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MIT researchers report a density-functional-theory and machine-learning approach for identifying catalyst alloys that could improve electrochemical ammonia production, though the proposed materials have not yet been made or tested.
Emerald AI, NVIDIA, EPRI, National Grid and Nebius tested software that reduced an AI factory’s electricity use during simulated grid emergencies while preserving selected high-priority workloads. One test reported roughly 30% less use within 40 seconds; commercial and grid-wide benefits remain unproven.
Ramp says Router gives developers one endpoint for multiple AI models and can lower inference costs through automated model selection, but key performance and retention details remain unverified.
AWS says an expanded Policy Authoring feature can convert written rules into Dogwood policies that restrict agent actions over time, enforce prerequisites and limits, inspect free-form text with Bedrock Guardrails, and constrain tool inputs.
Microsoft Research says Skala 1.1 improves accuracy over its predecessor while remaining near the computational cost of semi-local density-functional methods, and is now available in CP2K with integrations planned for other major chemistry packages.
An arXiv preprint reports that code agents can perform differently on semantically equivalent codebases, with effects varying by model, agent framework and benchmark.
This study presents an enhanced model combining Fuzzy Logic with the IEEE Key Gas Method (FL-KGM) that introduces refined membership functions, optimized fuzzy rule sets, and a novel separation of CO and CO2 to eliminate diagnostic inconsistencies.
An arXiv study evaluates looped and conventional language models on three tool-calling benchmarks, reporting stronger results on workflows that require multiple dependent API calls and a potentially more efficient adaptive-computation approach.
A new arXiv paper proposes a three-agent code-review protocol in which a reviewer evaluates an agent’s code and a critic audits that review before edits are made. The authors report improved benchmark results over tested baselines, while also identifying false consensus as a failure mode.
An arXiv preprint compares zero-shot and parameter-efficient fine-tuning for hate-speech detection in Roman Urdu. The authors report that LoRA adaptation raised F1 performance from 0.56 to above 0.93 on a corpus containing more than 72,000 annotated comments.
An arXiv paper introduces FraudBench, a benchmark for testing whether tool-using banking agents can detect fraud that unfolds across conversations. In a preliminary single-trial evaluation, four agents scored 49% to 65% on attack security.
A study of more than 2,000 language-model training runs says scarce target data can be repeated 15–20 times in mixtures, with the best rate varying by scale and compute.
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