代理工具

Agent、MCP、coding agent、工作流自動化與評測。

代理工具

CalibForge Calibrates 5,431 Terminal Tasks Using Strong and Weak Agents; 35B Model Reaches 47.57% on Terminal-Bench 2.0

Rather than retaining every executable synthetic task, CalibForge repeatedly revises tasks until different solver agents exhibit predefined success and failure patterns. Two models fine-tuned on calibrated trajectories improved across three coding-agent benchmarks, but full training required 64 NVIDIA H20 GPUs, and some results were based on only a single run.

時間序列與檢索增強

TS-RAG Fuses Historical Sequences via Reference Tokens, Reducing Average MSE to 0.310 Across Six Forecasting Datasets

Baidu researchers have adapted RAG from text generation to multivariate time-series forecasting, using a TCN to retrieve similar segments and cross-attention to fuse them. A channel-dependent variant outperformed the listed baselines across six datasets, but the code has not been released, and maintainers have warned that the conventional benchmarks used may no longer be sufficiently discriminative.

最新模型

Vision Models Can Zoom In on Images Without Necessarily Using the Evidence; Qwen3-VL-8B’s Net Gain Is Just 6.9 Percentage Points

A new study uses counterfactual cropping to test six open models, distinguishing between “calling a tool” and “the tool’s returned content actually changing the answer.” Most positive gains are concentrated in a small number of well-calibrated trajectories, suggesting that final accuracy alone may overstate visual tool capabilities.

3D 生成與具身 AI

iARCS Uses LLMs to Generate Reward Programs for 3D Scenes, Reducing the Object Collision Rate from 52.67% to 40.45%

iARCS first corrects collisions and walkability issues in an indoor scene generator, then compiles natural-language requirements into executable reward functions for a second stage of reinforcement learning. In 3D-FRONT experiments, it outperforms MiDiffusion on geometric metrics, but each new requirement requires a separately trained LoRA, and the authors have not yet released an implementation.

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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.