Silent Thinking: How LLMs Reason Without Writing It Down

Large language models (LLMs) have reshaped what we expect from natural-language systems. Still, getting them to solve multi-step problems reliably is hard. Chain-of-Thought (CoT) prompting — asking a model to “think step-by-step” and write down its internal chain — dramatically improved performance on many tasks, from grade-school math to complex planning. But forcing models to narrate every intermediate step is costly: decoding long sequences is slow, expensive, and sometimes brittle.

What if the model could do the thinking silently, inside its hidden layers, and only output the final answer? That is the promise of implicit reasoning: multi-step computation happening in latent space without emitting intermediate text. Implicit reasoning can reduce latency, lower decoding costs, and potentially allow richer internal computations that don’t have to be mapped back into natural language.

This article is a guided tour of a recent survey paper, “Implicit Reasoning in Large Language Models: A Comprehensive Survey.” I’ll summarize the main ideas, clarify the core taxonomy, highlight compelling experimental evidence, and discuss evaluation practices and open challenges. Along the way I’ll point you to representative methods and figures that illustrate the landscape.

Figure 1 contrasts explicit and implicit reasoning visually: on the left, the model writes out every step (explicit CoT); on the right, the internal model layers carry out the steps silently (implicit reasoning). Notice how implicit reasoning avoids repeated token generation and can instead use internal, parallel, or compressed representations to carry out multi-step computation.

Figure 2 (the paper’s taxonomy map) gives a high-level view of the survey’s organization: three technical paradigms for implicit reasoning (latent optimization, signal-guided control, and layer-recurrent execution), evidence for internal reasoning, evaluation practices, and challenges.

Why this survey matters: the literature on internal, “silent” reasoning has grown fast but fragmented. This paper contributes a functional, execution-centric taxonomy (how and where computation unfolds), synthesizes mechanistic and behavioral evidence that latent reasoning exists, and reviews evaluation protocols and datasets used in the field.

Below I unpack the main points in a way that should be practical for students and practitioners.

1. Preliminaries — defining explicit and implicit reasoning
2. Three execution paradigms for implicit reasoning
   • Latent optimization (token-, trajectory-, and internal-state-level)
   • Signal-guided control (single- and multi-type signals)
   • Layer-recurrent execution (looped / iterative architectures)
3. Evidence that LLMs reason implicitly (structural, behavioral, representation-based)
4. How to evaluate implicit reasoning: metrics and benchmarks
5. Challenges and research directions

If you’ve used CoT prompts, much of this will feel familiar in spirit — but implicit reasoning shifts the work from token outputs to the model’s continuous dynamics.

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1. Preliminaries — formalizing the difference

The survey frames LLM reasoning as a two-stage process. Given an input \(x\), the model constructs an internal trace \(z_{1:M}\) and then produces a final answer \(a\):

\[ z_{1:M} = (z_1, \dots, z_M) \]\[ z_{1:M} \sim \pi_\theta(\cdot \mid x), \qquad a \sim \pi_\theta(\cdot \mid x, z_{1:M}). \]

What distinguishes explicit from implicit reasoning is the form of \(z_{1:M}\):

• Explicit reasoning: \(z_{1:M}\) are textual steps (a chain-of-thought)

  \[ y_{1:T} \sim \pi_\theta(\cdot\mid x), \qquad a\sim\pi_\theta(\cdot\mid x,y_{1:T}). \]

  Explicit CoT is interpretable because the intermediate steps are visible, but it’s costly to generate.

• Implicit reasoning: \(z_{1:M}\) (or \(h_{1:L}\) in the survey’s notation) are hidden states, latent tokens, or repeated internal activations that never become visible text

  \[ h_{1:L}\sim\pi_\theta(\cdot\mid x),\qquad a\sim\pi_\theta(\cdot\mid x,h_{1:L}). \]

  The computation is silent; only the final answer is exposed.

Table 1 in the paper (reproduced conceptually as a figure in the original) compares these paradigms along practical dimensions: visibility, efficiency, interpretability, reasoning diversity, supervision granularity, and cognitive alignment. Explicit reasoning wins interpretability and supervisory directness; implicit reasoning wins efficiency and flexibility.

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2. Three execution paradigms for implicit reasoning

The survey organizes methods by where and how internal computation unfolds. These execution paradigms are complementary and often combined in practice.

• Latent optimization: directly manipulate or optimize latent variables, hidden tokens, or hidden states to encode reasoning.
• Signal-guided control: insert lightweight control signals (special tokens) that steer internal computation and allocate more computation where needed.
• Layer-recurrent execution: reuse one or more Transformer blocks iteratively (looped layers) to simulate multi-step reasoning without increasing parameters proportionally.

I’ll go through each paradigm and show representative techniques.

