Search: “multi-hop”

NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities

arXiv:2609.15695v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly power consumer-facing AI search, yet evaluating them on the diversity of everyday visual questions remains ch

agentsarxiv-cs-ai

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Pull: Lazy Materialization of Working Memory for Stateful LLM Conversations

DGX agent

arXiv:2609.14773v1 Announce Type: new Abstract: As LLM conversations grow to hundreds of turns, full-context injection incurs O(N^2) cumulative token costs, while lossy summarization or hard truncatio

model-releasesarxiv-cs-cl

15 Sept 2026

Question's Gambit: The First Move Matters in Agentic Deep Search

arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmark

Recurrent GraphNeural NetworkswithSet-BasedAggregation

arXiv:2609.15932v1 Announce Type: new Abstract: Recurrent GNNs iterate message passing to convergence, and their logical characterizations to date rely on multi-set aggregation, graded (counting) logi

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation,

TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps

arXiv:2609.14762v1 Announce Type: cross Abstract: Cloud-hosted large language models (LLMs) are increasingly used for root cause analysis (RCA) in AIOps pipelines, but they introduce data privacy risk

Verifiable by Construction: Claim-Level Evaluation of Verbatim Citation in Clinical Question Answering

arXiv:2609.15964v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted for clinical question answering (QA). Current systems can attach citations to their answers, but t

HypoKG: Evidence-Disciplined Biomedical Hypothesis Generation Beyond Endpoint Knowledge

arXiv:2609.12260v1 Announce Type: new Abstract: Large language models (LLMs) can generate biomedical hypotheses, but it remains unclear whether they truly reason from scientific evidence or simply pro

14 Sept 2026

LifeMem: Enabling Lifelong Experience Reuse for LLM Agents

arXiv:2609.12655v1 Announce Type: new Abstract: Large language model agents are expected to continuously adapt to new tasks and environments over their lifetime by reusing past experience. However, ex

agentsarxiv-cs-cl

14 Sept 2026

Tasks over Application Manuals: Revealing Gaps in Long-Horizon Procedural Reasoning for Language Models

arXiv:2609.13005v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance on a wide range of natural language tasks, and recent benchmarks suggest that they are inc

14 Sept 2026

Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents

arXiv:2609.11243v1 Announce Type: new Abstract: Autonomous research agents are increasingly expected to search the literature, analyze experimental evidence, and generate scientific hypotheses. These

12 Sept 2026

RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety

arXiv:2609.11758v1 Announce Type: new Abstract: Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However

11 Sept 2026

V-Retrver: Evidence-Driven Agentic Reasoning for Universal Multimodal Retrieval

arXiv:2602.06034v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently been applied to universal multimodal retrieval, where Chain-of-Thought (CoT) reasoning improv

agentsarxiv-cs-cv

11 Sept 2026

What Should an Agent Forget? Separating What Is Stored from What Is Used

arXiv:2609.10263v1 Announce Type: new Abstract: Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question.

agentsarxiv-cs-ai

11 Sept 2026

A Hierarchical Consistency Framework for Auditing Retrieval-Augmented Generation Systems

arXiv:2609.07075v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its re

Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering

arXiv:2505.13520v2 Announce Type: replace-cross Abstract: Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances have

Do Web Agents Investigate Before They Decide?

arXiv:2602.05354v3 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed in moderation and policy enforcement, where correct decisions often depend on evidence that is not i

Evaluating the Scalability and Adversarial Generalization of GRPO-Trained NLI Models

arXiv:2504.18376v3 Announce Type: replace-cross Abstract: Natural Language Inference (NLI) is a central task in natural language understanding with applications in fact-checking, question answering, a

Evidence-Grounded Retrieval for Investigation Hunt Lead Generation from CTI Reports

arXiv:2609.08790v1 Announce Type: cross Abstract: Threat hunting increasingly depends on converting unstructured knowledge (e.g., Cyber Threat Intelligence reports) into actionable hunt leads: concise

LogicSkills: A Structured Benchmark for Formal Reasoning in Large Language Models

arXiv:2602.06533v3 Announce Type: replace Abstract: Large language models perform well on many logical reasoning benchmarks, but it remains unclear which core logical skills they truly master. To addr

Hardware

RAGMark: A Comprehensive Framework for Benchmarking Retrieval-Augmented Generation Systems

arXiv:2609.05760v1 Announce Type: cross Abstract: We present RAGMark, a modular benchmarking framework for advanced Retrieval-Augmented Generation (RAG) systems targeting small-scale multi-GPU environ

hardwarearxiv-cs-ai

Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications

arXiv:2609.09569v1 Announce Type: new Abstract: News articles often contain omitted temporal expressions, such as day-only or month-only mentions, which must be interpreted with reference to the publi

researcharxiv-cs-cl

Eliciting Self-Verification in Multimodal Reasoning Agents with Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2609.08025v1 Announce Type: cross Abstract: Reasoning agents increasingly rely on external tools such as web search to answer complex queries. Reinforcement learning (RL) finetuning algorithms s

9 Sept 2026

GraphFAS: A Distributed System for Automated Graph Feature Generation and Selection in Industrial Transaction Networks

arXiv:2609.08970v1 Announce Type: new Abstract: Industrial fraud detection often relies on costly expert-crafted features that overlook graph-structured relational signals, while GNNs often do not mee

researcharxiv-cs-lg

9 Sept 2026

Applications

HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolation

arXiv:2609.08685v1 Announce Type: new Abstract: Standard open-set node classification methods rely on the homophily assumption, where connected nodes share labels. However, real-world graphs are often

applicationsarxiv-cs-lg

9 Sept 2026

CAGE: Coherence-Aware Graph Encoding for Retrieval-Augmented Generation

arXiv:2609.04647v1 Announce Type: new Abstract: Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, assembling context sets that may be individ

researcharxiv-cs-cl

Iris: Climbing to the Search Frontier

arXiv:2609.04304v1 Announce Type: new Abstract: We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data pipeline and training recipe be

NS-ST-GraphRAG: Neuro-Symbolic Spatio-Temporal GraphRAG for Literary Knowledge Processing

arXiv:2609.05139v1 Announce Type: new Abstract: Long-form literary narratives pose a distinctive information-processing challenge for retrieval-augmented generation: relevant evidence is distributed a

Reservoir-Based Graph Convolutional Networks

model-releasesarxiv-cs-cv

arXiv:2603.24131v2 Announce Type: replace-cross Abstract: Message passing is a core mechanism in Graph Neural Networks (GNNs), enabling the iterative update of node embeddings by aggregating informati

KhatianDoc: A Human-Verified Benchmark Diagnosing Multimodal LLM Failure on Bengali Legal Land Records

arXiv:2609.03597v1 Announce Type: new Abstract: Land ownership in Bangladesh is recorded in Ana-Ganda-Kora-Kranti-Til, a base-16 positional fraction system with dedicated Unicode glyphs, no mainstream

PACE: Towards Surfacing Hidden Conflicts in User Requests

arXiv:2609.03293v1 Announce Type: new Abstract: Personalized assistants should not only comply with user requests but also assess whether those requests are appropriate given the user's current circum

safetyarxiv-cs-cl

Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG

arXiv:2609.03749v1 Announce Type: cross Abstract: Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet

The Head Complexity of Boolean Functions in Single-Layer Attention

arXiv:2609.04046v1 Announce Type: cross Abstract: What can a single layer of self-attention compute? We study head complexity: the minimum number of attention heads required to compute a function in a

researcharxiv-cs-lg

Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads

arXiv:2602.22453v3 Announce Type: replace Abstract: Recent work has identified a subset of attention heads in Transformer as retrieval heads, which are responsible for retrieving information from the

FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment

arXiv:2603.19539v2 Announce Type: replace-cross Abstract: We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assess

HyGRAIL: Cost-Aware and Evidence-Grounded Scientific Hypothesis Discovery over Knowledge Graphs

arXiv:2609.02056v1 Announce Type: new Abstract: Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed l

agentsarxiv-cs-cl

Learning to Fuse LLMs with Ontology Rankers for Rare-Disease Diagnosis

arXiv:2609.02473v1 Announce Type: new Abstract: Ontology rankers remain useful for rare-disease diagnosis because each candidate can be traced to matched patient phenotypes. Large language models (LLM

MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity

arXiv:2609.02060v1 Announce Type: new Abstract: Mineral exploration requires integrating heterogeneous geochemical, geophysical, and geological evidence, yet existing prospectivity systems often provi

SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning

arXiv:2609.02336v1 Announce Type: new Abstract: Effective in-context learning (ICL) for complex reasoning relies on selecting the right demonstrations. Traditional retrieval methods based on surface s

Local Ai

Seed-Anchored Budget-Bounded Graph Rendering for Question Answering on Industry-Standard Power-Grid Information and Exchange Models

arXiv:2609.02011v1 Announce Type: cross Abstract: Large language model question answering over power-grid models must respect a fixed context budget. We introduce seed-anchored graph rendering, a dete

local-aiarxiv-cs-ai

Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion

arXiv:2609.02519v1 Announce Type: cross Abstract: Uncertain knowledge graphs (UKGs) extend knowledge graphs by assigning each triple a continuous confidence score. Since most possible triples lack obs

Trace as State: Reasoning Traces as Conditional States for Long-Context Transformers

arXiv:2609.02702v1 Announce Type: new Abstract: Transformers process information causally, but long-context reasoning may depend on task state discovered only later. We formalize this mismatch through

AdaSearch: Balancing Parametric Knowledge and Search in Large Language Models via Reinforcement Learning

arXiv:2512.16883v2 Announce Type: replace Abstract: Equipping large language models (LLMs) with search engines via reinforcement learning (RL) promises effective search agents. However, adaptively bal

researcharxiv-cs-cl

Automated Tree Knowledge Graph Construction using Ontology Expansion and Retrieval from Vietnamese History Textbooks

arXiv:2609.00763v1 Announce Type: new Abstract: Hierarchical Knowledge graph (KG)-based retrieval augmented generation (RAG) has emerged as a powerful approach for supporting large language models wit

Explore Before Committing: Hypothesis-Guided Search for Deep Research Agents

arXiv:2609.01294v1 Announce Type: new Abstract: Deep-research agents answer complex questions by interacting with search and browsing tools, yet they often search along a single evolving trajectory. O

agentsarxiv-cs-cl

From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification

arXiv:2609.01564v1 Announce Type: cross Abstract: Large language models (LLMs) struggle to classify text into taxonomies with many semantically similar labels, as the distinctions are domain-specific

One Policy, Any Budget: Internalizing Budget-Aware Search via Reinforcement Learning

arXiv:2609.00813v1 Announce Type: new Abstract: While reinforcement learning has enabled LLM-based search agents to invoke external tools, existing methods train under fixed budgets and cannot adapt w

Optimizing Byzantine Node Placement in Decentralized Federated Learning

arXiv:2609.01495v1 Announce Type: cross Abstract: Security evaluations of decentralized federated learning (DFL) typically focus on how Byzantine participants behave, while largely overlooking which p