Knowledge catalogue
arXiv:2608.20920v1 Announce Type: new Abstract: Open-web future event prediction requires agents to distill reliable signals from noisy, redundant, and incomplete evidence. Existing retrieval/memory m
arXiv:2608.21157v1 Announce Type: cross Abstract: High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolves
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:2601.06519v2 Announce Type: replace Abstract: Biomedical retrieval-augmented generation (RAG) can ground LLM answers in medical literature, yet long-form outputs often contain isolated unsupport
NVIDIA’s new GPUs in the Vera Rubin and Blackwell families set a new benchmark for agentic‑AI performance per watt on AgentX, an open‑source inference test that models realistic multi‑step, tool‑using
arXiv:2608.21172v1 Announce Type: new Abstract: Federated fine-tuning enables large language models to adapt on edge devices without centralizing private data, but practical deployments must address h
arXiv:2608.20365v1 Announce Type: cross Abstract: Sri Lankan parliamentary debates (Hansards) constitute a trilingual corpus of speeches in Sinhala, Tamil, and English, including code-mixed content, y
arXiv:2608.21230v1 Announce Type: cross Abstract: Persistent memory makes false information durable: once a false statement is stored, it can be retrieved into future sessions that match it. We measur
I'm curious what other people are using for local LLM coding / agentic coding with only 8 GB of VRAM. My current setup is: Intel Core i7-11800H RTX 3070 Laptop, 8 GB VRAM 32 GB DDR4 RAM openSUSE Tumbl
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
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.
I have been lurking on reddit for like 6 years, and this is the first time posting, so apologies for the mistakes. I'm not a native English speaker, so sorry for the bad English too. I used LLM to tra
A couple of days ago, I did a quick explainer on Claude’s new watermarking process and implementation. Since it’s such a popular topic and sparked such a lively discussion, I thought it might be inter
Im looking to set up a new local llm (probably on unsloth studio as that seemed to be doing pretty well last time I tested it). This one won't need to do agentic coding or app building or anything (no
Liquid AI currently possesses among the fastest LLM architectures around, and some of the best SLMs (in terms of utility IMO) around, so I'm very excited to see what a potential 100B LFM (3?) model wo
arXiv:2608.19299v1 Announce Type: new Abstract: Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic management ha
arXiv:2608.20320v1 Announce Type: new Abstract: Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated sepa
arXiv:2608.19220v1 Announce Type: cross Abstract: Rising immigration has intensified intergroup tensions in many countries. Traditional bias-reduction programs remain difficult to scale and increasing
arXiv:2608.20338v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly require selective removal of harmful or sensitive knowledge, called unlearning, yet existing methods and bench
arXiv:2608.20055v1 Announce Type: cross Abstract: Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet whether
arXiv:2608.19680v1 Announce Type: new Abstract: Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories dem
arXiv:2608.19893v1 Announce Type: cross Abstract: Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see? We dismantle a
arXiv:2608.19901v1 Announce Type: cross Abstract: Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a
arXiv:2608.19475v1 Announce Type: cross Abstract: This work introduces a formal semantic-block model for specifications and an execution-judged benchmark for evaluating specification quality independe
arXiv:2608.19303v1 Announce Type: new Abstract: When a tool call times out, the agent sees the failure and can route around it. A cached error page or negative price can instead arrive in the expected
Hi fellows fully-local halos, after manually following existing guides, I decided to build an LLM API endpoint installation and optimization guide that works even when autonomously followed by my pi a
arXiv:2608.19794v1 Announce Type: new Abstract: The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajectory toward gen
arXiv:2608.19529v1 Announce Type: cross Abstract: Many real-world AI systems represent entities, behaviors, and structured information using discrete machine-native symbols rather than natural languag
arXiv:2608.18088v1 Announce Type: new Abstract: Drone propeller faults can create safety and reliability risks when their effects are distributed across multiple flight-log channels rather than appear
