Knowledge catalogue
arXiv:2608.26655v1 Announce Type: new Abstract: Model merging promises to construct a single multi-task model from independently fine-tuned task models without accessing the original task data. This m
arXiv:2608.27176v1 Announce Type: cross Abstract: Understanding spoken dialogue requires joint reasoning over lexical content and paralinguistic acoustic signals such as emotion and conversational int
arXiv:2608.26123v1 Announce Type: new Abstract: Large language models (LLMs) are trained predominantly on English-language internet text that over-represents certain cultural narratives, raising conce
arXiv:2608.26385v1 Announce Type: cross Abstract: Volume-based accuracy rewards retrieval-augmented generation (RAG) systems for guessing: a system that answers everything outscores one that declines
arXiv:2608.27454v1 Announce Type: new Abstract: Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers
ok bit more context: it's actually a QAT Q2 for Qwen 3.8 27 B: https://huggingface.co/sdkyuan/qwen3.8-27B-qat-q2_0-gguf QAT Q2 for DFlash model: https://huggingface.co/HermiHg/Qwen3.8-27B-DFlash2-Q2_K
Yes, you can use other apps with Ollama's cloud too! https://x.com/ollama/status/2092453536634380763?s=20 Ollama v0.33 is here! You can now easily configure Claude Desktop to seamlessly work with Olla
You can now fine-tune GLM-5.3 with @tinkerapi. It’s the first GLM model on the platform and quite possibly the strongest model available there today. https://tinker-docs.thinkingmachines.ai/tinker/mod
arXiv:2608.26480v1 Announce Type: cross Abstract: Multi-agent large language model systems are widely reported to beat single-model baselines, but the evidence is mixed, and comparisons are usually co
100% Agree @GavinSBaker. Now, I'm going to debunk every single piece of mis-information about data centers that I've ever seen online: > DISINFORMATION: Data centers use a lot of water. FACT: Most AI
arXiv:2608.25479v1 Announce Type: new Abstract: Generative video models now synthesize footage nearly indistinguishable from reality. Their promise as interactive tools hinges on fine-grained control
arXiv:2512.21852v4 Announce Type: replace Abstract: The reasoning performance of large language models (LLMs) can be substantially improved by training them with reinforcement learning (RL). The RL ob
arXiv:2411.17629v3 Announce Type: replace Abstract: Motivation: Organic synthesis is fundamental to the chemical industry, particularly in domains such as pharmaceutical development. While artificial
arXiv:2608.25893v1 Announce Type: new Abstract: Foundation models have transformed molecular property prediction, yet it remains unclear whether a molecular foundation model, fine-tuned on a single ca
arXiv:2606.19660v2 Announce Type: replace-cross Abstract: Prompt injection is ranked as the most critical vulnerability in large language model (LLM) deployments by the OWASP Top 10 for LLM Applicatio
arXiv:2608.25022v1 Announce Type: new Abstract: As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such mod
arXiv:2608.26008v1 Announce Type: cross Abstract: Large language models (LLMs) remain vulnerable to jailbreak attacks that exploit techniques such as role-playing, obfuscation, code transformation, an
arXiv:2608.25940v1 Announce Type: new Abstract: Physical AI models are evaluated on suites of benchmarks that differ across model reports, leaving the model-by-benchmark matrix sparse and the relation
arXiv:2608.25489v1 Announce Type: cross Abstract: Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and expos
Jack Queen / Reuters: A US judge blocks the Pentagon from blacklisting Anthropic, ruling that its designation as a supply-chain risk was “illegal and baseless” — A U.S. judge on Thursday blocked the P
arXiv:2608.25315v1 Announce Type: new Abstract: Order-dependent activation statistics are often interpreted as evidence of interaction, but that interpretation can be confounded by where interventions
arXiv:2608.25524v1 Announce Type: cross Abstract: The Levenberg-Marquardt (LM) algorithm is the most widely used method for solving nonlinear least-squares problems, as it combines the robustness of s
arXiv:2608.25513v1 Announce Type: cross Abstract: Random feature methods provide a scalable approximation to kernel ridge regression (KRR), but the regularization parameter that yields the oracle lear
arXiv:2608.25559v1 Announce Type: new Abstract: Video deep research answers complex questions by jointly understanding video content and retrieving external knowledge from the open Web. However, diver
arXiv:2608.24954v1 Announce Type: new Abstract: Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We pr
Everything has changed in two months: DS4 0731 flash was the start of a wave that is taking open weights to paradise. It is easy to think that Qwen 4 and GLM 6 will be on par with Mythos. submitted by
arXiv:2608.26093v1 Announce Type: new Abstract: Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe
arXiv:2510.15987v3 Announce Type: replace Abstract: How do inference time and latent computations enable large language models (LLMs) to solve multi-step reasoning problems? We introduce AlgoTrace, a
Anthropic: Anthropic releases findings from a pilot that let three external researchers run studies on Claude usage; one study found users delegate high-stakes tasks — Earlier this year, we ran a pilo
arXiv:2608.25520v1 Announce Type: new Abstract: Audio-visual cross-modal Fine-Grained Visual Categorization (FGVC) aims to identify fine-grained categories by jointly leveraging visual and auditory in
