arXiv AI
VeriTrace: Evolving Mental Models for Deep Research Agents
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arXiv Computation and Language
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Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent
Mind2Report is a cognitive deep research agent designed to produce expert-level commercial reports from large, noisy web sources. It first clarifies detailed commercial intent to build a structured outline, then recursively gathers and validates evidence into a research memory that evolves with the outline, enabling iterative synthesis of comprehensive reports. The authors also introduce QRC‑Eval, a benchmark of 200 real-world commercial tasks, and show through extensive experiments that Mind2Report outperforms existing proprietary and open-source deep research agents, with ablation studies confirming the contribution of each component.
arXiv AI
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AREX: Towards a Recursively Self-Improving Agent for Deep Research
arXiv:2607. 21461v1 Announce Type: new Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints.
arXiv AI
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DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs
arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.
arXiv AI
Jun 9
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
arXiv:2606. 07603v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions.
arXiv AI
2d ago
LOCI: A Locator-Critic with Refinement Loop
LOC I (Locator‑Critic) is a training‑free framework that separates visual search from evidence verification in Vision‑Language Models. It uses a Locator agent to propose candidate visual evidence and a Critic agent to assess its relevance, engaging in an iterative refinement loop that progressively improves the evidence until it is sufficient to answer a question. The approach yields state‑of‑the‑art results on several complex visual benchmarks, boosting accuracy for both open‑weight models like Qwen3‑VL and proprietary models such as Gemini 2.5 Pro.
arXiv AI
Aug 19
G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution
G-ReAct is a reasoning framework that frames deep search as state evolution over a fixed-topology query graph, enabling explicit tracking of search progress and constraint preservation. It generates high-quality trajectories for fine-tuning and provides structured guidance during inference without extra fine-tuning. Experiments show that with only 1.9K generated trajectories, a Qwen3 model achieves strong accuracy on BrowseComp-ZH and XBench, outperforming larger open-source baselines, and consistently improves existing LLMs on deep-search tasks.