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新模型、開放權重、多模態、語音、影像與模型架構。

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SMRC-SD Verifies Agent State Before Distilling Successful Trajectories, Improving Qwen3-1.7B Task Success Rate by About 12 Percentage Points

Successful demonstrations may not apply to states reached after an agent takes a detour, and unconditionally providing the full trajectory can even reduce the probability of the correct action. SMRC-SD adds a self-distillation signal only when the state is compatible with a demonstration step, outperforming the full-path baseline on both ALFWorld and WebShop.

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ERA’s Reported 11% Edge in Retrospective Epidemic Forecasting Is Reassessed, With Data-Revision Leakage Explaining Nearly All of the Gain

A new analysis finds that Google’s ERA was backtested using season-finalized data, effectively giving it access to revisions that were unavailable to CDC models when their forecasts were made. The two achieved similar Weighted Interval Scores when the data underwent little or no revision, showing that AI forecasting benchmarks must preserve the version of the data available at each point in time.

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CIPO Assigns Step Rewards by Masking Retrieved Evidence, Raising Qwen2.5-7B’s Average F1 Across Seven QA Benchmarks to 0.504

CIPO compares an agent’s action probabilities with and without access to the latest retrieval results, directly rewarding next steps influenced by external evidence. In the authors’ experiments, it outperformed the strongest baseline by 4.7 F1 points, but the additional scoring increases training costs, and the method has so far been validated on only two Qwen2.5 models.

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u-OPSD Trains Solely Through Internal Model Voting, Boosting Qwen3-8B’s Five-Benchmark Math Average by 10.7 Points

u-OPSD uses eight reasoning trajectories from the same model to produce a majority-vote pseudo-answer, then distills token distributions conditioned on that consensus into trajectories that disagree. Qwen3-8B’s average across five benchmarks in non-thinking mode rose from 43.57 to 54.31, though incorrect consensus and the cost of repeated sampling still limit its applicability.

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BaKron Reduces Two-Sided Hessian Quantization to Cubic Complexity, With Its 8192-Square-Matrix Kernel 60× Faster Than YAQA

Using anti-diagonal parallelization and recursive divide-and-conquer, BaKron reduces total work from quartic to cubic complexity while preserving curvature information on both the input and output sides. In 2.81-bit experiments on Llama 3 8B, it improves perplexity over GPTQ, but total quantization time remains longer, and no public implementation is currently available.

最新模型

Hyper-ES First Uses Gradients to Build a Low-Dimensional Search Space, Averaging 1% Higher on Mathematical Reasoning Than GRPO-LoRA

Rather than applying random evolution directly to billions of model-weight dimensions, Hyper-ES constructs a subspace from a small number of fine-tuning directions and then searches for layer-wise model-merging coefficients. The authors report modest gains across three models and six mathematical reasoning datasets, although the method is not entirely gradient-free.

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OPERA Constrains Experimental Agents with Physics Residuals, Cutting Reward-Seeking Decisions with No Real Improvement from as High as 39.0% to 1.9%

Rather than returning only a single score, OPERA gives language models interpretable physics residuals before they select, compose, or generate optical operations. Three optical tasks show that residuals can curb specification gaming, but the public code does not include experimental instrument control or the complete statistical pipeline.