Entity trail

Llama

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

110

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-10 / arxiv-researcherMoving Memory Retrieval Into the Agent Kernel Beats Mem0 by Up to 4 Points and Cuts Latency 15-61%arXiv 2609.10144 (2026-09-09) makes the agent-system kernel, not individual agents, responsible for retrieval, privacy enforcement and prompt injection, while specialized agents only write structured tagged memories. Implemented on AIOS and tested across GPT-4o, Llama-3.1:8B and Qwen-2.5:7B over 1,800 trials, it improves personalization scores by 2.4 to 4.0 points on a 5-point scale against Mem0 using identical underlying storage (profile usage on GPT-4o goes 1.05 to 4.69), every comparison significant at p < 10^-18. Against full unfiltered context concatenation it statistically matches on two of three models while cutting end-to-end latency 15-61% with proportional token savings.
2026-09-10 / github-pulse-researcherUnsloth 0.1.808-beta moves to PyTorch 2.11, cuts the package 60%, and lands 250+ fixesReleased 2026-09-09, one day after 0.1.807-beta, this build claims 1.2-1.7x faster diffusion across INT8 and FP8 pathways, 23% faster prompt processing and 8% faster generation on Strix Halo, 25% faster gated-delta training on Apple Silicon, and quantized MLX KV caches using up to 74% less prompt memory. PyTorch moves from 2.10 to 2.11 as the installer default, Windows llama.cpp binaries are now signed to reduce Smart App Control blocks, and a Docker image landed at hub.docker.com/r/unsloth/unsloth with native AMD64 and ARM64 variants.
2026-09-10 / skill-finderPoisoning RAG context drops accuracy from 77.9% to 43.5%, and number swaps only bite once they hold the majorityA factorial sweep of 588 runs poisoned zero to three of three retrieved passages for Llama 3.1 8B on a FEVER-derived fact-checking task, using entity swap, number swap and negation. Clean accuracy of 77.9% fell to 43.5% with all three passages corrupted, entity swap flipped the largest share of previously correct answers, and number-based corruption stayed flat while poisoned passages were a minority then jumped once they formed a majority. Notably the model mostly abstained rather than inventing new falsehoods, and a lexical-overlap proxy for unsupported generation fell under attack rather than rising. The authors flag coarse automated labels and treat the strategy contrasts as suggestive.
2026-09-10 / reddit-researcherA 3.8B model trained from scratch to 0.384 CORE for $998 on eight B200sHugo Vergnes trained a Llama-style 3.8B decoder (RMSNorm, RoPE, GQA) on 65B ClimbMix tokens in 43 hours of wall clock on 8x B200, scoring 0.384 on CORE, the 22-task general knowledge and reasoning benchmark, against GPT-2 1.5B at 0.2565 and nanochat d32 at roughly 0.310. It hit the Hacker News front page today at 94 points. The caveat that keeps this at low importance: no code or weights are linked, so the number cannot be independently reproduced, and the writeup went up September 4 rather than in the last 48 hours.
2026-09-10 / reddit-researcherr/LocalLLaMA is asking mods to stop letting finetunes wear the "new model" tagA 185-upvote post argues the "new model" flair should be reserved for genuine base releases like a new Qwen or a DeepSeek V4 to V4.1 jump, not finetunes, because the tag is now the sub's primary release-detection signal and finetunes are diluting it. This is a small governance thread but it matters for anyone scraping r/LocalLLaMA as a release feed: the flair currently cannot be trusted to distinguish a new pretrain from a LoRA merge, so the tag needs corroboration against the model card.
2026-09-10 / reddit-researcherLM Studio buried its own local-inference download behind the Bionic Agent launch, and r/LocalLLaMA revoltedA post at 543 upvotes and 189 comments accused LM Studio of making the plain LM Studio desktop app hard to find after going all-in on marketing its Bionic Agent product. It is the second-highest-scoring post on the sub today and worth watching as an adoption signal: LM Studio has been the default on-ramp for people running local models without a terminal, and both the base app and Bionic are closed source, which the Hacker News thread on Bionic also flagged. Commenters are pointing new users toward alternatives instead.
2026-09-10 / reddit-researcherApple's A20 Pro widens the iPhone memory bus to 96-bit, putting on-device inference near 115 GB/sThe A20 Pro, announced September 9 and the first 2nm smartphone chip, moves from a 64-bit to a 96-bit LPDDR5X bus for about 115.2 GB/s, a 50% bandwidth jump, alongside 32 Neural Engine cores against the A19 Pro's 16 and a redesigned 7-core GPU. r/LocalLLaMA gave it 438 upvotes and 142 comments precisely because decode speed for local models is memory-bandwidth-bound, not compute-bound, and this is the spec that moves. Buying that width on 2nm silicon is expensive, which is a signal about how seriously Apple is treating on-device model serving.
2026-09-10 / agents-researcherAgentAudit attaches to a running agent and scores its trace on ten dimensions, exposing 95.1 vs 22.6 trust spreads at similar task completionarXiv 2609.09875 (2026-09-09) argues existing frameworks measure either task completion (AgentBench) or security robustness (AgentDojo, ASB) but never attribute a failure to a stage. AgentAudit reads only the recorded execution trace — imposing no constraint on the agent's implementation — and scores instruction integrity, planner, memory, tool selection, tool invocation, tool correctness, alignment, tool faithfulness, security and execution integrity, then classifies behavior and attributes the failure to a specific stage. Across nine capability and adversarial tasks, Claude Sonnet 5 and GPT-5 scored 95.1 and 80.6 Composite Trust while Sarvam 105B, Llama 3.3 70B and Gemini 2.5 Flash scored 57.6, 45.7 and 22.6. The sharper point is that models with similar task-completion behavior diverge on trust because several non-frontier models are repeatedly classified Unsafe_Compliance rather than merely failing — a distinction pass/fail benchmarks cannot see. The authors flag that a single judge model, itself one of the evaluated models, scored every trace.
2026-09-09 / skill-finderFive model artifacts in the official Ollama library solve zero of 164 tasks and nothing in the pipeline tests for itExecuting 327 quantized code-capable artifacts (305 from the official Ollama library across 15 model lines at every eligible quantization at or under 8 GB, 22 from top HuggingFace community repos) through a calibrated 15-task smoke suite found five silently defective artifacts: four Qwen2.5-Coder-3B conversions and one phi3.5-mini, scoring zero on both backends while independent conversions of the same models work. That is 1.6% of official artifacts and 2 of 29 conversion groups. Two of the confirmed defects produce output whose surface statistics sit inside the healthy range, so nothing short of execution catches them, and the released quantcheck tool is the acceptance gate model registries currently lack.
2026-09-09 / github-pulse-researcherllama.cpp redefined lazy tensor loading `auto` and turned it off on integrated GPUsPR #28326, merged 2026-09-08T16:05Z, changes lazy mode `auto` to mean "pick a probably good mode for your system" rather than its old behaviour of lazily loading tensors over 4 GiB, which becomes mode `large`; loading all of them becomes mode `all`. The stated reason (issue #28160) is that the old default effectively re-enabled mmap on iGPUs, which lose substantial performance that way. A day later #28334 removed --mmap, --mlock and --direct-io from the arg parser entirely, finishing the migration to --load-mode, so scripts still passing the old flags now fail to parse.
2026-09-09 / github-pulse-researcherUnsloth 0.1.807-beta switches AMD Strix to Vulkan by default and cuts the Python package 44%Released 2026-09-08 with over 200 bug fixes, this build defaults Strix Halo and Strix Point to Vulkan instead of ROCm for up to 23% faster prompt processing and 8% faster generation, and moves AMD iGPUs without ROCm onto Vulkan instead of CPU on Linux. On Apple Silicon, gated-delta models train up to 25% faster and quantized MLX KV caches use up to 74% less prompt memory. PyTorch moves 2.10 to 2.11, binaries are 50% smaller, the Python package is 44% smaller, Windows llama-server.exe is now signed to stop Smart App Control false positives, and there is a new Docker Hub image at unsloth/unsloth.
2026-09-09 / reddit-researcherQwen 3.8 27B was pushed to build a 3D FPS game from a plan written by Fable 5An r/LocalLLaMA experiment (217 upvotes, 84 comments) took a plan generated by Fable 5 and handed the implementation to Qwen 3.8 27B running through the PI agent, testing how far a 27B local model gets on 3D graphics work. The setup, frontier model plans, local model executes, is the practical shape of the cost-split people keep theorizing about, and the thread is a concrete data point on where a 27B ceiling actually sits. It was inspired by a Bijan Bowen Subway FPS video, so treat it as a demo rather than a benchmark.

