Knowledge catalogue
arXiv:2605.06055v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both p
arXiv:2605.07654v1 Announce Type: cross Abstract: Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority
arXiv:2605.07379v1 Announce Type: cross Abstract: Conventional visual object trackers localize targets using handcrafted spatial priors, often in the form of heatmaps. Such priors provide only surroga
arXiv:2605.06298v2 Announce Type: replace-cross Abstract: Training world models on vast quantities of unlabelled videos is a critical step toward fully autonomous intelligence. However, the prevailing
arXiv:2511.06571v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable progress across diverse tasks, yet their internal mechanisms remain largely opaque. In t
arXiv:2605.07174v1 Announce Type: new Abstract: We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work
arXiv:2601.16130v2 Announce Type: replace-cross Abstract: Motivated reasoning - the idea that individuals processing information may be motivated to either arrive at accurate beliefs or arrive at desi
arXiv:2510.00568v3 Announce Type: replace Abstract: Search agents powered by Large Language Models (LLMs) have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement l
arXiv:2605.07263v1 Announce Type: cross Abstract: Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition, but conventional analog aggregation schemes typi
arXiv:2605.07575v1 Announce Type: cross Abstract: Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall sho
arXiv:2605.06987v1 Announce Type: new Abstract: Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this sta
arXiv:2605.08000v1 Announce Type: new Abstract: Recent progress in dense optical flow has been driven by increasingly complex architectures and multi-step refinement for test-time scaling. While these
arXiv:2605.07307v1 Announce Type: new Abstract: Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must
arXiv:2605.07164v1 Announce Type: new Abstract: Self-evolving agents improve by accumulating and reusing experience from past interactions. Existing work has largely focused on how experience is const
arXiv:2605.07331v1 Announce Type: cross Abstract: Reinforcement learning, including reinforcement learning with verifiable rewards (RLVR), has emerged as a powerful approach for LLM post-training. Cen
arXiv:2605.07755v1 Announce Type: cross Abstract: The theory of state tracking in recurrent architectures has predominantly focused on expressive capacity: whether a fixed architecture can theoretical
arXiv:2602.04556v2 Announce Type: replace Abstract: Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output pr
arXiv:2605.06173v2 Announce Type: replace-cross Abstract: Diabetic Retinopathy (DR) is a leading cause of preventable blindness among working-age adults worldwide, yet most automated screening systems
arXiv:2605.05806v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decode
arXiv:2602.11162v2 Announce Type: replace Abstract: Recent studies have identified 'retrieval heads' in Large Language Models (LLMs) responsible for extracting information from input contexts. However
arXiv:2605.07106v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made remarkable progress on vision-language reasoning, yet most methods still compress visual evidence int
arXiv:2605.06764v1 Announce Type: cross Abstract: Learning from a sequence of interactions, as soon as observations are perceived and acted upon, without explicitly storing them, holds the promise of
arXiv:2605.07588v1 Announce Type: cross Abstract: Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choic
arXiv:2602.16548v2 Announce Type: replace Abstract: The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While
arXiv:2605.07413v1 Announce Type: new Abstract: Obtaining accurate class labels is often costly or unreliable, and may also be limited by privacy or other practical conditions. Compared with asking an
arXiv:2602.19974v2 Announce Type: replace Abstract: Recent advancements in image generation have achieved impressive results in producing high-quality images. However, existing image generation models
arXiv:2510.24736v3 Announce Type: replace-cross Abstract: Generating property-optimized mRNA sequences is central to applications such as vaccine design and protein replacement therapy, but remains ch
arXiv:2605.07752v1 Announce Type: new Abstract: Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradati
arXiv:2605.07634v1 Announce Type: cross Abstract: We consider a first order stochastic optimization framework where, at each iteration, K independent identically distributed (i.i.d.) data point sample
arXiv:2602.01372v2 Announce Type: replace-cross Abstract: Entropic regularization provides a simple way to approximate linear programs whose constraints split into two or more tractable blocks. The re
arXiv:2605.06686v1 Announce Type: new Abstract: Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper
