💬 LLM (Other)

📹 ICCV2025 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (3) · 🔬 ICLR2026 (56) · 💬 ACL2026 (62) · 🧪 ICML2026 (39) · 🤖 AAAI2026 (29) · 🧠 NeurIPS2025 (54)

🔥 Top topics: LLM ×3 · Continual Learning ×2

Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Models

This paper proposes the Analytic Subspace Routing (Any-SSR) framework, which eliminates inter-task interference by assigning each task an independent LoRA subspace, and trains a zero-forgetting analytic router via a recursive least squares (RLS) closed-form solution, enabling replay-free continual learning for LLMs.

Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Models

This paper proposes Analytic Subspace Routing (Any-SSR), which assigns an independent LoRA subspace to each new task to eliminate knowledge interference, while employing an analytic router based on a recursive least squares (RLS) closed-form solution to dynamically select subspaces. The approach provides theoretical guarantees against forgetting prior task knowledge, enabling replay-free continual learning for LLMs.

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization

This paper formalizes model merging as a constrained optimization problem and introduces FW-Merging, a Frank-Wolfe optimization-inspired method that iteratively selects the most relevant models and performs local merging. The approach achieves scalable and robust merging over large black-box model pools, surpassing the data-aware method AdaMerging by 8.39% when merging 20 ViT models.

ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer

This paper proposes the ShadowHack framework, which decomposes shadow removal into two subtasks—luminance restoration and color reconstruction. LRNet with Rectified Outreach Attention (ROA) recovers luminance and texture, followed by CRNet with cross-attention to reconstruct accurate color. The method achieves state-of-the-art performance on the ISTD+ and SRD datasets.

VA-GPT: Aligning Effective Tokens with Video Anomaly in Large Language Models

This paper proposes VA-GPT, a multimodal large language model for video anomaly event understanding. Through two modules—Spatial Effective Token Selection (SETS) and Temporal Effective Token Generation (TETG)—VA-GPT enables MLLMs to precisely align anomaly-relevant information in both spatial and temporal dimensions, achieving state-of-the-art performance on both in-domain and cross-domain anomaly detection benchmarks.

VIM: Versatile Interactive Motion-Language Model

This paper proposes VIM, the first multimodal large language model capable of simultaneously understanding and generating dyadic interactive motion and text within a unified framework. Accompanied by the Inter-MT² dataset containing 82.7K multi-turn interactive motion instruction samples, VIM supports a diverse set of tasks including text-to-motion, motion-to-text, reaction generation, motion editing, and motion reasoning.