Knowledge catalogue
arXiv:2605.08261v1 Announce Type: cross Abstract: Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically add
arXiv:2605.09855v1 Announce Type: new Abstract: Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-II
arXiv:2605.10916v1 Announce Type: cross Abstract: Recognition of handwritten Bangla compound characters remains a challenging problem due to complex character structures, large intra-class variation,
arXiv:2605.09760v1 Announce Type: new Abstract: A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a l
arXiv:2605.10793v1 Announce Type: new Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce
arXiv:2605.08112v1 Announce Type: cross Abstract: AI coding agents powered by large language models can read codebases and produce functional code, but they routinely violate team-specific product dec
arXiv:2605.09998v1 Announce Type: cross Abstract: Coding harnesses such as Claude Code and OpenHands wrap foundation models with tools, memory, and planning, but no equivalent exists for embodied agen
arXiv:2605.09867v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit a strong capacity for in-context learning: Given labeled examples, they can generate good predictions without par
arXiv:2605.08352v1 Announce Type: new Abstract: A convergence analysis is developed for the regularized Newton method for training neural networks (NNs) in the overparameterized limit. As the number o
arXiv:2605.08522v1 Announce Type: new Abstract: The evaluation of Large Language Models (LLMs) faces a critical challenge in construct validity, where fragmented benchmarks and ad hoc metrics frequent
arXiv:2605.10337v1 Announce Type: new Abstract: Intracranial electrocorticography (ECoG) offers high-signal-to-noise access to cortical activity for brain-computer interfaces, yet limited per-patient
arXiv:2605.09126v1 Announce Type: new Abstract: Asynchronous DiLoCo systems may receive pseudo-gradients computed several outer rounds earlier, yet the standard Nesterov outer optimizer does not expli
arXiv:2605.10426v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for end-to-end autonomous driving. However, existing reasoning mechanisms sti
arXiv:2605.10597v1 Announce Type: cross Abstract: Benchmarks for coding agents increasingly measure source-level software repair, and cybersecurity benchmarks increasingly measure broad capture-the-fl
arXiv:2603.09970v2 Announce Type: replace Abstract: A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE,
arXiv:2605.09875v1 Announce Type: new Abstract: Large language models from different families use different hidden dimensions, tokenizers, and training procedures, making behavioral directions difficu
arXiv:2605.08839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world sc
arXiv:2605.08960v1 Announce Type: cross Abstract: Crystal generative models mainly learn what stable crystals look like, with little explicit supervision for what makes them stable. We reveal a substa
arXiv:2605.09002v1 Announce Type: cross Abstract: In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLI
arXiv:2605.09486v1 Announce Type: cross Abstract: Graph Neural Networks (GNN) and Transformer-based architectures have achieved remarkable progress in graph learning, yet they still struggle to captur
arXiv:2605.08455v1 Announce Type: new Abstract: Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, mem
arXiv:2605.08467v1 Announce Type: new Abstract: Large language models show promise for automated CUDA programming, however even the strongest coding models (e.g., Claude-Opus-4.6) may still fall short
arXiv:2605.08793v1 Announce Type: cross Abstract: Optimal transport (OT) has emerged as a fundamental tool in modern machine learning, yet its computational cost remains a significant bottleneck for l
Release: datasette 1.0a29 New TokenRestrictions.abbreviated(datasette) utility method for creating '_r' dictionaries. #2695 Table headers and column options are now visible even if a table contains ze
arXiv:2605.10933v1 Announce Type: cross Abstract: While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates sign
arXiv:2605.08942v1 Announce Type: new Abstract: Large language models (LLMs) increasingly exhibit behaviors suggesting awareness of their evaluation context, often adapting their reasoning strategies
Deep learning hit a wall. Neurosymbolic AI rescued it. 🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 symbolic tools, 5
arXiv:2605.09890v1 Announce Type: cross Abstract: Differentially private stochastic gradient descent (DP-SGD) is a standard approach to privacy-preserving learning based on per-example clipping, subsa
arXiv:2508.06248v4 Announce Type: replace Abstract: The generalization of deepfake detectors to unseen manipulation techniques remains a challenge for practical deployment. Although many approaches ad
arXiv:2605.10564v1 Announce Type: new Abstract: End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual rea
arXiv:2605.09679v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) and AI agents have made significant progress in learning to analyze and reason about clinical images. However, e
arXiv:2605.09586v1 Announce Type: new Abstract: World models for deformable objects should recover not only geometry and appearance, but also underlying physical dynamics, interaction grounding, and m
