AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
OpenScholar
OpenScholar is a fully open, retrieval-augmented language model designed for scientific literature synthesis. It retrieves passages from a datastore of 45 million open-access papers, generates citation-backed responses, and iteratively refines outputs through a self-feedback loop. On ScholarQABench, the first large-scale multi-domain benchmark for literature search, OpenScholar-8B outperforms GPT-4o by 6% and PaperQA2 by 5.5% in correctness, while achieving citation accuracy on par with human experts and being preferred over expert-written answers 51-70% of the time.
Self-Evolving Search Agents Without Training Data
Dr. Zero introduces a framework for developing multi-turn search agents that improve themselves autonomously without labeled training data. A proposer generates diverse questions to train a solver initialized from the same base model, creating a self-evolution loop with automated curriculum difficulty scaling.
UniversalRAG
UniversalRAG introduces a RAG system that handles knowledge retrieval from heterogeneous sources containing multiple data types (text, images, videos) with varying granularities. Rather than forcing diverse modalities into a single embedding space where embeddings cluster by modality rather than meaning, it uses modality-aware routing to dynamically select appropriate corpus and granularity for each query, outperforming both unimodal and unified multimodal RAG baselines across 10 benchmarks.
CLaRa
CLaRa introduces a unified framework for retrieval-augmented generation that performs embedding-based compression and joint optimization in a shared continuous space. The approach addresses key RAG limitations around long contexts and disjoint retrieval-generation optimization.
FACTS Leaderboard
Google introduces the FACTS Leaderboard, a comprehensive benchmark suite for evaluating LLM factuality across diverse scenarios. The leaderboard aggregates performance across four specialized sub-benchmarks to provide a holistic measure of how accurately models generate factual text.
FINDER and DEFT
OPPO AI introduces FINDER, a fine-grained benchmark with 100 expert-curated research tasks and 419 structured checklist items for evaluating deep research agents, along with DEFT, the first failure taxonomy categorizing 14 failure modes across reasoning, retrieval, and generation dimensions.
OmniScientist
OmniScientist presents an end-to-end framework for building AI scientists capable of autonomously conducting research across the entire scientific lifecycle - from literature review and ideation to experimentation, writing, and peer review. The system establishes a collaborative ecosystem where human and AI scientists co-evolve within a shared scientific environment.
DR Tulu
DR Tulu-8B is the first open model directly trained for long-form deep research using Reinforcement Learning with Evolving Rubrics (RLER). Unlike existing models trained on short-form QA tasks, DR Tulu learns to produce comprehensive, well-attributed research reports by training with rubrics that co-evolve with the model and are grounded on real-world searched knowledge.
RL Enhances Knowledge Navigation
Researchers show that RL-enhanced models outperform base models by 24pp on hierarchical knowledge retrieval tasks (e.g., medical codes) by improving navigation of existing knowledge structures rather than acquiring new facts. Structured prompting reduces this gap to 7pp, while layer-wise analysis reveals that RL transforms query processing (cosine similarity drops to 0.65-0.73) while preserving factual representations (0.85-0.92). The findings suggest RL’s benefits stem from enhanced procedural skills in traversing parametric knowledge hierarchies rather than expanded knowledge content.
Tool-to-Agent Retrieval
PwC researchers introduce a unified retrieval framework that embeds both tools and agents in a shared vector space with metadata relationships, enabling efficient routing in multi-agent systems coordinating hundreds of MCP servers and tools.
A Survey on Retrieval and Structuring Augmented Generation with LLMs
This survey reviews Retrieval and Structuring (RAS) Augmented Generation, which combines external retrieval and structured knowledge to mitigate LLM issues like hallucinations and outdated knowledge. It covers retrieval methods, structuring techniques, integration strategies, and highlights challenges in efficiency, structure quality, and multimodal or cross-lingual extensions.
Rethinking RAG-based Decoding
REFRAG replaces most retrieved tokens with precomputed chunk embeddings at decode time, then selectively expands only the few chunks that matter. This exploits block-diagonal attention in RAG prompts to cut latency and memory while preserving accuracy across RAG, multi-turn dialog, and long-doc summarization.
