This paper evaluates the use of AI-generated summaries on search engine results pages (SERPs) in academic search for social science information.

This paper proposes a framework for summarizing dialogues, modeling semantic and emotion dynamics using multimodal inputs and an adapted hierarchical Chain-of-Agents approach.

The paper introduces AIBuildAI-2, a knowledge-enhanced agent that significantly improves the automatic building of AI models by integrating an external, evolving knowledge system, achieving state-of-t…

This paper proposes ASMR, a framework for automatically generating compact and informative schemas from historical ship maintenance reports using two specialized agents.

TalTech submitted top-ranking systems to the Beyond Transcription Challenge using fine-tuned Voxtral models and reinforcement learning against Open Medical Concept F1.

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper introduces a new benchmark for multi-target cross-lingual summarization (MTXLS) and proposes an activation steering method that significantly improves LLM performance by guiding the generati…

This paper investigates how the final prompt in conversational AI-search evaluations differs from the conversation history, using two corpora of commercial and PRISM conversations.

This paper introduces PromptMN, a domain-specific language for annotating natural language prompts to clarify roles, goals, and constraints for AI models, reducing context ambiguities and repair cycle…

PAPERCLAW is a multi-agent system that autonomously curates a domain, generates ideas, and writes venue-compliant papers using large language models and a stoppable hypothesis map.

The paper introduces I-WebGenBench, a framework and benchmark that converts static scientific papers into executable, interactive web systems, allowing users to dynamically explore the paper's mechani…

The authors conducted a user study to assess the effectiveness of their interactive visual query tracer and builder tools for Nemo, a Datalog reasoner, in helping students learn Datalog.

The paper introduces PlanRAG, a framework for Retrieval-Augmented Generation (RAG) that models exploratory reasoning problems as logical query trees, addressing representation and optimization gaps be…

MOOSE-Copilot is a novel web-based framework that unifies scientific hypothesis discovery by formalizing human-AI interaction, significantly improving performance over autonomous LLM baselines.

The paper introduces a supervised fine-tuning pipeline using large language models to accurately categorize sentence-level clinical provenance across multi-disciplinary hospital notes, demonstrating t…

The paper introduces TVIR, a new benchmark and multi-agent framework for deep research, to evaluate and improve the generation of factually reliable, text-visual interleaved reports.

The paper argues that purported anthropomorphic attributes of LLMs are not unique to language models but are substrate-dependent, demonstrating this by training a neural network on the game Age of Emp…

This paper proposes conversational AI review assistants for code review, systems that engage in conversation with developers instead of just generating comments.

This paper introduces MAGNET, a framework for long-form narrative generation and verification using a multi-agent goal-driven engine and a graph-based pipeline.