FOCUS uses selective branching to train long-horizon agent critics
At Qwen3.5-9B, held-out accuracy rises 7.2 points on BrowseComp-Plus and 4.1 on DeonticBench versus the strongest baseline.
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At Qwen3.5-9B, held-out accuracy rises 7.2 points on BrowseComp-Plus and 4.1 on DeonticBench versus the strongest baseline.
The headline cites 92.05% on BrowseComp-Plus using 12.6 search calls; the article body was not available.
ReGE-Syn raises accuracy 9.9 points for GPT and 8.8 for Claude while cutting guidance-construction tokens versus adapted AggAgent.
Mosaic improves multi-session agentic RL by crediting session contributions instead of broadcasting trajectory advantages.
SearchJev makes calibrated search decisions 5.2× faster than Qwen3.5 and lifts BrowseComp-Plus accuracy from 45% to 54%.
Context Language Models edit their own context file, beating external managers on agent benchmarks while cutting FLOPs.
Adaptive Consistency Graph raises GPT-5.6-luna long-horizon success from 44.5% to 50.2%, led by BrowseComp-Plus.
IterSynth decouples planning and synthesis for deep-search agents, and an 8B model scores 50.7.
Growing Harness converts recurring agent control into reusable code, cutting LLM calls by 76 to 92 percent.

Chinese lab AllSpark releases Iris-mini (35B) and Iris-pro (397B) open-weight search agents claiming best-in-class results on BrowseComp and other research benchmarks.

ByteDance Seed's HarnessDev benchmark finds LLM-built agent harnesses trail human engineering on code and search, with only 34 of 64 revisions generalizing.
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