Beyond Chain-of-Thought: Unpacking the Silent Reasoning of LLMs

If you’ve used large language models (LLMs) such as GPT-4 or Llama 3, you’ve probably seen Chain-of-Thought (CoT) prompting: ask a hard question, and the model walks you through intermediate steps before giving a final answer. That explicit, verbalized reasoning dramatically improves performance on many multi-step tasks, from math to commonsense puzzles.

But CoT has a cost: generating every intermediate token is slow, expensive, and sometimes unnecessary. What if the model could perform the same multi-step reasoning internally—“thinking silently”—and only output the final answer? That’s the core of implicit reasoning, an active research direction that aims to preserve deep reasoning ability while reducing latency, cost, and verbosity.

In this article I summarize and clarify the key ideas from the survey paper “Implicit Reasoning in Large Language Models: A Comprehensive Survey.” I’ll explain the core distinctions between explicit and implicit reasoning, walk through the execution-centric taxonomy the authors propose, highlight representative techniques, and discuss the evidence and benchmarks researchers use to study silent reasoning. Along the way, I’ll point out open challenges and promising directions.

A visual shorthand: explicit vs implicit reasoning

A comparison of explicit and implicit reasoning. Explicit reasoning shows a multi-step math problem solved with text. Implicit reasoning shows a series of internal model layers leading directly to the final answer.

Figure 1: Comparison between explicit and implicit reasoning in LLMs. Explicit reasoning shows each step by producing natural-language explanations (left). Implicit reasoning performs multi-step computation internally across layers or latent states without writing out each step (right), enabling faster, more compact processing.

Figure 1 captures the intuition: explicit reasoning exposes a token-level chain of intermediate thoughts; implicit reasoning keeps those intermediate states internal (hidden activations, latent tokens, or repeated layer computations) and only emits the final answer.

Both paradigms share a two-stage view of reasoning: the model builds some internal trace of computation and then uses that trace to produce the answer. They differ in whether the trace is verbalized.

Preliminaries — what we mean by reasoning

The survey formalizes LLM reasoning as a two-stage inference process. Given an input x, a model πθ first produces a trace z1:M and then produces an answer a conditioned on both x and the trace:

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

  • In explicit reasoning, z1:M are textual tokens, the CoT y1:T that you can read:

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

  • In implicit reasoning, z1:L are internal hidden states or latent variables h1:L that remain invisible to the user:

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

This shift—trading explicit token generation for internal computation—creates a different set of trade-offs: efficiency and compactness versus interpretability and controllability.

Table 1: Key differences between explicit reasoning and implicit reasoning in LLMs. The table compares dimensions like Reasoning Visibility, Efficiency, Interpretability, Diversity, Supervision, and Alignment with Human Thinking.

Figure 2 (table): High-level comparison of explicit vs implicit reasoning across visibility, efficiency, interpretability, trajectory diversity, supervision granularity, and alignment with human modes of thought.

An execution-centric taxonomy: three paradigms

The survey organizes implicit reasoning techniques by how and where internal computation unfolds. This execution-centric taxonomy groups approaches into three complementary paradigms:

  1. Latent optimization (directly optimize or inject latent representations),
  2. Signal-guided control (insert tokens or signals that steer internal computation), and
  3. Layer-recurrent execution (reuse layers or add recurrence to iterate computation).

Each family tackles the same core objective—perform multi-step reasoning without explicit tokenized CoT—but they do so at different levels of granularity and with different engineering trade-offs.

Figure 2: A taxonomy diagram showing the structure of the survey paper. It branches from LLM Implicit Reasoning into Technical Paradigms, which are broken down into Latent Optimization, Signal-Guided Control, and Layer-Recurrent Execution.

Figure 3: Taxonomy of implicit reasoning methods organized by execution paradigm.

Below I walk through each paradigm, highlight representative methods, and explain the intuition and practicality behind them.

Paradigm 1 — Latent optimization

Latent optimization methods directly manipulate the model’s internal representations (latent tokens, latent trajectories, or internal states) to encode reasoning. The approaches differ by the granularity of the optimization target:

  • Token-level: learn or insert special latent tokens that augment the input sequence.
  • Trajectory-level: compress full CoT traces into compact latent trajectories (“latent thoughts”).
  • Internal-state-level: distill or align internal activations so a student model can reason silently.

Figure 3 and the tables in the survey provide concrete model names and experimental summaries; here I cover the core ideas.

