Citation Closure in Regulatory QA Systems
Why regulatory QA needs per-rule attribution, citation closure, and traceable evidence beyond answer accuracy alone.
Why regulatory QA needs per-rule attribution, citation closure, and traceable evidence beyond answer accuracy alone.
DistractionIF shows how RAG systems misread instruction-like noise in documents and why pipeline design matters.
Explains how subscription and API billing differ, and why reselling AI access raises policy, security, and operational risks.
DMC suggests student-model compatibility, not just data quality, may matter more for reasoning distillation.
Why AI may matter more as a long-horizon task worker and strategic assistant in mathematics than as an answer generator.
A head-to-head test of Claude Code and Codex running an end-to-end gravitational wave analysis pipeline autonomously.
An arXiv study examines teacher-student-model collaboration and control frameworks for LLM use in K-12 writing.
Why agentic AI failures create governance and operational control risks beyond model accuracy alone.
SCDBench argues smart contract decompilation should be judged by semantic equivalence, not just source-like Solidity.
Examines synthetic data generation as a streaming learning problem, focusing on transfer, forgetting, and feedback loops.
TaxDistill argues pretraining data composition and distilled genome representations matter more than model size.
This study argues tokenized time series LLMs lose continuity and order, and proposes COM constraints to preserve temporal structure.
A look at a paper arguing that aggregating full reasoning traces can outperform answer-only consensus in multi-agent systems.
VitalAgent proposes an agent architecture for long-term ECG and PPG streams with reasoning, memory, and proactive monitoring.
Examines human-AI collaboration for replicability prediction, balancing speed and consistency against bias, accountability, and privacy risks.
MOV-Bench highlights evaluation gaps in multi-hop audio-visual reasoning and shows consistent gains from agentic search.
Examines whether offline RL can cut online RL costs in code generation post-training without sacrificing practical quality.
How under-specified applied ML papers can become executable benchmarks through agentic workflows and slot-based reporting.
How policy-as-code layers can govern generalist LLM agents by controlling tool use, approvals, and data exposure.
Examines how offloading and preemption affect multi-model LLM serving under GPU memory limits and model-specific costs.
COBALT proposes smartphone and cloud teleoperation to reduce data collection bottlenecks in robot imitation learning.
In handwritten math grading, process understanding matters more than OCR, requiring rubric-based review and human checks.
Multi-image prompts can bypass single-image filters, exposing structural safety gaps in multimodal LLM defenses.
A case of wrapping Florence-2 with ROS 2 topics, services, and actions for local inference and reproducible integration.