10 articles tagged with #token-generation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce HiSpec, a hierarchical speculative decoding framework that accelerates large language model inference by using early-exit models for intermediate verification, achieving up to 2.01× throughput improvements without sacrificing accuracy.
Google's DiffusionGemma AI model achieves 1,000 tokens per second by abandoning traditional word-by-word generation, offering free access but requiring substantial hardware that most users lack. This represents a significant speed breakthrough in AI inference, though practical adoption faces deployment barriers.
Researchers propose ADAS, a training-free reranking algorithm that improves parallel token decoding in masked diffusion language models by using attention weights as soft penalties to avoid committing to correlated predictions simultaneously. The method achieves 9-10 percentage point improvements on benchmarks like GSM8K and HumanEval with minimal computational overhead, advancing the efficiency of faster language model inference.
Researchers propose Multi-SPIN, a distributed speculative inference architecture that enables edge servers and resource-constrained devices to collaboratively generate language model tokens. The system optimizes draft-length control and bandwidth allocation to maximize throughput, achieving up to 88% goodput improvement over baseline methods in real-world testing.
Researchers propose Hybrid Verified Decoding, a technique that improves LLM inference speed by intelligently choosing between cache-based and model-based token drafting methods. The approach predicts draft acceptance rates before verification, achieving 2.73x average speedup on agentic workflows and outperforming existing methods like EAGLE3.
Researchers evaluated eight large Masked Diffusion Language Models (up to 100B parameters) and found they still underperform comparable autoregressive models despite promises of parallel token generation. The study reveals MDLMs exhibit task-dependent decoding behavior and propose a Generate-then-Edit paradigm to improve performance while maintaining parallel processing efficiency.
OpenSea Foundation has postponed its planned Q1 2026 token generation event (TGE) target. The company is simultaneously ending its rewards campaign, offering fee refunds to users, and reducing token swap fees to 0% starting March 31.
Researchers introduce soft-masking (SM), a novel approach for diffusion-based language models that improves upon traditional binary masked diffusion by blending mask token embeddings with predicted tokens. Testing on models up to 7B parameters shows consistent improvements in performance metrics and coding benchmarks.
March 2026 presents opportunities for cryptocurrency airdrop hunters as the Token Generation Event (TGE) calendar becomes less active. This creates a favorable environment for users to participate in airdrop campaigns during what's described as 'sowing season' for potential token distributions.
MegaETH has announced a tokenomics update just days before its scheduled mainnet launch on Monday. However, the project has not yet provided a defined date for its Token Generation Event (TGE).
