Bit Allocation Transfer for Perceptual Quality Enhancement of Traditional Video Codecs

Traditional block-based video codecs, such as H.264/AVC, H.265/HEVC and H.266/VVC, rely on hand-crafted Rate-Distortion Optimization (RDO) processes that primarily minimize Mean Squared Error (MSE), which correlates poorly with human perceptual quality. While neural video compression methods can easily optimize perceptually aligned metrics like MS-SSIM, their high computational complexity limits practical deployment. This paper proposes a novel bit allocation transfer framework that bridges these two paradigms to enhance the perceptual quality of conventional video codecs. Specifically, we train a quantization step generation model using a perceptual loss within a neural video compression framework (DCVC-FM). The model takes the original frame and a motion-compensated prediction as input and outputs a quantization step map. We then derive a block-wise bit ratio from this map and convert it into a Quantization Parameter (QP) map for a traditional video codec. Experimental results on the HEVC B$\sim$D dataset demonstrate that our method achieves 20.20\%, 8.25\%, and 8.37\% bitrate savings in terms of MS-SSIM compared with the standard reference software JM-19.0, HM-16.20, and VTM-23.0, respectively, with additional gains when utilizing predicted frames. Our approach effectively transfers the implicit perceptual importance learned by neural video compression models to guide block-level bit allocation in traditional video codecs without modifying their core decoding syntax.

Memento-Skills: Let Agents Design Agents

We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and improves task-specific agents through experience. The system is built on a memory-based reinforcement learning framework with \emph{stateful prompts}, where reusable skills (stored as structured markdown files) serve as persistent, evolving memory. These skills encode both behaviour and context, enabling the agent to carry forward knowledge across interactions. Starting from simple elementary skills (like Web search and terminal operations), the agent continually improves via the \emph{Read--Write Reflective Learning} mechanism introduced in \emph{Memento~2}~\cite{wang2025memento2}. In the \emph{read} phase, a behaviour-trainable skill router selects the most relevant skill conditioned on the current stateful prompt; in the \emph{write} phase, the agent updates and expands its skill library based on new experience. This closed-loop design enables \emph{continual learning without updating LLM parameters}, as all adaptation is realised through the evolution of externalised skills and prompts. Unlike prior approaches that rely on human-designed agents, Memento-Skills enables a generalist agent to \emph{design agents end-to-end} for new tasks. Through iterative skill generation and refinement, the system progressively improves its own capabilities. Experiments on the \emph{General AI Assistants} benchmark and \emph{Humanity's Last Exam} demonstrate sustained gains, achieving 26.2\% and 116.2\% relative improvements in overall accuracy, respectively. Code is available at https://github.com/Memento-Teams/Memento-Skills.

Context Adaptive Extended Chain Coding for Semantic Map Compression

Semantic maps are increasingly utilized in areas such as robotics, autonomous systems, and extended reality, motivating the investigation of efficient compression methods that preserve structured semantic information. This paper studies lossless compression of semantic maps through a novel chain-coding-based framework that explicitly exploits contour topology and shared boundaries between adjacent semantic regions. We propose an extended chain code (ECC) to represent long-range contour transitions more compactly, while retaining a legacy three-orthogonal chain code (3OT) as a fallback mode for further efficiency. To efficiently encode sequences of ECC symbols, a context-adaptive entropy coding scheme based on Markov modeling is employed. Furthermore, a skip-coding mechanism is introduced to eliminate redundant representations of shared contours between adjacent semantic regions, supporting both complete and partial skips via run-length signaling. Experimental results demonstrate that the proposed method achieves an average bitrate reduction of 18\% compared with a state-of-the-art benchmark on semantic map datasets. In addition, the proposed encoder and decoder achieve up to 98\% and 50\% runtime reduction, respectively, relative to a modern generic lossless codec. Extended evaluations on occupancy maps further confirm consistent compression gains across the majority of tested scenarios.