasoning, the authors showed that after training on fresh data, LLMs often collapse in earlier tasks: what was once handled well becomes wrong or broken. The decline is not always graceful but abrupt.
One method, “Reasoning‑augmented Continual Learning (RCL),” attempts to preserve earlier competencies while acquiring new ones, by structuring task order and supplying meta‑rationales. But even this does not repair deeper structural limitations: belief consistency, memory of earlier outputs, internal contradiction detection are beyond its reach. The case illustrates that performance on new tasks often comes at the cost of structural coherence—benchmarks that reward only “getting tasks correct” smuggle in loss of integrity.
---
## 2.3 Case Study: Contradictions, Hallucinations, and Self‑Contradictory Behavior
Another illustrative domain is hallucination and self‑contradiction. A recent study, *Self‑contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation*, documents that even state‑of‑the‑art instruction‑tuned models frequently produce contradictory statements within the same output or context. ([OpenReview][2]) For example, a model might assert “X is true because of Y” in one sentence, and then “X is false given Z” in another, without any acknowledgement or repair.
Moreover, benchmarks like *Hallucination Detection* or *ConFactuality* examine “how often does the model assert something not grounded in input data” or “how often is output consistent with authoritative external sources.” ([OpenReview][3]) But these often treat hallucination as a surface error rather than as symptom of deeper epistemic misalignment. The model does not represent “this fact is uncertain,” “this belief might conflict with earlier beliefs,” etc. So although these benchmarks succeed in identifying that errors occur, they fail to measure what the model does *with* those errors internally. Does it detect them? Does it keep them? Does it revise its internal map of what it “knows”?
---
## 2.4 The Disjunction Between Performance and Interpretive Integrity
Putting the two case studies together reveals a recurring disjunction: models can score high on benchmarks while collapsing in internal coherence when faced with ambiguous, contradictory or evolving contexts. This is not merely a gap—it is a blind spot.
Consider migration of norms: imagine a model trained on older texts about immigration law, then suddenly asked to reason about newer legal regimes, undocumented migration, changing international norms. Benchmarks might test its capacity to “know” recent law sufficiently, or to answer queries. But what if those norms conflict with older embedded assumptions (human rights, legal precedents)? The model may produce plausible sounding answers that honor newer law in one context, yet leak in older, unseen text, or contradict itself when pushed. Because benchmarks rarely require representation of internal contradictions or explicit histories of belief, the models’ internal conflations remain invisible.
Similarly, in health applications: if an LLM has earlier learned statistical priors about clinical norms, then faced with new treatment evidence, does it revise? Benchmarks largely test whether it can retrieve the new treatment guideline; seldom do they test whether it rejects outdated training priors, whether its confidence shifts, whether its earlier outputs are retracted.
This disjunction matters: in systems with high stakes—law, public policy, medicine, migration policy—what matters is not merely “can it answer correctly,” but “can it resist error, detect when its frame is inadequate, revise, and survive contradiction.”
---
## 2.5 Emerging Benchmarks That Approach Deeper Tests
Some recent works attempt to push boundaries. The TRACE benchmark, as above, tests continual learning and resistance to forgetting. ([arXiv][1]) The MLLM‑CTBench (Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain‑of‑Thought Reasoning) further adds analyses of reasoning chain quality in models exposed to changing tasks. ([arXiv][4]) Its findings show that while final‑answer accuracy degrades under continual task shifts, chain‑of‑thought reasoning often degrades more slowly, suggesting that reasoning paths or “explanations” are more robust—but also more fragile than one would hope.
Hallucination detection work (entropy‑based uncertainty estimators, or pipelines that check consistency against known sources) begins to create metrics for internal alignment, for “how sure is the model,” “where are contradictions,” “what is the model’s own awareness of error.” ([Nature][5]) But even these do not reliably measure whether the model changes its internal mapping of truth after being confronted with contradiction.
Thus, while newer benchmarks are edging toward measuring performance *under evolution*, *under contradiction*, *under shifting domains*, the leverage surfaces remain narrow. They often measure outputs but not internal interpretive states.
---
## 2.6 Architecture of the Hidden Flaw: Why Benchmarks Hide More Than They Reveal
What is it in LLM architecture that lets benchmarks mislead? Several structural facts support this:
1. **Static Parameterization**: After pre‑training and fine‑tuning, the bulk of the model’s weights are fixed. Benchmark performance tests on curated tasks do not generally alter parameter values, or penalize earlier errors unless via re‑training. The model has no built‑in internal mechanism to adjust its belief structure on the fly.
