Rethinking LLM Agents as Adaptive Computation Graphs
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Humanoids, autonomy, and embodied AI.
Hub content is updated incrementally.
View LLM agents as runtime-adaptive computation graphs to optimize accuracy, cost, latency, debugging, and control.
Minibal asks whether game AI should optimize not for dominance, but for balanced, engaging play against humans.
A look at markup proposals that separate instructions from data in LLM inputs and why structured interfaces matter.
A curated link roundup from recently collected official updates and tech news.
A curated link roundup from recently collected official updates and tech news.
In courts, AI outcomes hinge less on model accuracy than on judge uptake, override patterns, accountability, and TEVV.
In medical AI robotics, governance, validation, and monitoring matter more than performance demos alone.
Analyzes how segmentation signals in MLLMs weaken in the adapter and recover through LLM attention across the pipeline.
A curated link roundup from recently collected official updates and tech news.
Why agent governance is moving from static rules to execution paths, runtime logs, and timing-aware intervention.
Examines AI exposure in clerical work, automation pressure, and why task redesign and human accountability matter.
How LLMs can guide neural architecture search using only trial summaries while sensitive time-series data stays on-premises.
Models with identical predictions can still produce different feature attributions, challenging XAI reliability, audits, and governance.
How combining LLMs with computational argumentation could shift AI from making decisions for us to reasoning with us.
A curated link roundup from recently collected official updates and tech news.
A paper argues educational AI performance may depend less on model size and more on roles, skills, tools, runtime, and educator expertise.
Examines how LLMs should handle harmful user-provided text in harmless tasks like summarization, translation, and classification.
A curated link roundup from recently collected official updates and tech news.
ARROW extends DreamerV3 with dual buffers and distribution-matching replay to reduce forgetting under memory limits.
A minimal theory of multi-agent coordination through environmental memory, incentive fields, and feedback loops.
Examines how far automated evaluation can match human judgment in Mandarin-to-English LLM translation and where bias may distort results.
A low-cost teleoperation approach using a single RGB-D camera for hand tracking, 3D reconstruction, and robot retargeting.
A new estimator for stable dependence analysis across autoencoder inputs, latents, and reconstructions, beyond mutual information pitfalls.
A concise look at Stable Spike, dual consistency optimization, and bitwise AND for more stable low-latency SNN inference.