hexagon: support for multi-NPU devices (IQ9, IQ10) and fully asynchronous backend (#26501) hexagon: use non-host bufs by default and make the backend fully async hex-hb: remove optional hostbuf suppor
models : support nanbeige4.2-3B (#27730) Co-authored-by: admin lizongqiang@kanzhun.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43308396 macOS/iOS: m
llama : add --n-cpu-ffn option (#26622) common : dedupe --n-cpu-moe / --spec-draft-n-cpu-moe override loops common : add --n-cpu-ffn to CPU-offload dense FFN weights of first N layers common : general
metal : fix memory leaks due to missing autoreleasepools (#27758) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43372015 macOS/iOS: macOS Apple Silicon (a
args: add --video-* CLI arguments (#24318) args: add --video-* CLI arguments gen docs nits add mtmd_helper_init_opt Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attes
model : simplify MiniMax-01 graph (#27790) Co-authored-by: Stanisław Szymczyk sszymczy@gmail.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43387702 ma
spec: Add benchmark-only synthetic speculative acceptance options (#27711) Add benchmark-only synthetic speculative acceptance to llama-server and llama-cli Address review comments Address review comm
llama: model_loader: add TENSOR_READ_LAZY (#27794) llama: model_loader: add TENSOR_GET_ROW_LAZY add --tensor-read-lazy rename to TENSOR_READ_LAZY gen docs address comments Website: https://llama.app A
Feature: Added LIGHTNING_INDEXER support for Deepseek V4 ops on Vulkan Backend (#27453) vulkan: add LIGHTNING_INDEXER op vulkan: updated lightning_indexer.comp and ggml-vulkan.cpp with 128-lane dot-pr
quantize: cap working memory size to avoid loading big tensors onto RAM (#27795) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43468853 macOS/iOS: macOS A
opencl: add bin kernels kernel_gemm_moe_q4_0_q8_1_dp4a_bin, kernel_gemm_moe_mxfp4_q8_1_dp4a_bin (#27768) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/434
spec : add DFlash2 support (local convolution + candidate selector) (#27342) (#27816) spec : add DFlash2 support (local convolution + candidate selector) (#27342) support DFlash2 Add p_min in DFlash2
ci : bundle HIP runtime DLLs with Windows ROCm release (#26973) Copy amdhip64_7, amd_comgr and rocm_kpack next to the binaries so the correct HIP runtime loads over the driver's copy in System32. Fixe
model: add Qwen3.8-Flash-Next (qwen4exp) (#27742) gguf: add qwen4exp (Qwen3.8-Flash-Next) arch and converter Adds the GGUF-side plumbing for HF model_type qwen4_exp: MODEL_ARCH.QWEN4EXP plus tensors f
ci : build only the ggml-hip backend for windows-rocm release (#27753) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43506547 macOS/iOS: macOS Apple Silic
server: add ctx-per-slot (--kv-unified-per-slot) (#24124) Add ctx-per-slot argument for unifid KV cache Swap out ctx fractions for ctx pool slots Formatting cleanup Remove ctx-pool-slots, make ctx-per
hex-unary: fix RMS_NORM_MUL weight-offset bugs for grouped/broadcast norms (#27798) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/43526462 macOS/iOS: macO
ggml-hexagon: add HTP unary ops for ABS and LOG (#27786) Add HVX-accelerated implementations for GGML_OP_LOG and GGML_UNARY_OP_ABS on the HTP backend. Register HTP_OP_UNARY_ABS and HTP_OP_UNARY_LOG in
arXiv:2509.06346v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) has become a key architecture for scaling large language models (LLMs) efficiently. Recent fine-grained MoE designs
arXiv:2608.25761v1 Announce Type: new Abstract: One common trade-off in the use of large language models involves reducing the size of the model while increasing the amount of computation at inference
Breaking Claude Code Opus 5 Auto Mode Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently mad
arXiv:2608.24958v1 Announce Type: cross Abstract: An audio language model is a black box in a specific way: we see what it says, never what it works out on the way there, and chain-of-thought monitori
arXiv:2608.25148v1 Announce Type: new Abstract: Frozen hematology foundation-model (FM) embeddings reach near-saturated in-domain white-blood-cell (WBC) accuracy, but clinical deployment demands relia
arXiv:2608.25500v1 Announce Type: cross Abstract: Reusable skill libraries allow large language model (LLM) agents to reuse procedural knowledge across tasks, but they also turn memory access into a c
I installed the Claude Desktop via the ollama Mac app. Unfortunately, no connectors or plugins can be installed or none are listed. Is that normal or is there no possibility there submitted by /u/amne
Cohere's Chief AI Officer @jpineau1 has been named to this year's TIME 100AI list, recognizing the world's most influential people in artificial intelligence. Joelle has been shaping modern research i
Instinct, the developer of an artificial intelligence assistant popular among Silicon Valley tech workers, is reportedly raising 250 million in funding. The company told the Wall Street Journal on Wed
arXiv:2608.25344v1 Announce Type: new Abstract: Perceived risk in driving evolves over time and may be supported by specific scene entities, yet supervision is typically limited to coarse video-level
arXiv:2608.25370v1 Announce Type: cross Abstract: Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt thei
arXiv:2608.25539v1 Announce Type: new Abstract: A plant-health score can appear precise while resting on duplicated image families, a long-tailed label space, or a runtime file that was never evaluate