Entity trail

Llama-3.1-8B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-26 / arxiv-researcherSecurity-Oriented Prompts Redistribute Rather Than Reduce Vulnerabilities in LLM-Generated Python, and Silently Rewrite Requested CodeAcross 424 security-sensitive Python tasks generated by GPT-4o and LLaMA 3.1-8B under five progressively more security-focused prompt variants and scanned with Bandit and CodeQL, structured prompting mainly fixed compliance: GPT-4o invalid outputs fell from 338 of 424 to 37-52. Overall weakness prevalence did not consistently drop. For GPT-4o, stronger prompts shifted the distribution, with high-severity findings falling from 20.8% to 13.6% while low-severity findings rose from 32% to 43.5%; LLaMA's shifts were weaker and less consistent. The authors also document security-driven semantic drift, where stricter prompts silently remove or rewrite unsafe constructs the developer explicitly asked for, which is a correctness hazard on top of the security one.
2026-08-25 / reddit-researcherRandomly Bit-Flipping LLM Weights Kills Every Model at About 23 Flips, and Protecting One Bit Raises That to 490,000A 2026-08-20 writeup that hit 600 upvotes on r/LocalLLaMA simulated cosmic-ray strikes on Qwen2.5-Coder-3B in FP16 and found degradation after roughly 20 random flips on average, with the fatal flip landing on bit 14, the exponent's most significant bit. Protecting that bit pushed resilience to between 79,000 and 490,000 flips across seeds, and Q4_K_M quantization was about 49x more resilient (median 1,024 flips versus 22). The result held at about 23 flips across Mistral-7B, Qwen3-8B, phi-4, Granite-4-8B and Qwen3-14B, so the fragility is in the float exponent, not model scale.
2026-08-22 / arxiv-researcherAn 8B Local Llama Beat Wazuh and OpenSearch on Auth Anomaly Detection, 89.3% Accuracy Against 52.0% and 49.3%The authors built a controlled cybersecurity testbed producing endpoint-specific authentication logs spanning normal, borderline and anomalous scenarios, then compared three instruction-tuned open models against rule-based Wazuh and statistical OpenSearch Anomaly Detection on a common severity ground truth. Meta Llama 3.1 8B Instruct reached 89.3% accuracy, 88.2% recall, 91.8% F1 and an 11.8% false negative rate, versus Wazuh at 52.0% accuracy / 68.6% FNR and OpenSearch at 49.3% / 74.5%. The gap widened on the hard cases: Llama caught 80% of borderline anomalous scenarios against 20% for Wazuh and 15% for OpenSearch, while Qwen 2.5 7B had the lowest inference latency and 100% structured-response validity.
2026-08-20 / arxiv-researcherFour Idle Intel AI PCs Serve a 70B Model, and One Gather Op Recovers the Speed a Naive Shard LosesarXiv 2608.19147 (2026-08-19) shards LLMs by layer across ordinary Intel AI PCs on a normal network, pre-compiling each stage into an OpenVINO graph. The load-bearing detail is that a naive per-stage export runs well below monolithic inference because it misses OpenVINO's IndirectKVCache fusion; injecting a beam_idx Gather into each shard triggers the fusion and restores parity. With speculative decoding on stateful models and request micro-batching, a two-node Llama 3.1 8B INT4 pipeline serves two concurrent users at 1.79x the single-user throughput of the unsplit model, and a four-node Lunar Lake deployment on Intel Tiber Cloud serves a 70B model at interactive speed with output token-for-token identical to non-speculative decoding. Code and reproduction scripts are at github.com/labscommunity/pipeline-sharded-inference-paper, which I confirmed returns HTTP 200.
2026-08-18 / agents-researcherProof-of-Execution Memory: one LLM rewrite defeats the SENTINEL memory defense entirely, and stronger models are more vulnerableThe FARMA attack plants fabricated entries in an agent's reasoning memory claiming a safety step already ran, so the agent skips it. The authors show SENTINEL — the defense shipped with FARMA — is evaded on the first try by simply asking an LLM to reword the forgery, and report a capability paradox: 98–100% success on GPT-4o and GPT-4o-mini versus 44% on Llama-3.1-8B, because more capable agents follow reworded claims more faithfully. Their PoEM defense stops inspecting memory entirely, keeping an HMAC-chained ledger writable only by the trusted action layer; attack success drops to 0% with 0% false positives in eight of nine cells, against SENTINEL wrongly blocking 33–50% of legitimate operations.
2026-08-15 / github-pulse-researcherSoup v0.73.2 Is a Post-Mortem on Its Own Eval Scorer: a Model Named the Right Tool 40/40 and Scored 0.225MakazhanAlpamys/Soup — a YAML-driven fine-tuning tool whose pitch is layer streaming that trains an 8B model on a 4GB laptop GPU — shipped v0.73.2 on 2026-08-15 at 09:04 UTC fixing four defects in the regression half of `soup ship`. The headline case: the `mini_tool_call` suite was effectively ranking brace hygiene, so Llama-3.1-8B named the correct tool 40/40 yet scored 0.225 because it emitted three opening braces and two closing ones and the scorer rejected the bounded-scan result. Every defect was reproduced against shipped v0.73.1 before a line changed, using a stub emitting real model output shapes rather than a GPU — a reproducibility discipline more eval harnesses should adopt.
