8 citations · 13 across the 19 of their papers we have counts for
12 papers · 1 filter
PIGEON: VLM-Driven Object Navigation via Points of Interest Selection
Cheng Peng, Zhenzhe Zhang, Xiaobao Wei +7
Object navigation in unseen indoor environments requires agents to perform semantic search under partial observability. Vision-language models (VLMs) provide strong semantic-spatia…
Layer Specialization Underlying Compositional Reasoning in Transformers
Jing Liu
Transformers exhibit compositional reasoning on sequences not observed during training, a capability often attributed to in-context learning (ICL) and skill composition. We investi…
A Survey on Parallel Reasoning
Ziqi Wang, Boye Niu, Zipeng Gao +10
With the increasing capabilities of Large Language Models (LLMs), parallel reasoning has emerged as a new inference paradigm that enhances reasoning robustness by concurrently expl…
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Ryan Solgi, Parsa Madinei, Jiayi Tian +4
Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment.…
Circuit-Aware Reward Training: A Mechanistic Framework for Longtail Robustness in RLHF
Jing Liu
Reinforcement Learning from Human Feedback (RLHF) reward models exhibit systematic failures on longtail distributions, leading to reward hacking and misalignment. We propose a mech…
A2R: An Asymmetric Two-Stage Reasoning Framework for Parallel Reasoning
Ziqi Wang, Boye Niu, Zhongli Li +7
Recent Large Reasoning Models have achieved significant improvements in complex task-solving capabilities by allocating more computation at the inference stage with a "thinking lon…
Knowledge-Level Consistency Reinforcement Learning: Dual-Fact Alignment for Long-Form Factuality
Junliang Li, Yucheng Wang, Yan Chen +5
Hallucination in large language models (LLMs) during long-form generation remains difficult to address under existing reinforcement learning from human feedback (RLHF) frameworks,…
ViFP: A Framework for Visual False Positive Detection to Enhance Reasoning Reliability in VLMs
Ben Zhang, LuLu Yu, Lei Gao +3
During reasoning in vision-language models (VLMs), false positive (FP) reasoning occurs when a model produces the correct answer but follows an incorrect reasoning path, resulting…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…
Can AI automatically analyze public opinion? A LLM agents-based agentic pipeline for timely public opinion analysis
Jing Liu, Xinxing Ren, Yanmeng Xu +1
This study proposes and implements the first LLM agents based agentic pipeline for multi task public opinion analysis. Unlike traditional methods, it offers an end-to-end, fully au…
Empirical Computation: Prompting versus Programming
Eric Tang, Jing Liu, Marcel Böhme
Large Language Models (LLM) can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of spec…
Evaluating LLM-Based Regression Test Generation
Jing Liu, Seongmin Lee, Eleonora Losiouk +1
Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for pro…