Researchers present AIDE^2, a system that implements a loop of recursive self-improvement for an AI research agent by optimizing its own code. The system proposes changes to its internal logic, benchmarks modified versions on AI R&D tasks, and retains only those improvements that perform best on hidden evaluations.
The authors introduce Growing Harness, a training paradigm that learns an agent's control structure from task feedback rather than relying on standard context-heavy harnesses. By converting recurring control decisions into executable code and reserving LLM calls for semantic reasoning, the method aims to create reusable specialist agents.
Researchers present AIDE^2, a system that implements a loop of recursive self-improvement for a frontier AI research agent by optimizing its own code. The system proposes changes to its internal logic, benchmarks modified versions on AI R&D tasks, and retains only those updates that perform best on hidden evaluations.
Researchers introduce Agensh, a scalable self-organized multi-agent harness that eliminates the central orchestrator bottleneck by allowing concurrent workers to asynchronously claim sub-tasks and merge progress.
Researchers introduce TelecomGPT-R1, a family of open-source unified telecom reasoning models designed to automate engineering tasks by reasoning over standards, configurations, and logs. The model addresses the limitations of existing general-purpose and specialized LLMs through a structured approach covering protocol, knowledge, modeling, and fault axes.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance diagnostic and clinical healthcare reasoning. The training framework first applies rule-guided RL to MedBullets-derived questions for diagnosis, then utilizes 5.3k synthetic multi-turn scenarios with multi-dimensional rubrics for interactive clinical tasks.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning. The training framework first targets diagnostic accuracy using MedBullets-derived questions and then addresses multi-turn clinical interactions through 5.3k generated scenarios with multi-dimensional rubrics.
The authors introduce Fathom-Vaidya, a 30B parameter model that uses synthetic data and rubric-based reinforcement learning to enhance both diagnostic and clinical healthcare reasoning.
Microsoft Research has published and open-sourced RetroChimera, a new framework for retrosynthesis prediction that combines two complementary models to propose high-quality synthesis routes. The system integrates R-SMILES 2, a Transformer-based de-novo model, with NeuralLoc, a graph neural network (GNN) based model, using a learned ensembling strategy to rank predictions.
An author proposes a research design for a reasoning system that detects contradictions between two propositions by decomposing them into smaller components and searching for logical collisions, rather than relying on direct LLM judgment.
Researchers introduce CodeMidas, an agentic pipeline that constructs reinforcement learning environments from implemented functionality in open-source codebases, using source code as the sole input. The method allocates agentic compute to explore functionality, construct execution-grounded tests, and validate tasks through repeated rollouts, resulting in a dataset of 5,545 training tasks across 23 programming languages. Training MiMo-V2.5 on these tasks with GRPO improves performance on five diverse benchmarks, including DeepSWE (+11.7%), ProgramBench (+17%), and Terminal-Bench v2.1 (+8.5%). Trajectory analysis indicates the RL-trained agent exhibits better behaviors such as increased codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.
Researchers have introduced TrialAtlas, a memory-augmented multi-agent research organization designed to assist with clinical development planning (CDP). The system coordinates specialized agents for literature synthesis, competitive intelligence, and regulatory analysis to anticipate development risks.
MB-Bidram has released an experimental neural architecture called WideNDepth (WND) designed to separate reasoning capabilities from knowledge storage. The model consists of a "Wide part" that functions as memory and a "Depth part" that acts as the reasoner.
A researcher is requesting a single independent human annotator to label 100 Turkish narrative scenes in order to determine whether low inter-rater agreement stems from the interpretive nature of the task or from underspecified annotation definitions. The study found that four LLMs and a rule-based detector agreed with each other and the human reference at roughly chance level, with Cohen’s κ scores ranging from 0.000 to 0.185.
Independent researcher Mohamed Menasy has published GlucoEdge, an engineering study detailing an end-to-end on-device machine learning pipeline for five-class, 15-minute glucose trend forecasting from continuous glucose monitor data. The work introduces a compact 1D CNN containing only 2,909 parameters and covers the full workflow from PyTorch to LiteRT conversion.
A user on the Hugging Face forums proposes an experiment in progressive knowledge transfer, where a fixed-size student model (Qwen 4B) learns sequentially from increasingly larger teacher models rather than directly from the largest available one.
An independent researcher has published a literature review analyzing whether model compression techniques like quantization, pruning, and distillation actually reduce measured energy consumption or merely lower FLOPs.
The article introduces Human2Any, a method for human-to-robot transfer that utilizes constraint-aware compositional planning. No further details or body text are provided in the source.
OpenAI has released Open-1B, a language model trained under a regime where every operation during training is independently reproducible on heterogeneous commodity hardware with bitwise certainty. This approach addresses the reproducibility issues inherent in open-source models by imposing a definite order on sources of nondeterminism, such as GPU kernel reductions and data batch ordering.
Atria Dawn Preview is a foundation agentic language model designed for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments. Across 16 benchmarks spanning real-world research, engineering, and digital work, the model is competitive with frontier agents and achieves the highest reported score on five of them.