Best-worst comparisons, MaxDiff-style judging, and Plackett-Luce utility scores give agent teams a cleaner way to decide which configs to ship, prune, and route toward next. The post Stop Ranking Agent Configs by Average Score appeared first on Towards Data Science .
arXiv:2606. 19704v1 Announce Type: new Abstract: Agent benchmarks are growing fast, but no single benchmark touches more than four or five of the dimensions that deployment exposes.
How to set the rules that keep agents effective and out of trouble The post What AI Agents Should Never Do on Their Own appeared first on Towards Data Science .
arXiv:2607. 06624v1 Announce Type: new Abstract: We present AgentLens, a production-assessed benchmark for interactive code agents.
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost.
arXiv:2609.07785v1 Announce Type: new Abstract: An LLM-agent leaderboard invites a familiar inference: an agent ranked above another is the better agent. Public evaluation logs may not support that c...
The article introduces Guided Merge Sort, an optimized sorting technique that combines elements of ordinary merge sort and multi‑way merge sort. It highlights how the use of the "goto" operator becomes essential in this approach. The post explains the algorithm’s design and its potential advantages over traditional methods.
arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
The article titled "Your Agent’s Tools Can Make Decisions, Too" discusses decision models as tools and offers a hands‑on guide for their implementation. It highlights how agents can leverage these tools to make informed choices. The post was originally published on Towards Data Science.
The article "8 Tips for Writing Effective Agent Instructions" offers quick and simple guidance to improve the clarity and effectiveness of instructions given to agents. It presents a concise set of practical tips aimed at helping readers craft better agent instructions. The post is published on Towards Data Science.
The paper introduces Contrastive Attribution Topology Search (CATS), a method for selecting compact, high‑performing teams of agents within a larger multi‑agent system. CATS uses an LLM attributer to assign credit to agents based on execution traces, then iteratively prunes weak edges and agents to form a small, efficient team. Experiments show CATS improves accuracy on BigCodeBench by 5.3 percentage points over heuristic search while reducing deployment tokens by 54%, matches strong baselines on MBPP with fewer tokens, and successfully removes adversarial agents in a HotpotQA stress test.
Find the optimal way to interact with your coding agents The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science .