7 citations · 7 across the 17 of their papers we have counts for
17 papers
I'll Keep an Ear Out: Teaching AudioLLMs Proactive Audio Assistance
Amit Kumar Singh Yadav, Ritvik Shrivastava, Xuan Zhang +4
Audio large language models (AudioLLMs) operate reactively, responding only when queried. We introduce proactive audio assistance, where an AudioLLM monitors an audio stream and au…
S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
Xiaodong Wang, Xuanyi Zhao, Pedro Rodriguez +7
As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capability known as episodic memory.…
MoE-ViE: Mixture of Experts Vision Encoder for Efficient Image and Video Understanding
Bonan Zhang, Shiyu Dong, Quan Hung Tran +9
Vision encoders are a critical component of vision-language models, and scaling their capacity effectively improves performance. However, dense scaling increases compute cost and i…
Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness
Xilun Chen, Zhaleh Feizollahi, Ross Goodwin +5
Rubric-based evaluation of open-ended generation faces a fundamental tension between expressiveness and reliability. Authoring a faithful rubric requires expressing the structure o…
R3D: Quantitative 3D Spatial Reasoning for Egocentric Wearables
Maxwell Horton, Wei Lu, Quan Tran +6
Quantitative 3D spatial reasoning from egocentric RGB-D video is a critical capability for next-generation wearable assistants. Yet existing benchmarks do not reflect the challenge…
Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance
Kaustav Kundu, Ritvik Shrivastava, Maxim Arap +14
Proactive multimodal assistants must decide when guidance is needed and what to say, while tracking progress through extended procedures and helping users recover when they depart…
PixSearch: Region-Grounded Retrieval for Knowledgeable Large Multimodal Models
Jeonghwan Kim, Renjie Tao, Sanat Sharma +8
Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modular pipelines that separate visual…
TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models
Shima Imani, Seungwhan Moon, Lambert Mathias +2
Reliable mathematical and scientific reasoning remains an open challenge for large vision-language models. Standard final-answer evaluation often masks reasoning errors, allowing s…
SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code
Shima Imani, Seungwhan Moon, Adel Ahmadyan +3
We introduce, a large-scale synthetic benchmark of 15,045 university-level physics problems (90/10% train/test split). Each problem is fully parameterized, supporting an effectivel…
PRiSM: An Agentic Multimodal Benchmark for Scientific Reasoning via Python-Grounded Evaluation
Shima Imani, Seungwhan Moon, Adel Ahmadyan +3
Evaluating vision-language models (VLMs) in scientific domains like mathematics and physics poses unique challenges that go far beyond predicting final answers. These domains deman…
Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
Siddhant Arora, Haidar Khan, Kai Sun +14
End-to-end speech-in speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines, generating more natural, expressive responses with sig…
Proactive Assistant Dialogue Generation from Streaming Egocentric Videos
Yichi Zhang, Xin Luna Dong, Zhaojiang Lin +5
Recent advances in conversational AI have been substantial, but developing real-time systems for perceptual task guidance remains challenging. These systems must provide interactiv…
VisualLens: Personalization through Task-Agnostic Visual History
Wang Bill Zhu, Deqing Fu, Kai Sun +8
Existing recommendation systems either rely on user interaction logs, such as online shopping history for shopping recommendations, or focus on text signals. However, item-based hi…
Doppelgänger's Watch: A Split Objective Approach to Large Language Models
Shervin Ghasemlou, Ashish Katiyar, Aparajita Saraf +5
In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from thei…
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM
Jielin Qiu, Andrea Madotto, Zhaojiang Lin +7
Vision-extended LLMs have made significant strides in Visual Question Answering (VQA). Despite these advancements, VLLMs still encounter substantial difficulties in handling querie…
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
Zekun Li, Zhiyu Zoey Chen, Mike Ross +7
Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, thei…
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
Seungwhan Moon, Andrea Madotto, Zhaojiang Lin +10
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and…