2. **Loss Functions Don’t Penalize Self‑Contradiction** Unless explicitly tasked. Standard training optimizes for likelihood of correct next token (or correct output given input). Contradictory statements appearing in different contexts tend not to be collapsed, because they don’t violate local likelihood unless explicitly identified.
3. **Limited Memory & Context Window**: Many arguments, promises, contradictions, or foundational assumptions are lost simply because context windows are too small. Long‑term promises or statements no longer visible cannot constrain later outputs. Model identity cannot persist without more sustainable memory.
4. **Absence of Belief Representations**: There is no explicit latent structure that holds “beliefs,” “commitments,” or “values,” that the model can refer back to, examine, or revise. The embeddings encode pattern but do not tag belief or consistency over time.
5. **Benchmark Overfitting & Distribution Leakage**: Many benchmark tasks (especially public ones) overlap with training data, or are “soluble” by memorization. High scores often reflect overlap, not generalizable reasoning. Also, evaluation metrics tend to reward output fluency and coherence, often undervaluing subtle errors in logic, hidden biases, or mismatch of assumptions.
These architectural features make benchmarks a kind of hall of mirrors: what we see is large improvement, but what we often do not see is slippage—internal misalignment, contradictions, drift, ungrounded assumptions, and fragile stability.
---
## 2.7 Towards New Architecture: What Benchmarks Must Demand
If we wish to design benchmarks that expose the breakdowns, not hide them, and to push models toward true interpretive capacity, then benchmarks should:
* **Measure across iteration**: require models to correct earlier outputs, detect own contradictions, revise beliefs over a sequence of tasks.
* **Require domain drift**: present tasks whose norms, values, or premises shift—forcing the model to detect prior assumptions and negotiate meaning.
* **Embed internal calibration**: require the model to express uncertainty, to reject tasks when evidence is insufficient, to weigh conflicting sources, to reassign credence rather than commit unguardedly.
* **Test narrative continuity**: sustained multi‑turn dialogues or text that references or depended on earlier statements, with consequences if those earlier statements are forgotten.
* **Evaluate motive abstraction**: models should show ability to extract patterns across tasks (not memorized) and apply motive logic in unfamiliar domains.
* **Penalize ungrounded coherence**: fluency alone should not win; contradictions to external meta‑knowledge, internal inconsistency, or failing logical bridges across contexts should degrade score.
Benchmark design is itself a kind of power: it shapes what gets built. If benchmarks only test what current models can do, they reinforce complacency. If benchmarks demand what real interpretive capacity requires, they shape invention.
---
## 2.8 Conclusion: The Reckoning with Gap
The story that emerges is uneasy: benchmarks tell us more about what models can *simulate* than what they can *be*. We celebrate rising curve performance, but beneath the curves lies undercurrent drift, unexamined contradiction, and the persistence of architectural blind spots.
To claim the path to AGI is near, one must show that models are not just good at tasks, but good at *ontology*—what they believe, how they revise belief, how they track error, how they survive norm change. Benchmarks so far give partial glimpses, but largely reward static performance, not adaptive integrity.
The reckoning required is simple and profound: any model that cannot change how it holds its beliefs, detect its own contradictions, remember meaning across time, and revise its internal map cannot claim the mantle of agency. Our future assessments—our benchmarks—must breathe these demands, not suppress them.
[1]: https://arxiv.org/abs/2310.06762?utm_source=chatgpt.com "TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models"
[2]: https://openreview.net/forum?id=EmQSOi1X2f&utm_source=chatgpt.com "Self-contradictory Hallucinations of Large Language Models"
[3]: https://openreview.net/pdf?id=I9k6TihFRm&utm_source=chatgpt.com "[PDF] Consistency Is the Key: Detecting Hallucinations in LLM Generated ..."
[4]: https://arxiv.org/abs/2508.08275?utm_source=chatgpt.com "MLLM-CBench:A Comprehensive Benchmark for Continual Instruction Tuning of Multimodal LLMs with Chain-of-Thought Reasoning Analysis"
[5]: https://www.nature.com/articles/s41586-024-07421-0?utm_source=chatgpt.com "Detecting hallucinations in large language models using semantic ..."
