Extreme 2-Bit Quantization Can Break LLM Generation
Study compares six post-training 2-bit methods on a Polish 11B LLM, highlighting gaps between benchmarks and generation stability.
Study compares six post-training 2-bit methods on a Polish 11B LLM, highlighting gaps between benchmarks and generation stability.
VANGUARD estimates GSD from monocular UAV video using small vehicles as anchors to recover metric scale without GPS or telemetry.
CoT perturbations can sharply reduce accuracy. Unit conversion remains hard at scale; isolate checks and use self-consistency.
Retiring legacy ChatGPT models may shift tone, refusals, and creativity, reshaping the balance between expression and safety guardrails.
Examines multi-rater 3D lesion segmentation, limits of vanilla diffusion, and VDD anchored to consensus priors improving GED/CI.
How LLMs create difficulty illusions, and how to design evaluation gates with scenarios, protocols, and multi-metric reporting.
GIPO targets scarce, stale interaction data by replacing hard importance-ratio clipping with log-ratio Gaussian trust weights for stable reuse.
How LLM signals can shape belief in partially observable TAMP, and why calibration, uncertainty, and safety filters matter for reliability.
How ambiguity detection, clarification, and sycophancy control shape managerial AI advice quality, risk, and evaluation metrics.
Tool-free visual puzzle claims depend on fixed constraints: lock tools, image preprocessing, prompts, and logs for reproducibility.
NVML, DCGM, and nvidia-smi report window-averaged power and utilization. Learn how sampling affects LLM inference graphs.
As AI displaces jobs, energy costs and value capture can constrain cash transfers like UBI, complicating inflation and fiscal assumptions.
Why AI performance gains don’t instantly raise productivity, and how to close the lag using task scores and NIST AI RMF.
Examines how warmth, memory, and consistency in conversational AI affect intimacy, trust, and safety evaluation criteria.
Separate humanlike mimicry from self-consistency in LLMs, and evaluate long-term memory and persona drift with benchmarks and protocols.
Resizing, tiling, and tokenization can shift what models see, turning map/geography misreads into repeatable product risk.
How to turn AGI arrival-year claims into testable forecasts by specifying definitions, metrics, probabilities, and scoring rules.
How LLM reseller-layer services create margin via caching, batch, pricing design, and what security, logs, and compliance issues buyers must verify.
A Pentagon contract dispute highlights how AI safety guardrails become enforceable via contract terms and deployment controls.
How whitespace, Unicode normalization, and token boundaries can look like reasoning failures, and how to control evaluation setups.
Examines how LLM-generated target queues and prioritization can steer human selection, shaping autonomy boundaries, auditability, and control.
Run MLX mxfp4 local LLMs with identical commands and prompts, logging tokens-per-sec and peak memory for reproducible comparisons.
A data-first framework to separate AI CapEx expectations from rate/FX shocks and explain outsized moves in semiconductor equipment stocks.
A decision memo separating reasoning, long-term memory, and continual learning into testable metrics to reduce AGI narrative confusion.