MASCOT: Towards Multi-Agent Socio-Collaborative Companion Systems
MASCOT is a multi-agent framework for socio-collaborative companions that uses bi-level optimization—persona-aware behavioral alignment and collaborative dialogue optimization—to counter persona collapse and social sycophancy, improving role consistency and reducing redundant dialogue.
Sep 1, 2026
CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries
CultureVLM characterizes and improves cultural understanding of vision-language models across more than 100 countries using culturally-grounded benchmarks and training procedures.
Sep 1, 2026
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
SARA is a unified RAG framework that balances local factual precision with global coverage by combining natural-language spans with compact semantic compression vectors, achieving consistent gains under strict context budgets.
Jul 1, 2026
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations
A systematic study showing that reasoning capabilities alone are insufficient for LLMs in multi-turn mental health conversations, isolating failure modes that demand additional safety and empathy-aware design.
Jul 1, 2026
MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
Structure-aware multimodal RAG for enterprise Q&A, combining layout-aware report parsing, page-level slide representations, and inference-time context assembly. Includes FastRAGEval for efficient answer evaluation.
Jul 1, 2026
MedHalu: Hallucinations in Responses to Healthcare Queries by Large Language Models
MedHalu is a fine-grained benchmark for studying hallucinations in LLM responses to consumer healthcare queries, analyzing hallucination patterns across models, query types, and medical specialties.
Jun 1, 2026
Sysformer: Safeguarding Frozen Large Language Models with Adaptive System Prompts
Sysformer learns adaptive, query-conditioned system prompts to safeguard frozen large language models, providing fine-grained safety control without modifying model weights.
Apr 23, 2026
Beyond Magic Words: Sharpness-Aware Prompt Evolving for Robust Large Language Models with TARE
Sharpness-aware prompt evolution optimizes prompts for both performance and robustness by penalizing sharp regions in the prompt loss landscape, yielding prompts that transfer better across tasks and LLM families.
Apr 23, 2026
AgentArk: Distilling Multi-Agent Intelligence into a Single LLM Agent
Distills multi-agent debate into one LLM through reasoning-enhanced fine-tuning, trajectory-based augmentation, and process-aware distillation, moving computation from inference to training.
Feb 3, 2026
SlideAgent: Hierarchical Agentic Framework for Multi-Page Visual Document Understanding
We introduce SlideAgent, a versatile agentic framework for understanding multi-modal, multi-page, and multi-layout documents.
Nov 27, 2025