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arXiv:2602.23734v1 Announce Type: cross Abstract: One-stream Transformer-based trackers achieve advanced performance in visual object tracking but suffer from significant computational overhead that hinders real-time deployment. …
arXiv:2602.23863v1 Announce Type: cross Abstract: With the aim of detecting AI-generated images and identifying the specific models responsible for their generation, we propose a multi-modal …
arXiv:2602.23866v1 Announce Type: cross Abstract: Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is …
arXiv:2602.23881v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that …
arXiv:2602.23898v1 Announce Type: cross Abstract: Referring Expression Comprehension (REC) links language to region level visual perception. Standard benchmarks (RefCOCO, RefCOCO+, RefCOCOg) have progressed rapidly with …
arXiv:2602.24009v1 Announce Type: cross Abstract: Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across …
arXiv:2602.24040v1 Announce Type: cross Abstract: Reward models are central to aligning large language models (LLMs) with human preferences. Yet most approaches rely on pointwise reward …
arXiv:2602.23391v1 Announce Type: new Abstract: Large language models (LLMs) trained on webscale data can produce toxic outputs, raising concerns for safe deployment. Prior defenses, based …
arXiv:2602.23400v1 Announce Type: new Abstract: Generative Recommendation (GenRec) typically leverages Large Language Models (LLMs) to redefine personalization as an instruction-driven sequence generation task. However, fine-tuning …
arXiv:2602.23459v1 Announce Type: new Abstract: Psychiatric questionnaires are highly context sensitive and often only weakly predict subsequent symptom severity, which makes the prognostic relationship difficult …
arXiv:2602.23495v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) provide inherent interpretability by first mapping input samples to high-level semantic concepts, followed by a combination …
arXiv:2602.23504v1 Announce Type: new Abstract: Federated Learning (FL) enables a group of clients to collaboratively train a model without sharing individual data, but its performance …