arXiv:2605.09222v1 Announce Type: cross Abstract: Logs are ubiquitous in modern systems. Unfortunately, their unstructured nature in flat sequences limits understanding of execution behaviors, hinderi
arXiv:2604.01151v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human o
arXiv:2605.08286v1 Announce Type: cross Abstract: We introduce a spectral-injection diagnostic for measuring which angular frequencies a trained equivariant force-field backbone preserves: inject a co
arXiv:2605.08614v1 Announce Type: new Abstract: Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perfor
arXiv:2605.09275v1 Announce Type: new Abstract: Direct diffusion modeling of high-resolution spatiotemporal fields is computationally challenging. Parameter-efficient primitives address this by repres
arXiv:2605.08568v1 Announce Type: new Abstract: Large language models (LLMs) have rapidly grown in scale, creating substantial memory and computational costs that hinder efficient deployment. Singular
arXiv:2410.02543v3 Announce Type: replace-cross Abstract: In a convergence of machine learning and biology, we reveal that diffusion models are evolutionary algorithms. By considering evolution as a d
arXiv:2605.08446v1 Announce Type: new Abstract: We propose training Bayesian neural networks by directly minimizing the Bethe free energy rather than maximizing a variational lower bound. On tree-stru
arXiv:2605.08237v1 Announce Type: new Abstract: In grokking, a model first fits the training data while test accuracy remains low, and only later begins to generalize. We ask whether this transition c
arXiv:2605.08462v1 Announce Type: cross Abstract: Hallucination remains a persistent challenge in Large Language Models (LLMs), particularly in context-grounded settings such as RAG and agentic AI sys
arXiv:2605.08113v1 Announce Type: cross Abstract: Accurate predictions of smallholder maize yields across national boundaries are critical for food security planning in sub-Saharan Africa, yet most pu
arXiv:2605.08299v1 Announce Type: cross Abstract: Embedding-based code retrieval often suffers when encoders overfit to surface syntax. Prior work mitigates this by using LLMs to rephrase queries and
arXiv:2605.09315v1 Announce Type: new Abstract: Recent advances in LLM agents enable systems that autonomously refine workflows, accumulate reusable skills, self-train their underlying models, and mai
Do you actually own your document parsing infrastructure? 👀 At @llama_index, we wanted to make that easier, so we built 𝗹𝗶𝘁𝗲𝗽𝗮𝗿𝘀𝗲-𝘀𝗲𝗿𝘃𝗲𝗿, a lightweight HTTP backend built on top of LiteParse that can
arXiv:2605.08888v1 Announce Type: new Abstract: Evaluating whether Multimodal Large Language Models can produce trustworthy, verifiable reasoning over long, visually rich documents requires evaluation
arXiv:2605.08747v1 Announce Type: new Abstract: Standard embodied evaluations do not independently score whether an agent correctly commits to task completion at episode closure, a capacity we call te
arXiv:2605.09497v1 Announce Type: new Abstract: Vision-language model (VLM) based web agents demonstrate impressive autonomous GUI interaction but remain vulnerable to deceptive interface elements. Ex
arXiv:2504.21015v4 Announce Type: replace-cross Abstract: Training effective dense retrieval models typically relies on hard negative (HN) examples mined from large document corpora using methods such
arXiv:2605.09537v1 Announce Type: new Abstract: Despite rapid progress in Vision-Language-Action (VLA) models for robotic control, instruction drift remains a persistent failure mode in long-horizon t
arXiv:2605.08627v1 Announce Type: new Abstract: All-in-one image restoration aims to handle diverse degradations within a single model. However, existing methods often suffer from three key limitation
arXiv:2503.06047v2 Announce Type: replace Abstract: Large language model (LLM)-based agents are increasingly applied to complex strategic environments that demand long-horizon reasoning, multi-agent i
arXiv:2605.08441v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) generates hundreds of thousands of tokens per training step, with rollout generation dominating
arXiv:2602.06550v2 Announce Type: replace-cross Abstract: Zero-shot generalization in contextual reinforcement learning remains a core challenge, particularly when the context is latent and must be in
arXiv:2605.08177v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods be
arXiv:2405.12969v3 Announce Type: replace Abstract: Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable a
arXiv:2602.09514v3 Announce Type: replace-cross Abstract: Long-horizon planning is widely recognized as a core capability of autonomous LLM-based agents; however, current evaluation frameworks suffer
arXiv:2506.08136v3 Announce Type: replace Abstract: We introduce EconWebArena, a benchmark for evaluating autonomous agents on complex, multimodal economic tasks in realistic web environments. The ben
arXiv:2605.06644v2 Announce Type: replace Abstract: Fluorescent protein quantum yield (QY) is governed by the mature chromophore and its three-dimensional microenvironment rather than sequence identit