Knowledge catalogue
reminds me of the time Greg Brockman personally downloaded YouTube videos for training purposes, as reported in NYT. 🦔Apple is suing OpenAI for systematic trade secret theft weeks before the IPO. The
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning dep
Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information across long contexts
On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under which it is detrimental
While large language models (LLMs) and coding agents are often applied to user interface (UI) development, developers find it difficult to reliably assess their proficiency in visual and interaction d
Recent breakthroughs in instruction-based image editing have captured significant attention, as models are now capable of handling real-world editing demands with the practicality required by everyday
This study focuses on Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text, with both modalities aligned to the text conditions. Despite progress
The web is complex, open-ended, and constantly changing, making it challenging to scale training data for visual web agents. Existing data collection attempts remain limited to offline trajectories fo
Sparse Mixture-of-Experts (MoE) architectures route each token through a subset of experts at each layer independently. We propose viewing MoE computation through the lens of expert paths—the sequence
Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language models (LLMs) have been app
We address the fundamental incompatibility of attention-based encoder-decoder (AED) models with long-form acoustic encodings. AED models trained on segmented utterances learn to encode absolute frame
Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such
Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of a vector taken within a database (the keys) that best aligns with a given query. We pro
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing methods typically require
Large language models can exhibit emergent reasoning behaviors, often manifested as recurring lexical patterns (e.g., “wait,” indicating verification). However, complex reasoning trajectories remain s
Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. O
Understanding how transformer components operate in LLMs is important, as it is at the core of recent technological advances in artificial intelligence. In this work, we revisit the challenges associa
Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than execute fixed, pre-specified workflows. In such settings, effective coordination
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). Wh
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art
When annotators disagree on a label, the disagreement itself carries signal—and the number of annotators needed to capture it depends on the evaluation metric. We fine-tune NLI models on label distrib
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Exis
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective can numerically offset
We introduce a framework for learning latent representations of 4D objects which are descriptive, faithfully capturing object geometry and appearance; compressive, aiding in downstream efficiency; and
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despit
True spatial intelligence for multimodal agents transcends low-level geometric perception, evolving from knowing where things are to understanding what they are for. While existing benchmarks, such as
Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical
AI-driven sign language interpretation is limited by a lack of high-quality annotated data. New datasets including ASL STEM Wiki and FLEURS-ASL contain professional interpreters and 100s of hours of d
Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video gene
Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While
Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Y
Large Language Models (LLMs) demonstrate their reasoning ability through chain-of-thought (CoT) generation. However, LLM’s autoregressive decoding may limit the ability to revisit and refine earlier t
Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying thi
We present StereoFoley, a video-to-audio generation framework that produces semantically aligned, temporally synchronized, and spatially accurate stereo sound at 48 kHz. While recent generative video-
Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scene dynamics, exploring multiple possible futures
Sam Tobin / Reuters: A UK tribunal rules Microsoft must face a lawsuit alleging it overcharged UK businesses to run Windows Server on cloud services from Amazon, Google, and Alibaba — Microsoft (MSFT.
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the eval
This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026. Principled domain reweighting can substantially improve sample efficienc
Ollama v0.20.8-rc0 is a release candidate that introduces MLX support for Google's Gemma 4 model family on Apple Silicon, addressing a prior limitation where Ollama would throw a `Gemma4ForConditional
This paper provides a theoretical framework for interpreting acoustic neighbor embeddings, which are representations of the phonetic content of variable-width audio or text in a fixed-dimensional embe
This paper was accepted at the Workshop on Memory for LLM-Based Agentic Systems at ICLR. Language models have consistently grown to compress more world knowledge into their parameters, but the knowled
Enterprise multi-agent AI systems produce thousands of inter-agent interactions per hour, yet existing observability tools capture these dependencies without enforcing anything. OpenTelemetry and Lang
Ollama v0.20.4 is a minor patch release published on April 7, 2026, containing two changes: improved Apple Silicon M5 performance via NAX on the MLX backend, and enabled flash attention for the Gem...
ggml : add note about mul_mat_id + disable mul_mat_id_dup tests (#30501) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/56058008 macOS/iOS: macOS Apple Sil
vulkan: Use L2-friendly workgroup scheduling for cm2 mulmat (#30215) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/56139256 macOS/iOS: macOS Apple Silicon
Hey r/LocalLlama - Today we’re excited to release Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software e
graph : avoid reallocations in the 'no cache' path (#30439) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/55354972 macOS/iOS: macOS Apple Silicon (arm64)
cpu: fix AVX2 CPUs not using F16C when available (#30270) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54796975 macOS/iOS: macOS Apple Silicon (arm64) ma
cmake(s390x): disable z17 target for unsupported compilers (#30297) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54655455 macOS/iOS: macOS Apple Silicon
vulkan: fix rms_norm workgroup count overflow (#30145) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54337638 macOS/iOS: macOS Apple Silicon (arm64) macOS
CUDA: fix round issue, under MSVC the CPU and GPU agree (#30229) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54453988 macOS/iOS: macOS Apple Silicon (ar
vendor: apply deep nested json patch from upstream (#30253) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54488539 macOS/iOS: macOS Apple Silicon (arm64)
hexagon: improved GELU accuracy (#30104) Assisted-by: OpenCode Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53797268 macOS/iOS: macOS Apple Silicon (arm6
hex-mmadd: do not assume aligned read/write when bias-add is fused (#30133) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53932639 macOS/iOS: macOS Apple
llama: support MoE cache over multiple GPUs (#30112) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53988921 macOS/iOS: macOS Apple Silicon (arm64) macOS A
CUDA : looped PAD kernel for more than 65535 rows or slices (#30147) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54006924 macOS/iOS: macOS Apple Silicon
ggml-webgpu: no dawn native features on wasi (#27069) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53445261 macOS/iOS: macOS Apple Silicon (arm64) macOS
vulkan: fix amd iGPU slow checkpoint read (#30049) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53465294 macOS/iOS: macOS Apple Silicon (arm64) macOS App
ggml-webgpu: fix flash_attn supports_op check for overlapping KV (#28205) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53548008 macOS/iOS: macOS Apple Si
Tamara Djurickovic / Tech.eu: Hadrian, an agentic AI offensive security service, raised 40M co-led by Forgepoint Capital International and SmartFin, taking its total funding to 65M — Hadrian will use