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arXiv AI

Knowing When the Evidence Is Enough: Requirement-Directed Search with Horn-Closure Certificates

The paper introduces Certify, a system that determines whether retrieved evidence is sufficient for answering a query by using a requirement-directed search guided by a per-query hypergraph of facts. It decomposes the question, searches for each requirement, verifies entity links, and constructs a Horn-closure certificate that either provides minimal sufficient passages or indicates missing requirements. Experiments on three corpora show that Certify outperforms existing search agents and sufficiency judges, achieving higher decision accuracy and more correct answers.

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arXiv Computation and Language
3d ago

Less but Sufficient: Relation Knowledge Selects Compact Knowledge-Graph Contexts, and What External Checks Can and Cannot Catch

The paper presents a method that uses relation knowledge—either manually crafted or learned from training data—to select a compact set of knowledge‑graph facts for large language models (LLMs). It introduces derivability, consistency, and validation checks to determine whether an answer can be derived from the selected facts, whether the facts conflict, and whether the derived answer should replace the LLM’s output. Experiments show that seven open LLMs perform better with the compact context than with relevance‑ranked facts, achieving higher exact‑match accuracy on ComplexWebQuestions and outperforming SubgraphRAG across 53 LLM, benchmark, and prompt settings.

By Seunghui Jwa, Minsu Oh, Sangwoo Hong, Yeo Chan Yoon
llmsbenchmarks
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arXiv AI
Sep 7

Iris: Climbing to the Search Frontier

The paper introduces Iris-mini and Iris-pro, two search agents trained at 35B and 397B parameter scales. They use a novel data pipeline that constructs reverse‑engineered multi‑hop queries from web hyperlinks, filters trajectories, and alternates supervised fine‑tuning with reinforcement learning in a process called SFT‑RL climbing. Evaluations on several benchmarks show that, with inference‑time context management, the agents achieve the best open‑source results in their parameter ranges.

By Ziyuan Liu, Hengqi Liu, Zichuan Wang, Yang Qin, Jiachen Liang, Xu Chu, Shaowei Chen, Yuantao Gu, Mu Chuan
agentsreinforcement-learningfine-tuningbenchmarksdata-engineering
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arXiv AI
Oct 19

Optimist Deep Research: An Iterative, State-Centric Agent for Autonomous Literature Research

Optimist Deep Research is an open‑source agent that conducts literature research as an iterative, state‑centric process. It breaks a research question into a tree of subquestions, uses search intents to query multiple scholarly databases, and merges results into a provenance‑preserving document registry. The agent iteratively expands the registry via citation snowballing, evaluates evidence, and refines the research plan through a critic that can add, split, rephrase, deprioritize, or close subquestions, ultimately producing a report with every citation linked to a retrieved document.

By Gihan Panapitiya
ragagentsnlp
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arXiv AI
Sep 1

Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs

The paper evaluates how modern large language models use internal web search to answer factual questions. Using 783 static queries and 288 dynamic queries, the authors find that enabling retrieval improves accuracy on static questions but hurts confidence calibration. On dynamic queries, models often retrieve but still achieve less than 70% accuracy, mainly due to poor query formulation and source selection, indicating that internal web search works better as a quick verification tool than a full information‑retrieval system.

By Sahil Kale
llms
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