Token-level latent tokens

Token-level methods add a small number of additional embeddings—latent tokens—into the sequence that the model can use for private computation. These latent tokens can be:

  • Extracted concept vectors (e.g., from a sparse autoencoder),
  • Learned continuous tokens optimized by next-token prediction,
  • Discrete codebook tokens via vector quantization,
  • Or hybrid combinations interleaved with regular tokens.

Figure 3: Four diagrams showing different ways to acquire and use latent tokens, including from sparse autoencoders, through next-token prediction, via vector quantization, and by interleaving them in the input/output sequence.

Figure 4: Token-level latent optimization methods. Latent tokens act as compact computational scratch space that the model can read and write during reasoning without emitting words.

Why this helps: the model gains extra degrees of freedom for internal bookkeeping (counts, partial results, plan vectors) while preserving the original architecture and output format. The approach is lightweight and can be applied to pretrained backbones with fine-tuning or small adapter layers.

Trajectory-level latent thoughts

Trajectory-level methods compress entire CoT sequences into continuous latent trajectories—short sequences of embeddings that summarize multi-step reasoning. Typical patterns include:

  • Semantic anchoring: train latent trajectories to align with explicit CoT semantics, then replace textual CoT with the compressed latent gist.
  • Adaptive compression: dynamically decide how many latent tokens are needed per instance.
  • Progressive refinement: gradually internalize CoT through curriculum-like training.
  • Exploratory diversification: sample multiple latent trajectories to explore alternative reasoning paths in parallel.

Figure 4: Diagrams illustrating four trajectory-level optimization methods (CCoT, Coconut, CODI, LightThinker) that compress or replace explicit CoT steps with latent thoughts.

Figure 5: Representative trajectory-level approaches (e.g., CCoT, Coconut, CODI, LightThinker) that replace or compress explicit CoT into compact latent representations.

Intuition: compressing CoT to a short latent sequence preserves semantic information while avoiding token-by-token decoding. It can also make training more efficient (train to predict a shorter latent target) and enables instance-adaptive reasoning budgets.

Internal-state distillation

Internal-state methods transfer reasoning capability by teaching a student model to reproduce a teacher’s hidden activations. A typical pipeline:

  1. A strong teacher model generates explicit CoTs and hidden states.
  2. An emulator or auxiliary module learns to predict the teacher’s hidden states given the input.
  3. The student is trained to produce similar hidden states and then produce answers directly from those states—no text CoT needed.

Figure 5: A diagram showing how knowledge distillation works for internal-state optimization. A student model learns to emulate the hidden states of a teacher model that performs explicit reasoning.

Figure 6: Internal-state distillation pipelines (e.g., ICoT-KD, System-1.5) that transfer reasoning from explicit teachers into students’ latent representations.

This approach is powerful because it leverages existing explicit reasoning datasets and teachers while producing silent, efficient students in deployment. But it relies on good supervision (teacher hidden states) and careful alignment to avoid collapsing the representation.

Paradigm 2 — Signal-guided control

Signal-guided methods steer internal computation by inserting control signals—special tokens or dynamic embeddings—that prompt the model to allocate extra internal work. This category splits into single-type and multi-type signal strategies.

  • Single-type signals: a unified control token like [THINK], [PAUSE], or a learned “filler” that triggers additional internal passes or extended computation for subsequent tokens. These tokens are cheap and straightforward to implement: insert them at training or inference time and let the model use them as extra compute budget.
  • Multi-type signals: different tokens for different functions (e.g., <memory> for retrieval, <reason> for deduction, <short> vs <think> to select faster or deeper reasoning modes). They allow more structured and interpretable control.

Table 5: A summary of signal-guided control methods, including those that use “thinking tokens,” “pause tokens,” and “planning tokens.”

Figure 7: Signal-guided control methods provide a lightweight, architecture-compatible way to allocate internal computation with control tokens.

Signal-guided strategies are attractive because they require minimal architectural changes and can often be applied at test time (e.g., inserting pause tokens adaptively where the model shows low confidence). They also enable hybrid behavior: the model can sometimes operate in fast, explicit-output mode, and only “think more” silently when needed.

Paradigm 3 — Layer-recurrent execution

Layer-recurrent execution injects recurrence into the forward pass. Instead of strictly processing input through distinct layers once, looped or recurrent blocks reuse the same parameters multiple times to iteratively refine hidden states. This is effectively increasing depth at inference without proportional parameter increase.

Figure 6: A simplified diagram of layer-recurrent execution. It shows the input being processed by a block of layers (Li) that are repeated T times, refining the representation before decoding the answer.

