← Back to all news
arXiv AI By Qisheng Zhou, Zhen Xiong, Qiaoyu Tan

TRACE: Trajectory Selection for Parallel Scaling of Search Agents

Read the original on arXiv AI →

TRACE is a lightweight learned selector that ranks completed search trajectories by aggregating cross‑rollout evidence, preserving individual query and evidence occurrences while propagating information across shared content or document identity. Trained with answer‑level supervision over frozen text embeddings, TRACE selects an existing answer without additional search or autoregressive aggregation, and a single selector generalizes across rollout policies and agent backbones. Across six WebQA policies, six long‑horizon dataset‑backbone combinations, and multiple WebQA benchmarks, TRACE outperforms majority voting and generative aggregators, achieving higher accuracy and at least tenfold higher processing throughput.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Sep 7

Iris: Climbing to the Search Frontier

The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.

By Ziyuan Liu, Hengqi Liu, Zichuan Wang, Yang Qin, Jiachen Liang, Xu Chu, Shaowei Chen, Yuantao Gu, Mu Chuan
agentsreinforcement-learningfine-tuningbenchmarksdata-engineering
More like this →
arXiv AI
Aug 25

Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing

The paper introduces Enrich‑Retrieve‑Rank, a scalable method for discovering capabilities in large agent ecosystems. It replaces in‑context routing with an offline enrichment step that converts sparse metadata into searchable profiles, followed by an online retrieve‑then‑rank pipeline that returns a ranked shortlist without invoking candidates. Experiments show that as the number of capabilities grows from 10 to 7,278, the new approach maintains higher top‑1 accuracy and reduces cost by 70× compared to full‑context baselines.

By Nazib Sorathiya, Daniel Zhang, Bardiya Akhbari
llmsagents
More like this →
Hugging Face Trending Papers
Sep 8

Q2D-Web: A Large-Scale Benchmark for Retrieval in Agentic RAG Systems

Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.

llmsragagentsbenchmarks
More like this →