#search-agent

3 articles

01 ·Paper Deep-Dives·★ MEMBER·PAPER·10 min read Paper Explained: False Frontiers — Diagnosing and Breaking Co-Cheating in Self-Evolving Search Agents When the model that writes questions and the model that answers them start agreeing on the same wrong answers, internal reward climbs while real accuracy doesn't. This paper measures that 'co-cheating' with an external audit and breaks it with CrossFit: let a solver that never trained on a source grade the questions from it. Mechanism, results, cost, and limits. 02 ·Agents·★ MEMBER·PAPER·9 min read Paper Deep-Dive: Iris — Reverse-Built Search Tasks and SFT↔RL Climbing A technical report that reverse-builds multi-hop questions from web link structure and alternates SFT with RL to train search agents — and that measures every benchmark both with and without context management, so policy strength can be told apart from harness tricks. 03 ·Training & Alignment·★ MEMBER·PAPER·11 min read Paper Deep-Dive: ABSeeker — Training Long-Horizon Search Agents by Grading Each Step Backward from the Answer When an agent chains dozens of web searches, the only reward is whether the final answer was right — so every good move and every bad move along the way gets graded identically. We walk through ABC (Answer-Backtracked Credit Assignment), which recovers clues by working backward from the answer and scores every step against them, and ABSeeker, the 4B model trained with it — all from the paper itself.