Knowledge catalogue
arXiv:2605.08011v1 Announce Type: new Abstract: Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic
arXiv:2605.07444v1 Announce Type: cross Abstract: The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning mod
arXiv:2605.06900v1 Announce Type: cross Abstract: We present an accelerated relax-and-round algorithm for concave coverage problems, which generalize the classic maximum coverage problem. Building on
arXiv:2605.08048v1 Announce Type: new Abstract: Measuring the breadth of a word's meaning, or its spread across contexts, has become feasible with contextualized token embeddings. A word type can be r
arXiv:2605.07324v1 Announce Type: cross Abstract: Backdoor attacks on language models pose a significant threat to AI safety, where models behave normally on most inputs but exhibit harmful behavior w
arXiv:2605.08020v1 Announce Type: new Abstract: We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment predict
arXiv:2605.05703v2 Announce Type: replace-cross Abstract: Optimizing the communication structure of large language model based multi-agent systems (LLM-MAS) has been shown to improve downstream perfor
arXiv:2310.15288v3 Announce Type: replace Abstract: Reward learning techniques enable machine learning systems to learn objectives from human feedback. A core limitation of these systems is their assu
arXiv:2605.07857v1 Announce Type: new Abstract: Optimizing dynamic risk with stochastic policies is challenging in both policy updates and value learning. The former typically requires transition pert
arXiv:2605.07094v1 Announce Type: new Abstract: This paper introduces the Active-Importance-Sampling Actor-Critic (AISAC) algorithm, an extension of the Actor-Critic framework for reducing variance in
arXiv:2602.13357v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) achieve state-of-the-art performance in high-fidelity image and video generation but suffer from expensive infer
arXiv:2604.02525v2 Announce Type: replace Abstract: Hadamard transforms have become a key tool for stabilizing low-precision training, but existing methods apply them uniformly across tensors and comp
arXiv:2509.08461v3 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have demonstrated their remarkable capacity to process and reason over structured and unstruct
arXiv:2605.07002v1 Announce Type: new Abstract: A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptiv
arXiv:2605.08028v1 Announce Type: new Abstract: Traffic state estimation from sparse fixed sensors is challenging because physics-informed neural networks (PINNs) tend to over-smooth the shockwaves ad
arXiv:2605.06946v1 Announce Type: cross Abstract: Sequence models face a fundamental tradeoff between memory capacity and computational efficiency. Transformers achieve expressive context modeling at
arXiv:2605.07137v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a highly effective method for improving the reasoning abilities of Large Language Mod
arXiv:2605.07892v1 Announce Type: new Abstract: Sparse training reduces the memory and computational costs of deep neural networks. However, sparse optimization methods, e.g., those adding an ell_1 pe
arXiv:2605.07257v1 Announce Type: new Abstract: Generative personalization often suffers from the semantic collapsing problem (SCP), where a learned personalized concept overpowers the rest of the tex
arXiv:2605.07121v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, exis
arXiv:2605.07863v1 Announce Type: new Abstract: We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achie
arXiv:2605.06876v1 Announce Type: new Abstract: Adaptive density control in 3D Gaussian Splatting (3DGS) repeatedly grows the Gaussian population through fixed-cardinality random splitting to discover
arXiv:2605.06912v1 Announce Type: new Abstract: The proliferation of generative video technologies has intensified the need for reliable methods to detect and characterize synthetic media. To address
arXiv:2605.07885v1 Announce Type: new Abstract: The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments
arXiv:2605.06218v2 Announce Type: replace Abstract: Piecewise affine neural networks (PANNs) provide a principled geometric perspective on neural network expressivity by characterizing the input--outp
The FCC extended its deadline for software updates on foreign-made routers from March 1, 2027, to January 1, 2029 , allowing already-authorized devices to receive critical security patches longer desp
arXiv:2605.07142v1 Announce Type: new Abstract: Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a re
Agent engineering is hard Deep Agents hides a lot of the weird systems complexity, but still gives you more room to customize the harness than almost any agent SDK I've used This is harder to build th
Agent view is the best Claude Code native way to manage multiple sessions, kind of like tmux built for CC. We spent a lot of time getting the details right, I hope you enjoy it. New in Claude Code: ag
arXiv:2605.07926v1 Announce Type: new Abstract: As LLM-based agents increasingly rely on external tools, it is important to evaluate their ability to sustain tool-grounded reasoning beyond familiar wo
arXiv:2605.06713v1 Announce Type: cross Abstract: Agentic AI systems can plan, call tools, inspect code, interact with web applications, and coordinate multi-step workflows. These same capabilities ch
