1 citations · 3 across the 21 of their papers we have counts for
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DeepRewind: Predicting and Repairing Premature Commitments in Deep Research Agents
Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Lele Wang +2
Deep-research agents conduct long-horizon investigations through iterative search, evidence evaluation, belief revision, and synthesis. However, they may commit to claims before su…
UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs
Amirhossein Abaskohi, Amirhossein Dabiriaghdam, Liang Luo +4
We introduce UnpredictaBench, an evaluation that tests the ability of large language models (LLMs) to capture true underlying distributions. As LLMs are increasingly used as substi…
Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation
Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi +3
Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are relevant and the retrieved bund…
SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference
Amirhossein Abaskohi, Giuseppe Carenini, Peter West +1
Large language models increasingly operate over long contexts, where the KV cache becomes a dominant memory bottleneck: its size grows linearly with sequence length and must be ret…
MCompassRAG: Topic Metadata as a Semantic Compass for Paragraph-Level Retrieval
Amirhossein Abaskohi, Raymond Li, Gaetano Cimino +3
Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search…
SproutRAG: Attention-Guided Tree Search with Progressive Embeddings for Long-Document RAG
Amirhossein Abaskohi, Issam H. Laradji, Peter West +1
Retrieval-augmented generation (RAG) systems must balance retrieval granularity with contextual coherence, a challenge that existing methods address through LLM-guided chunking, si…
The Illusion of Multi-Agent Advantage
Prathyusha Jwalapuram, Hehai Lin, Chuyuan Li +7
Prevailing wisdom posits that Multi-Agent Systems (MAS) are superior to Single-Agent Systems (SAS), citing advantages like context protection, parallel processing and distributed d…
ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
Amirhossein Abaskohi, Yuhang He, Peter West +3
Computer-use agents (CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual tokens. As interaction trajector…
IntentGrasp: A Comprehensive Benchmark for Intent Understanding
Yuwei Yin, Chuyuan Li, Giuseppe Carenini
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces I…
When Minor Edits Matter: LLM-Driven Prompt Attack for Medical VLM Robustness in Ultrasound
Yasamin Medghalchi, Milad Yazdani, Amirhossein Dabiriaghdam +7
Ultrasound is widely used in clinical practice due to its portability, cost-effectiveness, safety, and real-time imaging capabilities. However, image acquisition and interpretation…
Improving Topic Modeling by Distilling Soft Labels from Language Models
Raymond Li, Amirhossein Abaskohi, Chuyuan Li +2
Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with…