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Parallel Web Systems reports that Particle-1 ranks #2 in efficiency on the Parallel Search Capability Leaderboard, matching DeepSeek V4.1 Flash's web research performance with 42% fewer output tokens. Suhail shared excitement about the independent validation of their efficiency claims on real-world tasks they couldn't train on.
提出 Self-Assessment 强化学习框架（SARL），让检索增强的 LLM 搜索智能体在训练中学会感知自身搜索状态、识别缺失信息，从而提升搜索效率而无需额外推理时评估，在多跳推理与深度研究基准上取得显著相对提升。
browsecomp-plus-mini 是一个 15 条查询的轻量子集，能以不到完整基准 2% 的成本，大致复现 BrowseComp-Plus 全部 830 条查询对深度研究智能体的排名（加权 Spearman 一致性 0.88），且无需 LLM 评分，仅用规范化精确匹配打分。
MMSearch-OPD 提出了一种结合可控数据精炼与联合强化学习和在线策略蒸馏的方法，以提升长时程多模态搜索智能体的能力，通过将简单问题扩展为困难且可学习的训练样本取得显著改进。
提出SOAR（Source-Aware Reinforcement Learning），通过将外部证据与内部知识解耦为可观测的并行流并引入Source-Consistent Reward，实现搜索代理的精确信用分配；在3B和7B模型上分别提升7.3%和5.4%，并将证据伪造率降低74.8%。
The paper introduces SEEDS, a preview-guided evidence admission framework for search agents that selectively decides which retrieved candidates should enter the agent's reasoning context, improving search effectiveness across nine knowledge-intensive QA benchmarks while reducing full-evidence consumption.
SearchJev introduces a fast, calibrated System-1 model for search agents that scores legal decisions without autoregressive generation, using Soft-Label Learning for Calibrated Decisions (SLCD) to reduce latency and improve confidence reliability. A dual-system agent combining SearchJev with a System-2 model achieves up to 4.7x speedups on BrowseComp-Plus while raising answer accuracy from 45% to 54%.
This paper identifies "co-cheating" in self-evolving search agents, where a proposer and solver converge on shared errors that inflate internal reward without improving true correctness. It proposes CrossFit, a cross-fitted verification scheme that substantially reduces false agreement and boosts performance on seven search benchmarks with Qwen3.5-4B/9B models.
FinFIRST introduces a benchmark for evaluating financial search agents by jointly assessing answers and supporting evidence through atomic rubrics, with 123 expert-authored tasks spanning difficulty levels.
The paper introduces Retrieval-Grounded Voting (RGV) to address the limitations of confidence-based voting in multi-turn search agents by using lexical overlap with retrieved documents, achieving up to 5.4% accuracy gains.
CAFE is a framework that couples a search agent and critic via shared parameters to learn in-trajectory corrective feedback, improving search performance and reducing hallucinations across benchmarks.
Introduces SSPO, a step-level self-distilled policy optimization method for training deep search agents, which uses evidence anchors and advantage weights to improve credit assignment beyond sparse outcome rewards. SSPO outperforms GRPO on benchmarks like BrowseComp and GAIA with only ~5% overhead per step.
This paper systematically studies context interference in multi-turn LLM-based search agents, finding that interference primarily arises from the latest retrieved documents, and introduces a distill-based context refiner to mitigate it. Incorporating context refinement into RL training pipelines significantly improves reliability and efficiency.
Search-G1 proposes a representation-based intrinsic reward framework for search-augmented language agents, using intervention-calibrated readouts to balance retrieval necessity and evidence reliance, improving search efficiency without costly annotations.
This paper introduces SearchAuditBench, a benchmark of 1,243 failed long-horizon search-agent trajectories with expert annotations, and SearchAuditor, a multi-perspective auditing framework that localizes, attributes, and repairs agent failures. Experiments show SearchAuditor outperforms baselines, achieving a 32.3% end-to-end pass rate with frontier models like GPT-5.5.
This paper proposes Answer-Backtracked Credit Assignment (ABC), a framework that converts sparse trajectory-level outcomes into dense step-level supervision for training long-horizon search agents. The resulting ABSeeker model, built on Qwen3.5-4B, achieves strong results on BrowseComp benchmarks, outperforming same-scale agents and matching larger models.
This paper introduces SESA, a self-evolving skill-augmented search agent that co-evolves task generation and skill memory via tool-augmented search self-play. It improves accuracy across seven QA benchmarks over baselines while supporting memory-free deployment.
This paper introduces Harness-G, a graph-structured retrieval framework that reformulates free-form query generation as finite action selection to reduce retrieval aliasing in RL-powered search agents. Across six QA benchmarks, Harness-G outperforms the strongest baseline Graph-R1 by 10.74 points at 1.5B and 3.98 points at 3B scale.
The article argues that AI visibility dashboards, which claim to track brand presence in AI search responses, are unreliable and lack predictive validity, comparing them to weighing smoke due to the inconsistency of AI outputs and the absence of meaningful correlation with business outcomes.
DiscoBench is a new benchmark that evaluates whether LLM-powered search agents can proactively identify ambiguity in user queries, ask clarifying questions, and recover correct reasoning paths through multi-turn interaction.