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Large language models (LLMs) hold significant potential for psychological support, capable of generating empathetic responses and simulating therapeutic conversations. However, existing LLM-based approaches often lack sufficient grounding in structured diagnostic reasoning and diverse therapeutic strategies, limiting their effectiveness in complex counseling scenarios. To address these limitations, we propose PsyLLM, the first large language model designed to incorporate diagnostic- and therapeutic-oriented reasoning for mental health counseling. To develop PsyLLM, we design a novel automated data synthesis pipeline that processes real-world mental health posts collected from Reddit, where users frequently express psychological distress and seek community support. The pipeline transforms these posts into multi-turn counseling dialogues and leverages LLMs guided by international diagnostic standards (e.g., DSM/ICD) and multiple therapeutic frameworks (e.g., CBT, ACT, psychodynamic) to generate structured, clinically informed reasoning traces. A rigorous multi-dimensional filtering mechanism further improves the quality and clinical fidelity of the synthesized dialogue data. In addition, we introduce a new benchmark and evaluation protocol, assessing counseling performance across four key dimensions. Our experiments, along with evaluations by professional counseling experts, demonstrate that PsyLLM consistently outperforms various baseline models on this benchmark. Code, data, and models are available at https://github.com/Emo-gml/PsyLLM.

Video compression at ultra-low bitrates remains challenging due to the loss of perceptually important details under limited bit budgets. Recent generative video compression methods leverage pretrained video priors to improve perceptual quality and temporal coherence, but many follow a compress-then-generate paradigm, where generative priors are primarily exploited during reconstruction. Moreover, representations learned for generation and reconstruction are not inherently optimized for entropy coding. In this work, we propose \method, which adapts pretrained video representations into compression-oriented representations while preserving their reconstruction capability. Specifically, we adapt the pretrained causal video VAE of MiniMax-H3 into a discrete video tokenizer and develop a small autoregressive entropy model that captures local and global token dependencies, with grouped prediction for parallel decoding. To further improve coding efficiency, we jointly optimize the tokenizer and entropy model using a differentiable rate surrogate, allowing rate gradients to adapt token assignments toward lower coding costs while preserving reconstruction quality. Experiments on UVG, MCL-JCV, and HEVC Class B demonstrate superior perceptual rate--distortion performance, achieving BD-LPIPS savings of 82.2\%--87.5\% and BD-DISTS savings of 78.3\%--85.0\% relative to GLC-Video.

As LLMs improve, data agents are emerging as a new interface for analytics, while semantic query processing engines use LLMs to operate over structured and unstructured data. Existing benchmarks largely evaluate agents and semantic query engines in isolation, leaving it unclear whether agents can decide when and how to use semantic operators in end-to-end analysis. We introduce AvalancheBench, a multimodal enterprise analytics benchmark that generates observed data from latent world specifications and evaluates data agents on their ability to recover that world. The benchmark's 3 validated tasks contain 58 questions spanning text, images, and documents. Questions are provided together as a multi-part analytical brief and may share intermediate results, exposing the avalanche effect: errors can propagate through legitimate dependencies, and inappropriate reuse can cause otherwise unrelated questions to fail. AvalancheBench measures analytical fidelity, monetary cost, and capability-adjusted token cost (CATC), a new metric that weights token use by model capability to separate technical efficiency from provider pricing. We evaluate 5 model-harness configurations across 3 agent harnesses using Snowflake Cortex AISQL as the engine, with the best configuration reaching macro analytical fidelity of 0.82. Agents fail primarily in constructing the analysis: failing to recover facts that require semantic computation costs 45.1% of lost points, yet incorrect operator outputs account for only 2.5%, versus 36.3% from agents misusing or avoiding semantic operators. Misinterpreting requirements costs the most, at 51.5%. Errors also spread between questions: 29% of lost points come from reusing another question's results. Finally, monetary cost and CATC produce different Pareto frontiers, distinguishing economically attractive configurations from those that are technically resource efficient.

Guiding pretrained flow-based generative models for conditional generation or to produce samples with desired target properties enables solving diverse tasks without retraining on paired data. We present ESS-Flow, a gradient-free method that leverages the Gaussian prior of the source distribution in flow-based models to perform Bayesian inference directly in the source space using elliptical slice sampling. ESS-Flow only requires forward passes through the generative model and observation process, making it applicable even when gradients are unavailable, such as with non-differentiable simulation-based observations or quantization in the generation process. We demonstrate its effectiveness on scientific applications including generating materials with target properties, protein backbone structures with improved designability, and synthesizable, drug-like molecules.