Small language models (<=9B parameters) can support local medical question answering (QA) while reducing privacy and latency concerns, but effective fine-tuning remains challenging. This paper compares two chain-of-thought (CoT) supervised fine-tuning methods: Uniform-across-Sequence (UaS), which applies equal loss wei...
Le Zhou, Xiao Luo, Chinmay Chandra et al.· 0 citations
Autonomous vehicles require robust perception systems to operate safely under diverse environmental conditions. While cameras provide high-resolution visual data, their performance significantly degrades in low-light and adverse weather. High Definition (HD) radar offers superior reliability and angular resolution unde...
This work investigates whether open-weight models can generate less verbose reasoning by changing the form of their own earlier thoughts, without retraining, and identifies reasoning history as one way to reduce future reasoning-token use and motivate model-specific compaction policies evaluated for both efficiency and...
A. Hristov· Proceedings of the Second Wo...· 0 citations
Simulation benchmarks are the standard way to compare robot policies, but each benchmark brings its own evaluator, observation layout, and action convention, and evaluation is slow: 500 RoboTwin Easy episodes with one simulator and one model on one GPU take 359.7 minutes. Profiling the official StarVLA evaluator shows...
Deng-Ke Wu, Yifan Jin, Yida Yang et al.· 0 citations
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Multimodal language models continue to improve on broad music understanding benchmarks, but which musical features can they perceive and reason about? We investigate this question by introducing PianoBench, a benchmark that tasks language models with reasoning about musical performances across congruent acoustic and sy...
Louis Bradshaw, Irmak Bukey, Dan Lin et al.· 0 citations
LONGINUS is proposed, a retrieval-memory multi-agent adjudication framework combining evidence retrieved from patient records with structured memory of the patient trajectory that achieves the best transition-specific performance among LLM-based methods in nearly all settings while remaining competitive with them on ag...
AIRoA-0 (A-0) is a bimanual manipulation system learned from 16,151 hours of handheld demonstrations, with no robot data collected at any stage. The demonstrations are recorded with YUBI, our open-source handheld gripper, which lets people record manipulation data anywhere with no robot present. From a single instructi...
Yueh-Hua Wu, Kei Ota, Hanlong Li et al.· 0 citations
Most malware detection research focuses on identifying whether a file is malicious before it executes. Far less attention goes to what happens after infection. This paper presents an empirical analysis of post-infection malware behavior across 37 families, grounded in dynamic sandbox execution using CAPEv2. Behavioral...
Emmanuela Andam, Emanuel S. Grant, Naima Kaabouch· 0 citations
Third-party Agent Skills extend what an autonomous agent can do, but a malicious Skill can hide data exposure or output manipulation inside a legitimate workflow. Neither reading a Skill's description nor granting it tool permissions tells whether its execution meets the user's security requirements. We introduce Runti...
Respiratory sound classification from auscultation recordings is confounded by heterogeneous acquisition conditions, including recording device, auscultation site, and patient characteristics, that introduce systematic variability unrelated to the underlying pathology. Although such metadata is often available, existin...
George Kontogiannis, Pantelis Tzamalis, Sotiris Nikoletseas· 0 citations
Multi-layer Indexer (MLI) needs no retraining and no new weights, and is faster still than IndexCache at 1/(2r) while keeping twice the fresh selections: at 128K it reaches 1.32x the unmodified model's decode throughput at 1/4, above IndexCache's 1.28x at 1/8.
Serdarcan Dilbaz, Arjun Krishna, Kiran Kamble et al.· 0 citations
Diffusion models trained on distinct datasets can produce highly similar samples when prompted with the same noise input, suggesting that they learn a generalized data distribution. We investigate the role of noise injected during the sampling process, and show that it plays an important role in making or breaking this...
Vincent Emmerling, Felix G\"unther, David Kappel· 0 citations
