distilling misaligned foundation models for lightweight scientific forecasting
a new method extracts useful knowledge from large time-series models that don't fit scientific data, creating small, fast forecasters for sensor networks.
aisummaries filed under research
a new method extracts useful knowledge from large time-series models that don't fit scientific data, creating small, fast forecasters for sensor networks.
aia new model shows llm agents have hidden internal beliefs that shape group decisions, explaining how confidence can exceed initial levels.
aia new policy framework extends beyond permit/prohibit to handle obligations, waivers, and conflict resolution for llm-driven agents.
aiorbital ai data centers promise abundant solar power and cooling but must overcome radiation, heat rejection, and high maintenance costs.
aiaura iteratively learns human-consistency signals to audit llm-as-a-judge decisions with minimal human verification.
aiinformation lattice learning can be interpreted as a method for learning the structure of probabilistic graphical models by projecting probability distributions onto partition lattices and lifting rules back.
aia new forecasting algorithm achieves optimal regret for both general and smooth proper losses, overcoming previous limitations in u-calibration.
aimosaicleaks shows that deep research agents often expose private information when they search the web, and training them to be better at tasks makes the leakage worse.
ainavi-orbital runs a vision-language model on a low earth orbit satellite to classify scenes, describe content, and answer operator questions in plain english.
aiadding human collaborators to ai teams can hurt performance without structured coordination, but shared memory and approval gates help.
aia new benchmark evaluates language model agents on long-horizon business management by simulating 500 days of running a startup.
aigoogle deepmind outlines a control roadmap to manage internal ai agents by treating them as potential insider threats and using layered monitoring.
aia graph neural network study finds adding sparse station data to radar forecasts gives minimal gains, while numerical weather prediction and satellite inputs matter more.
aia new mathematical framework connects shock-wave theory to the learning dynamics of stochastic gradient descent after removing parameter symmetries.
aia new method trains task generators using a lightweight probe instead of costly solver rollouts, making it practical to create frontier tasks for reinforcement learning.
aia statistical theory for offline policy optimization using only trajectory-level outcome labels instead of per-step rewards.
aia new method controls risk in ai agents that retrieve documents and use tools, even when their behavior changes over time.
aia new attention method uses probabilistic routing through learned gaussian components to achieve linear-time sequence mixing, avoiding the quadratic cost of standard attention.
aia new vision-language model pipeline uses closed-loop retrieval and verification to enforce evidence-based outputs and measure step-level faithfulness.
aiz.ai's glm-5.2, a 753b parameter mixture-of-experts model with 1m token context, leads open weights benchmarks and ranks second in code arena webdev.
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