arXiv:2605.06947v1 Announce Type: new Abstract: While autoregressive models have advanced 3D generation, creating physically stable brick structures remains a challenge due to the strict requirements
arXiv:2605.07129v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as a promising paradigm for next-generation recommender systems, offering strong semantic understanding and
arXiv:2605.07396v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white-box sce
arXiv:2605.08061v1 Announce Type: new Abstract: We argue that decomposing reward into weighted, verifiable criteria and using an LLM judge to score them provides a partial-credit optimization signal:
arXiv:2605.07760v1 Announce Type: new Abstract: Platform content moderation applies explicit policy rules and context-dependent conditions to decide whether user content is allowed, restricted, or rem
arXiv:2601.01285v2 Announce Type: replace Abstract: Medical image segmentation requires balancing local precision for boundary-critical clinical applications, global context for anatomical coherence,
arXiv:2503.05085v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have fundamentally reshaped speech-to-speech (S2S) systems, enabling increasingly natural spoken int
arXiv:2605.06230v2 Announce Type: replace Abstract: As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool
arXiv:2605.07630v1 Announce Type: cross Abstract: When a phone-use agent avoids harm, does that show safety, or simply inability to act? Existing evaluations often cannot tell. A harmful outcome may b
arXiv:2605.05995v2 Announce Type: replace-cross Abstract: The safety alignment of Large Language Models (LLMs) remains vulnerable to Harmful Fine-tuning (HFT). While existing defenses impose constrain
arXiv:2605.07102v1 Announce Type: new Abstract: Evaluating literary quality requires assessing interpretive dimensions such as cultural representation, emotional depth, and philosophical sophisticatio
arXiv:2605.05693v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is an effective approach for deploying large language models (LLMs) under memory and latency constraints. Most exis
arXiv:2605.07604v1 Announce Type: cross Abstract: 3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scene
arXiv:2605.07212v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained a
arXiv:2605.06908v1 Announce Type: cross Abstract: Adaptive test-time compute for LLM agents aims to invoke extra computation only when it improves performance. Existing methods typically use confidenc
arXiv:2605.07239v1 Announce Type: new Abstract: We establish sample complexity results for stochastic optimization over the integers, especially with a view to understand the complexity with respect t
arXiv:2605.07800v1 Announce Type: new Abstract: Recent video diffusion models (VDMs) synthesize visually convincing clips, yet still drop entities, mis-bind attributes, and weaken the interactions spe
arXiv:2605.07264v1 Announce Type: new Abstract: Accurate Digital Surface Model (DSM) reconstruction from satellite imagery is critical for applications such as disaster response, urban planning, and l
arXiv:2605.07181v1 Announce Type: new Abstract: Sparse-view satellite image surface reconstruction remains highly challenging, fundamentally because the reliability of multi-view matching under satell
arXiv:2511.16520v2 Announce Type: replace-cross Abstract: Foundation flow-matching (FM) models promise a universal prior for solving inverse problems (IPs), yet today they trail behind domain-specific
arXiv:2512.23770v3 Announce Type: replace-cross Abstract: In safety-critical domains, reinforcement learning (RL) agents must often satisfy strict, zero-cost safety constraints while accomplishing tas
arXiv:2508.15989v2 Announce Type: replace Abstract: Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in whic
arXiv:2509.00338v3 Announce Type: replace-cross Abstract: Hierarchical reinforcement learning (RL) has the potential to enable effective decision-making over long timescales. Existing approaches, whil
arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u
arXiv:2602.03473v2 Announce Type: replace-cross Abstract: Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research i
arXiv:2605.07823v1 Announce Type: new Abstract: Online group chats are social spaces with implicit behavior patterns that, when broken, are often met with social sanctioning from the group. The abilit
arXiv:2605.08043v1 Announce Type: cross Abstract: While text-to-image models have made strong progress in visual fidelity, faithfully realizing complex visual intents remains challenging because many
arXiv:2508.04056v2 Announce Type: replace Abstract: Enteric methane measurement from ruminant livestock faces fundamental trade-offs between accuracy and operational feasibility. Existing methods quan
arXiv:2602.15189v2 Announce Type: replace-cross Abstract: Producing output that conforms to a specified JSON schema underlies tool use, structured extraction, and knowledge base construction in modern