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2.1 Latent optimization

Latent optimization methods operate on latent representations rather than text. The survey breaks this paradigm down by granularity: token-level, trajectory-level, and internal-state-level.

Token-level latent optimization

Token-level methods introduce special latent tokens (continuous or discrete) into the sequence to represent compressed concepts or reasoning primitives. These tokens can be:

• Extracted concept tokens (e.g., via sparse autoencoders) and mixed into the input (CoCoMix).
• Learnable latent tokens that are trained alongside the model to guide computation.
• Discrete latent codes via vector quantization (codebooks) used as compact reasoning steps or preferences.

Figure 3 (below) shows how token-level latent tokens are discovered and used: concept extraction, learnable insertions, vector quantization, and typical usage patterns (interleaving with text tokens at different positions).

Why token-level methods? They are lightweight, can be added without large architecture changes, and provide a way to inject compact reasoning abstractions into existing LLMs.

Representative works (paper’s Table 2) include CoCoMix, Latent Token, LPC (Latent Preference Coding), and Token Assorted — spanning commonsense, reading comprehension, and mathematical tasks.

Trajectory-level latent optimization

Trajectory-level methods treat an entire reasoning trajectory (the chain-of-thought) as a single object and compress it into a compact latent trajectory or sequence of “latent thoughts.” This reduces decoding cost while attempting to preserve the semantic structure of the explicit chain.

Figure 4 illustrates variants of this idea: compressed CoT (CCoT), Coconut (continuous thought), CODI (self-distillation to latent space), and LightThinker (dynamic compression during generation).

Key flavors:

• Semantic anchoring: compress explicit CoTs into latent trajectories while aligning semantics (CCoT, HCoT).
• Adaptive efficiency: dynamically compress or expand latent traces at test time (LightThinker, CoLaR).
• Progressive refinement: gradually internalize CoT steps during training (ICoT-SI, Coconut) so the model transitions smoothly from explicit to implicit reasoning.
• Exploratory diversification: allow the model to represent multiple alternative latent trajectories simultaneously (SoftCoT, SoftCoT++, LaTRO, COT2).

Trajectory-level methods are attractive when you want to reuse or distill existing CoT supervision into a more efficient representation.

Representative summary (paper’s Table 3) shows many methods evaluated on math datasets (GSM8K, MATH), planning tasks, and general commonsense benchmarks.

Internal-state-level latent optimization

Internal-state methods target the model’s internal activations and aim to distill, manipulate, or control those states directly. Typical approaches include:

• Distilling explicit teacher hidden states into a student model so the student learns to produce answers from internal state alone (ICoT-KD, System-2→System-1 distillation).
• Adding implicit memory modules to store reasoning-like hidden summaries (Beyond Words).
• Router/adapter mechanisms that create dynamic shortcuts for trivial steps while allocating depth for harder steps (System-1.5 Reasoning).
• Latent-thought vectors inserted into cross-attention (Latent Thought Models — LTMs), treated with variational or posterior inference.

Figure 5 illustrates distillation of hidden states from teacher to student and the data-distillation pipeline used to convert System-2 data into System-1 training examples.

These approaches are often used when you have a powerful explicit-reasoning teacher and want a compact implicit student that runs faster at inference.

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2.2 Signal-guided control

Signal-guided control inserts special control signals (tokens) that steer the model’s internal computation. These signals may be static (same signal type everywhere) or multi-typed (different tokens for memory vs. reasoning).

Common single-type signals:

• Thinking tokens / pause tokens / filler tokens / planning tokens — dummy tokens that trigger extra internal computation.
• Dynamic insertion of [PAUSE] tokens at low-confidence positions (DIT) or test-time latent optimization (LatentSeek).

Why it works: dummy tokens create more steps for the model to allocate attention and computation, enabling additional internal processing without emitting natural language. Importantly, this often requires no architecture change and can be applied to pretrained models.

Representative works are summarized in the paper’s table (Figure/Table 5): thinking tokens, pause tokens, Quiet-STaR, filler tokens, planning tokens, LatentSeek, DIT, and memory-vs-reason tokens.

Multi-type signals separate concerns: for example, a <memory> token triggers retrieval, and a <reason> token triggers logical deduction. Methods like Memory & Reasoning and Thinkless use separate signal tokens to disentangle cognitive roles, and they often include optimization strategies (RL or GRPO) to select when and which token to insert.

Signal-guided control is practical for deployment: it is lightweight and can be applied as a prompt-time intervention or via small finetuning steps.

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2.3 Layer-recurrent execution

Layer-recurrent execution introduces iterative (looped) computation into the forward pass, reusing the same Transformer block multiple times to simulate deeper internal reasoning while keeping parameters small. This is particularly useful when you want token-adaptive compute — some tokens may require more iterations than others.

Figure 6 gives an intuitive schematic: the encoded input passes through a recurrent block \(T\) times (sharing weights each iteration), then the decoder produces the answer. The recurrent loop acts like thinking steps.