Figure 8: Layer-recurrent execution (looped Transformers, ITT, CoTFormer, Huginn, RELAY) performs iterative refinement using shared weights and adaptive looping for multi-step implicit computation.

Key ideas:

  • Weight sharing across iterations keeps parameters efficient.
  • Token- or instance-wise adaptive repetition lets harder tokens receive more “thinking” steps.
  • Training can use random iteration counts and truncated backprop to make the model robust to variable inference depth.

Looped models can simulate multi-step CoT-like computation internally. They are a natural fit for test-time scaling—invest extra iterations for tougher inputs—and for resource-constrained settings where parameter count is fixed but runtime compute may be varied.

Table 6: A summary of layer-recurrent execution methods, like ITT and looped Transformers, which use weight sharing and adaptive depth to perform implicit reasoning.

Figure 9: Representative layer-recurrent models and their tasks/datasets.

Evidence that something meaningful is happening inside

A central, and reasonable, question is: when these models “think silently,” are they truly performing structured multi-step reasoning or just memorizing shortcuts? Because implicit traces are hidden, the field relies on indirect evidence from three complementary angles:

  1. Layer-wise structural evidence,
  2. Behavioral signatures, and
  3. Representation-based analysis (probing and interventions).

Layer-wise structural evidence

Researchers observe that different layers often specialize in subtasks, and that intermediate layer activations sometimes predict final outputs well. Examples include:

  • Probing intermediate layers shows that linear classifiers can recover answers before final token generation—suggesting completion of computation within depth.
  • Analysis of trained looped transformers shows they can simulate iterative DAG-like computations across iterations.
  • Theoretical constructions demonstrate that compact transformer architectures can encode iterative search or graph reachability via continuous latent states.

These results suggest that models can allocate computation across depth to achieve multi-step inference internally.

Behavioral signatures

Behavioral analyses study how models act over training or inference:

  • The “grokking” phenomenon: prolonged training leads to sudden generalization improvements, interpreted as circuits shifting from memorization to algorithmic reasoning.
  • Step-skipping: fine-tuning can cause models to skip explicit intermediate steps while preserving accuracy, implying internalization of those steps.
  • Instance-adaptive behavior: models that accept pause tokens or dynamic latent compression often behave differently on easy vs. hard problems, implying conditional internal computation.

Behavioral signatures provide population-level indicators that internal multi-step processing is present and useful.

Representation-based analysis

The most direct approach is to analyze hidden states:

  • Probing: train lightweight classifiers to predict intermediate sub-results from hidden vectors. High probe accuracy implies the model encodes substeps internally.
  • Intervention: manipulate activations (steering vectors) to induce or suppress specific reasoning behaviors and observe the effect on output.
  • Attention and circuit analyses: some studies extract reasoning trees or chain structures from attention or activation patterns.

These methods are not definitive—probing can detect correlates rather than causal computations—but combined with interventions they build a reasonably strong case that meaningful, multi-step computation can be encoded in hidden states.

How we measure success

Evaluating implicit reasoning requires combining standard downstream metrics with new probes for internal computation and efficiency.

Key metric families:

  • Answer correctness: accuracy, exact match, Pass@k for code tasks.
  • Resource efficiency: decoding latency, output token count, FLOPs/FWPs; combined metrics such as Accuracy per Computation Unit (ACU) penalize both model size and decoding length.
  • Language modeling quality: perplexity (PPL) remains a baseline measure of general modeling competence.
  • Probing accuracy: training auxiliary classifiers to recover intermediate sub-results from hidden layers.

Because implicit methods do not yield textual traces, researchers increasingly use probing, interventions, and controlled benchmarks (see below) to understand and validate internal computations.

Benchmarks researchers use

The survey catalogs a broad set of datasets across five categories. I’ll summarize representative datasets used to evaluate implicit reasoning.

  • General knowledge & commonsense: CommonsenseQA, PIQA, WinoGrande, HellaSwag, TruthfulQA. (Table: images/025.jpg#center)

    Table 7: A list of general knowledge and commonsense reasoning benchmarks, including CommonsenseQA, PIQA, and TruthfulQA.

    Figure 10: Commonsense and general-knowledge benchmarks typically used to evaluate latent reasoning on everyday knowledge.

  • Mathematical reasoning & programming: GSM8K (grade-school math), MATH, MATH-500, SVAMP, HumanEval, MBPP. (Table: images/026.jpg#center)

    Table 8: A list of mathematical and programming benchmarks like GSM8K, MATH, and HumanEval.

