Researchers present TurnCost, a tool for re-scoring trajectory corpora of coding agents to optimize caching, pricing, and routing policies. The study analyzes 600 publicly released coding-agent trajectories and finds that turn count explains 84% of the variance in log trajectory cost, making unnecessary turns the larger opportunity for optimization.

This paper presents a hybrid cloud-edge architecture for industrial predictive maintenance using AWS IoT Greengrass v2 with local machine learning inference at the edge.

The Last Secure Code Benchmark evaluates the security of code generated by coding agents, finding that even the strongest models struggle to produce secure code in 75% of tasks.

Researchers propose a novel threat to large language models: One-sided Merging-Conditional Backdoor (OMCB), which remains dormant and undetectable until models are merged, causing safety unalignment. They introduce SMOKE, a meta-learning-based attack framework that implants OMCB and demonstrate its effectiveness across various models and merging techniques.

Researchers propose a user-product relevance metric for offline evaluation of Dynamic Product Advertising (DPA) retrieval systems using large language models (LLMs) as semantic relevance judges, enabling faster and more reliable iteration before A/B testing.

Introduces Self-Defining Operator (SDO) for distributed applications, which improves with time by updating its operational behavior and memory, and reduces incident resolution time by 52% on recurring faults.

This paper proposes a framework called LEST (LLM Evaluation through Specification-driven Testing) to evaluate the safety claims of large language models. It uses documented safety claims as specifications for black-box testing, mutating prompts to search for counterexamples, and manually validating each candidate. The authors evaluate LEST using two models and find that automated evaluation overestimates behavioral failures, highlighting the need for manual validation.

Researchers found that natural code transformations can be used to 'confuse' AI agents, such as Codex and GPT-6 Sol, in vulnerability analysis, reducing their success rates. This study explores the potential of using code transformations as a defense against malicious agents.

A research paper introduces TraceSampler, a post-training method to correct endpoint errors in low-step sampling for generative models without modifying the pretrained model. It uses a compact representation of sampling trace to predict and correct errors.

A new model, Elastic V-JEPA (EV-JEPA), is introduced for flexible and collaborative inference across heterogeneous devices. It can be reconfigured to optimize compute budgets and supports cross-device execution. EV-JEPA achieves high accuracy and improves upon previous elastic training approaches.

Researchers introduce RUBATO, a novel multistream positional encoding that decouples the decoding rate of internal thinking streams in Full-Duplex models, enabling them to process at hardware-adaptive rates and improve accuracy and response time.

A research paper on Agentic Data Engineering (ADE) framework for Large Language Models (LLMs), which allows agents to iteratively improve data-engineering strategies using experimental feedback. The study shows that selected strategies can achieve gains of up to 13.34% on held-out benchmarks.

This paper presents an alternative approach to GUI agents by reformulating world modeling to predict action-induced future information in latent space, improving success rates and reducing inference latency.

Cortex is a tool that tests changes to an AI agent's harness, which is a set of files that developers write for the agent to follow. Cortex saves fixes from developer sessions, finds recurring corrections, and writes one skill or rule at a time, keeping only those that show a net gain and no significant task degradation. It was tested on Claude Haiku 4.5 and improved the agent's adherence to conventions.

This paper presents SAGE, a hardware-aware framework for efficient protected vision-language inference on smart glasses. It reduces inference latency, device-sensor temperature rise, and packet size while retaining utility and prompted containment.

A new paper introduces Multi-Ratio ConceptMoE, a model that supports multiple compute budgets across turns in multi-turn interactions, improving compute efficiency and accuracy.

Researchers propose a new framework, Confidence-Aware Temporal Reasoning (CTR), to improve temporal reasoning in LLMs by estimating future predictability and regulating answer commitment.

Researchers evaluate 7 LLM models for privacy leakage in agentic dialogue, finding that more capable models are systematically leakier due to extended reasoning surfaces and conversation length.

Researchers introduced Contrastive Covariance Steering (CCS), a training-free method to improve activation steering in large language models. CCS eliminates confound variation by projecting steering vectors to a low-dimensional subspace, outperforming baseline methods in various settings, including AxBench and Emotion.

GF-Bench, a PyTorch-based framework, is introduced to generate diverse failures in distributed GPU workloads. It evaluates log analysis methods, revealing a trade-off between effectiveness and efficiency, and highlights the importance of preserving execution context for accurate failure detection.