FORT synthesizes shortcut-resistant search tasks by controlling four identified shortcut risks across entity selection, graph construction, question formulation, and refinement, producing training data that yields agents with longer search trajectories and top performance among open-source models on
Openseeker: Democratizing frontier search agents by fully open-sourcing training data
8 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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BioMedArena supplies a standardized open toolkit with 166 biomedical benchmarks, 75 tools, 6 harnesses, and 6 context strategies that improve 12 backbones and surpass prior SOTA by 15.01 points on average across 8 benchmarks.
LLMs drop from 71.1% to 38.0% accuracy on medical questions when misleading context is injected, measured via new MedMisBench benchmark with 10,932 items.
PBSD reweights RL trajectory advantages with Bayesian evidence scores from privileged answer-conditioned likelihoods, improving credit assignment in long-horizon search agents.
SAM is a standalone memory framework for long-horizon LLM agents that creates state-adaptive cues from interactions, preserves raw trajectories for intent-driven recall, and optimizes the module via expert supervision and RL, outperforming baselines on BrowseComp and related benchmarks.
A 4B deep research agent trained on 10K open data outperforms prior agents under 9B parameters and narrows the gap to 30B-class systems on research benchmarks.
MindDR combines a Planning Agent, DeepSearch Agent, and Report Agent with SFT cold-start, Search-RL, Report-RL, and preference alignment to reach competitive scores on research benchmarks using 30B-scale models.
SimpleSearch-VL improves Qwen3-VL multimodal agent baselines by 15.8-16 points on average using 7K total training examples and reaches parity with Gemini-3-Pro on the 30B variant.
citing papers explorer
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FORT-Searcher: Synthesizing Shortcut-Resistant Search Tasks for Training Deep Search Agents
FORT synthesizes shortcut-resistant search tasks by controlling four identified shortcut risks across entity selection, graph construction, question formulation, and refinement, producing training data that yields agents with longer search trajectories and top performance among open-source models on
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BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
BioMedArena supplies a standardized open toolkit with 166 biomedical benchmarks, 75 tools, 6 harnesses, and 6 context strategies that improve 12 backbones and surpass prior SOTA by 15.01 points on average across 8 benchmarks.
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Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
LLMs drop from 71.1% to 38.0% accuracy on medical questions when misleading context is injected, measured via new MedMisBench benchmark with 10,932 items.
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PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment
PBSD reweights RL trajectory advantages with Bayesian evidence scores from privileged answer-conditioned likelihoods, improving credit assignment in long-horizon search agents.
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SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent
SAM is a standalone memory framework for long-horizon LLM agents that creates state-adaptive cues from interactions, preserves raw trajectories for intent-driven recall, and optimizes the module via expert supervision and RL, outperforming baselines on BrowseComp and related benchmarks.
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DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
A 4B deep research agent trained on 10K open data outperforms prior agents under 9B parameters and narrows the gap to 30B-class systems on research benchmarks.
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Mind DeepResearch Technical Report
MindDR combines a Planning Agent, DeepSearch Agent, and Report Agent with SFT cold-start, Search-RL, Report-RL, and preference alignment to reach competitive scores on research benchmarks using 30B-scale models.
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SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search
SimpleSearch-VL improves Qwen3-VL multimodal agent baselines by 15.8-16 points on average using 7K total training examples and reaches parity with Gemini-3-Pro on the 30B variant.