Knowledge catalogue
arXiv:2605.07153v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge rema
arXiv:2605.04615v2 Announce Type: replace-cross Abstract: Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and d
arXiv:2605.07084v1 Announce Type: new Abstract: Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance be
arXiv:2602.23811v4 Announce Type: replace-cross Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., X
arXiv:2605.06890v1 Announce Type: new Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagn
arXiv:2605.07034v1 Announce Type: cross Abstract: Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-seman
arXiv:2605.07029v1 Announce Type: cross Abstract: Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects a
arXiv:2605.06863v1 Announce Type: new Abstract: We contribute Bi3, a dataset of social robot navigation among groups of people in a constrained lab space. Compared to prior data collection efforts for
arXiv:2605.06939v1 Announce Type: new Abstract: LLM-as-a-Judge evaluation has become a standard tool for assessing base model performance. However, characterizing performance via the naive estimator,
arXiv:2605.07277v1 Announce Type: cross Abstract: Many scientific and combinatorial problems admit multiple correct solutions, not a single label. Standard supervised learning resolves this ambiguity
arXiv:2605.07577v1 Announce Type: new Abstract: Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph struct
arXiv:2605.07306v1 Announce Type: cross Abstract: Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environment
arXiv:2605.07270v1 Announce Type: new Abstract: Many machine learning tasks are invariant under the action of a group G of transformations: signal classification can be invariant under translations, i
arXiv:2605.07878v1 Announce Type: new Abstract: Access to modern generative systems is often restricted to querying an API (the ``black-box' setting) and many properties of the system are unknown to t
arXiv:2512.19991v2 Announce Type: replace Abstract: We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array repr
arXiv:2602.17407v2 Announce Type: replace-cross Abstract: Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such a
arXiv:2509.21637v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen
arXiv:2605.07452v1 Announce Type: new Abstract: Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and c
arXiv:2605.07473v1 Announce Type: cross Abstract: To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends th
arXiv:2605.07650v1 Announce Type: new Abstract: Lens flares, caused by complex optical aberrations, severely degrade image quality especially in nighttime photography. Although recent restoration meth
arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reli
arXiv:2605.07605v1 Announce Type: new Abstract: Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained mani
arXiv:2605.07846v1 Announce Type: new Abstract: Coarse-mask local image editing asks a model to modify a user-indicated region while preserving the surrounding scene. In practice, however, rough masks
arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall
arXiv:2605.06936v1 Announce Type: cross Abstract: LLM-based agents are increasingly applied to the 'last mile' of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC
arXiv:2605.06969v1 Announce Type: new Abstract: Infrared-Visible image fusion (IVIF) aims to integrate thermal information and detailed spatial structures into a single fused image to enhance percepti
arXiv:2605.08057v1 Announce Type: cross Abstract: While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform wel
arXiv:2509.08318v2 Announce Type: replace Abstract: Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train-inference mismatch: branch
arXiv:2605.07251v1 Announce Type: new Abstract: Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous
arXiv:2601.21699v2 Announce Type: replace Abstract: Multi-turn reasoning agents solve complex questions by decomposing them into intermediate retrieval or tool-use steps, for accumulating supporting e
arXiv:2605.06919v1 Announce Type: new Abstract: Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to app
arXiv:2605.07138v1 Announce Type: new Abstract: Reinforcement learning from verifiable emotion rewards RLVER has produced language models with strong empathetic performance, evaluated on benchmarks th
arXiv:2605.07098v1 Announce Type: new Abstract: Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety-driven de
arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid sepa
arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computationally demanding and slow even on the most
arXiv:2605.06941v1 Announce Type: new Abstract: We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained t
arXiv:2605.07065v1 Announce Type: cross Abstract: Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by cha
arXiv:2605.07335v1 Announce Type: new Abstract: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Curr
arXiv:2602.01621v3 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. H
arXiv:2605.07783v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SL
arXiv:2605.07886v1 Announce Type: cross Abstract: Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, espec
arXiv:2605.07415v1 Announce Type: cross Abstract: Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision a
arXiv:2509.08350v2 Announce Type: replace-cross Abstract: Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometr
arXiv:2605.06861v1 Announce Type: new Abstract: State estimation is a critical task in scientific, engineering and control applications. Since the reliability of reconstructions depends on the number
arXiv:2605.07782v1 Announce Type: new Abstract: LLMs can generate hardware descriptions from natural language specifications, but the resulting Verilog often contains width mismatches, combinational l
arXiv:2605.07119v1 Announce Type: cross Abstract: Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is
arXiv:2311.08433v3 Announce Type: replace-cross Abstract: Few studies have investigated the diagnostic utilities of biomarkers for predicting bacteremia among septic patients admitted to intensive car
arXiv:2603.15525v2 Announce Type: replace Abstract: Deep learning models for chest X-ray diagnosis are constrained by limited coverage of clinically meaningful concept combinations in publicly availab
arXiv:2605.07162v1 Announce Type: new Abstract: Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However,
arXiv:2510.04606v2 Announce Type: replace Abstract: Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the lin
arXiv:2605.07194v1 Announce Type: cross Abstract: Dataset distillation compresses a large training set into a small synthetic set that preserves downstream training utility. While most existing method
arXiv:2605.07499v1 Announce Type: new Abstract: Accurate four-dimensional (4D) precipitation information is essential for understanding the Earth's energy and water cycles, yet remains observationally
arXiv:2605.07905v1 Announce Type: cross Abstract: Despite the rapid development of AI reviewers, evaluating such systems remains challenging: metrics favor overlap with human reviews over correctness.
arXiv:2403.18149v3 Announce Type: replace Abstract: Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware rem
arXiv:2605.07040v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to c
arXiv:2605.07764v1 Announce Type: new Abstract: Natural-language interfaces can make swarm robotics more accessible to non-expert operators, but they must translate ambiguous user intent into executab
arXiv:2510.07926v2 Announce Type: replace Abstract: Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce o
arXiv:2605.06720v1 Announce Type: cross Abstract: Antibody therapeutics are among the most successful modern medicines, yet computationally designing antibodies with desirable binding and developabili
arXiv:2605.07353v1 Announce Type: new Abstract: Large reasoning models often reach correct answers through flawed intermediate steps, creating a gap between final accuracy and reasoning reliability. E