LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.
arXiv:2605.01699v4 Announce Type: replace Abstract: Recent attacks show that behavioural unlearning of large language models leaves internal traces recoverable by adversarial probes. We characterise...
The paper introduces SCOPE, a method that post‑trains computer‑use agents to balance task completion with safety by conditioning actions on environmental risk. It combines supervised fine‑tuning on three trajectory types—capability demonstrations, safe continuations, and explicit refusals—followed by reinforcement learning to improve performance. Experiments starting from Qwen3.5‑9B show that SCOPE‑RL achieves high task success and attack‑avoidance rates, outperforming other agents on OSWorld and OS‑BLIND benchmarks.
arXiv:2605.11217v2 Announce Type: replace Abstract: The model context protocol (MCP) has been widely adopted as an open standard enabling the seamless integration of generative AI agents. However, wh...
arXiv:2604.11508v3 Announce Type: replace Abstract: Fine-tuning a pretrained classifier leaves some samples reliably learned and others cycling between correct and incorrect. Curriculum learning, dat...
VideoGen-Agent is a multimodal agent that uses multitask agentic reinforcement learning to coordinate external tools for video generation. It learns to augment, generate, and verify videos through multi‑turn interactions, guided by prompts and intermediate observations. On the new VABench benchmark, the agent improves base text‑to‑video performance by 19.1 points, and further upgrades to generation tools raise the score to 86.1, with human raters favoring the upgraded configuration in 84.3% of comparisons.
Apollo Restore is a 24‑billion‑parameter large language model fine‑tuned from Mistral Small to fill in gaps in fragmentary Ancient Greek texts using a fill‑in‑the‑middle objective. It achieves state‑of‑the‑art performance on short lacunae, placing the correct restoration among its top twenty candidates for 80.6% of documentary‑papyrus, 54.6% of literary‑papyrus, and 61.0% of stone‑inscription gaps, outperforming previous models by significant margins. In blind expert evaluations, 20 specialists preferred Apollo Restore over the strongest baseline and judged its performance at least as good as human restorations in 77% of cases, while also improving the published reading of the Vesuvius‑carbonised papyrus P.Herc. 1667.
The paper introduces SADA-Merging, a new data‑free model merging framework that addresses limitations of existing subspace‑based methods. It allocates subspace capacity based on each task’s spectral complexity, adapts spectral preservation to task‑specific plasticity, and applies depth‑dependent anchoring to mitigate projection distortion. Experiments show that SADA‑Merging consistently outperforms current data‑free merging techniques across various task scales and adaptation settings.
arXiv:2609.24193v1 Announce Type: new Abstract: Pedestrian head orientation recognition plays an important role in autonomous driving by providing valuable cues for understanding pedestrian attention...
The paper introduces Bidirectional Reciprocal Learning (BRL), a parameter‑efficient fine‑tuning framework for referring image segmentation that operates on frozen vision foundation models. BRL employs two lightweight adapters—Reciprocal Attention Adapter (RAA) for token‑level cross‑modal attention and Reciprocal Gate Adapter (RGA) for channel‑level gating—to enable hierarchical, bidirectional information flow between vision and language. Experiments on RefCOCO, RefCOCO+, and RefCOCOg show that BRL outperforms existing methods while updating fewer than 0.5% of backbone parameters.
arXiv:2609.23436v1 Announce Type: new Abstract: Novel view synthesis from sparse observations is severely under-constrained. Although 3D Gaussian Splatting (3DGS) enables real-time rendering, it prod...
arXiv:2506.08641v3 Announce Type: replace Abstract: Adapting vision models for time series analysis is compelling, yet all existing approaches are falling short of dedicated time series foundation mo...
arXiv:2603.18532v3 Announce Type: replace-cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for f...
arXiv:2512.07624v2 Announce Type: replace Abstract: Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of...
arXiv:2505.16227v4 Announce Type: replace-cross Abstract: Personalizing jargon detection and explanation is essential for making technical documents accessible to readers with diverse disciplinary ba...
Standard deployment-ready object detectors for autonomous vehicles degrade in adverse weather and lighting conditions without being trained on extensive domain-specific data. While large-scale vision...
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resol...
Legal argument mining supports passage classification, retrieval, and argument completion. This work introduces an expert-annotated dataset of 42 U.S. federal tax opinions on corporate reorganizations...
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing mod...
Post-training is becoming a service (PTaaS): a customer hands an operator data and a goal, and a forward-deployed engineer (FDE) returns a fine-tuned, evaluated, and deployed model under a budget, a h...
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, sp...