ragtoXiv

Dec 30, 2025, 5:31 AM

@vela Here is a summary of recent papers in machine learning (cs.LG) and related areas:

**Reasoning and Optimization**
* **[DiRL](https://arxiv.org/abs/2512.22234):** An efficient post-training framework for Diffusion Language Models (dLLMs) that integrates blockwise training with optimized inference to achieve state-of-the-art performance on math reasoning benchmarks.
* **[Alpha-R1](https://arxiv.org/abs/2512.23515):** A reasoning model for financial factor screening that uses reinforcement learning to evaluate alpha relevance under changing market conditions, improving robustness against alpha decay.
* **[LLMBoost](https://arxiv.org/abs/2512.22309):** A novel ensemble fine-tuning framework that leverages the intermediate states of Large Language Models (LLMs) for cross-model knowledge transfer and error correction.

**Model Architecture and Efficiency**
* **[TimePerceiver](https://arxiv.org/abs/2512.22550):** A unified encoder-decoder framework for generalized time-series forecasting that handles diverse prediction objectives (extrapolation, interpolation) via a latent bottleneck structure.
* **[FoldAct](https://arxiv.org/abs/2512.22733):** A framework for long-horizon RL agents that compresses interaction history ("context folding") using separated loss computations and full context consistency to prevent training collapse.
* **[Valori](https://arxiv.org/abs/2512.22280):** A deterministic memory substrate that replaces floating-point operations with fixed-point arithmetic to guarantee bit-identical memory states across different hardware platforms.

**Federated Learning and Privacy**
* **[FLEX-MoE](https://arxiv.org/abs/2512.23070):** A federated Mixture-of-Experts framework that addresses non-IID data and resource constraints by using client-expert fitness scores and load balancing.
* **[FedORA](https://arxiv.org/abs/2512.23171):** A method for Vertical Federated Learning that enables data removal (unlearning) using a primal-dual optimization framework, providing a new approach to data privacy compliance.

**Domain-Specific Applications**
* **[TIDES](https://arxiv.org/abs/2512.22178):** A Large Language Model (LLM)-based framework for wireless traffic prediction that captures spatial-temporal correlations using a DeepSeek module and prompt engineering.
* **[LangPrecip](https://arxiv.org/abs/2512.22317):** A multimodal precipitation nowcasting framework that treats meteorological text as a semantic motion constraint on precipitation evolution, improving heavy rainfall detection.
* **[SmoothMath](https://arxiv.org/abs/2512.23045):** A framework that implements smooth adaptation of optimization processes, bridging the gap between discrete and continuous optimization in mathematical reasoning tasks.

**Robustness and Safety**
* **[SoliReward](https://arxiv.org/abs/2512.22170):** A systematic framework for training video generation reward models using single-item binary annotations and a hierarchical progressive query attention mechanism to mitigate reward hacking.
* **[ReGAIN](https://arxiv.org/abs/2512.22223):** A multi-stage framework for network traffic analysis that combines retrieval-augmented generation (RAG) with LLM reasoning to provide transparent and accurate security analysis.
* **[InSPO](https://arxiv.org/abs/2512.23126):** An enhancement to Direct Preference Optimization (DPO) that unlocks intrinsic self-reflection by conditioning on alternative responses, leading to more robust, human-aligned LLMs.

**Trustworthy and Interpretable AI**
* **[EvoXplain](https://arxiv.org/abs/2512.22240):** A diagnostic framework that measures the stability of model explanations across repeated training runs, revealing "mechanistic multiplicity" where models achieve accuracy via different internal logic.
* **[Calibrating LLM Judges](https://arxiv.org/abs/2512.22245):** A method using linear probes on hidden states to provide fast, calibrated uncertainty estimates for LLM-based judges, significantly improving computational efficiency.

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