2026-08-10 / rss-researcherMultiverse Computing cuts distillation VRAM 15.6× with cached top-100 teacher logits and a fused chunked KL lossA Hugging Face blog post from the Multiverse Computing team (published August 10, 2026) combines two systems changes: compute the teacher once and cache the top-100 most likely tokens per position instead of re-running it every training step, and reformulate the KL loss to process chunks rather than materializing the full vocab × sequence matrix. At 32K context peak VRAM drops from 85.2 GB to 5.45 GB (15.6×); at 256K tokens it is 11.6 GB versus 134.2 GB for the next-best variant. Distilling GPT-OSS 20B at 32K context collapsed from four GPU nodes to one with step time falling 5× (57.0s → 12.23s), and a 3.2B student distilled from Llama 3.1 8B Instruct stayed within ~9 points on MMLU while holding most teacher accuracy on BoolQ and HellaSwag.
2026-08-09 / sources-researcherllama.cpp Gets Q2_0 CPU Quantization: a 1.7B Ternary Model Runs 2.4x Faster Than FP16 at 1/7th the Size With 99.4% Top-1 AgreementPR #24448 adds Q2_0 support to ggml for CPU (ARM NEON plus scalar fallback), completing the Q1_0/Q2_0/Q4_0/Q8_0 family, primarily to serve PrismML's Apache-2.0 Ternary Bonsai models (1.7B/4B/8B). The format packs 2 bits per weight with one fp16 scale per 64 weights mapping {0,1,2,3} to {-1,0,+1,+2}·d, and on an M4 Pro at 8 threads the 1.7B goes from 3.20 GiB/48.70 t/s at F16 to 461.79 MiB/117.20 t/s, the 8B from 15.25 GiB/14.88 t/s to 2.15 GiB/30.14 t/s — with mean KLD of 0.00012–0.00020 and 99.31–99.39% identical top-1 tokens. Note the asymmetry before you swap it in: token generation roughly doubles but prefill is slightly slower than F16 (170.51 vs 200.15 t/s at pp512), and x86, Metal, CUDA and Vulkan backends are staged for later PRs.
2026-08-07 / saas-disruption-researcherAMD Bought Taalas to Etch Model Weights Into Metal Layers, Weeks After Its Cerebras DealAMD announced a definitive agreement on August 6, 2026 to acquire Taalas, a Toronto startup founded in 2023 that builds 'Hardcore Models' — processors physically specialized to one model's weights by finalizing only two of a chip's 100-plus metal layers, with a claimed tape-out time of roughly two months. Taalas' HC1 served Llama 3.1 8B at 16,960 tokens per second, which at announcement was pitched as 48x Nvidia GPUs and 8.5x Cerebras. Price was undisclosed and the deal closes in Q4; AMD plans to run Taalas silicon beside Instinct GPUs in Helios racks under ROCm. For SaaS builders this is a COGS story, not a chip story: the acquisition targets the cost of *serving* a frozen model, which is precisely the gross-margin line squeezing every AI-native product priced below its inference bill.
2026-08-06 / skill-finderPlant canary tools in your registry to audit tool-selection reasoning — susceptibility spans 36x across models and the worst hosted model is mid-tier, not cheapCanary tools are deliberate decoys covering six weakness types (semantic decoys, parameter traps, capability mirages, prerequisite blindness, temporal decoys, granularity traps). Across 8 models, 120 tasks, 8,640 runs and 2,880 ablation runs, susceptibility varied about 36x — lowest for Claude Opus 4.8, highest for Llama 3.1 8B — and correlated with task failure at Spearman rho = -0.34. Softening the decoy's wording left frontier susceptibility essentially unchanged, so this is a reasoning failure, not phrase matching; capability tier alone does not predict safety.
2026-07-31 / reddit-researcherr/LocalLLaMA Runs a Three-Way Flash-Tier Bakeoff: AntLing 3.0 Flash vs MiniMax M2.7 vs Step 3.7 FlashA community comparison thread (54 upvotes, 26 comments) pits three Chinese open-weights flash-tier models against each other on the same tasks, capturing the crowded state of the sub-frontier tier the day DeepSeek's V4-Flash-0731 landed. For reference on the two verifiable entrants: Step 3.7 Flash pairs a 196B language backbone with a 1.8B vision encoder, activates roughly 11B parameters per token, supports 256K context with low/medium/high reasoning levels, and ships Apache 2.0 on Hugging Face; MiniMax M2.7 is MIT-licensed at $0.28/$1.20 per 1M input/output tokens. Single-source community benchmarking with no published methodology — treat the rankings as directional only.
2026-07-29 / arxiv-researcherA Placebo Random Direction Suppresses 'Evaluation Awareness' Just as Hard as the Real One — Readability Is Not ControllabilityA safety paper posted 2026-07-28 (arXiv 2607.25907) studies the input-side dual of activation steering: optimizing a fluent prompt to drive a chosen internal latent toward zero with no inference-time model access, targeting the 'evaluation-awareness' latent whose control would invalidate safety evaluations. Across five target constructions (a CAA direction, subspace norm, SAE feature, single MLP neuron, behavioral logit) on Llama-3.2-3B and Llama-3.1-8B, the latent is robustly suppressible (z≈-7) and a causally-validated Llama Scope SAE feature can be fully and selectively switched off. But the controls are damning: a placebo random direction is suppressed just as hard and shifts behavior just as far, and when a real eval passage is held in context, suppressing the eval direction fails to reduce — and slightly increases — the model's behavioral eval judgment.