Representative designs:

• ITT (Inner Thinking Transformer): adaptive token-wise depth with residual accumulation.
• Looped Transformer: K-layer transformer looped L times, with loop-based regularization to encourage stability.
• CoTFormer: token-wise adaptive repeats and budget-adaptive computation.
• Huginn: prelude–core–coda design where the core block is recurrent and iteration counts are sampled during training for scaleability.
• RELAY: looped transformer with iteration-wise alignment to CoT, enabling longer reasoning chains.

Layer-recurrent designs are attractive for test-time scaling: you can increase the number of iterations at inference time to trade compute for accuracy (test-time compute scaling).

Table 6 in the paper lists models, tasks, and datasets used to evaluate these looped architectures (math, reasoning primitives, reading comprehension, code tasks, etc.).

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3. Is implicit reasoning real? Evidence and probing

Because implicit reasoning is hidden, demonstrating that a model truly “reasoned” internally requires careful analysis. The survey groups the evidence into three complementary perspectives:

1. Layer-wise structural evidence
2. Behavioral signatures
3. Representation-based analyses

I’ll summarize each.

3.1 Layer-wise structural evidence

Several studies show consistent, layer-wise patterns that suggest internal computation:

• Intermediate-layer activations can often linearly predict the final output (Jump to Conclusions). That is, early layers already encode most of the final answer.
• Different layers specialize in subtasks: Internal Chain-of-Thought reports that certain layers encode particular subproblems and execute them in order, suggesting an internal, ordered computation.
• Theoretical constructions (Reasoning by Superposition) show that shallow transformers can encode multiple implicit search traces simultaneously in continuous superposition.
• Formal analysis comparing CoT to looped architectures (To CoT or To Loop) demonstrates how looped models can simulate multi-step computations via repeated application of a block.

Taken together, structural studies argue that the transformer’s depth can realize multi-step computations even without emitting tokens.

3.2 Behavioral signatures

Behavioral studies analyze how models behave under different training regimes or prompting:

• Grokking experiments show a phase transition from memorization to generalization with extended training; during this transition the model acquires internal, generalizable computation that can look like implicit reasoning.
• Step-skipping: LLMs can be trained to skip intermediate steps while preserving accuracy, implying that some steps are internalized.
• Reasoning-leap analyses show models sometimes arrive at correct answers without intermediate text but are sensitive to perturbations, indicating internal reliance on learned computation rather than deterministic token-level search.

These signatures indicate models can acquire non-trivial internal computation patterns behavioral indistinguishable from some forms of reasoning.

3.3 Representation-based analysis

The most direct way to study implicit reasoning is to probe and intervene in hidden representations:

• Probing: train a simple classifier on hidden states to predict intermediate sub-results. If a probe can recover step-1 results from a mid-layer representation, that’s strong evidence the model computed it internally.
• Causal interventions: steering vectors or activation-space manipulations can induce specific reasoning behaviors or correct errors, suggesting representations encode causal computation.
• Attention and activation patterns can reveal tree-like or sequential reasoning structures embedded in the network.

Collectively, these methods show that internal activations carry structured information consistent with multi-step inference, although careful causal analysis is still required to rule out trivial correlations.

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4. Evaluation: metrics and benchmarks

Implicit reasoning hides the intermediate steps, so evaluation must combine final-answer correctness with measures of efficiency and probes into internal representations.

4.1 Metrics

The survey categorizes metrics into four dimensions.

1. Answer correctness

   • Accuracy, Exact Match, Pass@k / Pass@1, BLEU/ROUGE/BERTScore for open-ended outputs.

   Accuracy (for \(N\) samples):

   \[ \text{Accuracy} = \frac{1}{N}\sum_{i=1}^N \mathbf{1}[a_{\text{pred}}^{(i)}=a_{\text{gt}}^{(i)}] \]

   Pass@k (for code/multi-sample setups):

   \[ \text{Pass@k} = 1 - \frac{\binom{n-c}{k}}{\binom{n}{k}},\qquad \text{Pass@1}=\frac{c}{n}. \]

2. Resource efficiency

   • Decoding latency, output length (token count), GPU memory/FLOPs, and composite metrics like Accuracy per Computation Unit (ACU): \[ \text{ACU} = \frac{\text{Accuracy}}{\#\text{Params}\times\#\text{Tokens}}. \]

   For looped or adaptive-depth models, measuring the actual iteration counts and dynamic compute is essential for fair comparison.