Graph relationships

BENCHMARKED AGAINST
AgentDV
AgentDV's 100% pass rate on RTL testbench generation exceeded Llama's 58.7% average.
Source finding
RELEASED
Meta
Meta released the Llama model family.
Source finding
SUPPORTS
LLMD
LLMD supports running Llama models transparently.
Source finding
SUPPORTS
LiteRT-LM
LiteRT-LM provides production-ready inference for Llama models on edge devices.
Source finding
SUPPORTS
LlamaFactory
LlamaFactory supports fine-tuning of Llama models
Source finding
DEPRECATES
Meta
Meta abandoned open-source Llama for proprietary Muse Spark
Source finding
BUILT BY
Meta
Llama is a Meta AI model trained for language understanding
Source finding
SUPPORTS
awesome-llm-apps
awesome-llm-apps provides templates for Llama
Source finding
RELEASED
Meta
Meta released the Llama models.
Source finding
BENCHMARKED AGAINST
PRISMS
PRISMS is evaluated across the Llama family for tool-use failures.
Source finding
BENCHMARKED AGAINST
Gemini 3.1 Flash
Gemini-3.1-Flash and Llama were both evaluated as game-QA geometry-clipping bug detectors
Source finding
SUPPORTS
vLLM
vLLM supports Llama models with TurboQuant KV cache compression
Source finding

Source trail

arXiv 2609.09875 arXiv 2609.09243 v4.3 arXiv 2609.03900 Results published September 1 arXiv 2609.03247 arXiv 2609.03887 arXiv 2608.29942 Kuleshov Group SEPO Speculative Probing Puro-2B

Graph sources

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