Hi all, it is my very first post, because I am so confused. First my setup: I have 2 X 7900XTX with rocm7.2.1 and I use it to fine tune small models and local llm etc. I run Qwen 3.8 27b 8 bit version
arXiv:2608.18312v1 Announce Type: new Abstract: Autonomous scientific agents now increasingly propose ideas, write code, run experiments, analyze results, and even draft papers. Observe and audit thos
arXiv:2608.18689v1 Announce Type: cross Abstract: We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni
arXiv:2608.18665v1 Announce Type: new Abstract: Industrial sensor diagnostics relies on preprocessing, representation, and classification pipelines, making automated pipeline search useful for reducin
arXiv:2608.18613v1 Announce Type: new Abstract: Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at query time. T
arXiv:2608.18147v1 Announce Type: cross Abstract: Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a given input wh
arXiv:2608.19047v1 Announce Type: new Abstract: We present Eureka, a task-conditioned Meta-Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit acceptance s
arXiv:2608.18289v1 Announce Type: new Abstract: The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sectors. While pr
arXiv:2608.18423v1 Announce Type: new Abstract: Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where actions have cumu
arXiv:2608.18292v1 Announce Type: cross Abstract: Consider a robot guide dog escorting a blind user to an available seat while a second assistive robot dog concurrently retrieves a cup of coffee and d
arXiv:2608.18336v1 Announce Type: new Abstract: When evaluating language models on human exams, benchmarks typically score each response as right or wrong and report the overall accuracy. This approac
arXiv:2608.18704v1 Announce Type: cross Abstract: Long-term memory is essential for agents that operate across extended interactions, yet existing memory systems and benchmarks predominantly focus on
Nice case study on using optimized functions whenever possible (except for educational purposes, though 😆) By switching from a hand-rolled GELU to PyTorch's built-in one, I improved my LLM training sp
arXiv:2608.18272v1 Announce Type: cross Abstract: Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is far larger
arXiv:2608.18144v1 Announce Type: cross Abstract: Modal auxiliaries such as must, should, and have to mark necessity and obligation within the contexts of speaker authority and interpersonal stance. W
arXiv:2608.18884v1 Announce Type: new Abstract: Reinforcement-learning training of reasoning LLMs (e.g., GRPO) is expensive and requires a controllable environment, committing every contribution to a
arXiv:2608.18521v1 Announce Type: new Abstract: Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrasti
arXiv:2608.17834v1 Announce Type: cross Abstract: Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting multi-step
arXiv:2608.17866v1 Announce Type: new Abstract: Reconstructing prompts that can elicit a desired answer or behaviour in an LLM is an open and important research topic. Optimisation methods which aim a
arXiv:2608.17756v1 Announce Type: new Abstract: Memory is a key capability of LLM agents. Persistent memory extends this across sessions---enabling recall, revision, and personalization. Yet its multi
arXiv:2608.18058v1 Announce Type: new Abstract: Autonomous LLM agents that converse on a user's behalf are an emerging design pattern in matching platforms, yet their viability depends on a condition
arXiv:2608.17524v1 Announce Type: new Abstract: This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functio
arXiv:2608.01324v2 Announce Type: replace Abstract: Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing approaches
arXiv:2608.17694v1 Announce Type: cross Abstract: Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that input is alrea
arXiv:2606.20560v2 Announce Type: replace-cross Abstract: LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging surpr
arXiv:2608.17393v1 Announce Type: new Abstract: Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execut
arXiv:2409.14644v4 Announce Type: replace-cross Abstract: The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embedd
arXiv:2608.16956v1 Announce Type: new Abstract: API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term or its omissio
arXiv:2608.17188v1 Announce Type: cross Abstract: Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitio
arXiv:2608.17530v1 Announce Type: new Abstract: Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often choose a globa
arXiv:2608.14765v1 Announce Type: new Abstract: Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observations. This pa