All Articles
19192 articles total
Kairos: A Native World Model Stack for Physical AI
Kairos is presented as a native world model stack for Physical AI, utilizing a cross-embodiment data curriculum and hybrid linear temporal attention.
The Faithfulness Gap: Certifying Semantic Equivalence Between Natural-Language and Formal Mathematical Statements
The authors introduce Bidirectional Provability Fingerprinting (BPF) to certify semantic equivalence between natural-language and formal mathematical statements.
ROSA-RL: Uncertainty-Aware Roundabout Optimized Speed Advisory with Reinforcement Learning
ROSA-RL is a reinforcement learning framework for uncertainty-aware speed advisory in roundabouts to improve autonomous driving safety.
TNODEV: Toolbox for Neural ODE Verification
TNODEV is introduced as the first sound formal verifier for neural ordinary differential equations, integrating falsification and reachability analysis.
ARB4WM: An Adversarial Robustness Benchmark for World Models in Continuous Control
The ARB4WM framework is introduced to benchmark the adversarial robustness of world models in continuous control systems under visual perturbations.
New way of making espresso with ultrasound
Exploration of using ultrasound technology to create espresso, potentially changing the extraction process.
Getting Creative with Perlin Noise Fields
A technical look at using Perlin Noise Fields for creative generative art and programming.
Trinket.io shutting down, so we saved it and hosted it a trinket.strivemath.org
The community saved the Trinket.io platform by hosting a mirror at trinket.strivemath.org after its shutdown.
Commodore Releases Flip Phone
Commodore has released a new flip phone, attempting to revive the brand in the mobile hardware market.
Looking Is Not Picking: An Attention-Segment Account of Tool-Selection Failures in LLM Agents
Research showing that LLM tool-selection failures happen at the decision readout stage rather than due to 'lost-in-the-middle' context issues.
Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions
Introduction of 'Posterior Twins,' a digital-twin approach for distributional behavioral simulation to improve enterprise decision-making.
When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting
A framework for quantifying and insuring autonomous AI risk using 'trace-economic underwriting' based on tool-use traces.
Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning
Tensor-Coord uses multilinear algebra (CP and Tucker decompositions) to prevent conflicts in multi-agent LLM planning.
Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents
EC-Script is a framework for generating narratives with controlled emotional trajectories to assist in AI-driven art therapy.
Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing
METIS introduces a loss-aware many-shot model merging protocol to reduce task interference and information erasure in multi-task LLMs.
After resurrecting an iconic PC brand, Commodore is getting into flip phones
Commodore is being revived by a retro gaming YouTuber, starting with a modern version of the Commodore 64 and expanding into flip phones.
Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
Researchers propose Latent Thought Flow (LTF), a method that moves LLM reasoning into continuous space to reduce inference overhead compared to traditional Chain-of-Thought.
SpecAlign: Efficient Specification-Grounded Alignment of Large Language Models via Synthetic Data
SpecAlign is introduced as a framework to align LLMs using synthetic data generated directly from structured provider specifications rather than abstract principles.
State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs
StateGen is a synthetic data generation platform for tool-augmented LLMs that uses an authoritative state manager to eliminate tool-call hallucinations.
Architectural Wisdom: A Framework for Governing Optimization in AI Systems
The authors propose 'architectural wisdom,' a governance layer for AI systems designed to interrogate objectives rather than just optimizing them to prevent structural failures.