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diffusion language models

7 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Oct 24, 2026 · Artificial Intelligence

HyperQ: 64 Qubits Enable 1.1B Model to Surpass 8B Models in Zero-Shot Tasks

Researchers from Université de Montréal and Mila introduce HyperQ, a quantum-augmented diffusion language model that uses a hypernetwork to generate token-adaptive quantum circuits; with 64 qubits, a 1.1B parameter model outperforms 8B models on six zero-shot benchmarks, and circuits are validated on IBM's 156-qubit quantum cluster.

HyperQIBM quantum clusterZero-Shot Learning
0 likes · 11 min read
HyperQ: 64 Qubits Enable 1.1B Model to Surpass 8B Models in Zero-Shot Tasks
Machine Heart
Machine Heart
Oct 23, 2026 · Artificial Intelligence

HyperQ: 64 Qubits Let 1.1B Diffusion Model Outperform 8B Models

HyperQ introduces per-token adaptive quantum circuits via a hypernetwork to augment diffusion language models; with 64 qubits, a 1.1B parameter model surpasses 8B models on six zero-shot tasks, and the quantum circuits are validated on IBM's 156-qubit cluster with minimal performance loss.

IBM quantum clusterZero-Shot LearningarXiv:2609.24657
0 likes · 10 min read
HyperQ: 64 Qubits Let 1.1B Diffusion Model Outperform 8B Models
Machine Heart
Machine Heart
Jul 15, 2026 · Artificial Intelligence

LAVE: Constrained Decoding for Diffusion Language Models (ISSTA 2026)

LAVE introduces a lookahead‑then‑verify constrained decoding technique that dramatically raises syntax correctness for diffusion language models across code, JSON, and SMILES generation, improves functional correctness, and adds only minimal inference overhead.

AIcode generationconstrained decoding
0 likes · 8 min read
LAVE: Constrained Decoding for Diffusion Language Models (ISSTA 2026)
Machine Heart
Machine Heart
Jul 9, 2026 · Artificial Intelligence

Can Your Self‑Distillation Model Do Without Reference Solutions? Introducing d‑OPSD for Diffusion LLMs

The paper presents d‑OPSD, the first on‑policy self‑distillation framework for diffusion large language models that eliminates reference solutions and extra teacher models, using only one‑tenth of RL steps while achieving equal or superior reasoning performance and markedly higher training efficiency, as demonstrated on multiple math‑reasoning benchmarks.

OPSDSelf-Distillationd-OPSD
0 likes · 7 min read
Can Your Self‑Distillation Model Do Without Reference Solutions? Introducing d‑OPSD for Diffusion LLMs
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Feb 12, 2026 · Artificial Intelligence

Fast Generation, Weak Intelligence? The Harsh Reality of Diffusion Models for Agents

A comprehensive evaluation shows that while diffusion language models achieve higher generation speed through parallel decoding, they suffer from severe causal reasoning and formatting deficiencies, lagging far behind autoregressive models on embodied and tool‑calling agent tasks.

AI EvaluationAgentic workflowsTool Calling
0 likes · 8 min read
Fast Generation, Weak Intelligence? The Harsh Reality of Diffusion Models for Agents
AI Frontier Lectures
AI Frontier Lectures
May 30, 2025 · Artificial Intelligence

Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?

Recent work from West Lake University's MAPLE Lab introduces a diffusion‑based “Divergent Thought Chain” that treats each intermediate denoising step of a diffusion language model as a reasoning step, using result‑based reinforcement learning to optimize non‑linear token generation and achieving state‑of‑the‑art performance on math and code tasks.

chain-of-thoughtcode generationdiffusion language models
0 likes · 14 min read
Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?