6. Beyond Ten Turns This paper introduces ASearcher, an open-source framework for training LLM-based search agents capable of long-horizon, expert-level search.
@dair_ai
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QA Hallucination Detectors: Evaluation Metrics Limitations
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7. Illusion of Progress The paper argues that common QA hallucination detectors look better than they are because evaluations lean on ROUGE.
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Top AI Papers of the Week: DINOv3, M3-Agent, and Medical Reasoning
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Top AI Papers of The Week (August 11-17): – DINOv3
– M3-Agent
– Illusion of Progress
– TRImodal Brain Encoder
– Efficient Architectures for LLMs
– Deep Dive into RL for LLM Reasoning
– GPT-5 for Multimodal Medical Reasoning Read on for more: -
DINOv3 Self-Supervised Vision Foundation Model Scales Data
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1. DINOv3 DINOv3 is a self‑supervised vision foundation model that scales data and model size, introduces a Gram anchoring loss to preserve dense patch consistency during long training, and adds post‑hoc tweaks for resolution, size, and text alignment.
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Building AI Solutions Systematically: New Course Launch
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We are focusing on building! Very little theory in this one. It's not about tricks and prompts; it's about building systematically and solving hard problems. This is useful for both beginners and experienced builders. The course launches tomorrow!
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Building Effective AI Agents: Framework and Training Guide
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Anyone can build useful AI Agents. But it requires having a solid framework to design and improve AI agents. That's what we'll teach in our new training on Building Effective AI Agents. Topics include context engineering, augmenting AI agents, multi-agent systems, and more.
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LLM Methods for Table Understanding Survey
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9. Tabular Data Understanding with LLMs This survey reviews LLM and MLLM methods for table understanding, outlining a taxonomy of tabular representations and tasks.
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Enhancing Medical Reasoning with LLMs: Training and Test-Time
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10. Medical Reasoning in the Era of LLMs This review categorizes techniques for enhancing LLM medical reasoning into training-time (e.g., fine-tuning, RL) and test-time (e.g., prompt engineering, multi-agent systems) approaches, applied across modalities and clinical tasks.
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Tool-Augmented RAG Agent Framework for Dynamic AI Search
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7. Tool-Augmented Unified Retrieval Agent for AI Search Presents a production-ready framework that extends the RAG (Retrieval-Augmented Generation) paradigm to support real-time, dynamic, and transactional queries through agentic tool use.
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Comprehensive Taxonomy of LLM Hallucinations: Intrinsic vs Extrinsic Errors
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8. A Comprehensive Taxonomy of Hallucinations Presents a detailed taxonomy of LLM hallucinations, distinguishing intrinsic vs extrinsic errors and factuality vs faithfulness, and covering manifestations from factual mistakes to domain-specific failures.