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Parallel Sampling

3 articles · Page 1 of 1

Sep 16, 2026 · Artificial Intelligence

Harness Evolution vs. Test-Time Scaling: Simple Retries Outperform Complex Self-Improvement

A study from AI2 and University of Washington finds that complex Harness Evolution for AI agents fails to consistently outperform simple test-time scaling methods like parallel sampling under equal compute budgets, and improvements rarely transfer to unseen tasks, questioning whether observed gains stem from genuine self-improvement or just extra attempts.

AI AgentsAgent EvaluationBenchmarking

0 likes · 14 min read

Harness Evolution vs. Test-Time Scaling: Simple Retries Outperform Complex Self-Improvement

Machine Heart

May 23, 2026 · Artificial Intelligence

Bengio’s New Paper Pushes Recursive Reasoning Limits with Parallel Trajectories

The paper introduces GRAM (Generative Recursive Reasoning Models), a probabilistic multi‑trajectory recursive reasoning framework that injects learnable randomness into each recursion step, enabling parallel sampling and achieving higher accuracy than deterministic baselines across tasks such as Sudoku‑Extreme, N‑Queens, ARC‑AGI and unconditional generation.

AI modelsGRAMParallel Sampling

0 likes · 12 min read

Bengio’s New Paper Pushes Recursive Reasoning Limits with Parallel Trajectories

HyperAI Super Neural

Apr 7, 2026 · Artificial Intelligence

MIT’s DRiffusion Achieves 1.4–3.7× Faster Diffusion Sampling via Draft‑and‑Refine Parallelism

MIT researchers introduce DRiffusion, a draft‑and‑refine parallel framework that uncovers intrinsic parallelism in diffusion models, delivering 1.4–3.7× speedup on three GPUs while preserving near‑lossless image quality across Stable Diffusion 2.1, SDXL and SD3 evaluated on MS‑COCO.

AI accelerationDRiffusionMS-COCO

0 likes · 14 min read

MIT’s DRiffusion Achieves 1.4–3.7× Faster Diffusion Sampling via Draft‑and‑Refine Parallelism