Knowledge catalogue
arXiv:2610.19424v1 Announce Type: new Abstract: LLM system capabilities depend not only on model parameters but also on the executable harnesses that organize information acquisition, tool use, reason
arXiv:2602.07549v3 Announce Type: replace-cross Abstract: In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But
arXiv:2610.14360v1 Announce Type: new Abstract: Large language model (LLM) alignment often assumes that the user interacting with a model is also the primary beneficiary of its behavior. Yet in many c
arXiv:2610.15492v1 Announce Type: new Abstract: Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to execu
arXiv:2610.15666v1 Announce Type: new Abstract: Converting natural-language instructions into executable action plans conditioned on a scene image is a core capability for embodied human-robot interac
arXiv:2610.17747v1 Announce Type: cross Abstract: Failure-attribution benchmarks ask an LLM judge which step or agent caused a failure. A high score should mean the judge checked the faulty step again
arXiv:2610.14427v1 Announce Type: cross Abstract: As watermarking of LLM-generated text enters deployment, users increasingly need to check text whose source is unknown. Existing detectors read the wa
arXiv:2610.14873v1 Announce Type: cross Abstract: Aspect Sentiment Quadruple Prediction (ASQP) is fundamentally a relational reasoning problem: identifying aspect-opinion-category-sentiment quadruples
arXiv:2610.16238v1 Announce Type: new Abstract: LLM agents increasingly control robots using VLA policies, perception models, and procedural knowledge. These modules are difficult to reuse because the
arXiv:2610.15244v1 Announce Type: new Abstract: Self-evolving mechanisms let vision-language model (VLM) agents continually adapt through evolving memory, but they also allow malicious behavior to eme
arXiv:2610.13995v1 Announce Type: new Abstract: Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to gener
Gary Marcus will be speaking at the @Bloomberg New Economy Forum in New Delhi tomorrow, October 15, on AI reliability, control and safety. @GaryMarcus Hope you’ll connect all the dots between… LLM bul
arXiv:2610.14224v1 Announce Type: cross Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a p
Hey everyone, I recently put together a local LLM machine using mostly second hand hardware. I wanted to experiment with local models for app development without spending a fortune on a new workstatio
arXiv:2509.13334v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on th
arXiv:2610.18851v1 Announce Type: cross Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple
arXiv:2610.18642v1 Announce Type: new Abstract: The Key-Value (KV) cache constitutes a dominant source of memory consumption during large language model (LLM) inference, particularly in long-context a
arXiv:2610.17143v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems are increasingly applied across diverse domains, yet different applications require different forms
arXiv:2610.18683v1 Announce Type: new Abstract: Reinforcement learning (RL) has emerged as the primary paradigm for optimizing Large Language Model (LLM) agents, yet efficient critic-free algorithms (
arXiv:2610.14921v1 Announce Type: new Abstract: A language model that keeps learning online must not slowly erode its own base capabilities. We ask which part of a modern LLM should absorb continual u
arXiv:2610.15924v1 Announce Type: cross Abstract: Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn complex outcomes into rewards. Ve
arXiv:2610.16761v1 Announce Type: cross Abstract: LLM-based information systems need safety guardrails. Most guardrails classify the prompt alone. If the prompt looks harmful, the system rejects it be
arXiv:2610.16857v1 Announce Type: new Abstract: Research on video classification models has witnessed significant growth recently, accompanied by a significant increase in the demand for datasets to t
arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri
arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk
arXiv:2610.15556v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models commonly generate robot actions token by token through Large Language Model (LLM) decoders, causing inference late
arXiv:2610.14731v1 Announce Type: new Abstract: Long-term memory benchmarks for language agents primarily evaluate whether information from earlier interactions can be retrieved and used when relevant
Learn how agent traces, human oversight, production evaluations, and log retention support EU AI Act compliance with a practical credit-assistant example. The post EU AI Act compliance for AI agents:
Lipwise is a [yet another] dictation app for Windows, macOS and Linux. You hold a hotkey, talk, and it types into whatever app has focus but with a twist. Since most of us that speak to our computers
