Knowledge catalogue
5 months later, I would say that AI is definitely on track to produce 90% of all bullshit within a few years. Congratulations LLM Industry, you are changing the world! Claim AI will generate 90% of kn
Trooper v2.1 is a tool that provides automatic fallback functionality from cloud-based LLM services to local Ollama instances when cloud quota limits are exceeded, incorporating context compaction to
I cannot verify the authenticity of this post, as the URL structure and timestamp appear inconsistent with actual X (Twitter) posts. The claim about fine-tuning a '1930 vintage LLM' is anachronistic,
arXiv:2604.27405v1 Announce Type: cross Abstract: We adapted the Reliable Change Index (RCI; Jacobson and Truax, 1991) from clinical psychology to item-level LLM version comparison on 2,000 MMLU-Pro i
Cool paper from Meta FAIR. It's on self-improving LLMs but on the pretraining side. (bookmark it) Most LLM safety, factuality, and reasoning fixes get bolted on at post-training. By then, the patterns
arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi
arXiv:2604.27267v1 Announce Type: cross Abstract: As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can
arXiv:2603.20965v2 Announce Type: replace-cross Abstract: This paper studies whether a lightweight supervised aggregator can combine diverse zero-shot large language model outputs into a stronger down
arXiv:2604.27283v1 Announce Type: cross Abstract: Large language model (LLM)-based coding agents increasingly rely on external memory to reuse prior debugging experience, repair traces, and repository
arXiv:2604.28178v1 Announce Type: new Abstract: Electroencephalogram (EEG) signals are vital for automated seizure detection, but their inherent noise makes robust representation learning challenging.
Our CEO @jerryjliu0 in @VentureBeat , on what's actually changing in the LLM stack: 'We've really identified that there's a core set of data that has been locked up in all these file format containers
arXiv:2604.27003v1 Announce Type: cross Abstract: Memory-augmented LLM agents offer an appealing shortcut to continual learning: rather than updating model parameters, they accumulate experience in ex
arXiv:2604.25933v1 Announce Type: cross Abstract: As large language models (LLMs) increasingly generate and process clinical text, scalable evaluation has become critical. LLM-as-a-Judge (LaaJ), which
arXiv:2604.26319v1 Announce Type: new Abstract: Stance detection identifies the attitude of a text author toward a given target. Recent studies have explored various LLM-based strategies for this task
arXiv:2604.26557v1 Announce Type: cross Abstract: The increasing deployment of Large Language Model (LLM) inference on edge AI systems demands efficient execution under tight memory budgets. A key cha
arXiv:2604.17612v2 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about. Coordination errors such as deadlocks or type-mismatc
arXiv:2604.26837v1 Announce Type: new Abstract: Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing onl
Tim Harford / Financial Times: A working paper uses an LLM to analyze political discourse on X, finding that anger is the dominant emotion expressed by US users, especially those over 65 — There is a
arXiv:2603.15954v2 Announce Type: replace Abstract: Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The
arXiv:2602.17697v2 Announce Type: replace Abstract: Large Language Models (LLMs) are being increasingly used across a wide range of tasks. However, their substantial computational demands raise concer
arXiv:2604.24468v1 Announce Type: cross Abstract: Fine-tuning unlocks large language models (LLMs) for specialized applications, but its high computational cost often puts it out of reach for resource
arXiv:2603.03555v2 Announce Type: replace-cross Abstract: As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. Howeve
Congrats to @poolsideai on Laguna XS.2 release. Day 0 support in @vllm_project. FP8, INT4, and NVFP4 quantized checkpoints created with LLM Compressor also ready today. 33B params, 3B active. Apache 2
arXiv:2604.24320v1 Announce Type: new Abstract: Large language model (LLM) agents that follow the sequential 'reason-then-act' paradigm have achieved superior performance in many complex tasks.However
arXiv:2604.23277v1 Announce Type: cross Abstract: Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context co
arXiv:2602.17547v3 Announce Type: replace Abstract: This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model
arXiv:2604.23355v1 Announce Type: new Abstract: Existing LLM-based EDA agents are often isolated task-specific systems. This leads to repeated engineering effort and limited reuse of successful design
arXiv:2604.23837v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors in high-stakes domains -- answering medical questions, interpreting legal documents, recomme
arXiv:2604.23600v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where m
arXiv:2604.22888v1 Announce Type: cross Abstract: Agent skills introduce a new and more severe form of indirect injection for LLM agents: unlike traditional indirect prompt injection, attackers can hi
