Opinions
AI, Meet CBA
This article examines how labor unions are negotiating artificial-intelligence protections into collective bargaining agreements across multiple industries. It highlights NewsGuild…
Opinions
Google’s Search Monopoly Money Will Let It Purchase the AI Market
Asad Ramzanali and Joel Thayer argue that Google’s dominance in search has provided the financial resources and data infrastructure enabling…
Opinions
AI Surveillance in Schools Raises Safety and Equity Concerns
Rashawn Ray and Rebekah Boitey examine how AI surveillance systems, including weapon-detection cameras and audio-monitoring devices, are expanding in American…
Engineering
LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes
Researchers introduce LLaDA-Image, an open framework pairing a 6B diffusion transformer with a frozen vision-language module built on the LLaDA2.0-Mini…
Engineering
Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training
A new paper formulates conditional experience transfer for autonomous LLM post-training, addressing when past training-update evidence remains valid after a…
Engineering
EarlyEval: Cheaper Agent Evaluation via Early Outcome Prediction
EarlyEval introduces early outcome prediction as a way to cut the cost of evaluating LLM agents, training lightweight classifiers to…
Engineering
Language Models Can Control Their Own Attention
Researchers introduce Declarative Attention (DA), a protocol that has a language model explicitly declare in its chain-of-thought which parts of…
Engineering
S3Gym: Can LLMs Turn Self-Testing and Self-Judging into Self-Improvement?
S3Gym is a new interactive benchmark testing whether LLM agents can turn accumulated experience into genuine self-improvement, evaluating three coupled…
Engineering
RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
A new study finds that real-world coding requests differ sharply from the curated GitHub issues used in SWE-bench-style benchmarks: 88%…
Engineering
Cliff: Learning Process Rewards from the First Mistake
Cliff is a new reward-shaping method for reinforcement learning with verifiable rewards (RLVR) that uses an off-the-shelf LLM to identify…
Engineering
Using Grounded Theory for Agent Behavior Analysis at Scale
Researchers adapt grounded theory, a decades-old qualitative research method from the social sciences, into AutoTraceGT, the first automated multi-agent pipeline…
Engineering
Environment Evolution for Terminal Agents
A new paper proposes environment evolution, a method that incrementally increases the difficulty of training environments for terminal agents off-policy…