DeepSeek-OCR
DeepSeek-OCR explores compressing long text contexts into visual representations using a novel vision encoder architecture (DeepEncoder) that achieves 10-20x compression ratios while maintaining high OCR accuracy.
Continual Learning via Sparse Memory Finetuning
Meta AI researchers address catastrophic forgetting in language models through sparse memory finetuning, updating only memory slots most activated by new knowledge while achieving 89% less performance degradation than standard finetuning.
When Models Manipulate Manifolds
Anthropic researchers investigate how Claude 3.5 Haiku learns to predict line breaks in fixed-width text, revealing geometric representations analogous to biological place cells and boundary cells in biological brains.
Bayesian Influence Functions for Hessian-Free Data Attribution
Classical influence functions struggle with deep neural networks due to non-invertible Hessians and high-dimensional parameter spaces. This work introduces the local Bayesian influence function (BIF), which replaces Hessian inversion with loss landscape statistics estimated via stochastic-gradient MCMC sampling.
Reasoning with Sampling
Base language models achieve reasoning performance matching or exceeding RL-posttraining through inference-time power distribution sampling, using MCMC techniques that require no training, datasets, or verifiers.
Lookahead Routing for LLMs
Lookahead is a response-aware LLM routing framework that predicts latent representations of potential model outputs to enable more informed routing decisions without full inference.
Ring-1T
Ring-1T is the first open-source thinking model with 1 trillion parameters (~50B active per token), achieving breakthrough results through three innovations for trillion-scale RL training.
ColorAgent
ColorAgent is a mobile OS agent combining step-wise RL and self-evolving training with a multi-agent framework for personalized user engagement. It achieves 77.2% success on AndroidWorld and 50.7% on AndroidLab (SOTA among open models), while scoring 58.66% on MobileIAR for personalized intent alignment and 68.98% on VeriOS-Bench for trustworthiness.
Prompt-MII
CMU researchers propose Prompt-MII, an RL framework that meta-learns instruction induction across 3,000+ HuggingFace datasets, achieving 4-9 F1 point improvements on 90 unseen tasks while requiring 3-13x fewer tokens than in-context learning. Unlike APE (2000 LLM calls) and GEPA (150 calls), it generates compact instructions in a single forward pass and is training-free at test time.
Enterprise Deep Research
Salesforce AI researchers present EDR, a transparent multi-agent framework for enterprise deep research with human-in-the-loop steering via todo-driven task management and steerable context engineering. It achieves SOTA on DeepResearch Bench (49.86), 71.57% win rate on DeepConsult, and 68.5% on ResearchQA while consuming 4x fewer tokens than LangChain’s open deep research.
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