arXiv Machine Learning
Asymmetric Capacity Allocation in Self-Refinement Pipelines
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arXiv AI
Oct 26
Green Code or Costly Code? Evaluating the Energy Costs of Iterative LLM-Based Code Optimizations
Large Language Models (LLMs) are increasingly used to optimize code for performance, but this study shows that such optimizations often increase energy consumption rather than reduce it. Across 11 open‑source models and 1,000 benchmark problems, inference energy ranged from 0.79 to 8.68 MJ, and 42 of 44 configurations produced code that used 4% to 886% more energy than human‑written reference solutions. Mixture‑of‑Experts architectures and token‑limit constraints can reduce inference energy, yet performance‑optimized code remains energy‑heavier, indicating that speed and sustainability are not always aligned goals.
arXiv AI
Aug 11
Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines
arXiv:2604. 01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction.
arXiv AI
Jul 28
Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation
arXiv:2607. 22653v1 Announce Type: new Abstract: Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model.
arXiv Machine Learning
Sep 10
MetaRSI / RSI2: A Meta-Recursive Self-Improving System for Recursive Self-Improving Systems Themselves
arXiv:2609.06396v2 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet...
arXiv AI
Oct 15
ClawLoop: Self-Improving Agents via Harness-Native Training Loop
arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents o...
arXiv Machine Learning
Sep 2
Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation
arXiv:2609.01604v1 Announce Type: cross Abstract: LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the int...