Time-Scaling Is What Agents Need Now

• URL: http://arxiv.org/abs/2601.02714v1
• Date: Tue, 06 Jan 2026 05:01:17 GMT
• Title: Time-Scaling Is What Agents Need Now
• Authors: Zhi Liu, Guangzhi Wang,
• Abstract summary: Humans solve problems under limited cognitive resources through temporalized sequential reasoning.<n>Recent models like DeepSeek-R1 enhanced performance through explicit reasoning trajectories.<n>Time-Scaling refers to architectural design utilizing extended temporal pathways.
• Score: 14.04852184032241
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early artificial intelligence paradigms exhibited separated cognitive functions: Neural Networks focused on "perception-representation," Reinforcement Learning on "decision-making-behavior," and Symbolic AI on "knowledge-reasoning." With Transformer-based large models and world models, these paradigms are converging into cognitive agents with closed-loop "perception-decision-action" capabilities. Humans solve complex problems under limited cognitive resources through temporalized sequential reasoning. Language relies on problem space search for deep semantic reasoning. While early large language models (LLMs) could generate fluent text, they lacked robust semantic reasoning capabilities. Prompting techniques like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) extended reasoning paths by making intermediate steps explicit. Recent models like DeepSeek-R1 enhanced performance through explicit reasoning trajectories. However, these methods have limitations in search completeness and efficiency. This highlights the need for "Time-Scaling"--the systematic extension and optimization of an agent's ability to unfold reasoning over time. Time-Scaling refers to architectural design utilizing extended temporal pathways, enabling deeper problem space exploration, dynamic strategy adjustment, and enhanced metacognitive control, paralleling human sequential reasoning under cognitive constraints. It represents a critical frontier for enhancing deep reasoning and problem-solving without proportional increases in static model parameters. Advancing intelligent agent capabilities requires placing Time-Scaling principles at the forefront, positioning explicit temporal reasoning management as foundational.

Related papers

• Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction [57.67217258741752]
  Thinker is a hierarchical thinking model for deep search through multi-turn interaction.<n>It decomposes complex problems into independently solvable sub-problems.<n> dependencies between sub-problems are passed as parameters via these logical functions.
  arXiv  Detail & Related papers  (2025-11-11T07:48:45Z)
• Real-Time Reasoning Agents in Evolving Environments [52.21796134114843]
  We introduce real-time reasoning as a new problem formulation for agents in evolving environments.<n>Our work establishes real-time reasoning as a critical testbed for developing practical agents.
  arXiv  Detail & Related papers  (2025-11-07T00:51:02Z)
• AI Agents as Universal Task Solvers [94.49762121230042]
  We show that the optimal speed-up that a universal solver can achieve using past data is tightly related to their algorithmic information.<n>We argue that the key quantity to optimize when scaling reasoning models is time, whose critical role in learning has so far only been indirectly considered.
  arXiv  Detail & Related papers  (2025-10-14T02:17:54Z)
• Decoupling Understanding from Reasoning via Problem Space Mapping for Small-scale Model Reasoning [22.582715282848795]
  We propose a new framework that decouples understanding from reasoning by mapping natural language problems into a canonical problem space.<n>Within this framework, we introduce DURIT, a three-step algorithm that iteratively aligns reasoning trajectories through self-distillation.<n>Experiments show that DURIT substantially improves SLMs' performance on both in-domain and out-of-domain mathematical and logical reasoning tasks.
  arXiv  Detail & Related papers  (2025-08-07T01:13:30Z)
• A Survey on Latent Reasoning [100.54120559169735]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities.<n>CoT reasoning that verbalizes intermediate steps limits the model's expressive bandwidth.<n>Latent reasoning tackles this bottleneck by performing multi-step inference entirely in the model's continuous hidden state.
  arXiv  Detail & Related papers  (2025-07-08T17:29:07Z)
• Reasoning in machine vision: learning to think fast and slow [10.430190333487957]
  Reasoning is a hallmark of human intelligence, enabling adaptive decision-making in complex and unfamiliar scenarios.<n>Machine intelligence remains bound to training data, lacking the ability to dynamically refine solutions at inference time.<n>Here we present a novel learning paradigm that enables machine reasoning in vision by allowing performance improvement with increasing thinking time.
  arXiv  Detail & Related papers  (2025-06-27T10:03:05Z)
• Adaptive Deep Reasoning: Triggering Deep Thinking When Needed [28.575411507835973]
  Large language models (LLMs) have shown impressive capabilities in handling complex tasks through long-chain reasoning.<n>We propose a novel approach that autonomously switches between short and long-chain reasoning chains based on problem complexity.<n>This advancement enhances the practicality of reasoning in large language models for real-world applications.
  arXiv  Detail & Related papers  (2025-05-26T15:08:51Z)
• From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models [46.02816479205161]
  We present textbfAtomic Reasoner (textbfAR), a cognitive inference strategy that enables fine-grained reasoning.<n>AR decomposes the reasoning process into atomic cognitive units, employing a cognitive routing mechanism.<n>Results show AR's superior reasoning capabilities without the computational burden of exhaustive solution searches.
  arXiv  Detail & Related papers  (2025-03-20T08:34:53Z)
• From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning [66.98861219674039]
  Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions. Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
  arXiv  Detail & Related papers  (2023-10-24T19:46:04Z)

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.