1 citations · 2 across the 11 of their papers we have counts for
13 papers
Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems
Jianxin Gao, Runze Li, Tianyi Yu +3
Multi-agent systems built on large language models (LLMs) restate observations as a matter of course: relays forward them, shared boards repeat them and discussion rounds echo them…
Interchangeable in Role, Not in History: Replacing Members of Formed LLM Teams
Jianxin Gao, Tianyi Yu, Linna Deng +4
Multiagent LLM systems often treat a role as a slot: when a member is replaced, any agent competent at the role is expected to fill it. We test this view on teams whose members kee…
MirrorCraft: Paired Evaluation under Hidden Rule Changes in Minecraft
Jianxin Gao, Beini Hu, Runze Li +7
With the rapid progress of large language models (LLMs), an interesting question has emerged: how do LLM-based agents perform in Minecraft? To date, most Minecraft benchmarks evalu…
EKLAV: Elicited Key CoT Learning via Answer only superVision
Adarsh Singh, Aman Singh, Kushal Raj Bhandari +4
Reasoning distillation trains a student to reproduce both the teacher's chain-of-thought trace and its final answer. We ask whether the trace is better used as privileged training…
EvoFlux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Kushal Raj Bhandari, Ling Yue, Ching-Yun Ko +4
Small language models (1.5B-4B parameters) offer low-cost, low-latency tool use but often produce plausible plans that fail during execution due to unavailable tools, missing argum…
Intermediate Representations are Strong AI-Generated Image Detectors
Zhenhan Huang, Pin-Yu Chen, Tejaswini Pedapati +1
The rapid advancement in generative AI models has enabled the creation of photorealistic images. At the same time, there are growing concerns about the potential misuse and dangers…
Improving Robustness of Tabular Retrieval via Representational Stability
Kushal Raj Bhandari, Adarsh Singh, Jianxi Gao +2
Transformer-based table retrieval systems flatten structured tables into token sequences, making retrieval sensitive to the choice of serialization even when table semantics remain…
From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents
Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko +6
Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval,…
Unraveling the cognitive patterns of Large Language Models through module communities
Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao
Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and…
CRAFT: Training-Free Cascaded Retrieval for Tabular QA
Adarsh Singh, Kushal Raj Bhandari, Jianxi Gao +2
Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional dense retrieval models such as DT…
Forecasting Open-Weight AI Model Growth on HuggingFace
Kushal Raj Bhandari, Pin-Yu Chen, Jianxi Gao
As the open-weight AI landscape continues to proliferate-with model development, significant investment, and user interest-it becomes increasingly important to predict which models…
Optimizing Waste Management with Advanced Object Detection for Garbage Classification
Everest Z. Kuang, Kushal Raj Bhandari, Jianxi Gao
Garbage production and littering are persistent global issues that pose significant environmental challenges. Despite large-scale efforts to manage waste through collection and sor…
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis
Kushal Raj Bhandari, Sixue Xing, Soham Dan +1
Large Language Models (LLMs), already shown to ace various unstructured text comprehension tasks, have also remarkably been shown to tackle table (structured) comprehension tasks w…