arXiv:2605.06717v1 Announce Type: cross Abstract: Coding agents are rapidly changing the landscape of software development, moving from inline completion to autonomous systems that edit repositories,
Ben Thompson / Stratechery: Agentic inference is set to be different than today's inference, and will change compute infrastructure because speed won't matter when humans aren't involved — Subscribe t
arXiv:2605.06869v1 Announce Type: new Abstract: AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no u
arXiv:2512.10371v2 Announce Type: replace Abstract: The rapid development of mobile GUI agents has stimulated growing research interest in long-horizon task automation. However, building agents for th
Agents + file sandboxes are all in the range in 2026 🤖🗃️ This is a nifty reference implementation by @itsclelia showing you how to run your agent over a collection of docs (PDFs, images, Office) with
# Agents as Products This post likely explores how AI agents can be effectively deployed as standalone products, discussing their practical applications and advantages over traditional software soluti
arXiv:2605.07963v1 Announce Type: new Abstract: Aggregating conformal predictors is a standard way of balancing their predictive and computational efficiency while retaining their validity, at least a
arXiv:2602.04672v3 Announce Type: replace Abstract: Reconstructing dynamic hand-object interactions from monocular videos is critical for dexterous manipulation data collection and creating realistic
arXiv:2605.06841v1 Announce Type: new Abstract: In model-based learning, the agent learns behaviors by simulating trajectories based on world model predictions. Standard world models typically learn a
Ah yes, chatGPT, tell me more about the 'colony mind', 'foraging patrol' and 'nost building' behaviors of the human liver. For anyone predicting near-term physician replacement by LLM-based AI, please
arXiv:2605.06965v1 Announce Type: cross Abstract: As language-based AI systems become more anthropomorphic, the question of whether they can have subjective experience is increasingly pressing. I focu
arXiv:2605.06607v2 Announce Type: replace-cross Abstract: Recent LLM-based agents have closed substantial portions of the scientific discovery loop in software-only machine-learning research, in chemi
AI existential crisis for software engineers is to go live in the woods and read poetry. AI existential crisis for creatives is to make things until 4am every day because you can't stop now. is this w
arXiv:2605.07003v1 Announce Type: new Abstract: The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and pot
Nous Research announced free access to Qwen 3.6 Plus and additional features available through their subscription portal at portal.nousresearch.com. This promotion likely provides users with complimen
arXiv:2605.07104v1 Announce Type: new Abstract: Establishing almost sure convergence rates for stochastic approximation and reinforcement learning under Markovian noise is a fundamental theoretical ch
arXiv:2605.07744v1 Announce Type: new Abstract: The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets a
Am old enough to remember when @GeoffreyHinton told me I was stupid for saying that LLMs regurgitate training data. He was wrong. LLM regurgitation is now one of the best-established findings in the f
Amazon Quick helps turn your large enterprise data into fast and accurate AI-powered decisions. In this post, you will learn about five new capabilities of Amazon Quick that accelerate how data profes
Enterprise AI adoption has crossed a threshold: The question is no longer whether to invest, but how to do it wisely. As agentic workloads multiply and inference costs rise, AI choice — the ability to
arXiv:2602.12162v3 Announce Type: replace Abstract: In structurally constrained molecular optimization, state-of-the-art methods restart an expensive oracle-driven search from scratch for every new in
arXiv:2511.09598v5 Announce Type: replace Abstract: Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational condition
arXiv:2605.07317v1 Announce Type: cross Abstract: Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Exis
An abbreviated history of Cognition... Scott Wu is the co-founder of Cognition AI, one of the fastest-growing companies in history. He’s also the greatest competitive programmer the US has ever produc
arXiv:2504.12922v3 Announce Type: replace-cross Abstract: We provide a general theorem on the asymptotic behavior of stochastic processes that conform to a relaxed supermartingale condition. The disti
arXiv:2605.06759v1 Announce Type: new Abstract: The decline of natural pollinators has created a major challenge for crop production in controlled indoor agriculture, particularly in vertical farming
@trq212: An Anthropic engineer argues HTML is a better output format for AI agents than Markdown, citing information density, ease of sharing, and two-way interaction — Using Claude Code: The Unreason
arXiv:2605.07719v1 Announce Type: cross Abstract: Long-context inference increasingly operates over CPU-resident KV caches, either because decoding-time KV states exceed GPU memory capacity or because
arXiv:2605.07125v1 Announce Type: cross Abstract: Sequential recommendation has increasingly shifted toward generative recommenders that combine sequential patterns with semantic item information. Yet