Predictive, Asymmetric, Asynchronous Critic (PAAC): A Framework for Real-time, Zero-Latency Self-Improvement in Large Language Models
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2025
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| _version_ | 1866902097032642560 |
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| author | Kondo, Kazunori |
| author_facet | Kondo, Kazunori |
| contents | <p>Large Language Models (LLMs) rely on human feedback for improvement, but this feedback is often limited to low-resolution signals like scores or binary choices. Meanwhile, approaches that perform self-correction at inference time suffer from a fundamental user experience challenge: increased latency. This paper proposes a novel framework, the Predictive, Asymmetric, Asynchronous Critic (PAAC), to resolve these challenges simultaneously. PAAC combines three core components: (1) a Predictive Critique mechanism that uses the semantic "gap" between a predicted user reply and the actual reply as a high-resolution feedback signal; (2) an Asymmetric Architecture that offloads this critique process to a lightweight, specialized model; and (3) an Asynchronous Implementation that executes this critique computation in the background during the user's natural thinking and typing time. This framework demonstrates the feasibility of a next-generation LLM architecture that can continuously self-improve from rich learning signals in real-time, without any perceived latency by the user.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15751270 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Predictive, Asymmetric, Asynchronous Critic (PAAC): A Framework for Real-time, Zero-Latency Self-Improvement in Large Language Models Kondo, Kazunori LLM Large Language Models <p>Large Language Models (LLMs) rely on human feedback for improvement, but this feedback is often limited to low-resolution signals like scores or binary choices. Meanwhile, approaches that perform self-correction at inference time suffer from a fundamental user experience challenge: increased latency. This paper proposes a novel framework, the Predictive, Asymmetric, Asynchronous Critic (PAAC), to resolve these challenges simultaneously. PAAC combines three core components: (1) a Predictive Critique mechanism that uses the semantic "gap" between a predicted user reply and the actual reply as a high-resolution feedback signal; (2) an Asymmetric Architecture that offloads this critique process to a lightweight, specialized model; and (3) an Asynchronous Implementation that executes this critique computation in the background during the user's natural thinking and typing time. This framework demonstrates the feasibility of a next-generation LLM architecture that can continuously self-improve from rich learning signals in real-time, without any perceived latency by the user.</p> |
| title | Predictive, Asymmetric, Asynchronous Critic (PAAC): A Framework for Real-time, Zero-Latency Self-Improvement in Large Language Models |
| topic | LLM Large Language Models |
| url | https://doi.org/10.5281/zenodo.15751270 |