Knowledge catalogue
arXiv:2608.23018v1 Announce Type: cross Abstract: Federated fine-tuning of on-device large language models (LLMs) faces a significant computing burden. To overcome this limitation, split learning (SL)
arXiv:2608.21393v1 Announce Type: new Abstract: Running large language models inside enterprise environments has always bumped up against a practical wall: the data lives in one place, the AI horsepow
arXiv:2608.22538v1 Announce Type: new Abstract: Policy-governed agents must interpret case evidence while following an authorized procedure. We present extsc{Stage}, an executable-graph framework that
arXiv:2608.23564v1 Announce Type: cross Abstract: Modern software systems accumulate technical debt over decades of development, which makes migration expensive and largely manual. As coding agents be
arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent,
arXiv:2608.22128v1 Announce Type: new Abstract: Printability assessment in additive manufacturing is typically conducted at the geometry level before printing to determine whether a computer-aided des
We've closed our Series B, bringing total funding to $232M under new leadership. This round welcomes entertainment titans Electronic Arts, Sony Music Group, Universal Music Group, Warner Music Group,
arXiv:2608.22793v1 Announce Type: cross Abstract: Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the
arXiv:2608.22263v1 Announce Type: cross Abstract: Vision-language models can be personalized in a training-free manner by directly providing user profiles, preferences, or visual references at inferen
arXiv:2608.23020v1 Announce Type: cross Abstract: Large language models (LLMs) require effective unlearning to address privacy regulations and safety concerns. However, achieving precise forgetting wi
arXiv:2608.23370v1 Announce Type: new Abstract: Large Language Models (LLMs) recognise patterns but do not natively track the path of exclusions that a coherent discourse demands. When an input rests
Now, some of you might not believe me from the get go, and it's completely fine, But for those who are willing to give Tap a chance, a whole new world of voice dictation is going to unlock for you. Ta
arXiv:2608.20566v1 Announce Type: new Abstract: AI agents extend large language models from single prompt-response interactions to long-running, goaldirected workflows that issue many model calls, inv
arXiv:2508.14313v4 Announce Type: replace-cross Abstract: Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or sear
arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks
arXiv:2608.20405v1 Announce Type: new Abstract: Persuasive argument generation requires modeling audience beliefs, rhetorical strategies, and factual grounding. Despite recent advancements, existing m
Deepseek 4: -sm tensor (#26490) DSV4: sm tensor set coarser granularity for head splits fix dspark add model saving for dsv4 + allow dflash to return on specific device add comment about dsv4 seq_rm s
after 3 months and 800 burned... Unbounded Labs is proud to introduce Bart, our vintage LLM: 2.82B parameters trained from scratch on 20.1B tokens of English written before 1931. You can talk to it ri
after 3 months and 800 burned... Unbounded Labs is proud to introduce Bart, our vintage LLM: 2.82B parameters trained from scratch on 20.1B tokens of English written before 1931. You can talk to it ri
arXiv:2510.16211v2 Announce Type: replace Abstract: Label noise is a common problem in real-world datasets, affecting both model training and validation. Clean data are essential for achieving strong
arXiv:2608.20346v1 Announce Type: new Abstract: Speech systems used in customer-facing applications often require domain-specific language coverage. We present a synthetic Bengali speech dataset for t
arXiv:2608.20357v1 Announce Type: cross Abstract: Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift a
arXiv:2606.15867v2 Announce Type: replace Abstract: Multi-subject reference-based image generation requires jointly preserving multiple human identities, binding per-person objects and fashion items,
arXiv:2602.09716v2 Announce Type: replace Abstract: Computing the importance of nodes in networks is a long-standing fundamental problem that has driven extensive study of various centrality measures.
