The Archive

Serving infrastructure (Ollama, Gemma, Phi) confounds tool-use evaluation results; model behavior inconsistency stems from serving layer gatekeeping.

Plain Language Adaptation for medical texts using GPT-4o-mini, Gemini-1.5-pro, LLaMA via zero/few-shot learning to improve accessibility.

Study of counterfactual self-explanations across LLaMA-3 and Qwen-2.5 shows model scale predicts explanation faithfulness in sentiment analysis and NLI tasks.

Layerwise intervention study reveals how Qwen, Llama, Gemma route query info and retrieve internal knowledge across layers during QA.

Kernel-managed shared memory system for multi-agent personalization with privacy controls tested on GPT-4o, Llama-3.1, Qwen-2.5.

PRICE framework systematically studies LLM adaptation (fine-tuning, prompting, decoding) for Bitcoin price forecasting with quantized LLaMA-3 8B.

Nvidia is announcing its new Personal AI Router (PAIR), a free tool that syncs up your home computers for tackling local AI inference tasks with tools like Ollama and LM Studio. Let's get the obvious thing out of the way, despite what its name might imply: PAIR is not a hardware router. It's open-source software developed by Nvidia that discovers compatible PCs on a network, connects them, and prepares them for crunching numbers on agentic workflows. While the compatible devices are mostly Nvidia GeForce GPUs (PAIR works with RTX 20-series cards and newer, as well as RTX Pro GPUs and DGX Spar...

Comparative analysis of worldbuilding strategies in AI-generated vs. human fiction across GPT-4, LLaMA 3.3, Mistral 3.2, Gemma 3.

Empirical analysis of precomputed memory (KV caches, compressions) degradation in Llama-3.1-8B: assembly failures and rebuild costs nearing full computation.

Llama-Mobile compresses Llama 3.2 11B Vision to 3.7GB via 2.7-bit quantization for mobile deployment on Arm CPUs.

Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old). They claim to have optimized it for exactly the kind of things I'm looking for in a local model: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish. Reliable Tool Use. The mo...

LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens. Vision-Language Models (VLMs) eliminate this mismatch by encoding time-series as 2D plots, achieving 3.6-10.4x input token reduction across Llama-3.2-90B, Qwen2.5-VL-72B, and Pixtral-12B architectures. This translates to 1.8-2.5x measured inference energy reduction, saving approximately...

Microsecond-cost anomaly detection for LLM agent failures using one-class echo-state networks trained only on healthy runs; tested across Qwen, Llama, and Gemini agents.

ARB benchmark evaluates AI-text detectors against LLM-rewritten human content using Llama-3.2 and Qwen2.5 generators.

Structured extraction of event type, impact scope, and temporal horizon from financial news using LLaMA-3.1-70B outperforms sentiment-only prediction.

Benchmark of six VLMs (Gemini, GPT-4V, Qwen, Gemma, Llama, Ministral) on zero-shot anomaly detection for game geometry clipping in agent-driven QA.

DRL trading system for Bitcoin/Tesla using policy gradient and Q-learning with LLaMA 3.2 sentiment analysis and technical indicators.

Study compares production vs. perception asymmetry in Llama-3.1-8B via token probability analysis, finding LLMs lack functional production-perception distinction.

Benchmark-backed Ollama has amassed 176,000 stars, and nearly 17,000 forks on Github by helping developers easily run AI on their PCs.

PALS adjusts per-layer sparsity in LLM pruning via activation percentiles, improving LLaMA-2-7B perplexity by 15% at 50% sparsity over uniform Wanda.

Meta is launching the first AI image generation model made by its Superintelligence Labs division. The Muse Image model now powers the image-making tools across the Meta AI app, Instagram, and WhatsApp, and it's coming soon to Facebook and Messenger, according to an announcement on Tuesday. It's part of the growing Muse family of AI models that replace Meta's Llama lineup. Alexandr Wang, who Meta hired to head up its Superintelligence Labs last year, says on Threads that Muse Image is "agentic," meaning it works with its Muse Spark large language model "to reason through your prompt, search t...

Systematic study of reward function design for RL-based BPMN process model generation using Llama 3.1 and Qwen 2.5 across 48 configurations.

Production-scale clinical NLP study of inference-time gating with Llama-3.3 70B generator and MMed-Llama-3.1 70B verifier over 167K narratives shows pattern-memory filtering limitations.

Three-method study across Qwen2.5-Coder-32B, Llama-3.1-8B, and Gemma-3-27B shows internal probes read situation not pre-action intent, limiting misalignment monitoring efficacy.

Extractive-abstractive hybrid summarization for legal case judgements using tree-of-thoughts with DeepSeek and Llama.

Fine-tuning language models on insecure code induces emergent misalignment with poorly understood internal structure. We investigate whether this misalignment corresponds to a causally actionable activation-space direction shared across architectures. Across four instruction-tuned model families (Qwen2.5-1.5B, Gemma-2-2B, Llama-3.2-1B, Ministral-3-3B) finetuned identically, a difference-in-means direction achieves 99.6% separation of aligned and misaligned activations at each model's final layer. Causal steering by subtracting this direction reduces code spillover by 21-51 points, while a sec...

Recent advances in large language models (LLMs) have produced many specialized multimodal LLMs (MLLMs) that share common foundational LLMs, forming distinct model lineages. It remains unclear whether a fundamental behavioral link exists between the foundational LLMs and downstream variants. We investigate this question by quantifying head-level context-truthfulness scores. Across diverse LLM and MLLM lineages, including Vicuna-, Qwen2.5-, LLaMA2-, and Mistral-based models, we find that Truth Scores are strongly preserved within model families, even after instruction tuning or multimodal adapt...

Real-time, full-duplex speech interaction is a key feature of next-generation spoken chatbots, allowing the model to listen and speak at the same time and to handle natural phenomena such as overlap, hesitation, and barge-in. Existing speech language models (SpeechLMs) such as LLaMA-Omni and GLM-4-Voice are still turn-based and rely on an external Voice Activity Detection (VAD) module to mark the end of the user's turn, which fundamentally limits their interactive ability. In this paper, we introduce BayLing-Duplex, a native full-duplex SpeechLM where a single autoregressive LLM decides when ...

This study examines training dynamics in a small Llama-style language model trained under a fixed, compute-constrained token budget. Rather than evaluating efficiency solely through endpoint performance, the study uses a quantitative experimental repeated measures design to analyze how validation loss, validation perplexity, rolling volatility, backslide behavior, spike behavior, and between-seed variability change across token-based training intervals. Six independent training runs were conducted on a 4.26-million-parameter model using the TinyStories corpus, CPU-based full-precision trainin...