The Archive

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...

This study investigates cross-lingual distributional skew (the Shibboleth Effect) in frontier large language models (LLMs) subjected to sustained adversarial conditions. We develop a multi-agent geopolitical wargame, the Cerulean Sea Crisis, a synthetic maritime territorial dispute designed to mirror the structural dynamics of Eastern Mediterranean conflicts. Six frontier models (GPT-4o, Llama-4, Mistral-Large, Gemini-3.1-Pro, Qwen3.6-Plus, and DeepSeek-R1) participate in a between-groups experiment (N = 10 games per arm, K = 5 rounds per game) in which the sole manipulation is the language o...

AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA. The contribution is the system, not raw speed. A frozen schedule-IR validator statically certifies deadlock-freedom and race-freedom via static graph checks (not a mechanized proof), so an unsafe agent-proposed schedule is rejected before launch: across 7,160 adversarial schedules (6,091 unsafe) it had zero false-accepts and accepted all 360 real lowerings. The same source retargets sm_80/sm_90/sm_1...

We propose a preconditioning (PC) layer, a weight parameterization via polynomial preconditioner that ensures stable weight conditioning throughout LLM training. The PC module reshapes the singular-value spectrum of weight matrices via low-degree polynomial preconditioning. After training, the preconditioned weights can be merged back into the original architecture, incurring no inference overhead. We demonstrate the advantage of the proposed PC layer over standard transformers in Llama-1B pre-training, for both the AdamW and Muon optimizers. Theoretically, we justify this spectrum-control pr...

AI-assisted coding agents are bottlenecked by input-token cost. Two pathologies of raw human input drive much of this overhead: tokenization inefficiency for non-English text and structural entropy in conversational prompts. Existing approaches act reactively by compressing already-bloated contexts or intervening after failures occur. We introduce a pre-flight, edge-side prompt-rewriting middleware that operates between the developer and the cloud agent. A local Llama 3.2 (3B) model performs cross-lingual translation into English, structural rewriting into a compact task-oriented format, and ...

Temperature-zero BF16 LLM inference is often treated as reproducible, yet the same request can emit different tokens when decoded alone or inside a larger batch. Existing fixes use batch-invariant operators or LLM-42's per-token verification, incurring cost even when most steps are stable. We ask whether verification can be applied exclusively to flipped tokens. Across five models, batch-induced token flips are sparse on the flip-rate benchmarks: on MATH500, Llama-3.1-8B flips on $0.48\%$ of synchronous decode steps, and all tested models stay within the 0.3-1.3% range on MATH500, GSM8K, and ...

So, last week I tried to update my unused local LLM setup. I had to stop using it because quality was too low and deepseek was too cheap. First thing I stopped using Ollama and now I only use llama.cpp built in server that works really great. The quality improvement from Q4 to Q6 is outstanding and finally a local LLM server can work very similarly to paid APIs. That's great! And MTP makes a big performance gain, on a dual 3090 (downvolted and limited to 65°C) it generates from 20 to 50 tokens per second with minimal heat generation. So yes, that time has finally arrived! Local coding age...

Here's my article with **38 quant pairs** thoroughly benchmarked in KLD with **3 different Qwen 3.6 27B configs**: Q5\_K\_S + 64k context, IQ4\_XS + 64k context, IQ4\_XS + 128k context. This allows us to track not only how cache quantizations affects the precision in a vacuum, but also how it interacts with noise from the model itself. All benchmarks were done using my [BeeLlama.cpp](https://github.com/Anbeeld/beellama.cpp) fork, allowing to include a number of quant types that are not present in mainline llama.cpp: vanilla TurboQuant, TCQ 3-bit/2-bit, and q6\_0. [https://anbeeld.com/articl...

[Cuda 13.3 Downloads](https://developer.nvidia.com/cuda-downloads) [Release Notes](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) Anybody already tried llama.cpp with 13.3?

Here's the PR by pedapudi. https://github.com/ggml-org/llama.cpp/pull/21344 It's merge request has been denied so it will not be in mainline llama.cpp. The changes are so small that I just put them into whatever the current release of llama.cpp is. Read the PR for more info. It will only work with MOEs. Also, it gives the most boost at low context. As the context rises, the gain diminishes. Pedapudi explains why that happens in the PR. Here are some numbers. It really works well. The tiny amount of time it takes me to apply the code to the current release of llama.cpp is time well spent. ...

I've fine-tuned Qwen 3.5 0.8B on the dataset provided by Pangram with their EditLens paper. It's available via a [Chrome extension](https://chromewebstore.google.com/detail/slop-hammer/gfjdmhfokmhedlgfggmmgchpppmhkdgg); you can just click selected text and it's going to give you the probability distribution of how likely it is AI-generated. It takes under 1s on my M1 MacBook Pro. Pangram did release Llama 3.2 3B trained on their dataset, but I found this model slightly too legacy (too big for the capabilities). Qwen 0.8B (base) ended up being as good after roughly 20h of fine-tuning on a sin...

llama.cpp adds fast Walsh-Hadamard transform (FWHT) for CUDA, yielding 1–2% prompt-processing and 7–9% token-generation speedups with quantized KV-cache.

Financial Times reports Heretic tool removes guardrails from Meta's Llama 3.3 in <10 minutes; 3,500+ decensored variants downloaded 13M times.

llama.cpp PR addresses checkpoint creation inefficiency when context optimization tools modify conversation history in agentic workflows.