Standard constrained RL assumes consequences are immediate and attributable to the current action. This breaks down whenever violations are delayed and stochastic, which is most real-world settings yo
arXiv:2608.21057v1 Announce Type: new Abstract: Agentic large language model (LLM) systems are reshaping scientific workflows in chemistry and drug discovery, but evaluating their open-ended, tool-aug
arXiv:2505.16222v2 Announce Type: replace Abstract: With the growing use of large language models(LLMs) as evaluators, their application has expanded to code evaluation tasks, where they assess the co
arXiv:2608.20799v1 Announce Type: new Abstract: We study dynamic context scheduling as a training instrument for contextual re- inforcement learning. Rather than treating intra-episode context variati
arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challengi
arXiv:2608.20910v1 Announce Type: new Abstract: With large pretrained models, existing methods have effectively improved instruction-based video editing. However, most of them rely on an in-place edit
arXiv:2608.20840v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval have improved the ability to retrieve information from visually rich documents such as PDFs and reports. Howev
arXiv:2608.20984v1 Announce Type: cross Abstract: Narratives are central to how social communication is framed, making their detection critical for understanding and analysing public discourse. Prior
Nice little survey on Terminal Agents. It provides good information on what exactly is a terminal agent, and why do harness comparisons keep contradicting each other? Paper: https://arxiv.org/abs/2608
arXiv:2608.20342v1 Announce Type: new Abstract: Large language model (LLM) coding agents start each session with an empty context window, discarding accumulated knowledge from prior work. We present P
arXiv:2608.20845v1 Announce Type: new Abstract: Nearly every retrieval-augmented question-answering system in production ships with a hidden interpreter: on each query a language model re-derives the
arXiv:2608.20812v1 Announce Type: new Abstract: We develop constructive approximation and learning guarantees for shallow neural models with infinite-dimensional inputs observed through finitely many
arXiv:2608.21024v1 Announce Type: new Abstract: Modern neural network training increasingly uses matrix-aware optimizers, yet their conditioned matrix step is typically added directly to the weight, j
arXiv:2601.09487v2 Announce Type: replace Abstract: The rapid evolution of Large Language Models (LLMs) has fostered diverse paradigms for automated slide generation, ranging from code-driven layouts
arXiv:2608.20631v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated the ability to solve multi-step tasks requiring planning, tool use, and external information access,
arXiv:2608.20810v1 Announce Type: cross Abstract: Multimodal information systems increasingly route generated visual content back through the same vision-language index that informed its production, s
arXiv:2608.21281v1 Announce Type: new Abstract: Recent advances in field technology have led to a massive influx of in-the-wild video data for ecological science. The primary bottleneck in leveraging
arXiv:2608.21049v1 Announce Type: cross Abstract: With the progression in open and disaggregated 6G radio access networks, it is expected that the system will be able to host multi-vendors. In order t
model : support MTP in GLM-4.5-Air (#26534) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42447665 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Sili
Hey r/Ollama, If you run local models on mid-range hardware, you probably know how fast VRAM disappears when you try to set up document memory or local RAG pipelines. I built Hillock (https://github.c
I've tried a few Gemma 4 models, for example 'gemma4:26b-mlx'. It says 'I cannot process images.'. On their website it mentions images, audio etc. https://ollama.com/library/gemma4/tags I’m using Olla
I noticed that a recent Ollama/llama.cpp setup was using a surprising amount of CPU despite the model being fully GPU-offloaded. My setup: Ryzen 9 9950X VM with 16 vCPUs 3× RTX 4060 Ti 16GB Qwen 3.8 2
Prior to Fable, it felt silly to waste too much time improving your coding harness or context strategies. A new model would arrive at the same price (or cheaper!) and paper over most of your problems.
Our devs got their hands on it a few days ago. One wired it into Codex to compare with GPT Luna, our usual workhorse right now for its cost effectiveness. Another tried it out on one of our OCR pipeli
model: add dots3-note (#27060) text: conversion init impl address review comments fix rope move to a new llama_kv_cache_dsa_iswa Website: https://llama.app Attestations: https://github.com/ggml-org/ll
model : support DSpark for bailingmoe3 (#27508) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/42309942 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple
Anthropic’s Claude models embed a subtle statistical watermark in generated text by biasing token choices during decoding. Sebastian Raschka’s 48‑minute lecture walks through this sampling scheme, exp
Good afternoon, everyone! I wanted to show the work I've been doing around porting Ninfer over to the CMP170HX (Github) So, first, I do want to call out the amazing work that Neroued, Sergiuszm and sp
I was finally able to replicate tensor level allocation outside the Gemma family. https://huggingface.co/ByteOtter/Qwen3.5-4B-CADA-IQ2_XS After the Gemma 4 12b, e4b and gemma 3 4b results, I attempted
(To make this work you need to run the ChatGPT or Claude apps on your computer & turn on browser control, which means you need to trust the models to do that. Definitely check the work carefully first
arXiv:2608.20318v1 Announce Type: new Abstract: Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inherits the improv
arXiv:2608.20172v1 Announce Type: new Abstract: Attention directly derives normalized information flow from pairwise scores. We introduce Relation, an alternative token-mixing primitive that first org
arXiv:2608.20161v1 Announce Type: new Abstract: Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit plan, and a
https://github.com/deepseek-ai/deepseek-harness/releases/tag/dsh-v0.1.1-rc.1 The DeepSeek adapter adds the multimodal visual understanding model DeepSeek-V4-Flash-Vision-Exp. It also supports configur
arXiv:2608.20149v1 Announce Type: new Abstract: This article explores the performance of analytical and neural-based hillshading methods in a dense urban environment using high-resolution digital elev
arXiv:2608.19891v1 Announce Type: new Abstract: How to efficiently finetune robot policies to learn new tasks on the fly? State of the art robotic manipulation policies are based on behaviour cloning