arXiv:2604.23747v1 Announce Type: cross Abstract: Recent mixed-policy optimization methods for LLM reasoning that interleave or blend supervised and reinforcement learning signals report improvements
arXiv:2604.23263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. How
The new LLM trained only on pre-1931 text is small enough that it can potentially run on device, so, with the right tools, you can get a fully vintage version of Siri, but from the era of Downton Abbe
arXiv:2604.23750v1 Announce Type: cross Abstract: Hypernetwork-based methods such as Doc-to-LoRA internalize a document into an LLM's weights in a single forward pass, but they fail systematically on
arXiv:2603.11831v2 Announce Type: replace Abstract: The field of Computer-Aided Design (CAD) generation has made significant progress in recent years. Existing methods typically fall into two separate
arXiv:2505.13360v3 Announce Type: replace Abstract: Prompt underspecification is a common challenge when interacting with LLMs. In this paper, we present an in-depth analysis of this problem, showing
arXiv:2604.22756v1 Announce Type: cross Abstract: Conjoint analysis is a cornerstone of market research for estimating consumer preferences; however, traditional methods face persistent challenges reg
arXiv:2601.14287v2 Announce Type: replace Abstract: External memory systems are pivotal for enabling Large Language Model (LLM) agents to maintain persistent knowledge and perform long-horizon decisio
Kimi K2.6 has achieved the top ranking on OpenRouter's weekly LLM leaderboard, marking a significant achievement for the model. The announcement expresses gratitude to developers using Kimi and indica
ml-intern now notifies you when it needs input! Amazing for those 12h+ autonomous LLM training runs The ML Intern can now ping you on Slack when it's finished training models, generating datasets, or
This post compares TensorRT-LLM and llama.cpp (GGUF) as inference frameworks for running coding agents like Cline and RooCode on RTX 5090 GPUs, examining the tradeoff between inference speed and VRAM
arXiv:2601.03779v2 Announce Type: replace Abstract: We explore intrinsic dimension (ID) of LLM representations as a marker of linguistic complexity. Specifically, we test whether ID differences across
arXiv:2604.22273v1 Announce Type: new Abstract: Iterative self-correction is widely used in agentic LLM systems, but when repeated refinement helps versus hurts remains unclear. We frame self-correcti
arXiv:2604.21377v1 Announce Type: new Abstract: Large language models are increasingly being explored as interfaces between humans and robotic systems, yet there remains limited evidence on how such t
arXiv:2604.21308v1 Announce Type: cross Abstract: Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a us
arXiv:2604.20924v1 Announce Type: new Abstract: Timely and interpretable early warning of sepsis remains a major clinical challenge due to the complex temporal dynamics of physiological deterioration.
arXiv:2604.20987v1 Announce Type: new Abstract: Long horizon interactive environments are a testbed for evaluating agents skill usage abilities. These environments demand multi step reasoning, the cha
arXiv:2604.21598v1 Announce Type: cross Abstract: Multi-agent frameworks are widely used in autonomous code generation and have applications in complex algorithmic problem-solving. Recent work has add
arXiv:2509.21275v3 Announce Type: replace-cross Abstract: Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication
arXiv:2604.21525v1 Announce Type: new Abstract: Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (L
arXiv:2604.21564v1 Announce Type: new Abstract: Large language models increasingly shape the information people consume: they are embedded in search, consulted for professional advice, deployed as age
New Frontier LLM Orchestrator Model @SakanaAILabs The fugu model series provides a novel paradigm for test-time scaling. The models are trained to call LLMs and infer not only which individual model i
arXiv:2604.20911v1 Announce Type: cross Abstract: LLM agents deployed in production operate under operator-defined behavioral policies (system-prompt instructions such as prohibitions on credential di
arXiv:2604.21231v1 Announce Type: cross Abstract: Efficient inference for on-device Large Language Models (LLMs) remains challenging due to limited hardware resources and the high cost of the prefill
These pelicans are kind of angry looking! Left is deepseek-v4-flash, right is deepseek-v4-pro - both generated using OpenRouter via my LLM tool 🚀 DeepSeek-V4 Preview is officially live & open-sourced!
arXiv:2604.21223v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has ra
arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin
arXiv:2604.19779v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of stan
arXiv:2604.20726v1 Announce Type: cross Abstract: This work explores the role of prompt design and judge selection in LLM-as-a-Judge evaluations of free text legal question answering. We examine wheth
arXiv:2604.19775v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments.