Knowledge catalogue
arXiv:2509.23155v3 Announce Type: replace Abstract: Robotic manipulation benefits from foundation models that describe goals, but today's agents still lack a principled way to learn from their own mis
arXiv:2608.22274v1 Announce Type: cross Abstract: Machine translation tests masked diffusion language models (dLLMs) because every source token must be rendered faithfully, while fixed canvas decoding
arXiv:2510.11358v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) is typically optimized for topical relevance, yet its success ultimately depends on whether retrieved pas
arXiv:2507.09924v2 Announce Type: replace-cross Abstract: Continually updating model-based indexes in generative retrieval with new documents remains challenging, as full retraining is computationally
arXiv:2608.23179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poo
arXiv:2608.21599v1 Announce Type: cross Abstract: Voice has been proposed as a low-cost screening signal for chronic obstructive pulmonary disease (COPD). COPD is strongly age-associated and voice cha
arXiv:2607.14935v2 Announce Type: replace Abstract: Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications
arXiv:2608.21369v1 Announce Type: cross Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchmar
What do I want in AI? Here are just a few things. I want voice-enabled everything. I want things to be more proactive. I want to manage AI’s escalations (versus delegation). I want those escalations t
arXiv:2608.21140v1 Announce Type: cross Abstract: Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generat
arXiv:2608.20554v1 Announce Type: cross Abstract: The critical failure modes in deployed large language models (LLMs) are cross-dimensional: a model can score 99.3 in safety alignment while refusing o
arXiv:2608.20797v1 Announce Type: new Abstract: Evaluating language-guided mobile agents has recently shifted from rule-based to model-based approaches to achieve scalable and automated assessments. H
arXiv:2608.20851v1 Announce Type: cross Abstract: Agentic engineering systems have shown strong performance on general-purpose benchmarks, yet their effectiveness in enterprise resource planning (ERP)
arXiv:2608.20427v1 Announce Type: cross Abstract: Dense causal attention remains expensive at long context even when implemented with highly optimized exact kernels. We study BF1, a deterministic bloc
arXiv:2608.21286v1 Announce Type: new Abstract: Conditional Flow Matching trains generative models by regressing a network onto the velocity of a prescribed noise-to-data interpolation path. The inter
arXiv:2608.20392v1 Announce Type: cross Abstract: LLM-powered meeting assistants are deployed at scale, yet systematic evaluation of their grounding fidelity remains limited to static benchmarks that
arXiv:2608.20518v1 Announce Type: new Abstract: In Federated Learning (FL), the communication topology is a runtime variable rather than a fixed design choice, since links and edge devices drop in and
arXiv:2608.20749v1 Announce Type: new Abstract: Identity-preserving video generation aims to synthesize videos that follow natural-language instructions while maintaining the visual identity of a give
arXiv:2603.23463v2 Announce Type: replace-cross Abstract: Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text-
arXiv:2512.03262v3 Announce Type: replace-cross Abstract: Vibe coding is a new software development paradigm in which human engineers prompt a large language model (LLM) agent to complete complex codi
arXiv:2608.21087v1 Announce Type: new Abstract: Large language models have significantly enriched the toolkit for computational humor research, particularly in the automated generation of jokes and pu
arXiv:2608.20882v1 Announce Type: new Abstract: Modern generators faithfully model macroscopic semantics, producing synthetic images that appear highly realistic. Consequently, decisive forensic cues
arXiv:2608.20635v1 Announce Type: cross Abstract: Discrete diffusion models have demonstrated strong performance across a range of datasets, including natural language data and graph-structured data.
