Long-Horizon State Tracking in LLMs: Executing MD5 through a Deep Sequence of Dependent Tool Calls
arXiv:2609.00012v1 Announce Type: new
Abstract: Long-horizon tasks remain uncommon in large language model (LLM) evaluation, and for a reason: when each step depends on the last, per-step accuracy that looks excellent in isolation decays catastrophically, as errors cascade and the end-to-end failur...
REFACTOR-VLA: Unsupervised Library Learning of Typed Motor Programs
Most current vision-language-action (VLA) models—such as OpenVLA, π0, RT-2, and RDT-1B—are “monolithic.” This means they generate raw motor commands or very short sequences of actions, without organizing behaviors into reusable, well-defined abstractions. As a result, these models perform poorly on ...
NVIDIA and CrowdStrike Strengthen Agentic Cybersecurity Frontier
“We’re at an inflection point in cybersecurity,” Jensen Huang told a sold-out crowd at CrowdStrike’s Fal.Con 2026 in Las Vegas Tuesday. Attacks are now automated. Defense has to be, too. The NVIDIA founder and CEO joined CrowdStrike CEO and founder George Kurtz to announce CrowdStrike SafeMind, its...
Anthropic Releases Claude Fable 5.1 and Claude Mythos 5.1: 52.6% on Terminal-Bench-Science and 75% Cheaper Cache Reads
Anthropic has released Claude Fable 5.1 and Claude Mythos 5.1, the same model behind two different safeguard layers. Fable 5.1 is generally available on the Claude API, AWS, Google Cloud, and Microsoft Foundry; Mythos 5.1 remains restricted to vetted organizations under Project Glasswing. Fable 5.1 ...
Researchers from Princeton, Ant Group and Stanford Introduce AQuA: A Two-Part Agentic Framework for Autonomous Factor Discovery and Model Development in Quantitative Finance
Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do not close this, because autho...
Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch
Five failure modes that survive constrained decoding, and why your schema validator will never catch them.
The post Your JSON Is Valid but Your Data Is Wrong: Five Failure Modes LLM Structured Outputs Won't Catch appeared first on Towards Data Science.
Fambot introduces an ‘AI chief of staff’ for families
Fambot is building an AI “chief of staff” to help families manage the emails, calendars, school updates, sports schedules, and other logistics of raising kids.
John Grass | Sr. Manager, EngineeringA Fundamental TransformationAn AI team is fundamentally more than just a group whose members incorporate AI tools into their existing workflows. The journey to becoming an AI team necessitates a fundamental and comprehensive paradigm shift in how the team defines...
Path to Astra: critical capabilities and frontier safeguards
Astra is the first OpenAI model to meet the Critical cybersecurity capability threshold under the Preparedness Framework, with stronger safeguards for release.
3 Ways to Enhance Your AI Model’s Interpretability
In this article, you will learn three concrete techniques for making machine learning model predictions interpretable, covering both global and local explanations across tree-based and...
ERR+: Sequential Entropy Resolution for Efficient and Decisive LLM Reasoning
arXiv:2608.28771v1 Announce Type: new
Abstract: Large reasoning models achieve strong performance on complex tasks by generating extended chain-of-thought (CoT) traces via reinforcement learning with verifiable rewards (RLVR). While current RLVR methods have achieved strong results with correctness...
Curvature Cryptanalysis of Smooth Transformer Feed-Forward Networks
arXiv:2608.28843v1 Announce Type: new
Abstract: We show that smooth two-layer feed-forward networks (FFNs) expose an additional structural model extraction channel under a chosen-input raw-output oracle at the FFN branch; consider transformer FFN branches with GELU or SiLU activations under chosen-...
Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs
arXiv:2608.28853v1 Announce Type: new
Abstract: Equivariant graph neural networks provide a principled way to model geometric systems, but efficient first-order architectures remain limited in how vector information can be transformed as it moves across a graph. We introduce \textsc{ESNN}, an Equiv...
The Halt Vector: Internalizing a Causal Steering Intervention for Efficient Reasoning
arXiv:2608.28859v1 Announce Type: new
Abstract: Reasoning models do not stop when they know the answer. On DeepSeek-R1-Distill-Qwen-7B the chain of thought runs about twice as long as the model's own answer probability takes to settle, and how much of that excess is removable varies from problem to...