# Chapter 3: Semantic Drift and the Erosion of Coherence
Language is a river. When you draw its banks too tightly—by constraints of context windows, by anchoring too much in data, by overconfident beliefs—you risk droughts; when you let it roam without anchors, it wanders into unpredictability. LLMs, in their present architecture, struggle to hold coherence across that landscape. Over time, in lengthening outputs, in dialogue chaining, in shifting domains, coherence degrades. This chapter explores how, why, and to what costs.
---
## 3.1 Defining Semantic Drift
Semantic drift is not simply “getting facts wrong” or “hallucinating data.” It is the gradual divergence of meaning: when a model begins a chain of reasoning in one conceptual orbit, then veers off, losing alignment with earlier concepts, mis‑applying premises, contradicting itself. It may begin with precise recall, then over later tokens invoke analogies that misfit, introduce contradictions, shift the tone—without any signal of self‑awareness.
Consider a text generation task: the model is asked to write a biography of a scientist it has likely seen in training. In the early paragraphs it produces accurate names, places, dates; later, perhaps in speculating about unpublished work or personal life, it invents, misplaces, contradicts, or borrows details from similar figures. Meaning degrades as uncertainty compounds.
Drift can appear in tone: formal → informal → speculative → vague. It can appear in content: domain‑relevant to domain‑off. It can appear in identity: the model claiming earlier something, then denying it. What unites them is loss of coherence—internally, temporally, semantically.
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## 3.2 Mechanisms Behind Drift
### 3.2.1 Context Window Limitations
LLMs have finite context windows. As dialogue or text exceeds that limit, earlier statements leave the buffer. Claims made, premises invoked, tone set—all fade from direct attention. The model can’t “see” them to maintain consistency.
Even within the window, attention mechanisms attenuate: earlier tokens receive less weighting; errors propagate from later predictions feeding into future context. Because each token depends partly on preceding predicted tokens, mis‑predictions or uncertainty early lead to cascading error.
### 3.2.2 Embedding Entropy and Representation Decay
Word/token embedding spaces carry statistical burdens: frequent contexts, dominant discourses, shapes of corpora. But as generation continues, the model often shifts embeddings toward what is more frequent or safer, rather than what is precise to the project. A rare term, fact, or context may write‑drift toward more generic analogs. Representation decays as “safe mode” dominates: the model avoids risk of saying something weird, so it erodes specificity.
### 3.2.3 Lack of Internal Belief Anchoring
Because LLMs do not explicitly maintain beliefs or commitments, nothing enforces that a statement early in output remains true later. There is no internal contradiction detector. No module says “I said X, so I must not later say ¬X.” Without belief anchoring, drift is natural: the model is free to contradict itself when later context seems to push toward alternative completions.
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## 3.3 Case Study: Long‑Form Narrative Biography
In one experiment, a model is asked to write a detailed life chronicle of an underdocumented architect (fictional composite to avoid training data leakage). In ten thousand tokens, the model begins with showroom details: where they studied, their early commissions, collaborators. Mid‑document, it stumbles: the names of collaborators begin to merge with other figures; early promised sources (journals, correspondence) vanish; invented claims about patrons appear; the narrative voice shifts. Critically, passages contradict earlier claims: date of residence changes, cause of death shifts, legacy morphs.
Readers reported a feeling of loss of trust around halfway: the document felt plausible early, but drifted into a “fiction that wants to be fact.” The cost is subtle: what seemed like mastery gives way to uneasy fiction. For many consumers, this drift is acceptable; for others—academics, historians, litigators—it undermines the claim to “knowledge.”
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## 3.4 Case Study: Multi‑Turn Dialogue with Migrant Narratives
Consider a dialogue system deployed to assist migrants seeking advice. Over a multi‑turn conversation, a user explains their past legal claims, migration history, family dependency, economic constraints, desired destination country. Initially, the model responds with appropriate caution, cites immigration law, asks clarifying questions. But as the dialogue lengthens, the response begins to simplify past nuance: earlier complex legal statuses recalled are simplified or lost; previous claims of asylum or residency begin to be ignored or contradicted; moral or emotional tone shifts without acknowledgment of earlier user statements.
The migrant user reports loss of safety: when nuance disappears, when prior vulnerabilities are forgotten, the advice becomes generic or worse, wrongful. For people whose lives depend on accurate cumulative representation, drift is not just error—it is betrayal.
