Knowledge catalogue
arXiv:2604.24977v1 Announce Type: new Abstract: User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary
arXiv:2510.19669v4 Announce Type: replace Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility
arXiv:2604.25525v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used not only for instrumental tasks, but as always-available and non-judgmental confidants for emotional
arXiv:2604.25634v1 Announce Type: cross Abstract: We report a striking statistical regularity in frontier LLM outputs that enables a CPU-only scoring primitive running at 2.6 microseconds per token, w
arXiv:2601.13288v2 Announce Type: replace Abstract: Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operat
arXiv:2604.23853v1 Announce Type: new Abstract: Skill-distillation pipelines learn reusable rules from LLM agent trajectories, but they lack a key signal: how much each step costs. Without per-step co
arXiv:2604.23057v1 Announce Type: new Abstract: We investigate whether explicit belief graphs improve LLM performance in cooperative multi-agent reasoning. Through 3,000+ controlled trials across four
arXiv:2604.23059v1 Announce Type: new Abstract: Clinical framing -- the linguistic manner in which clinical information is presented -- can influence patient understanding and decision-making, with im
arXiv:2604.23090v1 Announce Type: new Abstract: Automatically generating formal ontologies from unstructured natural language remains a central challenge in knowledge engineering. While large language
arXiv:2604.22534v1 Announce Type: cross Abstract: Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and struc
arXiv:2502.17011v2 Announce Type: replace-cross Abstract: Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In
arXiv:2604.22597v1 Announce Type: new Abstract: Recent advancements in large language models have led to significant improvements across various tasks, including mathematical reasoning, which is used
arXiv:2604.22237v1 Announce Type: cross Abstract: Diagnosing student problem behaviors requires teachers to synthesize multifaceted information, identify behavioral categories, and plan intervention s
Sebastian Raschka has updated his LLM architecture gallery with higher resolution figures and improved summaries, including coverage of the DeepSeek V4 model architecture. This resource provides visua
arXiv:2604.21579v1 Announce Type: cross Abstract: LLM-based automated program repair (APR) techniques have shown promising results in reducing debugging costs. However, prior results can be affected b
Feel like very few serious people make the argument Britain should develop its own LLM? Certainly not an argument you see made in Westminster often during the sovereignty debate. Also some other eyebr
arXiv:2602.01493v2 Announce Type: replace-cross Abstract: Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrate
arXiv:2604.21769v1 Announce Type: new Abstract: LLM leaderboards are widely used to compare models and guide deployment decisions. However, leaderboard rankings are shaped by evaluation priorities set
arXiv:2412.03594v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly play an important role in a wide range of information processing and management tasks in industry. M
arXiv:2604.07798v3 Announce Type: replace Abstract: Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable informat
arXiv:2604.20261v1 Announce Type: new Abstract: Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable
arXiv:2604.19774v1 Announce Type: cross Abstract: Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (
arXiv:2604.19793v1 Announce Type: new Abstract: LLM agents must select tools from large API libraries and order them correctly. Existing methods use semantic similarity for both retrieval and ordering
Yann LeCun (@ylecun ): Sillion Valley is 'completely LLM-pilled' 'In the end, if you’re interested in building systems that have the intelligence of, let’s say, a cat, let alone humans, you need commo
arXiv:2508.21184v3 Announce Type: replace-cross Abstract: We propose a general-purpose approach for improving the ability of large language models (LLMs) to intelligently and adaptively gather informa
arXiv:2604.19354v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly proposed for autonomous cybersecurity tasks, but their capabilities in realistic offensive settings r
arXiv:2604.19071v1 Announce Type: new Abstract: Evaluating the writing capabilities of large language models (LLMs) remains a significant challenge due to the multidimensional nature of writing skills
arXiv:2505.11765v3 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recentl
arXiv:2604.19447v1 Announce Type: new Abstract: We present a dual-track pipeline for detecting biblical allusions in literary fiction and apply it to the novels of Cormac McCarthy. A bottom-up embeddi
arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r
Yann LeCun (AMI Labs Founder): 'The AI industry is completely LLM-pilled. Everybody is working on the same thing. They're all digging the same trench.' LeCun explains why no lab dares break from the p
arXiv:2604.17716v1 Announce Type: new Abstract: The validity screen (Cacioli, 2026d, 2026e) classifies LLM confidence signals as Valid, Indeterminate, or Invalid. We test whether these classifications
arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods
arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou
arXiv:2604.16555v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) aims to automatically discover high-performing deep neural network (DNN) architectures. However, conventional algorit
arXiv:2604.16763v1 Announce Type: new Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goal
arXiv:2604.17512v1 Announce Type: new Abstract: Serialization formats designed for document interchange impose structural overhead that becomes prohibitive when large language models consume operation
arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k
arXiv:2604.17842v1 Announce Type: new Abstract: LLM benchmarks are increasingly dynamic: instead of containing a fixed set of questions, they define templates and parameters that can generate an effec
arXiv:2604.17714v1 Announce Type: new Abstract: LLM confidence signals are used for abstention, routing, and safety-critical decisions. No standard practice exists for checking whether a confidence si
arXiv:2604.18464v1 Announce Type: new Abstract: Semantic Tube Prediction (STP) leverages representation geometric to regularize LLM hidden-state trajectories toward locally linear geodesics during fin
arXiv:2506.06485v4 Announce Type: replace Abstract: Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely e
arXiv:2604.15001v2 Announce Type: replace Abstract: LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple
arXiv:2509.25300v4 Announce Type: replace-cross Abstract: While scaling laws for large language models (LLMs) during pre-training have been extensively studied, their behavior under reinforcement lear
The second most important release of the LLM era (after GPT-3.5), featuring what was likely the most important chart. Still seems surprising to me that OpenAI told everyone about the biggest advance i
arXiv:2604.15343v1 Announce Type: cross Abstract: We report a detailed autoethnographic case study of a single-subject who deliberately constructed and operated a multi-modal prompt-engineering system
arXiv:2511.15915v2 Announce Type: replace-cross Abstract: We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerator
arXiv:2604.14961v1 Announce Type: new Abstract: Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We pro
arXiv:2604.14691v1 Announce Type: cross Abstract: LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often
arXiv:2604.14159v1 Announce Type: new Abstract: Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce person
arXiv:2511.01014v3 Announce Type: replace Abstract: Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imp
arXiv:2604.14321v1 Announce Type: new Abstract: We tasked GPT-4.1 to read what baseball fans wrote about their game-day experience and predict the overall experience rating each fan gave on a 0-10 sur
arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder
'The post all LLMs hate and want you to NOT SEE' ^^ Serious title, you can also discuss this with the LLM, it would be biased against it, as it's known at this point that memory is the true long term
arXiv:2604.13717v1 Announce Type: new Abstract: LLM-as-a-judge, using a language model to score or rank candidate responses, is widely used as a scalable alternative to human evaluation in RLHF pipeli
arXiv:2604.13899v1 Announce Type: new Abstract: Instruction-tuned LLMs can annotate thousands of instances from a short prompt at negligible cost. This raises two questions for active learning (AL): c
arXiv:2602.24119v2 Announce Type: replace Abstract: Purpose: This study evaluates the quality of commercial large language model (LLM) machine translation (MT) for Ancient Greek technical prose and be
Release: llm-anthropic 0.25 New model: claude-opus-4.7, which supports thinking_effort: xhigh. #66 New thinking_display and thinking_adaptive boolean options. thinking_display summarized output is cur
arXiv:2604.13062v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for sym
very excited for this one! a year ago, most of what was being traced to LangSmith was 'LLM apps'. now everything is becoming an agent. with that shift, it's getting harder to know what your software i