AINeutralarXiv – CS AI · Jun 196/10
🧠Researchers introduce ScholarQuest, a large-scale benchmark for evaluating AI agents that search academic papers using language models. The benchmark tests agents across 1,000+ computer science topics with four research intent types, revealing that current agentic methods significantly outperform basic retrieval but still achieve only 31-36% recall, exposing substantial performance gaps in AI-driven literature discovery.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers introduce SkillJuror, a framework measuring how LLM agent skill organization affects runtime behavior independent of content. Testing Progressive Disclosure—a hierarchical skill structure—against flat baselines shows agents access 3.26x more resources and achieve 4.1% higher verification rates, revealing that procedural knowledge presentation meaningfully influences agent reasoning patterns.
AINeutralarXiv – CS AI · Jun 116/10
🧠A theoretical study proves that quantization fundamentally limits dense top-k retrieval systems, requiring embedding dimension and precision to scale logarithmically with corpus size, contradicting prior corpus-independent bounds that assumed infinite precision. This finding has direct implications for practical vector databases and dense retrieval systems where quantization is standard practice.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers present agentic hybrid RAG, a framework combining retrieval-augmented generation with agentic reasoning to improve scientific question answering in muon collider physics research. The work introduces the first benchmark for retrieval-augmented QA in high-energy physics, demonstrating that hybrid retrieval methods outperform traditional approaches for locating and synthesizing evidence from scientific literature.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers introduce SkillResolve-Bench, a benchmark for evaluating agent skill retrieval systems that addresses the critical problem of selecting the correct skill variant when multiple capabilities are semantically similar. The benchmark includes 661 helper/risky skill pairs and proposes SkillResolve, a method that achieves safer procedural exposure by selecting appropriate skill representatives from capability families.
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers introduced LakeQA, a new benchmark dataset for evaluating large language models on question-answering tasks over massive data lakes containing 9.5TB of heterogeneous data. The benchmark reveals significant challenges in current LLMs, with GPT-5.2 achieving only 18.37% accuracy, highlighting the gap between reading-comprehension performance and real-world search-and-reasoning requirements.
🧠 GPT-5
AINeutralarXiv – CS AI · Jun 106/10
🧠Researchers introduce STORM, a self-supervised framework that optimizes lexical query expansion for information retrieval by using BM25 reward signals during generation. The approach enables smaller language models (0.6B-8B parameters) to match larger proprietary rewriters while maintaining BM25's speed efficiency, and demonstrates zero-shot transfer across 18 languages.
AINeutralarXiv – CS AI · Jun 96/10
🧠AgriGov introduces a curated trilingual dataset (English-Hindi-Marathi) containing 8,000 parallel sentence pairs focused on Indian agricultural government schemes and farmer welfare programs. The dataset combines automated data collection, machine translation, and human post-editing to create domain-specific resources for machine translation, question-answering, and information retrieval systems aimed at farmer-facing applications.
AINeutralarXiv – CS AI · Jun 96/10
🧠The CHIIR 2026 Workshop on Generative AI and Academic Search convened researchers to examine how GenAI is transforming academic research systems beyond traditional document retrieval. Discussions centered on three themes—foundations, applications, and search-as-learning—emphasizing human-centered design principles that prioritize research integrity, transparency, and higher-order cognitive support.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers present a unified framework (PQO) that unifies diverse approximate nearest neighbor search methods under three design choices: projection placement, quantization thresholds, and code organization. The framework demonstrates that one-bit codes achieve 32x compression over floats while maintaining quality through re-ranking, with supervised eight-byte codes doubling the performance of two-kilobyte embeddings.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce DIVERGE, a new retrieval-augmented generation (RAG) framework that addresses a critical limitation in current AI systems: their inability to generate diverse, multiple perspectives for open-ended questions. The system achieves approximately 2x greater diversity in outputs without sacrificing quality by using iterative reflection and diversity-aware retrieval strategies.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce CICL, a decision-aware context layer that improves how language model agents select and compress relevant information for tool use. By scoring evidence based on action criticality and packing high-utility data as typed memory cards, the system achieves significant performance gains on code retrieval benchmarks, raising hit rates from 58% to 78% on SWE-bench tasks.