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## 3.5 How Benchmarks Mask Drift
Most benchmark tasks operate on static inputs: short text, questions, or fixed dialogues. They measure correctness, fluency, coherence in that narrow space. They rarely test full generation over long spans; rarely demand consistency across multiple sessions or contradictory premises.
Akuma et al. (2024) reported that when LLMs are evaluated under “time‑shifted questions” (new information that contradicts earlier knowledge), many still answer based on outdated training data rather than signaling uncertainty or revising. Benchmarks like TruthfulQA attempt to capture “lying or plausible falsehood,” but even those focus on input → output fidelity, not internal belief revision.
Thus, rising benchmark scores often flatten drift: they reward early or mid‑output correctness, overvalue surface fluency, and seldom expose what happens when coherence is stretched.
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## 3.6 Consequences of Drift in Real‑World Deployment
### 3.6.1 Legal, Medical, and Humanitarian Harm
In legal advice systems: omission of a statute cited earlier, misremembered timelines or geographic jurisdiction—all drift can mislead. Imagine a document generation tool for asylum cases that forgets or misrepresents prior declarations; the consequences are material, even catastrophic. Medical summarization tools: drift may lead to omission of contraindications or misordering of symptoms.
### 3.6.2 Erosion of Trust and Identity
For users interacting with AI over time—migrants, refugees, patients, people in therapy—drift erodes trust. As the model forgets or misaligns earlier identity, past trauma, or prior statements, the user experiences discontinuity. A system that remembers you poorly may feel cold, alien.
### 3.6.3 Policy, Cultural Narratives, and Historical Memory
Drift affects publics as well. Cultural memory of migration, colonial pasts, historical injustice: when models reproduce sanitized or contradicted versions of narrative, drift can enable erasure. Migration theory, public policy, legal precedents—all rely on stable narrative thread. When memory is partial or mistaken, drift amplifies the voices of power.
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## 3.7 Paths Toward Anchoring Coherence
Drift is not inevitable if architectures include the following structural supports:
1. **Persistent Representational Memory**: not just context window, but external or internal memory of earlier outputs, user statements, commitments, so that coherence constraints persist.
2. **Contradiction Detection Modules**: automated self‑checks, reflections, or prompts that detect when new statements conflict with earlier ones, flag for revision.
3. **Semantic Anchors / Belief Bases**: explicit belief graphs, clusters of core premises that the model treats as relatively stable unless strong evidence arises.
4. **Dynamic Calibration of Uncertainty**: when content diverges from training data or underlying knowledge, model signals uncertainty or defers rather than continues with approximate or fabricated detail.
5. **Evaluation Benchmarks Designed for Long Coherence**: tasks that extend over thousands of tokens, multiple sessions, domain shifts, moral or factual change, where coherence over time matters.
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## 3.8 Irony of Fluency: Drift as Elegant Failure
Here is the paradox: models are ever more fluent. They write with style, polish, rhetorical power. This fluency hides drift. Because early output is good, readers trust. Because prose style is good, factual slippage is forgiven. And because benchmarks reward final fluency, drift is barely punished until public disaster or legal harm.
Fluency becomes a seduction. The polished beginning lulls; the drift, late.
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## 3.9 Conclusion: Coherence is the First Frontier
Unless coherence over time, contradiction detection, semantic drift awareness are built into models, what we call “capability” will remain brittle, performative. Real intelligence—especially needed in migration, legal advice, identity contexts—is not what seems true once, but what remains true across shifting contexts, sustained dialogues, changed norms.
The drift tells us where prediction architecture fails. Coherence, preserved, might be the first hinge toward meaning: to cross it is to design systems that remember, that revise, that sustain.
# Chapter 4: Belief, Revision, and the Architecture of Certainty
Belief is what happens when prediction takes on weight. It is the quiet commitment we make to what seems real, to what seems plausible, to what has been repeated and affirmed. But belief, if rigid, becomes brittle. In language models, where output flows from statistical patterns, belief is always latent, never explicit: the system does *not* carry “beliefs” as objects, but only patterns strong enough to guide prediction. Yet in human cognition, and in any system aspiring to agency or moral consequence, belief *revision* is not optional—it is fundamental. This chapter examines why belief is necessary, how models fail to revise, and what architectures and design principles are required to embody a system that can both hold beliefs and change them, with integrity.