    Figure 11: Arithmetic, competition math, and code-generation datasets that stress precise multi-step reasoning.

  • Language modeling & reading comprehension: PTB, WikiText, LAMBADA, SQuAD, DROP. (Table: images/027.jpg#center)

    Table 9: A list of language modeling and reading comprehension benchmarks, such as SQuAD, RACE, and TriviaQA.

    Figure 12: Core language understanding and reading comprehension tasks.

  • Multi-hop and multidisciplinary QA: HotpotQA, 2WikiMultiHopQA, StrategyQA, MMLU, BIG-Bench Hard. (Table: images/028.jpg#center)

    Table 10: A list of multi-hop and multidisciplinary QA benchmarks, including HotpotQA, MMLU, and BIG-Bench Hard.

    Figure 13: Multi-hop and multidisciplinary tasks require compositional inference across multiple facts.

  • Multi-modal reasoning: LLaVA-CoT-100K, MMStar, MathVista, ScienceQA, TheoremQA. (Table: images/029.jpg#center)

    Table 11: A list of multi-modal reasoning benchmarks, such as MMStar, MathVista, and ScienceQA.

    Figure 14: Benchmarks that combine visual and textual inputs to evaluate cross-modal implicit reasoning.

These benchmarks let researchers evaluate final-answer quality across diverse reasoning types. Complementing them with probing datasets—where intermediate labels or symbolic proofs are available—helps verify internal computation.

Challenges and directions that matter

Implicit reasoning is promising, but it raises important scientific and engineering challenges. The survey highlights six core limitations and suggests directions for progress.

  1. Limited interpretability and latent opacity. Because intermediate states are hidden, debugging and trusting implicit reasoning is hard. We need better causal probing, trajectory visualization, and interventions that reveal not just correlations but causal pathways.

  2. Limited control and reliability. Silent failures are dangerous. Future models should expose confidence signals, support adjustable reasoning budgets, and allow partial intervention or verification hooks to balance silence with oversight.

  3. Performance gap vs. explicit CoT. Many implicit methods still lag behind explicit CoT in accuracy on difficult compositional tasks. Hybrid strategies—silent thinking plus lightweight verification or selective explicitization—may close this gap.

  4. Lack of standardized evaluation. The field lacks benchmark suites designed for implicit reasoning (latent annotations, probing protocols, robustness tests). A standardized evaluation framework would greatly improve comparability and reproducibility.

  5. Architecture and generalization constraints. Several methods depend on architecture-specific components that are tricky to scale or port. Architecture-agnostic techniques and pretraining objectives that encourage latent reasoning would improve adoption across model families and sizes.

  6. Dependence on explicit supervision. Many implicit approaches rely on explicit CoT supervision (teacher hidden states or textual traces) during training. Unsupervised or self-supervised objectives that discover useful latent trajectories would reduce dependence on expensive annotations.

Taken together, these challenges suggest a practical path: develop hybrid systems that combine silent latent reasoning with targeted explicitization and verification; build better probes and interventions to inspect latent computation; and construct benchmarks that measure not only final answers but the fidelity and robustness of internal reasoning.

Takeaways

  • Implicit reasoning rethinks multi-step inference: instead of emitting every intermediate step, models perform internal computation in latent space and output the final answer. This promises efficiency gains and new scaling behaviors, especially at test time.
  • The survey organizes methods into three execution paradigms—latent optimization, signal-guided control, and layer-recurrent execution—each offering different trade-offs between implementation complexity, interpretability, and runtime flexibility.
  • Evidence from layer-wise analyses, behavioral phenomena, and representation probing suggests LLMs can and do perform meaningful internal computation, though substantial caution remains: probing results are correlational and intervention-based causality is required.
  • A major open area is evaluation: the community needs standardized benchmark suites, probing protocols, and metrics that assess not just correctness but the quality, reliability, and controllability of internal reasoning.
  • Short term, hybrid approaches (silent reasoning + selective explicit verification) look most promising for practical systems. Longer term, we need better mechanistic understanding of how latent trajectories implement algorithmic reasoning.

Implicit reasoning is an exciting, rapidly moving space with both practical engineering implications and deep scientific questions. As LLMs become more central to real-world systems, figuring out how to let them “think silently” while remaining understandable and controllable will be essential.

If you’re exploring this area—whether to build faster inference pipelines, design new pretraining objectives, or probe internal computation—this survey is a useful roadmap. It ties together a broad literature, clarifies key mechanisms, and highlights concrete open problems. The silent thinkers among our models have a lot to teach us—but we need better ways to listen.