REVIEW 41 cited by
Let's Think Dot by Dot: Hidden Computation in Transformer Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Let's Think Dot by Dot: Hidden Computation in Transformer Language Models
read the original abstract
Chain-of-thought responses from language models improve performance across most benchmarks. However, it remains unclear to what extent these performance gains can be attributed to human-like task decomposition or simply the greater computation that additional tokens allow. We show that transformers can use meaningless filler tokens (e.g., '......') in place of a chain of thought to solve two hard algorithmic tasks they could not solve when responding without intermediate tokens. However, we find empirically that learning to use filler tokens is difficult and requires specific, dense supervision to converge. We also provide a theoretical characterization of the class of problems where filler tokens are useful in terms of the quantifier depth of a first-order formula. For problems satisfying this characterization, chain-of-thought tokens need not provide information about the intermediate computational steps involved in multi-token computations. In summary, our results show that additional tokens can provide computational benefits independent of token choice. The fact that intermediate tokens can act as filler tokens raises concerns about large language models engaging in unauditable, hidden computations that are increasingly detached from the observed chain-of-thought tokens.
Forward citations
Cited by 41 Pith papers
-
Transformers Provably Learn to Internalize Chain-of-Thought
L-layer transformers under Log-ICoT curriculum provably learn k-parity with poly(n) samples and log k stages, matching explicit CoT efficiency without inference overhead.
-
The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies
Corruption studies of CoT faithfulness largely measure explicit answer placement in prompt format rather than computational importance of reasoning steps.
-
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
DiscoLoop adds a decoded token-embedding channel to looped transformers, fixing a representation mismatch that limited implicit multi-hop reasoning and improving OOD generalization.
-
Does Verbose Chain-of-Thought Really Help? In-Distribution Evidence that Content, Not Length, Matters
In-distribution sampling across 25 models and controlled interventions with DAG-verified content show that semantic reasoning and validation content, not token count, drive CoT gains.
-
PearlVLA: Progressive Embodied Action-Plan Refinement in Latent Space
PearlVLA achieves SOTA on LIBERO by separating VLM representations into visual grounding and an iterative latent plan branch refined via world model queries and RefineNet with process-reward RL.
-
Demystifying Hidden-State Recurrence: Switchable Latent Reasoning with On-Policy Reinforcement Learning
SWITCH uses explicit <swi> and </swi> boundary tokens to make latent chain-of-thought compatible with on-policy RL (GRPO) and open to causal mechanistic probing, outperforming prior hidden-state recurrence methods.
-
Unlocking the Working Memory of Large Language Models for Latent Reasoning
RiM trains LLMs to perform latent reasoning via fixed memory blocks processed in one forward pass using a two-stage curriculum, matching or exceeding prior latent methods on benchmarks.
-
What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation
CoT probe-time gains arise primarily from lexical activation and short-range token co-occurrence rather than sentence-level logical derivation.
-
Training-Free Looped Transformers
Training-free looped transformers retrofit recurrence to frozen models via damped ODE sub-steps on mid-stack blocks, yielding gains such as +2.64 pp on MMLU-Pro for Qwen3-4B.
-
CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning
CopT reverses CoT by eliciting a draft answer first then using continuous-embedding contrastive verification and on-policy thinking to reflect and correct, yielding up to 23% higher accuracy and 57% fewer tokens witho...
-
The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies
Corruption studies on CoT chains detect the position of explicit answer statements rather than computational steps, as evidenced by format ablations collapsing suffix sensitivity 19x and models following conflicting a...
-
Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost
Post-Reasoning boosts LLM accuracy by reversing the usual answer-after-reasoning order, delivering mean relative gains of 17.37% across 117 model-benchmark pairs with zero extra cost.
-
PLUME: Latent Reasoning Based Universal Multimodal Embedding
PLUME uses latent-state autoregressive rollouts and a progressive training curriculum to deliver efficient reasoning for universal multimodal embeddings without generating explicit rationales.
-
Enabling Agents to Communicate Entirely in Latent Space
Interlat lets LLM agents exchange last hidden states in latent space for communication, outperforming CoT baselines across models while enabling up to 24x faster inference via compression.
-
Learning a Continue-Thinking Token for Enhanced Test-Time Scaling
A learned continue-thinking token, trained via RL on its embedding alone, improves math benchmark accuracy more than fixed-token budget forcing in a frozen language model.
-
Training Large Language Models to Reason in a Continuous Latent Space
Coconut lets LLMs perform reasoning directly in continuous latent space by recycling hidden states as inputs, outperforming standard chain-of-thought on search-intensive logical tasks with better accuracy-efficiency t...
-
J-CoT: Chain-of-Thought in J-Space
J-CoT improves LLM reasoning by passing a sparse, vocabulary-indexed coefficient state—not text or full hidden vectors—between recurrent computation cycles.