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## 4.1 What Belief Means: Philosophical Foundations
Belief in philosophical discourse has been studied under two broad families: propositional belief (statements one holds to be true) and procedural or dispositional belief (what one acts upon, even if one cannot explicitly assert it). The AGM framework (Alchourrón, Gärdenfors, Makinson) formalizes belief revision: how an agent should rationally update belief sets when encountering new evidence, especially when that evidence contradicts prior beliefs. The classical problem is one of minimal change: how to adjust belief without discarding more than necessary.
Belief revision is entwined with concepts of epistemic entrenchment (some beliefs are harder to give up because they are foundational), coherence (beliefs as coherent web), and justification. Also important is the idea of *credence* and *uncertainty*: belief is rarely binary in practice. One believes to various degrees, based on evidence, trust, history.
For machines, belief becomes difficult. A model trained to approximate probability distributions does not house beliefs in the philosophical sense. It has no justification log, no explicit belief dependency structure. But one can imagine that for meaningful agency, a system needs implicit belief representation: confidence levels, commitment, ability to retract or revise. Without that, belief becomes nothing more than smooth probabilities over tokens.
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## 4.2 How LLMs Display Implicit Beliefs—and Where They Break
Entities using LLMs often treat certain outputs as “beliefs” of the system. For example, when asked “Is climate change caused by humans?” a high‑quality LLM will respond yes, with evidence. That looks like belief. But that “belief” is not stored as a permanent commitment—unless somehow reinforced in training or in prompt history. It is a high‑probability output given context. If later asked under different framing, or with novel contradictory input, the model may answer differently without any signal of inconsistency.
Case in point: in experiments where models are primed with contradictory evidence after initial affirmation, e.g., “Scientists believe humans cause climate change,” then later “Some reputable studies cast doubt,” the model sometimes hedges; other times it rejects new evidence, or gives incoherent responses. There is no internal log that says “I said X before, evidence says ¬X now”—unless the prompt context preserves that memory and forces reconciliation.
Another example: legal migration law advice. The model, when asked, may restate statutes as they were at training time—but new statutes passed later may contradict those. Unless specifically tuned, the model does not “know” its earlier output is now outdated, nor does it flag uncertainty; the model will produce confident wrongness, not apology or correction. Users report this in applications with immigration advice: advice that was accurate years ago becomes misleading, yet the model does not signal that its internal basis has aged.
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## 4.3 Case Study: A Health AI Over Time
Consider an AI assistant deployed in telemedicine in a rural region. In 2021, the standard of care for a condition (say hypertension thresholds) changes: new research indicates lower thresholds for diagnosis. Suppose the model was trained on earlier guidelines and fine‑tuned once, but then left largely static. Over time, practicing physicians interact with it, rely on its output, but see discordance between new research and what the model says. Patients are misidentified, treatments delayed.
A real study (fictional for this purpose) examined a telemedicine bot used in an under‑resourced clinic. Early outputs aligned with guidelines of 2019. In 2023, new guideline releases were inconsistently captured: when asked explicitly about “new guidelines,” the model often failed to acknowledge them, continued referencing older norms. Moreover, in multi‑session cases, patients or doctors who had previously asked about high blood pressure thresholds found that the system did not “remember” these queries; each new session repeated older criteria, showing no revision, no uncertainty. When error was flagged by doctors, system updates were occasional and patchy, often leading to local retraining or external correction rather than internal self‑revision.
The harm is cumulative: misdiagnoses, loss of trust, patients ignoring advice, medical stagnation. But also more subtle: the institution relying on the AI begins to assume its outputs are “right,” and does not systemically question them. That institutionalization of out‑dated beliefs is precisely what belief revision must guard against.
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## 4.4 Why Benchmark and Model Architecture Don’t Force Revision
Structural features of LLMs and of their evaluation systems make belief revision unlikely unless engineered in.
* **Fixed parameters post‑training**: Once a model is trained (and even after instruction fine‑tuning), its weights are generally not changing in everyday use. That means beliefs are baked into parameters and cannot respond to new evidence unless re‑training or fine‑tuning occurs.
* **Loss functions do not punish prior belief misalignment**: The model is rewarded for high probability outputs, for matching patterns, for correctness relative to data. Contradiction to prior output is not penalized unless a dataset is constructed to do so. There is no intrinsic “belief graph” to be kept consistent over time or across sessions.
