Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs

• URL: http://arxiv.org/abs/2607.27415v1
• Date: Wed, 29 Jul 2026 19:31:50 GMT
• Title: Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs
• Abstract summary: We propose a novel framework, textitGAMER(Graph-based Action-centric Memory with Episodic Reasoning), that bridges the gap between inference scaling and episodic memory.<n>Our approach models historical reasoning as a dynamic textitAction-Centric Graph.<n> Experiments on multiple benchmarks demonstrate that textitGAMER achieves superior performance by textbf20.81%/6.17% for success/progress rate compared to vanilla baselines.
• Score: 48.754678397305355
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in inference-time scaling have significantly unlocked the complex reasoning capabilities of Large Language Models~(LLMs). However, for agents, these approaches suffer from a critical inefficiency, operating in a stateless manner and engaging in redundant search processes. Existing memory mechanisms largely rely on the reasoning capabilities of LLMs, leading to prohibitive computational costs. In this paper, we propose a novel framework, \textit{GAMER}~(Graph-based Action-centric Memory with Episodic Reasoning), that bridges the gap between inference scaling and episodic memory. Our approach models historical reasoning as a dynamic \textit{Action-Centric Graph}. By decoupling the memory mechanism from LLMs, our method can save token/money usage by providing less memory context than memory mechanism baselines. To extract knowledge from the graph effectively, we use a dual-stream Temporal Difference learning mechanism to estimate the positive~(suggestion) and negative~(avoidance) value of action nodes based on past successes and failures. During the inference phase, this learned value function optimizes decision-making bi-directionally, so that positive values provide action suggestions, while negative values indicate high-risk actions. By performing efficient searches on the graph, our method significantly improves the efficiency of inference scaling. Experiments on multiple benchmarks demonstrate that \textit{GAMER} achieves superior performance by \textbf{20.81\%/6.17\%} for success/progress rate compared to vanilla baselines.

Related papers

• MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning [78.46301394559903]
  Large Language Models (LLMs) are increasingly used for long-duration tasks.<n>Current methods face a trade-off between cost and accuracy.<n>MemSifter is a novel framework that offloads the memory retrieval process to a small-scale proxy model.
  arXiv  Detail & Related papers  (2026-03-03T02:57:38Z)
• NextMem: Towards Latent Factual Memory for LLM-based Agents [58.35585202907478]
  NextMem is a latent factual memory framework that utilizes an autoregressive autoencoder to efficiently construct latent memory.<n>Extensive experiments demonstrate that NextMem achieves superior performance.
  arXiv  Detail & Related papers  (2026-02-26T14:35:27Z)
• Recurrent Preference Memory for Efficient Long-Sequence Generative Recommendation [27.325586037888]
  We introduce Rec2PM, a framework that compresses long user interaction histories into compact Preference Memory tokens.<n>Experiments show that Rec2PM significantly reduces inference latency and memory footprint while achieving superior accuracy compared to full-sequence models.
  arXiv  Detail & Related papers  (2026-02-12T05:51:52Z)
• Power Law Guided Dynamic Sifting for Efficient Attention [0.8198759882422455]
  We propose SiftAttention, a novel approximate attention method that replaces the top-k$ step with a computationally efficient element-wise filtering operation based on a threshold value.<n>Our evaluation demonstrates that SiftAttention preserves model quality better than existing approximate attention methods while reducing memory usage when loading vectors.
  arXiv  Detail & Related papers  (2025-06-05T17:50:32Z)
• Quantifying Memory Utilization with Effective State-Size [73.52115209375343]
  We develop a measure of textitmemory utilization'<n>This metric is tailored to the fundamental class of systems with textitinput-invariant and textitinput-varying linear operators
  arXiv  Detail & Related papers  (2025-04-28T08:12:30Z)
• Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation [29.139579820699495]
  This work strives to reduce memory overhead in fine-tuning from perspectives of activation function and layer normalization. We apply our Approx-BP theory to backpropagation training and derive memory-efficient alternatives of GELU and SiLU activation functions. In addition, we introduce a Memory-Sharing Backpropagation strategy, which enables the activation memory to be shared by two adjacent layers.
  arXiv  Detail & Related papers  (2024-06-24T03:09:15Z)
• Constant Memory Attention Block [74.38724530521277]
  Constant Memory Attention Block (CMAB) is a novel general-purpose attention block that computes its output in constant memory and performs updates in constant computation. We show our proposed methods achieve results competitive with state-of-the-art while being significantly more memory efficient.
  arXiv  Detail & Related papers  (2023-06-21T22:41:58Z)
• Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers [29.319666323947708]
  We present a novel approach that dynamically prunes contextual information while preserving the model's expressiveness. Our method employs a learnable mechanism that determines which uninformative tokens can be dropped from the context. Our reference implementation achieves up to $2times$ increase in inference throughput and even greater memory savings.
  arXiv  Detail & Related papers  (2023-05-25T07:39:41Z)
• A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning [56.450090618578]
  Class-Incremental Learning (CIL) aims to train a model with limited memory size to meet this requirement. We show that when counting the model size into the total budget and comparing methods with aligned memory size, saving models do not consistently work. We propose a simple yet effective baseline, denoted as MEMO for Memory-efficient Expandable MOdel.
  arXiv  Detail & Related papers  (2022-05-26T08:24:01Z)
• ABC: Attention with Bounded-memory Control [67.40631793251997]
  We show that bounded-memory control (ABC) can be subsumed into one abstraction, attention with bounded-memory control (ABC) ABC reveals new, unexplored possibilities. First, it connects several efficient attention variants that would otherwise seem apart. Last, we present a new instance of ABC, which draws inspiration from existing ABC approaches, but replaces their memory-organizing functions with a learned, contextualized one.
  arXiv  Detail & Related papers  (2021-10-06T03:53:25Z)

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.