1 citations · 4 across the 12 of their papers we have counts for
15 papers
Learning Who Talks to Whom: Role-Aware Communication Topology Scheduling for Efficient LLM Multi-Agent Collaboration
Haibin Li, Wenya Yu, Xuefeng Jiang +4
Multi-agent systems driven by large language models (LLMs) rely on structured communication topologies to facilitate information exchange and collaborative reasoning. However, effe…
Ares: Adaptive Reasoning Effort Selection for Efficient LLM Agents
Jingbo Yang, Bairu Hou, Wei Wei +2
Modern agents powered by thinking LLMs achieve high accuracy through long chain-of-thought reasoning but incur substantial inference costs. While many LLMs now support configurable…
PromptBridge: Cross-Model Prompt Transfer for Large Language Models
Yaxuan Wang, Quan Liu, Zhenting Wang +4
Large language models (LLMs) underpin applications in code generation, mathematical reasoning, and agent-based workflows. In practice, systems access LLMs via commercial APIs or op…
DRAGON: Guard LLM Unlearning in Context via Negative Detection and Reasoning
Yaxuan Wang, Chris Yuhao Liu, Quan Liu +4
Unlearning in Large Language Models (LLMs) is crucial for protecting private data and removing harmful knowledge. Most existing approaches rely on fine-tuning to balance unlearning…
WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks
Jingbo Yang, Bairu Hou, Wei Wei +2
Large language model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that re…
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
Gyuhak Kim, Sumiran Singh Thakur, Su Min Park +2
Supervised fine-tuning (SFT) has become an essential step in tailoring large language models (LLMs) to align with human expectations and specific downstream tasks. However, existin…
Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control
Alireza Rezazadeh, Zichao Li, Ange Lou +3
Complex tasks are increasingly delegated to ensembles of specialized LLM-based agents that reason, communicate, and coordinate actions-both among themselves and through interaction…
Enhancing Retrieval Systems with Inference-Time Logical Reasoning
Felix Faltings, Wei Wei, Yujia Bao
Traditional retrieval methods rely on transforming user queries into vector representations and retrieving documents based on cosine similarity within an embedding space. While eff…
Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS
Shafiuddin Rehan Ahmed, Ankit Parag Shah, Quan Hung Tran +5
Climate change has intensified the need for transparency and accountability in organizational practices, making Environmental, Social, and Governance (ESG) reporting increasingly c…
H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
Martin Kuo, Jianyi Zhang, Aolin Ding +6
Large Reasoning Models (LRMs) have recently extended their powerful reasoning capabilities to safety checks-using chain-of-thought reasoning to decide whether a request should be a…
KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse
Jingbo Yang, Bairu Hou, Wei Wei +2
We describe KVLink, an approach for efficient key-value (KV) cache reuse in large language models (LLMs). In many LLM applications, different inputs can share overlapping context,…
LLM Unlearning via Loss Adjustment with Only Forget Data
Yaxuan Wang, Jiaheng Wei, Chris Yuhao Liu +6
Unlearning in Large Language Models (LLMs) is essential for ensuring ethical and responsible AI use, especially in addressing privacy leak, bias, safety, and evolving regulations.…
From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs
Alireza Rezazadeh, Zichao Li, Wei Wei +1
Recent advancements in large language models have significantly improved their context windows, yet challenges in effective long-term memory management remain. We introduce MemTree…
Improving Data Efficiency via Curating LLM-Driven Rating Systems
Jinlong Pang, Jiaheng Wei, Ankit Parag Shah +6
Instruction tuning is critical for adapting large language models (LLMs) to downstream tasks, and recent studies have demonstrated that small amounts of human-curated data can outp…
Harnessing Business and Media Insights with Large Language Models
Yujia Bao, Ankit Parag Shah, Neeru Narang +30
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performan…