7 papers
One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu +1
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness under trivial constraints? We show that simple lexical constraints…
Power-SMC: Low-Latency Sequence-Level Power Sampling for Training-Free LLM Reasoning
Seyedarmin Azizi, Erfan Baghaei Potraghloo, Minoo Ahmadi +2
Many recent reasoning gains in large language models can be explained as distribution sharpening: biasing generation toward high-likelihood trajectories already supported by the pr…
SkipKV: Selective Skipping of KV Generation and Storage for Efficient Inference with Large Reasoning Models
Jiayi Tian, Seyedarmin Azizi, Yequan Zhao +7
Large reasoning models (LRMs) often incur significant key-value (KV) cache overhead, due to their linear growth with the verbose chain-of-thought (CoT) reasoning. This incurs both…
From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance
Ardalan Aryashad, Parsa Razmara, Amin Mahjoub +3
Autonomous driving perception systems are particularly vulnerable in foggy conditions, where light scattering reduces contrast and obscures fine details critical for safe operation…
Activation Steering for Chain-of-Thought Compression
Seyedarmin Azizi, Erfan Baghaei Potraghloo, Massoud Pedram
Large language models (LLMs) excel at complex reasoning when they include intermediate steps, known as "chains of thought" (CoTs). However, these rationales are often overly verbos…
VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis
Tina Khezresmaeilzadeh, Parsa Razmara, Seyedarmin Azizi +2
Stock price prediction remains a complex and high-stakes task in financial analysis, traditionally addressed using statistical models or, more recently, language models. In this wo…