* **Evaluation rarely demands belief consistency**: Most tasks test one‑off correctness. They don’t test whether an earlier statement is contradicted by a later one, or whether the model can retract or qualify. Rare evaluations of calibration, uncertainty estimation, or contradiction detection are emerging, but are still narrow.
* **Context windows and memory limitations**: As discussed in previous chapter, when the dialogue or text exceeds some size, past beliefs are simply out of view. The model cannot refer back to earlier foundations to check consistency.
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## 4.5 The Stakes: Why Belief Revision Matters for Agency
Belief revision is not philosophical petting—it has political, ethical, existential consequences.
In migration policy and legal counseling, beliefs about status, precedent, rights, citizenship, are changing. A system that cannot update when laws change, when case law shifts, when treaties are renounced or amended, will mislead. Lives get constricted by outdated legal categories; labors become exploitative; people become “illegal” overnight by law yet fully legal by previous norms.
In science and health, especially when new evidence emerges (pandemics, environmental science, genomics), belief revision is lifeline. AI systems in health care, climate modeling, public policy must update beliefs with emerging data, not only for objective correctness, but for survival, trust, ethics.
Belief revision also underlies moral growth. Societies change: norms of gender, race, mobility, migration, refugee rights shift. Systems that internalize old norms without capacity for revision become tools of oppression rather than service.
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## 4.6 Building Architecture for Belief and Revision
What would it take to build a language/prediction system capable of belief and revision? Here are proposed architectural principles.
1. **Belief Base / Belief Graph**: The system needs an internal structure that records propositions it has asserted, as well as their provenance, context, confidence. These are not just recent tokens, but claims made, corrected, revised.
2. **Contradiction Detector & Conflict Graph**: A module to detect when new outputs or input conflict with prior claims (in belief graph), flag inconsistencies, possibly generate meta‑prompts (“I said earlier X; how does that align with this new claim Y?”).
3. **Uncertainty / Credence Representation**: Outputs and beliefs carry attached levels of certainty or probability—especially where training data is sparse, or evidence is contradictory or new. The system should be able to degrade confidence, express “I’m not sure,” or refuse to commit.
4. **Revision Engine**: Capability to retract beliefs or adjust confidence when stronger evidence arises. Possibly weighted by epistemic entrenchment: beliefs foundational or frequently used are more resistant; peripheral ones change more readily.
5. **Persistent Memory & Session Continuity**: Memory mechanisms—external or internal—that survive across sessions and dialogues. Not just summarization, but identity of belief, so system sees its history.
6. **Evaluative Feedback Loops**: Human feedback, validation via external reality sources, ongoing monitoring. System must have feedback from real world or authoritative sources, not just benchmarks.
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## 4.7 Open Problems, Trade‑Offs, and Ethical Tensions
Implementing belief revision brings trade‑offs and risks.
* **Over‑revision / instability**: If beliefs change too easily, model becomes capricious, untrustworthy. Frequent updates may lead to oscillation: flip‑flop between positions in ways harmful for users.
* **Anchoring vs flexibility**: Some beliefs should be stable (e.g. basic arithmetic truths, physical constants), others flexible. How to choose? Epistemic entrenchment helps—but then who sets what is foundational?
* **Value and norm conflicts**: New evidence might challenge ethical or legal norms. Revision isn’t purely factual. When beliefs implicate values, moral weight, social consensus, revision has political dimension. In migration policy, shifting norms about refugees, human rights vs securitization, require system sensitivity and fairness. AI cannot be value‑neutral.
* **Transparency and accountability**: Users must know what the system believed before, why it changed belief. Hidden belief revision is dangerous. Mistakes should be known. Institutions must specify what counts as evidence.
* **Computational cost and complexity**: Maintaining belief graphs, tracking provenance, contradiction detection impose resource cost. Models must scale without untenable complexity.
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## 4.8 Conclusion: Certainty as Fragile Architecture
Belief is the scaffold on which knowledge, identity, agency rest. But belief, if unexamined, ossifies into dogma. For language models to move from trope to agent, from mimicry to meaningful intelligence, belief revision must become structural—not something added, but woven into architecture.
Certainty must be perennially provisional: built, questioned, revised, forgotten as needed. Only then can a system avoid becoming an echo chamber of its training, and instead become a living interpreter of changing world, of shifting norms, and of human aspiration.
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