A preprint is a signal, not a finished product or an independently confirmed result. We therefore track research papers separately and show the actual state of supporting evidence for each one.
293
published research events
Latest work
A significant claim from a single source is published only after further confirmation.
Research on arXiv introduces OMatG-flash, a flow model for predicting and generating crystalline structures. According to the authors, it achieves performance comparable to the best models, but requires orders of magnitude fewer computational steps and less machine time. It applies Reinforce Adjoint Matching for optimization.
The research team published the SynCraft framework, which uses large language models to predict sequences of atom-level edits to molecules to improve their synthesizability. The system outperforms baseline methods in generating synthesizable analogues with high structural fidelity and works with both proprietary and…
A research paper presents Probe Guidance, a new method for steering diffusion language models. The method uses the frozen internal states of an existing model and eliminates the need for an additional forward pass during inference. On a model with 1.7B parameters, it achieves improved performance on multiple-choice benchmarks…
Antropic is building its own biological laboratory in San Francisco, where the Claude model will control robots conducting experiments with drugs with minimal human involvement. The company has developed Claude Science (an AI workspace for researchers) and Model Hardware Standard (control of laboratory equipment). In April…
The article introduces the SafeTune library for addressing safety drift in fine-tuned LLMs. It unifies four approaches: weight regeneration, constrained fine-tuning, gradient-based unlearning and inference-time steering. The library is open-source and includes tools for interpretability, evaluation and…
The research team introduced BabelArena, a benchmark with 16 146 tasks across 23 languages and 702 canonical prompts. Tests of five frontier models revealed that no model dominates universally and agents in low-resource languages have higher rates of tool and control-flow errors, while consuming up to 2× more…
The research team presents Calibrated Clipping, a method that addresses instability during FP8 quantization in RL training of LLMs. The method compares FP8 clipping bounds with BF16 distributions; it was tested on GRPO and DAPO algorithms for models with 8B to 32B parameters.
The research paper presents a two-stage framework for personalized sleep guidance. In the first stage, a multi-agent language model pipeline generates structured guidance from wearable device records without manual annotations. In the second stage, the approach is distilled into small models suitable for local deployment.…
The research paper introduces StepKV, a method for compressing the KV cache in LLM agents. It addresses the problem of "Reasoning Continuity Disruption" by pruning tokens at the level of reasoning steps rather than individual tokens. It combines step utility with token-level saliency and improves the efficiency-accuracy tradeoff…
The research analyzed 364 thousand posts on X from four climate actions using an AI lexical model; it detected a paradox: during campaigns, happiness rises (by 9 percentage points above baseline), but action language declines (by 10.75 points). Happier posts had lower reach through retweets.
The research team introduced FLARE, a method for training AI agents to solve software engineering tasks. It uses a generative reward model to provide a dense supervision signal during training. According to the results, it achieves 5× lower token consumption compared with a competing approach, with a 19% improvement…
The system for UNLP 2026 Shared Task extracts information from Ukrainian PDFs by combining BM25, BGE-M3 and Cross-Encoder reranking with the 4-bit quantized model LapaLLM 12B on NVIDIA T4 GPUs. OCR completed within the 9-hour Kaggle limit in an offline environment, with the remaining 2 hours used for LLM inference on 500 questions.…
A research team introduces LE4Mob, a framework for learning geolocation representations that preserves geographic distances and enables prediction for previously unseen locations. It combines contrastive language-location pre-training with distance-aware regularization, outperforming baseline methods in inductive settings…
Academic Sherry Turkle is releasing the book "Artificial Intimacy: Who We Come When We Talk to Machines" on 29 September, in which she warns that ChatGPT, Replika and other conversational AI agents lead people to misinterpret machine behavior as care, resulting in a decline in their capacity for empathy.
Scientists from University of Bristol propose a "Learning Ensemble" framework for testing medical AI in three areas: system limitations and data, reliability across patient groups, clinical suitability. Inspired by the pharmaceutical process. It addresses problems where AI learns from irrelevant patterns and fails for…
Hugging Face publishes a research paper that formulates block removal from LLMs as an Ising glass optimization with all pairwise interactions. At 50% compression of the Llama-3.3-70B-Instruct model, it achieves a score on MMLU that is 23 percentage points higher than competing block removal methods.
Author Nathan Lambert analyzes competition among open models. According to him, Chinese companies (Alibaba with Qwen, DeepSeek, Kimi K3, GLM-5.2) have led since July 2025 and have surpassed American Llama. He distinguishes open-weight from true open-source. GLM-5.2 and Kimi K3 have achieved agentic capabilities comparable to Claude…
The research team introduces SpecOpt, a method for optimizing existing drugs using an agentic framework with an LLM and protein docking. On a dataset of 915 compounds from ChEMBL, the method improved binding specificity in 84.8 % of drugs, shifted the average binding gap from −0.72 to +0.47 kcal/mol, and maintained similarity to the original…
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Each row represents one event — not one article. At a glance, you can see its significance, credibility and main point.
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Importance: ▮▮▮ majorOpenAIModels✓ 6
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▮▮▮ major · ▮▮ important · ▮ we're tracking = how significant the change is✓ 6 = six independent publishers, not the number of articles or feeds✓ official = a clear release, law or incident is substantiated by the relevant authority1 source = no independent confirmation yetbold = who is behind the changegray text = a brief summary of what happened
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The same news, two practical uses
We first summarize each event in the same way for everyone. Based on those same facts, we then explain what the change means for your own use and what it could mean for how a company operates.
For individualsWhat you can use or try, how the change can help you at work and what to watch out for.
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Today’s briefing is the same for everyone. Pages
For individuals and For businesses
can be found in the main navigation — they select only events relevant to the given use case.