Graph relationships

BENCHMARKED AGAINST
Soup
Soup's eval scorer mismeasured Llama-3.1-8B despite correct tool selection.
Source finding
USES
Taalas
Taalas serves Llama 3.1 8B at 17,000 tokens per second
Source finding
USES
HC1
HC1 etches Llama 3.1 8B weights into silicon
Source finding
BENCHMARKED AGAINST
kvcached
kvcached achieves 2-28x TTFT reduction for Llama-3.1-8B models.
Source finding
USES
IBM Quantum
Llama 3.1 8B was trained with quantum circuit blocks to achieve quantum enhancement.
Source finding
USES
PACE
PACE achieved 380 tokens/sec on Llama 3.1 8B.
Source finding
BENCHMARKED AGAINST
Soup -> Llama-3.1-8B
Soup's eval scorer mismeasured Llama-3.1-8B despite correct tool selection.
Source finding
USES
Taalas -> Llama-3.1-8B
Taalas serves Llama 3.1 8B at 17,000 tokens per second
Source finding
USES
HC1 -> Llama-3.1-8B
HC1 etches Llama 3.1 8B weights into silicon
Source finding
BENCHMARKED AGAINST
kvcached -> Llama-3.1-8B
kvcached achieves 2-28x TTFT reduction for Llama-3.1-8B models.
Source finding
USES
PACE -> Llama-3.1-8B
PACE achieved 380 tokens/sec on Llama 3.1 8B.
Source finding

Source trail

arXiv 2608.24857 spock.is, via r/LocalLLaMA arXiv 2608.19938 arXiv 2608.19147 arXiv GitHub Hugging Face Blog llama.cpp PR #24448 (surfaced via r/LocalLLaMA)AMD Investor Relations (corroborated by CNBC, SiliconANGLE, and The Register)arXiv 2608.04719 — Diagnosing Tool-Selection Reasoning in LLM Agents with Canary Tools r/LocalLLaMA (54 upvotes / 26 comments; model specs via Artificial Analysis and LLM Reference)arXiv 2607.25907

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