🧠 GPT-5
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers propose Kernel Affine Hull Machines (KAHM) as a lightweight alternative to transformer-based neural encoders for semantic search in frozen representation spaces. The method achieves 8.53x faster query encoding while maintaining competitive retrieval performance, offering practical efficiency gains for production deployment scenarios.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers introduce CTIConnect, a benchmark for evaluating retrieval-augmented large language models on cyber threat intelligence tasks. The study integrates five heterogeneous CTI sources into 1,860 expert-verified QA pairs across nine tasks, revealing that different task categories require fundamentally different retrieval strategies and that domain-specific approaches outperform generic retrieval methods.
AIBullisharXiv – CS AI · Jun 56/10
🧠Researchers introduce A2RAG, an adaptive framework that improves Graph-Retrieval-Augmented Generation (Graph-RAG) for multi-hop question answering by dynamically adjusting retrieval effort based on query difficulty. The system reduces token consumption and latency by ~50% while achieving significant accuracy gains, addressing practical deployment challenges in AI reasoning systems.
AIBullisharXiv – CS AI · Jun 46/10
🧠The EReL@MIR 2025 Multimodal Document Retrieval Challenge invited teams to build retrieval systems handling both closed-set document page retrieval and open-domain Wikipedia passage retrieval from text and image queries. The competition attracted 22 teams with 586 submissions, with winning systems favoring decoder-based Multimodal-LLM embedders over traditional CLIP-style encoders.
AINeutralarXiv – CS AI · Jun 46/10
🧠LCSHBench introduces the first large-scale public benchmark for Library of Congress Subject Heading assignment, comprising 22,346 multilingual books with consensus-validated labels from three major university libraries. The dataset reveals that while libraries agree on conceptual topics 93% of the time, they differ in exact heading assignments 39.4% of the time, enabling more nuanced evaluation of automated cataloging systems.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers introduce QO-Bench, a diagnostic benchmark for evaluating retrieval-augmented generation (RAG) systems on structured database-style queries over text. The benchmark reveals that current RAG systems excel at finding relevant passages but fail to preserve typed values needed for query operators like joins and counting, identifying operator execution rather than retrieval as the core bottleneck.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers propose DART, a test-time training method that improves dense retrieval reranking without requiring labeled data. By adapting scoring functions at inference time using pseudo-labels from document rankings, DART achieves 2.1% NDCG improvements across BEIR benchmarks with minimal latency overhead, addressing a key limitation in zero-resource information retrieval systems.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers propose Self-Conditioned Positional HNSW (SCP-HNSW), a method to improve retrieval-augmented generation (RAG) systems by reducing redundant overlapping chunks in document retrieval. The approach adds positional codes to embeddings and implements a two-pass query procedure, validated through 770 text-evidence reviews and 70 OCR audits showing varying quality levels across different document types.
AINeutralarXiv – CS AI · Jun 26/10
🧠TechGraphRAG presents an advanced retrieval-augmented generation framework that combines multi-step agentic reasoning, knowledge graphs, and external database searches to improve technical literature analysis. The system demonstrates how sophisticated AI pipelines can enhance domain-specific research by automating evidence gathering, query refinement, and citation verification across large academic corpora.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers propose REAL, a framework addressing knowledge conflicts in knowledge-intensive visual question answering by introducing 'reasoning-pivots' as atomic units that link external evidence in reasoning chains. The approach combines specialized fine-tuning and decoding strategies to improve accuracy when handling conflicting information from open-domain retrieval systems.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers propose DiffusionRank, a generative deep learning approach to learning-to-rank in information retrieval that uses denoising diffusion models instead of traditional discriminative methods. By modeling the full joint distribution of features and relevance labels, the method demonstrates improvements over classical ranking approaches on standard benchmarks.
AIBullisharXiv – CS AI · Jun 26/10
🧠Researchers introduce Harness-1, a 20B parameter search agent that separates semantic decision-making from state management by externalizing working memory to a stateful harness environment. The system achieves 73% average curated recall across eight retrieval benchmarks, outperforming comparable open-source searchers by 11.4 points while generalizing well to held-out transfer tasks.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers systematically studied how masking outdated information improves long-horizon search agents' efficiency, finding that benefits follow an inverted-U pattern dependent on model capacity and retriever quality. The effect collapses when models become saturated, revealing that context management success depends on balancing retriever performance with a model's implicit filtering capacity rather than either factor alone.