-
Training Continuous Chain of Thought Models: A Tale of Two Regimes
C-MTP, a multi-token-prediction direct supervision method, matches slower latent-reasoning methods on short math traces, but all continuous CoT variants lose ~65% accuracy on long realistic traces.
-
Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers
LARM enables test-time compute scaling in non-autoregressive ASR via depth-conditioned looping with CTC checkpoints, supervision embeddings, FiLM conditioning, and posterior feedback, yielding lower WER on LibriSpeech...
-
CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models
CIRF tokenizes CoT traces into functional units, fine-tunes models to autoregressively emit these tokens plus optional results, and reports improved accuracy-latency trade-offs on math, symbolic, and commonsense benchmarks.
-
What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation
Chain-of-thought rationales help LLM answers mainly through word presence and 2-3-token local co-occurrences, not sentence-level reasoning order.
-
Understanding and Mitigating Premature Confidence for Better LLM Reasoning
Premature confidence in LLM chains of thought predicts flawed reasoning and is mitigated by progressive confidence shaping, a label-free RL objective that yields accuracy gains on arithmetic, math, and science tasks.
-
Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts
Reasoning language models extract answers from sparse, order-shuffled chain-of-thought traces with little accuracy loss.
-
SeLaR: Selective Latent Reasoning in Large Language Models
SeLaR selectively applies latent soft reasoning in LLMs via entropy gating and contrastive regularization, outperforming standard CoT on five benchmarks without training.
-
Enabling Agents to Communicate Entirely in Latent Space
Exchanging LLM hidden states instead of text beats chain-of-thought communication in the paper's tests and can be compressed to 8 steps with a 24× speedup.
-
Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought
LLMs interleave true causal reasoning steps with decorative ones in CoT, with only ~2.3% of steps having high causal impact on AIME for Qwen-2.5, and a steering direction can force internal use of specific steps.
-
Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought
Chain-of-thought steps in LLMs vary in causal influence; many are decorative, TTS identifies them, and a latent steering direction can switch whether a model 'thinks' through a step.
-
Mind-Paced Speaking: A Dual-Brain Approach to Real-Time Reasoning in Spoken Language Models
MPS proposes a dual-brain architecture separating formulation reasoning from articulation to achieve real-time CoT in SLMs with accuracy comparable to full pre-computation but much lower latency.
-
Performative Thinking? The Brittle Correlation Between CoT Length and Problem Complexity
A transformer trained to imitate A* produces traces whose length reflects similarity to training data, not true problem complexity, so long chain-of-thought should not be read as more 'thinking'.
-
Not All LLM Reasoning is Visible in the Chain-of-Thought
Semantically empty filler tokens improve accuracy across several frontier LLMs on synthetic math tasks and let Claude Opus 4.5 satisfy a hidden modular constraint, evidence of computation invisible in output tokens.
-
Conversable Complexity: Agentic LLM Collectives as Interpretable Substrates
Agentic LLM collectives are proposed as natural-language-interpretable computational substrates for ALife research.
-
DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
DiscoLoop adds a discrete embedding channel to looped transformers to fix representational misalignment in two-hop reasoning, yielding near-perfect accuracy on synthetic tasks and better pretraining loss on real data.
-
Latent Recurrent Transformer: Architecture Exploration, Training Strategies, and Scaling Behavior
Latent Recurrent Transformer augments autoregressive transformers with a cross-layer recurrent latent pathway from prior hidden states and uses interleaved parallel training to improve loss and in-context learning at ...
-
NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning
Injecting noise into LLM latent trajectories creates diverse reasoning paths whose agreement acts as a confidence signal for selective abstention, cutting error rates from 40-70% to under 15% on math tasks.
-
LLM Reasoning Is Latent, Not the Chain of Thought
LLM reasoning is primarily mediated by latent-state trajectories rather than by explicit surface chain-of-thought outputs.
-
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
A large survey organizes latent-space work in language-based models by foundation, evolution, four mechanisms, seven abilities, and open challenges.
-
NEST: Nascent Encoded Steganographic Thoughts
Frontier LLMs can embed short digit sequences in sentence acrostics (Claude Opus 4.5: 92% per-digit at D=4) but fail to jointly solve hidden reasoning tasks and encode the solution.
-
Diagnosing Pathological Chain-of-Thought in Reasoning Models
Three log-probability-difference metrics — Necessity, Paraphrasability, Substantivity — are proposed and tested on deliberately fine-tuned 'model organisms' to detect post-hoc, encoded, and internalized chain-of-thoug...
-
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
A survey organizing techniques to achieve efficient reasoning in LLMs by shortening chain-of-thought outputs.
-
Integrated and Cross-Architecture Interpretation of LLM Reasoning
Proposes IAR framework using MIP token isolation, DTR overlap analysis, and Jaccard stability to interpret reasoning patterns in Qwen and Llama models across math, code, logic, and commonsense domains.
-
Measuring AI Reasoning: A Guide for Researchers
Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.