Knowledge catalogue
arXiv:2610.11559v1 Announce Type: cross Abstract: Coding assistants such as Claude Code and Codex have become a major application of LLM agents, yet existing benchmarks remain far from real-world use,
arXiv:2610.10619v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly reshaping software engineering, accompanied by an explosion of new code benchmarks. Yet nearly all exist
arXiv:2610.12281v1 Announce Type: cross Abstract: Over 90% of disease-associated variants from genome-wide association studies fall in noncoding regulatory regions, yet their functional interpretation
arXiv:2610.12338v1 Announce Type: new Abstract: While caching key-value (KV) states accelerates Large Language Model (LLM) decoding, this cache can dominate memory usage at long context lengths. One s
I've been building ANCHOR, a web automation agent that runs entirely on Ollama. You describe a task in plain language ('Register Maria Silva, IT department, contractor, and save'), point it at a site,
arXiv:2610.12061v1 Announce Type: new Abstract: Large language model (LLM)-based agents have demonstrated strong capabilities on complex tasks. They typically perform reasoning before each action thro
arXiv:2610.12367v1 Announce Type: new Abstract: Skills, reusable procedural guidance added at inference, can substantially improve LLM downstream performance (Li et al., 2026). Prior work retrieves sk
arXiv:2610.11561v1 Announce Type: new Abstract: LLM-based agents now interact with their environments continuously, shaped by such organizational channels as user instructions, peer messages, and long
The NVIDIA KGMON team secured second place in the KDD Cup 2026 Data Agents competition by building a constrained harness around a small fixed LLM, unifying heterogeneous data into an SQLite database w
arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than
arXiv:2610.09202v1 Announce Type: cross Abstract: Extreme low-bit LLM compression is most challenging when weights, activations, and KV caches are quantized together: their distributions differ, and q
arXiv:2610.09489v1 Announce Type: new Abstract: Many annotation projects begin before experts have a stable guideline or enough labels to train a task-specific model. We present Goldsmith, an agentic
Had fun nerding out on the @_odsc AI podcast about: • Building in public as both market diligence and a way to meet founders • Jev experiments: knowledge graphs, relevance filters, and emotional blobs
arXiv:2606.13994v2 Announce Type: replace-cross Abstract: LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world.
arXiv:2610.08811v1 Announce Type: new Abstract: As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference. Existing met
arXiv:2610.09079v1 Announce Type: cross Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete re
arXiv:2610.08858v1 Announce Type: new Abstract: Low-rank compression reduces the cost of pretrained language models by replacing linear transformations with low-rank factorizations. However, conventio
arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final
arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r
arXiv:2610.10444v1 Announce Type: cross Abstract: Much knowledge work produces new deliverables from files a workspace already holds, and LLM agents are beginning to take such work over. Through direc
arXiv:2602.08234v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have shown stunning results in complex tasks, yet they often operate in isolation, failing to learn from past expe
arXiv:2610.08773v1 Announce Type: cross Abstract: Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent
arXiv:2610.07191v1 Announce Type: cross Abstract: Modern edge Systems-on-Chip (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, each with distinct performance and ene
arXiv:2610.08622v1 Announce Type: cross Abstract: System administrators of Internet-scale services need to resolve failure incidents to maintain reliability of such services. Ideally, we want a troubl
arXiv:2610.08101v1 Announce Type: new Abstract: Shared memory coordinates agents' actions, but correct records do not establish that those actions satisfy task requirements. Memory governance and fail
arXiv:2610.08173v1 Announce Type: cross Abstract: Automated Program Repair (APR) with language models is usually evaluated by whether a generated patch passes the test suite, which can hide difference
arXiv:2610.07177v1 Announce Type: cross Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of-
arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate
arXiv:2610.07075v1 Announce Type: new Abstract: High-quality datasets are essential for reliable machine learning, but dataset curation remains costly and hard to generalize across domains. Existing m
arXiv:2610.07816v1 Announce Type: new Abstract: Small language models (SLMs) are attractive as local agent controllers because they reduce remote inference, latency, and deployment footprint, yet stru
arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their e
arXiv:2610.07620v1 Announce Type: new Abstract: Scientific law discovery requires selecting measurements and converting evidence into a governing equation. We evaluate an explore-then-commit protocol
arXiv:2508.10020v2 Announce Type: replace-cross Abstract: Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially
Upgrade Question If we say the 'budget max' is 1700-1800, and this could include upgrading to a Taichi motherboard: With a new motherboard, no need to bifurcate 1. Would you add a second 5060Ti (16GB)
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, ex
arXiv:2610.08063v1 Announce Type: cross Abstract: This paper presents the HINTT system submitted to the 2nd Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM). We ad
arXiv:2610.08577v1 Announce Type: cross Abstract: While unlearning seeks to negate undesired capabilities acquired through learning, little research has examined how the way models learn shapes their
arXiv:2610.07186v1 Announce Type: new Abstract: Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we dist
arXiv:2610.07607v1 Announce Type: cross Abstract: Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide r
arXiv:2610.08250v1 Announce Type: new Abstract: Large language models are increasingly used to simulate clients for counselor training and psychological counseling research, but reliable simulation re
arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory
arXiv:2610.07661v1 Announce Type: new Abstract: Massive activations, a phenomenon in which a small number of hidden channels exhibit exceptionally large magnitudes, are pervasive in large language mod
arXiv:2610.08095v1 Announce Type: new Abstract: Natural-language access to RDF knowledge graphs is a core Semantic Web ambition. Large language models (LLMs) have advanced Text-to-SPARQL, yet on unfam
arXiv:2610.07787v1 Announce Type: new Abstract: Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question an
arXiv:2610.07725v1 Announce Type: new Abstract: Modern voice assistants may be shared by multiple users and should be able to answer questions about earlier conversations such as 'When did I originall
arXiv:2610.07342v1 Announce Type: new Abstract: On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectivenes
arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with st
arXiv:2610.08093v1 Announce Type: cross Abstract: Developing reliable models for clinical tasks, such as Medical Question Answering (QA), is severely constrained by the limited availability of high-qu
arXiv:2610.07886v1 Announce Type: new Abstract: Dietary nutrition planning plays an important role in chronic disease management and maintaining a healthy body. In applications, it must simultaneously
arXiv:2610.07121v1 Announce Type: new Abstract: Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based t
arXiv:2610.08403v1 Announce Type: new Abstract: Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitig
arXiv:2610.07763v1 Announce Type: new Abstract: The rapid progress of LLM-based multi-agent systems (MAS) has shown that they largely outperform single agents on coding, math, and QA tasks, where exec
arXiv:2610.07625v1 Announce Type: cross Abstract: Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay
arXiv:2610.07332v1 Announce Type: cross Abstract: Long-horizon LLM agents are frequently implemented using sparse mixture-of-experts (MoE) models, yet the co-design of agentic behavior and MoE structu
arXiv:2610.08098v1 Announce Type: cross Abstract: AI agents increasingly rely on modular third-party 'skills' that are dynamically selected by skill routers to execute complex tasks. While recent stud
arXiv:2610.06994v1 Announce Type: cross Abstract: Backdoor-defense leaderboards print a clean-accuracy drop and read it as removal cost. Measured on the poisoned victim alone, the drop cannot separate
arXiv:2610.08138v1 Announce Type: new Abstract: Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. Howev
arXiv:2610.08630v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, us
arXiv:2610.07755v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as judges for automated AI evaluation. A common practice is to randomize prompt sequences and avera
arXiv:2610.08463v1 Announce Type: new Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an en