EMNLP 2026

Headlines · 2

1. 4d
   SpecTr-GBV unifies multi-draft speculation with block verification

   Researchers from Renmin University of China's Center for Applied Statistics and the China Medical University's School of Intelligent Medicine propose SpecTr-GBV, which folds multi-draft speculative decoding and greedy block verification (GBV) into one framework, per Machine Heart. The method casts verification of multiple draft token blocks against the target token block as an optimal transport problem, seeking a longer expected acceptance length while preserving output-distribution consistency. The authors prove that, within their conditionally independent couplings class, SpecTr-GBV attains the optimal expected acceptance length for a fixed draft count K, that the bound rises with K and reduces to GBV at K=1. Experiments on HumanEval, GSM8K, MGSM, LM1B and Alpaca with DeepSeek-33B/1.3B and DeepSeek-6.7B/1.3B target-draft pairs report higher block efficiency and speedup; the article notes that wall-clock speedup is typically below block efficiency, that speedup first rises then falls as draft length grows, and that the current theory covers i.i.d. draft sequences but not tree-structured candidates.

   机器之心 · 🔥 0
2. Oct 11
   MIT's HugAgent tests whether AI 'digital twins' can predict people in new scenarios

   Researchers at MIT and other institutions propose HugAgent, a benchmark that uses interviews with 54 real participants to test whether large models can portray a specific person—not just restate current views but predict how that person would answer previously unseen hypothetical scenarios on universal healthcare, public surveillance and housing densification, across 1,742 evaluation items; the paper was accepted as an EMNLP 2026 Main Oral. Models did best at inferring a person's current beliefs (top accuracy 77.56%; GPT-4o 74.66%, against an 84.84% two-week self-consistency ceiling), while on the belief-dynamics update task the best result was 68.61% (Claude Sonnet 4.5), then LLaMA 3.3 70B at 67.57% and GPT-4o at 63.11%, against an 85.66% human ceiling. Personal interviews clearly helped prediction (GPT-4o rose from 39.83% to 63.11%), but adding more interview rounds did not consistently improve forecasts for new situations.

   虎嗅 AI · 🔥 0

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