Tmax presents the strongest open RL recipe for terminal agents, achieving 27% on Terminal-Bench 2.0 with only 9B parameters. It uses a novel data taxonomy to generate over 2.5x more terminal environments than prior datasets, enabling efficient training with a simple, outcome-only recipe. The dataset, models, and code are open-sourced at https://github.com/hamishivi/tmax.
Tmax: A Simple RL Recipe for Terminal Agents
Benchmarks
| Benchmark | Model | Score |
|---|---|---|
| Terminal-Bench 2 | Tmax | 27% |
HealthClaw: open-source agent with self-evolving memory for longitudinal personal health management
Researchers developed HealthClaw, an open-source agent architecture designed to update support as a person's routines, preferences, measurements, and risks change over time. It separates shared safety rules and medical knowledge from private longitudinal memory containing profile facts, reusable procedures, and episodic traces.
DiaLLM investigates robustness-generation gap in English dialect adaptation
The DiaLLM study addresses the disconnect between understanding and producing dialectal English by continually pretraining three open-weight language model families on the International Corpus of English. It applies implicit and explicit post-training paradigms combined with three alignment strategies to compare Australian, Indian, and Northern British English.
DiaLLM investigates robustness-generation gap in English dialect adaptation
The DiaLLM study reveals that dialectal robustness and generation are dissociated in large language models, as benchmarks shaped by continual pretraining do not capture how alignment reshapes actual output. The authors continually pretrain three open-weight model families on the International Corpus of English and apply implicit and explicit post-training paradigms combined with three alignment strategies for Australian, Indian, and Northern British English.
BERTomelo: Your Portuguese Encoder Best Friend
This article introduces BERTomelo, a next-generation monolingual encoder specifically optimized for the Portuguese language using the ModernBERT architecture.
AOHP: An Open-Source OS-Level Agent Harness for Personalized, Efficient and Secure Interaction
The Android Open Harness Project (AOHP) is an open-source operating system-level agent harness built on the Android Open Source Project. It addresses the mismatch between current application-centric operating systems and the needs of autonomous AI agents by treating agents as first-class OS actors. The design introduces three key mechanisms: personalized service composition, efficient agent interfaces, and secure information flow. These features enable adaptive user interfaces and agent-friendly runtime environments while preserving the existing Android ecosystem. Preliminary experiments on challenging tasks demonstrate significant performance improvements over conventional systems. Specifically, AOHP achieved a 21.12% increase in task completion rates compared to baseline methods. It also reduced token execution costs by 51.55%, highlighting its efficiency gains. Furthermore, the system showed improved compliance with security policies during agent-mediated interactions.