Capabilities and Fundamental Limits of Latent Chain-of-Thought
- URL: http://arxiv.org/abs/2602.01148v1
- Date: Sun, 01 Feb 2026 10:46:00 GMT
- Title: Capabilities and Fundamental Limits of Latent Chain-of-Thought
- Abstract summary: Latent Chain-of-Thought (Latent CoT) models promise efficient reasoning via continuous representations, yet exhibit puzzling performance inconsistencies.<n>We reveal that this trade-off is governed by decisional certainty.
- Score: 9.586079816543178
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Latent Chain-of-Thought (Latent CoT) models promise efficient reasoning via continuous representations, yet exhibit puzzling performance inconsistencies: excelling at exploration (ProsQA: 97.0%) but failing at computation (GSM8K: 34.1%). We reveal that this trade-off is governed by decisional certainty. Our contributions are threefold: (1) We theoretically characterize the fundamental Exploration-Execution Trade-off, proving that high certainty enables precise execution but inhibits exploration, while low certainty facilitates search but causes error accumulation. (2) We introduce the Symbolic Index--quantifying decisional commitment--as the core mechanism governing this trade-off and establish its causal relationship with both execution stability and exploration capability. (3) We prove that curriculum learning is theoretically necessary, as direct training provably fails due to distributional mismatch. Our framework shifts the design paradigm from binary architectural choices toward adaptive systems that dynamically regulate decisional certainty based on task demands.
Related papers
- Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning [55.99621402404222]
Reinforcement learning with verifiable rewards without human-annotated data, often referred to as zero RL, has emerged as a powerful paradigm for eliciting chain-of-thought reasoning.<n>We aim to elicit high-quality reasoning behaviors from the model.<n>We find that naive scaling often suffers from poor readability, token redundancy, and a lack of adaptive reasoning depth.
arXiv Detail & Related papers (2026-07-14T06:14:55Z) - Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models [46.26628756478016]
Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data.<n>We show that reasoning conflicts are internally detectable, as confidence scores significantly drop during conflicting episodes.<n>Our findings establish that while LLM reasoning is anchored to concrete instances, active mechanistic interventions can effectively decouple logical schemata from data.
arXiv Detail & Related papers (2026-04-29T22:55:40Z) - Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure [58.89643769707751]
We study latent chain-of-thought as a manipulable causal process in representation space.<n>We find that latent-step budgets behave less like homogeneous extra depth and more like staged functionality with non-local routing.<n>These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and improving latent reasoning systems.
arXiv Detail & Related papers (2026-02-09T15:25:12Z) - TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents [51.30998248590416]
Trajectory-Aware Comprehensive Evaluation (TRACE) is a framework that holistically assesses the entire problem-solving trajectory.<n>Our contributions include the TRACE framework, its novel metrics, and the accompanying DeepResearch-Bench with controllable complexity.
arXiv Detail & Related papers (2026-02-05T13:28:57Z) - Agentic Uncertainty Quantification [76.94013626702183]
We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
arXiv Detail & Related papers (2026-01-22T07:16:26Z) - CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs [53.199517625701475]
CoG is a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation.<n>CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.
arXiv Detail & Related papers (2026-01-16T07:27:40Z) - The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents [0.6117371161379209]
We propose a formal probabilistic framework that provides agents with a non-altruistic motive for bidirectional knowledge exchange.<n>We show how these accumulated belief states serve as verifiable reward signals for Reinforcement Learning from Human Feedback (RLHF) and high-quality data filters for Supervised Fine-Tuning (SFT)<n> Simulation results validate that this uncertainty-driven strategy significantly outperforms random baselines in heterogeneous environments.
arXiv Detail & Related papers (2025-12-24T02:02:25Z) - Rediscovering Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning [55.59724323303857]
We propose a framework that balances exploration and exploitation via three components: difficulty-aware coefficient allocation, initial-anchored target entropy, and dynamic global coefficient adjustment.<n>Experiments on multiple mathematical reasoning benchmarks show that AER consistently outperforms baselines, improving both reasoning accuracy and exploration capability.
arXiv Detail & Related papers (2025-10-13T03:10:26Z) - FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning [62.452350134196934]
FaithCoT-Bench is a unified benchmark for instance-level CoT unfaithfulness detection.<n>Our framework formulates unfaithfulness detection as a discriminative decision problem.<n>FaithCoT-Bench sets a solid basis for future research toward more interpretable and trustworthy reasoning in LLMs.
arXiv Detail & Related papers (2025-10-05T05:16:54Z) - Deliberative Reasoning Network: An Uncertainty-Driven Paradigm for Belief-Tracked Inference with Pretrained Language Models [7.095344389368656]
Deliberative Reasoning Network (DRN) is a novel paradigm that reframes logical reasoning from probability to uncertainty minimization.<n>DRN achieves intrinsic interpretability by explicitly tracking belief states and quantifying uncertainty for competing hypotheses.<n>We position DRN as a foundational, verifiable System 2 reasoning component for building more trustworthy AI systems.
arXiv Detail & Related papers (2025-08-06T11:33:35Z) - CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
arXiv Detail & Related papers (2025-07-10T21:32:18Z) - Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning [22.885363238296858]
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities.<n>We propose a causal framework that characterizes CoT reasoning through the dual lenses of sufficiency and necessity.
arXiv Detail & Related papers (2025-06-11T15:22:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.