LLaMA-3.1-8B

Headlines · 4

1. 3d
   Agent MechSuits Adds Internal Safety Controls to CLI Agents

   Researchers from Georgia Tech, Rutgers, the University of Southern California and other institutions proposed Agent MechSuits, a NeurIPS 2026-accepted method that reads hidden-state safety signals before each command-line agent action and intervenes on 10 dimensions without updating the main model. Across LLaMA-3.1-8B, Qwen-2.5-7B and Gemma-2-9B, the reported MAS current-step detection accuracy averaged 97.32%, while Qwen-2.5-7B's malicious-step ASR fell from 91.30% to 4.35%. The method also had limitations: generation-collapse rate rose from 2.24% to 18.54%, the evaluation used language-model judging, and the probe did not reliably detect prompt injection arriving through environment feedback.

   新智元 · 🔥 0
2. Oct 26
   CrossProfile Exposes Memory Failures in LLM Group Decisions

   A newly submitted paper introduces CrossProfile, a diagnostic benchmark with 60 templates, automatically verified answer keys, and 480 hidden-profile tasks. Testing Qwen2.5-7B, Llama-3.1-8B, and two DeepSeek models, the study finds that rarity- and relevance-based memory selection can fail systematically, while the final decision model may overvalue facts attributed to many agents. The authors also report that a complete natural discussion transcript can yield lower accuracy than using no notes.

   Hugging Face · Papers · 🔥 0
3. Oct 25
   llama.cpp CUDA PR fuses FFN operations for reported speed gains

   llama.cpp pull request #29948 proposes fusing CUDA FFN gate/up matrix multiplications and GLU in MMQ with interleaved gate/up rows. Its llama-bench results report gains of 1.9%–10.7% over master for listed quantizations of Qwen3.8-27B, Qwen3.6-27B, and Llama-3.1-8B on NVIDIA DGX Spark and RTX 5090. Tests used full GPU offload, Flash Attention, a 16,384-token prompt, and five repetitions.

   r/LocalLLaMA · 🔥 0
4. Oct 7
   SparseDecoding: decoding-aware pruning for faster LLM inference

   The paper introduces SparseDecoding, a training-free network pruning framework aimed at the decoding stage of LLM inference. It builds calibration matrices from layer-wise activations collected during dense-model autoregressive generation (excluding prefill) so the pruning objective matches decoding activations, and pairs this with an N:M sparse matrix-vector kernel using bitmask indexing and fixed-step traversal. The authors report that on Llama-3.1-8B, Llama-3.3-70B and Qwen3-14B/32B it outperforms fixed-text calibration on long-form generation benchmarks, with up to 1.48x end-to-end wall-clock decoding speedup on A100 GPUs.

   Hugging Face · Papers · 🔥 0

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OBLITERATUS•3d Pinned

OBLITERATUS: An open-source toolkit for understanding and removing refusal behaviors from large language models.

What problem it solves Refusal mechanisms in transformer models can block legitimate research, creative writing, and red-teaming alongside genuinely harmful content. Researchers need transparent, reproducible ways to locate refusal representations, evaluate their relationship to other capabilities, and apply controlled interventions without retraining or fine-tuning. Key features Refusal direction…

github.comhttps://github.com/elder-plinius/OBLITERATUS