3. Perplexity

   • Perplexity reflects next-token modeling quality and serves as a proxy for foundational language understanding: \[ \text{PPL} = \exp\Big(-\frac{1}{N}\sum_{i=1}^N \log p_\theta(w_i\mid w_{

4. Probing accuracy

   • Train probes to predict intermediate labels from hidden states; high probing accuracy suggests internal encoding of sub-results. A typical probing loss: \[ \mathcal{L}_{\text{probe}}=\frac{1}{N}\sum_{i=1}^N \ell(f_\phi(h^{(i)}), z^{(i)}), \] and ProbingAcc is the fraction of correct probe predictions.

Probing should be complemented by causal interventions (ablations, activation edits) to show representations are not merely correlated with intermediate results but causally involved.

4.2 Benchmarks

The field uses many standard benchmarks. The survey groups them into five categories; I summarize representative ones here and include the paper’s table images for convenient reference.

1. General knowledge and commonsense (CommonsenseQA, PIQA, WinoGrande, HellaSwag, TruthfulQA).

2. Mathematical reasoning and programming (GSM8K and variants, MATH, MATH-500, AQUA-RAT, MultiArith, HumanEval, MBPP).

3. Language modeling and reading comprehension (PTB, One Billion Word, WikiText, LAMBADA, SQuAD, RACE, DROP).

4. Complex multi-hop and multidisciplinary QA (HotpotQA, 2WikiMultiHopQA, StrategyQA, MMLU, BIG-Bench Hard, GPQA).

5. Multi-modal reasoning (LLaVA-CoT, MMStar, MMBench, MathVista, ScienceQA, TheoremQA).

The paper emphasizes the need for benchmarks tailored to implicit reasoning: standardized probing datasets, latent annotations, and tasks measuring reasoning-depth and robustness to distribution shifts.

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5. Challenges and research directions

Implicit reasoning is promising, but several hard challenges remain. The survey highlights these and suggests directions:

1. Limited interpretability and latent opacity

   • Because reasoning is hidden, it’s hard to diagnose and trust. Future work needs causal probing, trajectory visualization, and state-trajectory attribution tailored to dynamic latent computation.

2. Limited control and reliability

   • Without intermediate outputs, failures are silent. Research should develop confidence-aware execution, reversible computation, and hybrid designs that allow partial inspection or verification (e.g., silent thinking plus optional verification prompts).

3. Performance gap vs explicit CoT

   • In many evaluations, explicit CoT still outperforms implicit approaches on complex tasks. Closing this gap may require hybrid training objectives (latent fidelity losses, verifier heads, or auxiliary explicit supervision) and better inductive biases for latent computation.

4. Lack of standardized evaluation

   • The field needs consensus benchmarks and protocols that measure internal consistency, depth of reasoning, and robustness, not just final-answer accuracy.

5. Architecture and generalization constraints

   • Many techniques are tied to specific architectures or small models. Creating architecture-agnostic methods and scaling them to large backbones remain open problems.

6. Dependence on explicit supervision

   • Most latent techniques rely on explicit CoT traces (teacher models or annotated chains) for training. Unsupervised or self-supervised signals that induce latent reasoning would make the approach more scalable.

Taken together, these challenges point to a rich research agenda: better interpretability tools, hybrid explicit-implicit training, evaluation standards, and architecture-agnostic latent objectives.

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6. Practical takeaways and recommendations

If you’re a practitioner or researcher thinking about implicit reasoning, here are practical guidelines distilled from the survey:

• When to use implicit reasoning:

  • Production systems where decoding latency and token costs matter.
  • Scenarios where intermediate text is not required or desirable (privacy, compact responses).
  • When you can afford additional model engineering (latent tokens, distillation).

• When to prefer explicit CoT:

  • Research and debugging (you need interpretability).
  • High-stakes domains where auditability is essential.
  • Tasks where the structure of intermediate steps matters (complex proofs, formal reasoning with verifiable intermediate states).

• Hybrid strategies are practical:

  • Distill explicit CoT teachers into implicit students (gain efficiency while preserving performance).
  • Use implicit reasoning by default and fall back to explicit CoT or verification prompts on low-confidence cases (adaptive reasoning budget).

• Evaluation best practices:

  • Always report final-answer accuracy alongside compute/latency metrics.
  • Use probing and causal interventions to support claims about internal computation.
  • For looped or dynamic-depth models, report average iteration counts and iteration-wise performance.

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7. Conclusion

Implicit reasoning reframes how we think about LLM computation: from visible chains of natural language to latent sequences of internal states. The survey provides a clear execution-centric taxonomy and synthesizes evidence that LLMs can and do perform useful latent computations. The potential benefits — faster inference, compact representations, richer internal search — are real, but the field must confront opaque internal dynamics, control and reliability issues, and a lack of standardized evaluation.

The likely near-term path forward is hybrid: leverage explicit CoT for supervision and interpretability, distill it into implicit representations for efficiency, and develop probes and interventions that open the latent processes enough to make them auditable and controllable. If successful, implicit reasoning could provide the efficiency of silent thinking with just enough transparency to be trusted.