Knowledge catalogue
arXiv:2603.17837v3 Announce Type: replace-cross Abstract: During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal c
arXiv:2605.00754v1 Announce Type: cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time sc
arXiv:2605.00438v1 Announce Type: cross Abstract: Long-horizon robotic manipulation requires plans that are both logically coherent and geometrically grounded. Existing Vision-Language-Action policies
arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I
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arXiv:2605.00414v1 Announce Type: new Abstract: Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work u
arXiv:2605.00052v1 Announce Type: new Abstract: Hybrid-capture novel view synthesis combines images at substantially different camera distances (e.g., aerial drone and ground-level views). Standard 3D
arXiv:2605.00365v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved substantial gains in single-attempt accuracy (Pass@1) on reasoning tasks, yet often
arXiv:2605.00364v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existin
arXiv:2312.12339v3 Announce Type: replace Abstract: Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose exti
arXiv:2605.00538v1 Announce Type: new Abstract: Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment
arXiv:2506.11991v3 Announce Type: replace-cross Abstract: In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which
arXiv:2603.22285v2 Announce Type: replace Abstract: Long video understanding remains challenging for multimodal large language models (MLLMs) due to limited context windows, which necessitate identify
arXiv:2505.03500v5 Announce Type: replace Abstract: Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, c
arXiv:2605.00725v1 Announce Type: new Abstract: Combinatorial complexes have unified set-based (e.g., graphs, hypergraphs) and part-whole (e.g., simplicial, cellular complexes) structures into a commo
A Reddit post from r/ChatGPT where a user expresses frustration with ChatGPT's argumentative behavior and perceived inaccuracies, contrasting their experience with Claude which they found superior aft
(Sorry, after seeing so many of these, could not resist): 🚨 BREAKING: Google just dropped a NEW paper that completely deletes RNNs from existence. No recurrence. No convolutions. Nothing. Just one mec
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,
This post from Swyx discusses Grok (xAI's AI assistant) in the context of efficiency, likely highlighting performance metrics, speed improvements, or resource optimization of the model compared to alt
arXiv:2604.27421v1 Announce Type: cross Abstract: Large Language Models (LLMs) are now widely used for query reformulation and expansion in Information Retrieval, with many studies reporting substanti
arXiv:2509.22017v4 Announce Type: replace Abstract: Bipartite knowledge graphs in niche domains are typically data-poor and edge-sparse, which hinders link prediction. We introduce AEGIS (Authentic Ed
arXiv:2602.11897v3 Announce Type: replace-cross Abstract: Cybersecurity decision-making increasingly occurs in environments characterized by uncertainty, partial observability, and adversarial manipul
arXiv:2601.01885v2 Announce Type: replace Abstract: Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory mana
arXiv:2604.27253v1 Announce Type: new Abstract: Recent advances in multimodal large language models (LLMs) have revolutionized web agents that can automate complex tasks on websites. However, their ac
arXiv:2507.12414v2 Announce Type: replace-cross Abstract: Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations su
arXiv:2604.27394v1 Announce Type: cross Abstract: Conditional Average Treatment Effect (CATE) estimation in practice demands three properties simultaneously: heterogeneous effects au(x), calibrated un
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
arXiv:2602.01265v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a machine learning framework that transfers knowledge from a teacher model to a student model. The vanilla KD propose
arXiv:2604.27667v1 Announce Type: cross Abstract: Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and of
arXiv:2604.27415v1 Announce Type: new Abstract: With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-d
arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss functio
arXiv:2604.27149v1 Announce Type: cross Abstract: Conformal Prediction provides distribution-free prediction intervals with guaranteed coverage, but its reliance on a single global calibration thresho
arXiv:2604.27707v1 Announce Type: new Abstract: Current agentic memory systems (vector stores, retrieval-augmented generation, scratchpads, and context-window management) do not implement memory: they
arXiv:2604.28159v1 Announce Type: new Abstract: Topological features play an essential role in ensuring geometric plausibility and structural consistency in image analysis tasks such as segmentation a
arXiv:2604.27137v1 Announce Type: new Abstract: This paper introduces a systematic evaluation framework grounded in the Interagency Language Roundtable (ILR) Skill Level Descriptions and applies it to
arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec
arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious
arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen
arXiv:2604.27741v1 Announce Type: new Abstract: We study the problem of understanding where two populations differ within a feature space, which we formalize in the concept of a differential subgroup:
arXiv:2603.22078v3 Announce Type: replace Abstract: Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predictin
arXiv:2604.27987v1 Announce Type: new Abstract: Fine-tuning pretrained models has become a standard approach to adapting pretrained knowledge to improve the accuracy on new sparse, imbalance datasets.
arXiv:2604.28011v1 Announce Type: new Abstract: Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of
arXiv:2604.27004v1 Announce Type: cross Abstract: We propose EdgeSpike, a co-designed spiking neural network (SNN) framework for autonomous low-power sensing in edge Internet of Things (IoT) architect
arXiv:2602.00095v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. H
arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov
arXiv:2603.02226v2 Announce Type: replace Abstract: Real-world sequential signals, such as audio or video, contain critical information that is often embedded within long periods of silence or noise.
arXiv:2604.27178v1 Announce Type: new Abstract: Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tas
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:2604.28016v1 Announce Type: new Abstract: 3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control
arXiv:2601.06992v2 Announce Type: replace-cross Abstract: Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics,
arXiv:2604.27510v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates und
arXiv:2306.10407v3 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an
arXiv:2604.28115v1 Announce Type: cross Abstract: Existing learning-based occupancy prediction methods rely on large-scale 3D annotations and generalize poorly across environments. We present FreeOcc,
arXiv:2604.27353v1 Announce Type: new Abstract: Gait recognition has emerged as a compelling biometric modality for surveillance and security applications, offering inherent advantages such as non-int
arXiv:2604.28193v1 Announce Type: new Abstract: Reconstructing 3D scenes from sparse, unposed images remains challenging under real-world conditions with varying illumination and transient occlusions.
arXiv:2604.27918v1 Announce Type: new Abstract: Existing talking avatar methods typically adopt an image-to-video pipeline conditioned on a static reference image within the same scene as the target g
arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm
arXiv:2604.28144v1 Announce Type: new Abstract: Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy mea
arXiv:2503.01817v2 Announce Type: replace Abstract: A fundamental challenge in neurosymbolic systems is applying continuous gradient-based optimization to discrete logical domains. While fuzzy relaxat
arXiv:2604.27895v1 Announce Type: new Abstract: As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient predi