Fast, Slow, and Tool-augmented Thinking for LLMs: A Review | Paper Citation Record

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This paper cites Claude sonnet 4, 2025.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Claude sonnet 4, 2025

Reference 1

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This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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This paper cites Openai o3, 2025.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Openai o3, 2025

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This paper cites Grok 4, 2025 18 Front.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Grok 4, 2025 18 Front

Reference 4

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This paper cites From System 1 to System 2: A Survey of Reasoning Large Language Models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review From System 1 to System 2: A Survey of Reasoning Large Language Models

Reference 5

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This paper cites Thinking, fast and slow.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Thinking, fast and slow

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This paper cites DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review DynaThink: Fast or Slow? A Dynamic Decision-Making Framework for Large Language Models

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This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Chain-of-thought prompting elicits reasoning in large language models

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This paper cites Tree of thoughts: Deliberate problem solving with large language models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Tree of thoughts: Deliberate problem solving with large language models

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This paper cites Towards mitigat- ing llm hallucination via self reflection.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Towards mitigat- ing llm hallucination via self reflection

Reference 10

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This paper cites Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification

Reference 11

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This paper cites Let llms break free from overthinking via self-braking tuning.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Let llms break free from overthinking via self-braking tuning

Reference 12

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This paper cites Large language models are zero-shot reasoners.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Large language models are zero-shot reasoners

Reference 13

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This paper cites Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspective

Reference 14

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This paper cites Confident adaptive language modeling, 2022.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Confident adaptive language modeling, 2022

Reference 15

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This paper cites How Much Knowledge Can You Pack Into the Parameters of a Language Model?.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review How Much Knowledge Can You Pack Into the Parameters of a Language Model?

Reference 16

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This paper cites Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back Home

Reference 17

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This paper cites Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Self-Route: Automatic Mode Switching via Capability Estimation for Efficient Reasoning

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This paper cites AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning

Reference 19

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This paper cites ThinkSwitcher: When to Think Hard, When to Think Fast.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review ThinkSwitcher: When to Think Hard, When to Think Fast

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This paper cites Active retrieval augmented generation.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Active retrieval augmented generation

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This paper cites MUR: Momentum Uncertainty guided Reasoning for Large Language Models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review MUR: Momentum Uncertainty guided Reasoning for Large Language Models

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This paper cites AdaptThink: Reasoning Models Can Learn When to Think.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review AdaptThink: Reasoning Models Can Learn When to Think

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This paper cites Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Learning when to think: Shaping adaptive reasoning in r1-style models via multi-stage rl

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This paper cites Certainty- guided reasoning in large language models: A dy- namic thinking budget approach.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Certainty- guided reasoning in large language models: A dy- namic thinking budget approach

Reference 25

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Dynamic early exit in reasoning models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Seakr: Self-aware knowledge retrieval for adaptive re- trieval augmented generation, 2024

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Adaptive Retrieval-Augmented Generation for Conversational Systems

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This paper cites Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Sugar: Leveraging contextual confidence for smarter retrieval

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Smart: Select, mix, and reinvent–a strategy fusion framework for llm-driven reasoning and planning

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Cognitive load during problem solving: Ef- fects on learning

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Au- tol2s: Auto long-short reasoning for efficient large lan- guage models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review TL;DR: Too Long, Do Re-weighting for Efficient LLM Reasoning Compression

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Self-rag: Learning to retrieve, generate, and critique through self- reflection

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Toolformer: Language models can teach themselves to use tools,

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review WebGPT: Browser-assisted question-answering with human feedback

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review KAT-V1: Kwai-AutoThink Technical Report

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Think Only When You Need with Large Hybrid-Reasoning Models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Exaone 4.0: Unified large language models integrating non-reasoning and reasoning modes

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Introducing gpt-5, 2025

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Thinking with nothinking calibration: A new in-context learn- ing paradigm in reasoning large language models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Z1: Efficient Test-time Scaling with Code

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Qwen3 Technical Report

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Llama-nemotron: Efficient reasoning models

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This paper cites Active retrieval augmented gener- ation.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Active retrieval augmented gener- ation

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This paper cites The Web Can Be Your Oyster for Improving Large Language Models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review The Web Can Be Your Oyster for Improving Large Language Models

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This paper cites Dragin: Dynamic retrieval augmented generation based on the information needs of large language models, 2024.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Dragin: Dynamic retrieval augmented generation based on the information needs of large language models, 2024

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This paper cites Retrieve only when it needs: Adaptive retrieval augmentation for hallucination mitigation in large language models.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Retrieve only when it needs: Adaptive retrieval augmentation for hallucination mitigation in large language models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Alignment for Efficient Tool Calling of Large Language Models

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation

Reference 56

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization

Reference 57

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review ReTool: Reinforcement Learning for Strategic Tool Use in LLMs

Reference 58

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review R3-rag: Learning step-by- step reasoning and retrieval for llms via reinforcement 20 Front

Reference 59

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning

Reference 60

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Toolken+: Improving LLM Tool Usage with Reranking and a Reject Option

Reference 61

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Confidence in the reasoning of large language models

Reference 62

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 63

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Wizardlm: Empowering large pre- trained language models to follow complex instructions

Reference 64

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Observation 4d84c411-6f2f-4454-850b-4e153cf46648 · outbound

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Gaia: a benchmark for general ai assistants, 2023

Reference 65

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review When do llms need retrieval augmentation? mitigating llms’ overconfidence helps retrieval augmentation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:17.648945Z

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Observation 79704caa-8239-40dc-a548-246fa479ba51 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:17.625979Z

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review The real barrier to llm agent usability is agentic roi

Reference 68

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Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Multi-Agent Collaboration Mechanisms: A Survey of LLMs

Reference 69

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Unavailable: canonical work link unavailable.

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This paper cites The interaction between text modality and the learner’s modality preference influ- ences comprehension and cognitive load.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review The interaction between text modality and the learner’s modality preference influ- ences comprehension and cognitive load

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:30:17.598455Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fac1af7e-6c2d-4094-96fa-39b6da72ebe9 · outbound

This paper cites Toolformer: Language Models Can Teach Themselves to Use Tools.

Fast, Slow, and Tool-augmented Thinking for LLMs: A Review Toolformer: Language Models Can Teach Themselves to Use Tools

Reference 2023

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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