I’ve been building SonicBoom, a native tensor runtime written in C++/CUDA with a C ABI and no Python anywhere in the stack. I just tagged v0.0.1, and the first target is local LLM inference on consume
arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single
Distributed inference splits a large AI model into sub‑components that run concurrently on multiple compute nodes, allowing each node to process only part of the workload. This parallelism boosts thro
arXiv:2610.13494v1 Announce Type: new Abstract: Existing LLM-based agent approaches for long-horizon machine learning engineering (MLE) often use context engineering, implemented through custom harnes
Over the last few months at @river_ai_inc we’ve developed what we believe is the best system in the world for building your own internal custom models, driven by 10 years of experience training top mo
arXiv:2610.13399v1 Announce Type: new Abstract: Long-term personalized tutoring requires language models to translate a learner's evolving history into appropriate instructional support. Existing LLM-
arXiv:2606.16939v2 Announce Type: replace-cross Abstract: A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly prod
arXiv:2610.13166v1 Announce Type: new Abstract: Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool
Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaffolds mitigate it wi
arXiv:2610.12596v1 Announce Type: new Abstract: Agentic large language models now tackle complex tasks, yet strategic reasoning over long horizons is under-studied: most agentic benchmarks are short a
arXiv:2610.12864v1 Announce Type: new Abstract: Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress e
Evaluate open models with Together AI in Arize AX. Compare model performance, run LLM-as-a-Judge evaluations, and explore lower-cost alternatives. The post Together AI integration: Evaluate open model
arXiv:2610.13695v1 Announce Type: new Abstract: Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable. Despite their advantages, current LLM-
Release: web-edit 0.1a0 I was working on a project from my phone using Codex Remote when I found myself wanting to edit a credentials file without round-tripping those credentials through the LLM itse
arXiv:2610.12561v1 Announce Type: new Abstract: Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests. For embodied agent
arXiv:2610.12847v1 Announce Type: cross Abstract: A simulator summarizes an environment in a state that users can read and change. Generative models learn representations of the world without explicit
arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact
A conversational AI plush toy where nothing leaves my network. Everything runs on my LAN: STT: Whisper LLM: Hermes 3 Llama 3.1 8B (Q8_0) TTS: Kokoro Memory: per-person (LangGraph). It knows who it's t
arXiv:2507.14897v2 Announce Type: replace Abstract: Methods to build LLM agents have evolved from prompt engineering and supervised finetuning to agentic reinforcement learning (agentic RL). However,
arXiv:2610.12002v1 Announce Type: cross Abstract: Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settin
arXiv:2610.12445v1 Announce Type: cross Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box deception d
arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical
arXiv:2610.11655v1 Announce Type: new Abstract: Improving a long-horizon LLM agent means evolving the harness around a frozen model or training its weights. We let a self-evolving harness make the sys
arXiv:2610.12303v1 Announce Type: new Abstract: Declarative logic programs offer a powerful and interpretable abstraction for encoding relational structure and neurosymbolic reasoning, by expressing d
It's been a long time coming, and I should have done this in the first place. But LumaBrowser is now open source. Since I first released it a year ago, it's had several thousand installs and an absolu
arXiv:2507.18074v2 Announce Type: replace Abstract: Recent progress in LLM agents has advanced the prospect of autonomous research. Yet whether AI can complete difficult long-horizon tasks, especially
arXiv:2610.10833v1 Announce Type: new Abstract: We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using avai
arXiv:2610.11358v1 Announce Type: new Abstract: Cross-model KV-cache reuse remains a key challenge in modern LLM serving. Coding agents and multi-model systems increasingly route a shared context acro
arXiv:2610.11773v1 Announce Type: new Abstract: Guard models are increasingly used to safeguard LLM-based agents, primarily by identifying actions that agents are forbidden to perform. However, identi
arXiv:2610.10929v1 Announce Type: cross Abstract: Speculative decoding is a popular technique for increasing the speed and reducing the costs of large language model (LLM) inference by verifying multi
arXiv:2610.10942v1 Announce Type: new Abstract: Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic ben