The AI Economist Agent uses RAG, knowledge graphs, and LLMs to generate economic narratives grounded in theory and data. It enables model-based analysis, evidence retrieval, and report generation, ensuring economic coherence and traceability through explicit model computations.
The AI Economist Agent uses RAG, knowledge graphs, and LLMs to generate economic narratives grounded in theory and data. It enables model-based analysis, evidence retrieval, and report generation, ensuring economic coherence and traceability through explicit model computations.
A four-arm comparison shows that retrieval is essential for accurate statutory citation under the Ontario Residential Tenancies Act. The SFT+RAG hybrid model achieves 0.481 exact-match with zero hallucinations, outperforming base and SFT-only models, and matches a pipeline using larger, specialized models without needing more data or larger training sets. Results are based on a small, human-verified real-world evaluation set and are preliminary.
Multi-Agent Transactive Memory (MATM) enables population-level storage and retrieval of agent-generated trajectories. It allows producer agents to share procedural knowledge with consumer agents, improving task performance and reducing interaction steps in interactive environments like ALFWorld and WebArena without coordination or joint training.
A study measures tool-intent stabilization in Streaming RAG, defining when speculative tool queries converge to correct answers. On the CRAG benchmark, 73.9% of queries allow substantial latency hiding, with early stabilization observed in questions with verbatim retrievable evidence. Question type significantly predicts early versus late stabilization, informing when speculative triggers are effective.
CATCH-ME introduces the first large-scale, multilingual dataset of contextually annotated, multi-turn counterspeech dialogues targeting hate and misinformation. The dataset covers five languages and focuses on seven marginalized groups, with dialogues grounded in verified fact-checking sources and including document- and chunk-level span annotations for RAG systems.
LFM2.5-Embedding-350M is a dense bi-encoder that provides fast multilingual retrieval with one vector per document, achieving best-in-class accuracy for its size and inference speed comparable to smaller models. LFM2.5-ColBERT-350M is a late interaction retriever with best-in-class multilingual accuracy, enabling cross-lingual retrieval by storing one vector per token and supporting retrieval in multiple languages with high precision. Both models are designed as drop-in replacements for existing RAG pipelines.
PhysAssistBench introduces a benchmark for interactive doctor-patient-EHR assistance using real MIMIC-IV cases. It features 1,296 manually reviewed, physician-validated turns and reveals that current LLMs struggle with coordinating clinical knowledge, communication, and EHR system interaction.
DICE, a training-free method, splits long documents into chunks, encodes them independently, and aggregates the results into a single vector. It reduces the Evidence Dilution Index in 92.8% of cases on LongEmbed, significantly improving retrieval performance for slices over 4k tokens across four backbones.
Extend AI has released an open source UI kit with 15 components for PDF, DOCX, and XLSX viewers, including bounding box citations, file upload, e-signature, and file systems. The toolkit, MIT licensed and fully customizable, was initially internal but is now open source due to customer demand, and is maintained for scalability and edge case handling in high-volume document processing.
ProvenanceGuard introduces a source-aware verifier for MCP-based LLM agents that detects cross-source conflation by routing claims to specific evidence sources and comparing stated attribution with actual source ownership. It achieves block F1 of 0.802 and source accuracy of 0.858 on 260 source-eligible claims, outperforming source-blind baselines, and detects all injected attribution swaps in 50 clinical probes.
HistoRAG introduces architectural changes to Retrieval-Augmented Generation based on historiographical principles. It separates retrieval and generation, implements temporal windowing for balanced source representation, and uses LLM-as-judge evaluation for transparent relevance judgments. Evaluated on 102,189 Der Spiegel articles (1950-1979), the framework addresses deficiencies in standard RAG, including temporal skew and weak retrieval correlation, and proposes Zwischentexte as a responsible integration method for LLM-generated content in scholarly work.
ProvenanceGuard introduces a source-aware verifier for MCP-based LLM agents that detects cross-source conflation by routing claims to specific evidence sources and comparing stated attribution with actual source ownership. It achieves block F1 of 0.802 and source accuracy of 0.858 on 260 source-eligible claims, outperforming source-blind baselines, and detects all injected attribution swaps in 50 clinical probes.
HyGRAG introduces a hierarchical graph RAG framework that integrates contextual and relational information through synthesized summaries. It enables emergent knowledge retrieval via context and relation-aware search across abstraction levels and supports dynamic updates with local re-summarization. Experiments show a 9.7% improvement in multi-hop reasoning accuracy.
The MLLP-VRAIN group submits a cascaded SimulST system using Parakeet and Qwen 3.5 models with adaptive black-box policies. For En→De, It, Zh, it employs ASR word-boosting and RAG with pre-translated exemplars in the new context track, achieving +5.82 XCOMET-XL improvement on MCIF En→De and an additional +1.03 gain via context integration.
MODE-RAG proposes a multi-agent system using Variational Free Energy to dynamically gate interventions and reduce cross-modal hallucinations in retrieval-augmented generation. It integrates Monte Carlo Tree Search and logit perturbations to address causal fabrications and sycophancy, with dedicated agents ensuring factual verification and formatting stability. Evaluated via ModeVent, a subset of MultiVent, the system significantly improves robustness against logical fabrications.