arXiv:2605.29522v2 Announce Type: replace Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
The paper introduces a repeat‑run evaluation framework for AI agents that process large unstructured datasets, focusing on theme churn and volume disagreement as key metrics. Experiments on three customer‑feedback tasks across eight frontier models show that a taxonomy‑grounded agent (TGA) dramatically reduces theme churn by 86–88% and eliminates volume disagreement compared to raw generation and hierarchical decomposition. The framework is applicable beyond customer feedback, to any recurring synthesis of unstructured corpora such as financial reports, legal documents, incident records, and scientific literature.
arXiv:2608.30023v1 Announce Type: cross Abstract: Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying...
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
The paper introduces ElephantBench, a closed‑book knowledge probe with 1,094 multi‑account factual questions generated via an auditable graph‑based pipeline that pulls documents from a low‑exposure web corpus and identifies naturally occurring disagreements. Across 32 large language models, even the best model only recovers both divergent accounts on 52.4% of questions, and most models recall one account while omitting the other, indicating persistent epistemic myopia. The study shows that scaling model size and inference‑time reasoning improves recall but does not eliminate incompleteness, and that exposure imbalance in the corpus biases models toward the dominant account.
The paper evaluates the use of large language models (LLMs) as judges for assessing conversational voice agents, comparing human judgments with GPT‑4.1 and GPT‑5 across telecom and retail interactions. It examines agreement, metric‑level correlations, and consistency across three evaluation configurations (p0, p1, p2) to determine how reliably LLMs can judge conversational quality and safety. The study finds that LLM‑based evaluation can be effective but its reliability varies by metric and configuration, suggesting a hybrid approach where LLMs handle scalable assessment while humans focus on metrics requiring contextual interpretation.
TRACE is a new framework that uses agentic Large Language Models to automatically enrich e-commerce product catalogs with missing or buried attributes. It employs a ScoutAgent to gather multimodal evidence from merchant catalogs, syndicated feeds, and web search, and a JudgeAgent to verify and publish the proposed attribute values. In offline evaluation, TRACE achieved 98.2% accuracy with 74.7% coverage, and in production it increased enrichment coverage by 90.4% and boosted checkout conversion by 0.48%.
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
SurveyGraph introduces a framework that uses a Methods‑Entity Knowledge Graph (MEKG) to encode relationships such as follow‑on, competition, and shared‑resource links between research methods. The graph enables core‑paper discovery, hybrid outlining, and evidence‑gated narrative synthesis, outperforming flat lexical baselines in recall and achieving strong citation support and semantic edge validation. The study demonstrates MEKG’s effectiveness through controlled benchmarks, diagnostic tests, and human adjudication, positioning it as an auditable structural layer for survey generation.
arXiv:2608. 05876v1 Announce Type: new Abstract: User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it.
SimInter is a pipeline that enriches user profiles for conversational recommender systems by conducting synthetic interviews between two large language model agents, grounded in users’ statistical profiles and historical conversational evidence. An audit agent flags potential fabrication and stereotyping at each turn before enrichment. In experiments on movie recommendation, SimInter significantly increases the diversity of expressed preference aspects and improves simulator performance with two recommenders, with high human agreement on enriched attributes and auditor decisions.
arXiv:2609.08869v1 Announce Type: cross Abstract: Analysts in emerging equity markets keep answering the same questions. Did fundamentals match the market's response? How does the local currency co-m...