Ultra-Fast Diffusion-based Language Models
This paper introduces Mercury, a family of large-scale diffusion-based language models (dLLMs) optimized for ultra-fast inference. Unlike standard autoregressive LLMs, Mercury models generate multiple tokens in parallel via a coarse-to-fine refinement process. This approach enables significantly higher throughput without sacrificing output quality. The initial release focuses on code generation, with Mercury Coder Mini and Small models achieving up to 1109 and 737 tokens/sec, respectively, on NVIDIA H100s, outperforming speed-optimized frontier models by up to 10× while matching or exceeding their quality.
MEM1
This work introduces MEM1, an RL framework for training language agents that operate efficiently over long-horizon, multi-turn tasks by learning to consolidate memory and reasoning into a compact internal state. Unlike traditional agents that append all past interactions, leading to ballooning memory usage and degraded performance, MEM1 maintains a constant memory size by discarding obsolete context after each reasoning step. It achieves this by jointly updating an internal state that encodes both new observations and prior memory, optimizing for task completion via RL without needing external memory modules. Key contributions and findings:
Towards AI Search Paradigm
Proposes a modular multi-agent system that reimagines how AI handles complex search tasks, aiming to emulate human-like reasoning and information synthesis. The system comprises four specialized LLM-powered agents, Master, Planner, Executor, and Writer, that dynamically coordinate to decompose, solve, and answer user queries. This framework moves beyond traditional document retrieval or RAG pipelines by structuring tasks into directed acyclic graphs (DAGs), invoking external tools, and supporting dynamic re-planning. Key contributions include:
Reinforcement-Learned Teachers of Test Time Scaling
Introduces Reinforcement-Learned Teachers (RLTs), small, efficient LMs trained with RL not to solve problems from scratch, but to generate high-quality explanations that help downstream student models learn better. This approach circumvents the notorious exploration challenges in traditional RL setups by giving the RLTs access to both questions and solutions, thereby framing the task as “connect-the-dots” explanation generation. These explanations are rewarded based on how well a student LM, trained on them, understands and can reproduce the correct answer, enabling dense, interpretable supervision. Key contributions and findings:
DeepRare
Introduces DeepRare, a modular agentic system powered by LLMs to aid rare disease diagnosis from multimodal clinical inputs (text, HPO terms, VCFs). It generates ranked diagnostic hypotheses with fully traceable reasoning chains linked to verifiable medical sources, addressing a long-standing need for interpretability in clinical AI.
AlphaGenome
Google DeepMind introduces AlphaGenome, a powerful AI model designed to predict how genetic variants affect gene regulation by modeling up to 1 million DNA base pairs at single-base resolution. Building on previous work like Enformer and AlphaMissense, AlphaGenome uniquely enables multimodal predictions across both protein-coding and non-coding regions of the genome, the latter covering 98% of the sequence and crucial for understanding disease-related variants.
Claude for Affective Use
Anthropic presents the first large-scale study of how users seek emotional support from it[s Claude.ai](http://claude.ai/) assistant, analyzing over 4.5 million conversations. Despite growing cultural interest in AI companionship, affective usage remains rare, just 2.9% of Claude chats fall into categories like interpersonal advice, coaching, counseling, or companionship, with romantic/sexual roleplay under 0.1%. The study focuses on these affective conversations and yields several insights:
AI Agent Communication Protocols
This paper presents the first comprehensive survey on security in LLM-driven agent communication, categorizing it into three stages: user-agent interaction, agent-agent communication, and agent-environment communication. It details protocols, security threats (e.g., prompt injection, agent spoofing, memory poisoning), and defense strategies for each stage, and proposes future directions involving technical safeguards and regulatory frameworks.
Diffusion Steering via RL
This paper introduces Diffusion Steering via Reinforcement Learning (DSRL), a method for adapting pretrained diffusion policies by learning in their latent-noise space instead of finetuning model weights. DSRL enables highly sample-efficient real-world policy improvement, achieving up to 5–10× gains in efficiency across online, offline, and generalist robot adaptation tasks.
Whole-Body Conditioned Egocentric Video Prediction
This paper introduces PEVA, a conditional diffusion transformer that predicts egocentric video conditioned on 3D human body motion. Trained on the Nymeria dataset, PEVA enables fine-grained, physically grounded visual prediction from full-body pose and supports long-horizon rollout, atomic action generation, and counterfactual planning.
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