arXiv:2608.20770v1 Announce Type: new Abstract: Modern AI video generation models can produce videos with high visual fidelity and seemingly smooth temporal transitions. However, visual realism does n
arXiv:2608.20438v1 Announce Type: cross Abstract: Population-level behavior in large-language-model (LLM) agents cannot be characterized by single-agent benchmarks. We introduce PV-SST, a peer-voted s
arXiv:2608.20825v1 Announce Type: new Abstract: Artificial intelligence systems increasingly make consequential judgments - which patient is deteriorating, which building is safe to enter, whether an
arXiv:2608.20757v1 Announce Type: cross Abstract: We describe the PSK submission to the WMT 2026 Multilingual Instruction Shared Task. Our system uses the 3.35B-parameter Tiny Aya Global model with th
arXiv:2608.20656v1 Announce Type: cross Abstract: Traffic sensors commonly record flow, speed, and occupancy, but standard traffic flow forecasting benchmarks and models rarely exploit all three raw m
still more bad news for Anthropic. if they don’t scale back expectations for their IPO, it’s going to be a trainwreck. NEW: Thomson Reuters wanted to rely less on Claude. So it built its own AI model
arXiv:2608.20485v1 Announce Type: new Abstract: Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, to
arXiv:2608.20376v1 Announce Type: new Abstract: LLM agents can now generate realistic shilling profiles, fluent reviews, and coherent ratings at scale, systematically defeating recommender-system defe
Try it here: https://ocean.blackbeardlabs.dev/ Model: Qwen 3.8 27b Q8_X_KL Unsloth Hardware: 3 x RTX3090 Harness: DeepSeek Harness Prompt: /goal I want you to create a **JavaScript + Node.js WebGL pro
arXiv:2608.21070v1 Announce Type: cross Abstract: Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Tr
arXiv:2608.21290v1 Announce Type: cross Abstract: We propose VT-MUSE, a Multimodal Unified SEquential representation learning framework for visuotactilemanipulation. Existing approaches often encode v
arXiv:2601.00894v2 Announce Type: replace-cross Abstract: Large language models apply uniform computation to all inputs, regardless of difficulty. We propose PonderTTT, a gating strategy using the TTT
arXiv:2607.15218v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly serve as high-level planners for embodied agents, where linguistically benign instructions can become unsa
been building ShardFlow for the past few months, a distributed LLM inference framework that splits any HuggingFace transformer across N GPU machines and uses neural speculative decoding to deal with W
I love the Ornith 35B local models, 1.0 has been running my HAM radio rig for me. I have a hackRF receiver and a 5 watt quansheng portable the both run headless through the PC. I tried out the new Orn
Great paper if you are tracking progress in recursive self-improvement (RSI). (bookmark it) There is so much hype around RSI, so I think it's worth understanding why current models are not able to do
That is the second comparison and the last one. I will not be spamming again ;) Continuation from: https://www.reddit.com/r/LocalLLaMA/comments/1vu0u2v/qwen_38_27b_pi_agent_vs_opencode/ That is one of
This is one of the most effective ways to improve your agentic workflows. If you are building computer-use agents, this one is worth your time. Task Model Induction takes a raw recording of someone wo
Right now standard CoT is a one-way street. You generate forward, dump a pile of scratchpad tokens into the KV cache, and pray the model doesn’t hallucinate halfway through. On phones/laptops that cre
arXiv:2608.19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are of
arXiv:2608.20204v1 Announce Type: new Abstract: Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of LLMs. Contrac
arXiv:2608.19758v1 Announce Type: new Abstract: Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, partic
arXiv:2608.19659v1 Announce Type: new Abstract: Choosing tensor-parallel (TP) degrees and replica counts for an LLM serving fleet is difficult because performance is not monotonic in TP and the feasib
arXiv:2608.20112v1 Announce Type: cross Abstract: Generative models have shown strong potential for positron emission tomography (PET) image reconstruction. Although diffusion model-based reconstructi
arXiv:2608.19535v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overhead: retrieve
arXiv:2608.19355v1 Announce Type: cross Abstract: Educational visual question answering, or VQA, requires models to solve curriculum-oriented multiple-choice questions using both language and visual e
I'm confused by something I've noted in the recent discourse since Kimi K3 has been released on subscription. Has anyone done the (I know Ollama should display this) math as well on how much K3 is usi
arXiv:2608.20256v1 Announce Type: new Abstract: Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which
arXiv:2608.19369v1 Announce Type: new Abstract: Statistical watermarks for language models live in the freedom of the signifier: they choose among tokens that are nearly equivalent in meaning, and the
arXiv:2608.19278v1 Announce Type: cross Abstract: The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they carry land gl
arXiv:2501.14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raise
arXiv:2608.19957v1 Announce Type: new Abstract: Natural language code retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly
arXiv:2608.19993v1 Announce Type: new Abstract: Loading reusable skill documents into a bounded context window is now the primary way large language model (LLM) agents acquire task-specific capabiliti
Our general-purpose coding agent just scored 100% on the ARC-AGI-3 interactive reasoning benchmark. NVIDIA AVO completed all 183 levels across all 25 public environments, figuring out what to do with
arXiv:2608.19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP
arXiv:2608.20290v1 Announce Type: new Abstract: Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and loses. Tracking t
https://www.reddit.com/r/LocalLLaMA/comments/1j7r47l/i_just_made_an_animation_of_a_ball_bouncing/ This post inspired me to make that test after a year ;) That is one of my many tests I make comparing