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Weekly Digest · 70 papers ·

This week in AI research: better results start below the model

Execution harnesses, scoring rules and control interfaces explain consequential differences that model-level comparisons can miss.

Illustration: AI teacher adapts its hints as a language model learns, boosting performance across four domains
Paper · Academic

AI teacher adapts its hints as a language model learns, boosting performance across four domains

Zijian Zhang et al. · University of Minnesota, Twin Cities, JPMorgan AI Research

Zhang et al. introduce AutoScaffold, a framework where a frozen language model teacher uses a student's training experience to design adaptive scaffolds, strategies for task classes and hints for hard tasks, that are removed at evaluation. Across ALFWorld, search-based QA, mathematical reasoning, and GPU kernel generation, students trained with AutoScaffold outperform those trained with standard RL baselines, with gains of 7.2 points in ALFWorld success and 4.0 points in average QA exact match, and on math, the student surpasses QuestA's reported accuracy using about one-third as many training steps.

Illustration: Agent traces can be compiled into workflows that are 130x faster and 24% cheaper
Paper · Big Tech

Agent traces can be compiled into workflows that are 130x faster and 24% cheaper

Kaiyuan Hou et al. · Columbia University, Amazon

The authors present ReTrace, which extracts decision rules from logged agent executions that include reasoning and compiles them into a static workflow. On 79 production procedures, the workflows achieved 93.8% completion compared with 96.8% for the agent, at 24% lower cost per task. On τ^2-Bench, runtime dropped from 170 seconds to 1.3 seconds while task success fell from 80.6% to 75.8%; after repair, success reached 81.2%, matching the agent.

Illustration: Drug discovery AI agents beat expert chemists at ranking molecule poses
Paper · Top University

Drug discovery AI agents beat expert chemists at ranking molecule poses

Megha Srivastava et al. · Stanford University, Genesis Molecular AI

The authors introduce Pyrite, a benchmark of 1,500 pose pairs from protein-ligand complexes, and show that expert chemists rank poses near random chance. Two LLM-based agents, one using direct reasoning and one generating code for feature engineering, achieve 67% and 74% accuracy respectively, with reinforcement learning boosting open-weight models to match closed-source ones.