iterative-refinement

SABET-QA introduces an iterative framework for temporal knowledge graph question answering that enhances multi-hop reasoning through bidirectional entity-temporal scoring and contextualization, showing consistent improvements over baselines on benchmarks like CronQuestions and TimeQuestions.

This paper proposes ODE-based Transformer decoders using Runge-Kutta integration methods to improve iterative refinement for sign language translation, achieving better BLEU scores than the IPSLT baseline without increasing model size.

This paper proposes a novel contextual bandit algorithm that explicitly models reward decay for progressive content refinement in LLMs, using EM to estimate arm-specific and decay parameters. Experiments on Sentiment Reversal and GSM8K show significant gains over strong baselines.

This paper proposes WILC, a framework for coordinating multiple LLMs through complementarity-driven iterative collaboration, demonstrating superior performance across benchmarks at lower estimated cost compared to GPT-5.2.

Introduces IRIS, a framework that learns dynamic user personas from implicit interaction streams without explicit feedback, outperforming static and memory-only baselines on decision prediction.

MusiChat presents a conversational system for human-AI music co-creation that enables iterative refinement through natural language interaction, achieving high accuracy in multi-turn editing.

A comprehensive blog post by Volodymyr Kuleshov's Cornell group explains how to build diffusion language models, covering core techniques like masked diffusion, iterative refinement, variable-length generation, controllable generation, fast samplers, and RL post-training, using open-source models such as Mercury, Gemma Diffusion, and Nemotron Diffusion as examples.

Proposes treating semantic compression as a diffusion noise function for handling massive context beyond model windows, using multi-pass reading at decreasing compression levels. Untrained-model experiments show components work in isolation but the full chain needs training to resolve binding bottleneck.

ScholarSum is a hierarchical reflective graph-based framework for scientific abstractive summarization that emulates a student–teacher writing process. It uses a hierarchical knowledge graph to capture global structure, generates an initial draft, and iteratively refines it via evidence retrieval and teacher-like review to improve both fluency and factual faithfulness.

This paper introduces Reflective Masking, a lightweight post-training method that enables mask diffusion models to perform multi-turn self-revision through token-level revision policies and history references, improving performance on reasoning tasks like Sudoku, math, code generation, and image editing.

This article describes a scaffold that scales test-time compute on Qwen-3.6-27B and Gemma-4-31B using iterative corrections and branch exploration to surpass Claude Mythos in code optimization. It includes a paper link and GitHub repository.

KForge is a cross-platform framework that uses two collaborating LLM-based agents to automatically generate and optimize high-performance compute kernels for diverse AI accelerators, achieving significant speedups on NVIDIA B200 and Intel Arc B580 hardware.

This paper frames LLM-generated reward shaping for sparse structured RL as a debugging problem, identifying failure modes like reward flooding and semantic misunderstanding. The authors propose diagnostic-driven iterative refinement, achieving dramatic success rate improvements (e.g., DoorKey-8×8 from 2.3% to 97.6%) compared to one-shot generation.

This paper introduces the Iterative Refinement Neural Operator (IRNO), which augments pretrained neural operators with a learned refinement module applied via fixed-point iteration to mitigate spectral bias. IRNO progressively corrects high-frequency errors, achieving up to 56% improvement on turbulent flow and showing stable extrapolation beyond the trained iteration count.

Research Math Agents (RMA) is an agentic framework for automated reasoning on research-level mathematical problems, achieving state-of-the-art results on the First Proof benchmark by solving 8 out of 10 problems, outperforming strong baselines like GPT-5.2R and Aletheia.

This paper introduces a critique-and-routing controller for multi-agent LLM systems that formulates coordination as a sequential decision problem. It uses policy gradients to optimize the controller for iterative refinement, outperforming baselines while reducing reliance on top-tier models.

This paper introduces Attractor Models, which use fixed-point solving and implicit differentiation for efficient iterative refinement, achieving superior language modeling and reasoning performance with reduced computational costs compared to traditional transformers.

This paper proposes an epistemic state graph representation and an order-gap termination criterion for recursive reasoning systems, addressing how to manage evolving reasoning states and when to stop iteration.

The paper introduces WiCER, an iterative algorithm for compiling domain knowledge into LLM Wiki systems to minimize information loss and catastrophic failure rates during knowledge distillation. It demonstrates that this approach improves upon full-context KV cache inference by preserving critical facts better than blind compilation methods.

GPT-Image-2 now has the ability to review its own generated outputs and iteratively refine them until satisfied with correctness, though this process can take around 11 minutes per image.