On the Theoretical Limitations of Embedding-based Retrieval
Single-vector dense retrievers cannot realize all possible top-k relevance combinations once queries demand sufficiently many “mix-and-match” document sets. The paper ties this failure to the sign-rank of the relevance matrix, proves lower and upper bounds on the embedding dimension needed, and then stress-tests models with a simple but adversarially combinatorial dataset (LIMIT).
Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains
A test-time reasoning framework that replaces a single linear chain with multiple parallel, entity-grounded chains over medical knowledge graphs. MIRAGE decomposes a query into sub-questions, runs adaptive graph retrieval in Anchor and Bridge modes, then reconciles answers via cross-chain verification, yielding higher accuracy and clearer provenance than linear ToT or web-centric agentic RAG.
Retrieval-Augmented Reasoning with Lean Language Models
A domain-tuned pipeline that fuses RAG and reasoning into a single small-footprint model. The team distills reasoning traces from a frontier model into Qwen2.5 variants, uses summarization to keep context small, and shows that a 32B local model approaches frontier accuracy on an NHS A‑to‑Z clinical QA task.
ReaGAN
This paper introduces ReaGAN, a graph learning framework that reconceptualizes each node in a graph as an autonomous agent capable of planning, reasoning, and acting via a frozen LLM. Instead of relying on static, layer-wise message passing, ReaGAN enables node-level autonomy, where each node independently decides whether to aggregate information from local neighbors, retrieve semantically similar but distant nodes, or take no action at all. This node-agent abstraction addresses two key challenges in graph learning: (1) handling varying informativeness of nodes and (2) combining local structure with global semantics.
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. Unlike conventional RAG systems that rely on static web snapshots, TURA enables LLM-based systems to interact with external APIs and databases, addressing user intents that require up-to-date or structured information (e.g., train schedules, weather forecasts).
Graph-R1
Introduces a novel RAG framework that moves beyond traditional one-shot or chunk-based retrieval by integrating graph-structured knowledge, agentic multi-turn interaction, and RL. The core goal is to improve factual accuracy, retrieval efficiency, and reasoning quality in knowledge-intensive tasks.
Deep Researcher with Test-Time Diffusion
Rethinks how deep research agents generate long-form reports. Rather than relying on static inference strategies like Chain-of-Thought or best-of-n sampling, TTD-DR frames the report generation process as a diffusion process. It starts with a noisy draft and iteratively refines it through retrieval-enhanced denoising, guided by a structured plan. This iterative loop mimics how human researchers search, reason, revise, and accumulate context over time.
A Survey of Context Engineering for LLMs
This survey defines Context Engineering as a formal discipline for optimizing information given to LLMs, outlining its core components, retrieval, processing, and management, and their integration in systems like RAG, memory, and multi-agent frameworks. It identifies a key gap: LLMs can understand complex input but struggle to generate equally complex long-form output, pointing to a major direction for future research.
HIRAG
HIRAG is a new instruction fine-tuning method that enhances the capabilities of RAG models by teaching them to think before answering. Key ideas:
Agentic RAG for Personalized Recommendation
Introduces a multi-agent framework that enhances traditional RAG systems with reasoning agents tailored to user modeling and contextual ranking. Developed at Walmart Global Tech, ARAG reframes recommendations as a structured coordination problem between LLM agents.
Towards AI Search Paradigm
Proposes a modular multi-agent system that reimagines how AI handles complex search tasks, aiming to emulate human-like reasoning and information synthesis. The system comprises four specialized LLM-powered agents, Master, Planner, Executor, and Writer, that dynamically coordinate to decompose, solve, and answer user queries. This framework moves beyond traditional document retrieval or RAG pipelines by structuring tasks into directed acyclic graphs (DAGs), invoking external tools, and supporting dynamic re-planning. Key contributions include:
RAG+
Introduces RAG+, a modular framework that improves traditional RAG systems by explicitly incorporating application-level reasoning into the retrieval and generation pipeline. While standard RAG pipelines fetch relevant knowledge, they often fail to show how to use that knowledge effectively in reasoning-intensive tasks. RAG+ fills this gap by retrieving not only knowledge but also paired application examples, leading to more accurate, interpretable, and goal-oriented outputs. Key highlights: