MVSS: A Unified Framework for Multi-View Structured Survey Generation

Scientific surveys require not only summarizing large bodies of literature, but also organizing them into clear and coherent conceptual structures. Existing automatic survey generation methods typically focus on linear text generation and struggle to explicitly model hierarchical relations among research topics and structured methodological comparisons, resulting in gaps in structural organization compared to expert-written surveys. We propose MVSS, a multi-view structured survey generation framework that jointly generates and aligns citation-grounded hierarchical trees, structured comparison tables, and survey text. MVSS follows a structure-first paradigm: it first constructs a conceptual tree of the research domain, then generates comparison tables constrained by the tree, and finally uses both as structural constraints for text generation. This enables complementary multi-view representations across structure, comparison, and narrative. We introduce an evaluation framework assessing structural quality, comparative completeness, and citation fidelity. Experiments on 76 computer science topics show MVSS outperforms existing methods in organization and evidence grounding, achieving performance comparable to expert surveys.

LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities

Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews the emerging paradigm of LLM-based multi-agent systems, examining their applications across the Software Development Life Cycle (SDLC), from requirements engineering and code generation to static code checking, testing, and debugging. We delve into a wide range of topics such as language model selection, SE evaluation benchmarks, state-of-the-art agentic frameworks and communication protocols. Furthermore, we identify key challenges and outline future research opportunities, with a focus on multi-agent orchestration, human-agent coordination, computational cost optimization, and effective data collection. This work aims to provide researchers and practitioners with valuable insights into the current forefront landscape of agentic systems within the software engineering domain.

Unique Word Channel Estimation for Oversampled OTFS

Practical aspects of orthogonal time frequency space (OTFS), such as channel estimation and its performance in fractional delay-Doppler (DD) channels, are a lively topic in the OTFS community. Oversampling and pulse shaping are also discussed in the existing literature, but not in the context of channel estimation. To the best of our knowledge, this paper is the first to address the problem of data-to-pilot and vice versa energy leakage caused by oversampling and pulse shaping in OTFS. Theoretical analysis is performed on an oversampled, pulse-shaped OTFS implementing the embedded pilot channel estimation technique, revealing a trade-off between the amount of energy leakage and excess bandwidth introduced by the pulse shape. Next, a novel variant of OTFS is introduced, called UW-OTFS, which is designed to overcome the leakage problem by placing the pilot in the oversampled time domain instead of the DD domain. The unique structure of UW-OTFS offers 36 percent higher spectral efficiency than the OTFS with embedded pilot. UW-OTFS also outperforms traditional OTFS in terms of bit error ratio and out-of-band emissions.

A Deep Dive into OpenStreetMap Research Since its Inception (2008-2024): Contributors, Topics, and Future Trends

OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information (VGI) project into a global, multi-disciplinary research nexus. This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces. By evaluating 1,926 publications from the Web of Science (WoS) Core Collection and 782 State of the Map (SotM) presentations up to June 2024, we quantify publication growth, collaboration patterns, and thematic evolution. Results demonstrate simultaneous consolidation and diversification within the field. While a stable core of contributors continues to anchor OSM research, themes have shifted from initial concerns over data production and quality toward advanced analytical and applied uses. Comparative analysis of OSM-related research in WoS and SotM reveals distinct but complementary agendas between scholars and the OSM community. Building on these findings, we identify six emerging research directions and discuss how evolving partnerships among academia, the OSM community, and industry are poised to shape the future of OSM research. This study establishes a structured reference for understanding the state of OSM studies and offers strategic pathways for navigating its future trajectory.The data and code are available at https://github.com/ya0-sun/OSMbib.

Full Disclosure, Less Trust? How the Level of Detail about AI Use in News Writing Affects Readers' Trust

As artificial intelligence (AI) is increasingly integrated into news production, calls for transparency about the use of AI have gained considerable traction. Recent studies suggest that AI disclosures can lead to a ``transparency dilemma'', where disclosure reduces readers' trust. However, little is known about how the \textit{level of detail} in AI disclosures influences trust and contributes to this dilemma within the news context. In this 3$\times$2$\times$2 mixed factorial study with 40 participants, we investigate how three levels of AI disclosures (none, one-line, detailed) across two types of news (politics and lifestyle) and two levels of AI involvement (low and high) affect news readers' trust. We measured trust using the News Media Trust questionnaire, along with two decision behaviors: source-checking and subscription decisions. Questionnaire responses and subscription rates showed a decline in trust only for detailed AI disclosures, whereas source-checking behavior increased for both one-line and detailed disclosures, with the effect being more pronounced for detailed disclosures. Insights from semi-structured interviews suggest that source-checking behavior was primarily driven by interest in the topic, followed by trust, whereas trust was the main factor influencing subscription decisions. Around two-thirds of participants expressed a preference for detailed disclosures, while most participants who preferred one-line indicated a need for detail-on-demand disclosure formats. Our findings show that not all AI disclosures lead to a transparency dilemma, but instead reflect a trade-off between readers' desire for more transparency and their trust in AI-assisted news content.

Uplink Multi-User MIMO Implementation in OpenAirInterface for a Cell-Free O-RAN Testbed

Cell-Free Multiple-Input Multiple-Output (MIMO) and Open Radio Access Network (O-RAN) have been active research topics in the wireless communication community in recent years. As an open-source software implementation of the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) protocol stack, OpenAirInterface (OAI) has become a valuable tool for deploying and testing new ideas in wireless communication systems. In this paper, we present our OAI based real-time uplink Multi-User MIMO (MU-MIMO) testbed developed at Fraunhofer HHI. As a part of our Cell-Free MIMO testbed development, we built a 2x2 MU-MIMO system using general purpose computers and commercially available software defined radios (SDRs). Using a modified OAI next-Generation Node-B (gNB) and two unmodified OAI user equipment (UE), we show that it is feasible to use Sounding Reference Signal (SRS) channel estimates to compute uplink combiners. Our results verify that this method can be used to separate and decode signals from two users transmitting in nonorthogonal time-frequency resources. This work serves as an important verification step to build a complete Cell-Free MU-MIMO system that leverages time domain duplexing (TDD) reciprocity to do downlink beamforming over multiple cells.

LISP -- A Rich Interaction Dataset and Loggable Interactive Search Platform

We present a reusable dataset and accompanying infrastructure for studying human search behavior in Interactive Information Retrieval (IIR). The dataset combines detailed interaction logs from 61 participants (122 sessions) with user characteristics, including perceptual speed, topic-specific interest, search expertise, and demographic information. To facilitate reproducibility and reuse, we provide a fully documented study setup, a web-based perceptual speed test, and a framework for conducting similar user studies. Our work allows researchers to investigate individual and contextual factors affecting search behavior, and to develop or validate user simulators that account for such variability. We illustrate the datasets potential through an illustrative analysis and release all resources as open-access, supporting reproducible research and resource sharing in the IIR community.

Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphs

Retrieval-Augmented Generation for software engineering often relies on vector similarity search, which captures topical similarity but can fail on multi-hop architectural reasoning such as controller to service to repository chains, interface-driven wiring, and inheritance. This paper benchmarks three retrieval pipelines on Java codebases (Shopizer, with additional runs on ThingsBoard and OpenMRS Core): (A) vector-only No-Graph RAG, (B) an LLM-generated knowledge graph RAG (LLM-KB), and (C) a deterministic AST-derived knowledge graph RAG (DKB) built with Tree-sitter and bidirectional traversal. Using 15 architecture and code-tracing queries per repository, we measure indexing time, query latency, corpus coverage, cost, and answer correctness. DKB builds its graph in seconds, while LLM-KB requires much longer graph generation. LLM-KB also shows indexing incompleteness: on Shopizer, 377 files are skipped or missed, reducing embedded chunk coverage and graph size compared to DKB. End-to-end cost is modest for DKB relative to the vector-only baseline but much higher for LLM-KB, especially as repository scale increases. Query latency is similar for No-Graph and DKB, while LLM-KB is slower and more variable. On the Shopizer question suite, DKB achieves the highest correctness, LLM-KB is close behind, and the vector-only baseline performs worst on upstream architectural queries and has the highest hallucination risk. Overall, deterministic AST-derived graphs provide more reliable coverage and multi-hop grounding than LLM-extracted graphs at substantially lower indexing cost.

SwiftMem: Fast Agentic Memory via Query-aware Indexing

Agentic memory systems have become critical for enabling LLM agents to maintain long-term context and retrieve relevant information efficiently. However, existing memory frameworks suffer from a fundamental limitation: they perform exhaustive retrieval across the entire storage layer regardless of query characteristics. This brute-force approach creates severe latency bottlenecks as memory grows, hindering real-time agent interactions. We propose SwiftMem, a query-aware agentic memory system that achieves sub-linear retrieval through specialized indexing over temporal and semantic dimensions. Our temporal index enables logarithmic-time range queries for time-sensitive retrieval, while the semantic DAG-Tag index maps queries to relevant topics through hierarchical tag structures. To address memory fragmentation during growth, we introduce an embedding-tag co-consolidation mechanism that reorganizes storage based on semantic clusters to improve cache locality. Experiments on LoCoMo and LongMemEval benchmarks demonstrate that SwiftMem achieves 47$\times$ faster search compared to state-of-the-art baselines while maintaining competitive accuracy, enabling practical deployment of memory-augmented LLM agents.

PrivGemo: Privacy-Preserving Dual-Tower Graph Retrieval for Empowering LLM Reasoning with Memory Augmentation

Knowledge graphs (KGs) provide structured evidence that can ground large language model (LLM) reasoning for knowledge-intensive question answering. However, many practical KGs are private, and sending retrieved triples or exploration traces to closed-source LLM APIs introduces leakage risk. Existing privacy treatments focus on masking entity names, but they still face four limitations: structural leakage under semantic masking, uncontrollable remote interaction, fragile multi-hop and multi-entity reasoning, and limited experience reuse for stability and efficiency. To address these issues, we propose PrivGemo, a privacy-preserving retrieval-augmented framework for KG-grounded reasoning with memory-guided exposure control. PrivGemo uses a dual-tower design to keep raw KG knowledge local while enabling remote reasoning over an anonymized view that goes beyond name masking to limit both semantic and structural exposure. PrivGemo supports multi-hop, multi-entity reasoning by retrieving anonymized long-hop paths that connect all topic entities, while keeping grounding and verification on the local KG. A hierarchical controller and a privacy-aware experience memory further reduce unnecessary exploration and remote interactions. Comprehensive experiments on six benchmarks show that PrivGemo achieves overall state-of-the-art results, outperforming the strongest baseline by up to 17.1%. Furthermore, PrivGemo enables smaller models (e.g., Qwen3-4B) to achieve reasoning performance comparable to that of GPT-4-Turbo.