News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 4d ago Bandit Submodular Maximization under Matroid Constraints: Learning Compressed Exchange Policy arXiv:2608.24627v1 Announce Type: new Abstract: We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank-$k$ matroid on $n$ elements, we give a randomized oracle-polynomial algorithm that makes one feasible value query per… 7 arXiv — Machine Learning research 4d ago Data Leakage Inflates Generalizability of Power Outage Prediction Models arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whether these models generalize to the novel conditions such applications require.… 6 arXiv — Machine Learning research 4d ago A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal observations. We introduce the oFM, a foundation model developed on a real-world… 4 arXiv — Machine Learning research 4d ago On-policy Distillation with Verifiable Reward arXiv:2608.24696v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training large language models. However, RLVR suffers from sparse task-level feedback, while… 26 arXiv — Machine Learning research 4d ago Single State Update Predictive Coding training for Time Series Forecasting and Anomaly Detection arXiv:2608.24697v1 Announce Type: new Abstract: Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates. However, the main bottleneck of PC Networks (PCN) is the sequential backwards error propagation. To tackle this, we… 37 arXiv — Machine Learning research 4d ago Parameter-Level Attribution of Symmetry in Trained Networks Though Parameter-Wise Functional Sensitivity arXiv:2608.24700v1 Announce Type: new Abstract: When a network has learned a function with a known symmetry, can that symmetry be moved through the parametrisation---is there a motion in parameter space realising the group action in function space? We formulate this as a lifting… 9 arXiv — Machine Learning research 4d ago Constrained Hyperparameter Optimization for Streaming Data arXiv:2608.24712v1 Announce Type: new Abstract: Optimization of hyperparameters is a critical factor to obtain optimal model performance. While existing research has predominantly concentrated on batch-learning scenarios, addressing the complexities inherent in data streams… 37 arXiv — Machine Learning research 4d ago Enhancing Bayesian Optimization and Active Learning Through Kernel Diversity arXiv:2608.24721v1 Announce Type: new Abstract: Hyperparameter selection remains a key challenge in Bayesian optimization (BO) and Bayesian active learning (AL), as model misspecification can lead to suboptimal performance, while more accurate fully Bayesian treatments typically… 35 arXiv — Machine Learning research 4d ago Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets arXiv:2608.24727v1 Announce Type: new Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for… 10 arXiv — Machine Learning research 4d ago Optimal Alternating Regret for Online Learning and Games arXiv:2608.24731v1 Announce Type: new Abstract: We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimization (OLO) and online convex optimization (OCO). For OLO over the probability… 5 arXiv — Machine Learning research 4d ago $(\text{DNN})^2$: Doubly Non-Negative Relaxations for Deep Neural Networks arXiv:2608.24743v1 Announce Type: new Abstract: Existing linear program (LP) and semidefinite program (SDP) relaxations for rectified linear unit (ReLU) neural network (NN) verification yield overly-conservative safety guarantees due to significant relaxation gaps. While the… 6 arXiv — Machine Learning research 4d ago Beyond Uniform Local Isometry and Topology: FactoMap for Disentangled Representations arXiv:2608.24762v1 Announce Type: new Abstract: Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collapse, or have position-dependent geometry. We introduce factor-space structure,… 8 arXiv — Machine Learning research 4d ago LION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning arXiv:2608.24795v1 Announce Type: new Abstract: Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data… 14 arXiv — Machine Learning research 4d ago MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification arXiv:2608.24812v1 Announce Type: new Abstract: Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minority representations, while conventional node and neighborhood information… 28 arXiv — Machine Learning research 4d ago Effective Learning Rate Governs Loss Dynamics in Language Model Pretraining arXiv:2608.24814v1 Announce Type: new Abstract: We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynamics primarily through their ratio, the effective learning rate (ELR). When ELR is matched across runs, their loss… 12 arXiv — Machine Learning research 4d ago A Geometric Theory of Robust Fairness Audits arXiv:2608.24818v1 Announce Type: new Abstract: Neighborhood-based fairness audits evaluate individual fairness by comparing predictions among similar individuals in feature space. Despite their widespread use, little is known about the robustness of the auditing procedure… 17 arXiv — Machine Learning research 4d ago BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences. Existing histology--transcriptomics objectives can emphasize instance-level… 10 arXiv — Machine Learning research 4d ago Bellman Calibration for Marginalized Importance Weighting in Offline Reinforcement Learning arXiv:2608.24858v1 Announce Type: new Abstract: Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characterized by an adjoint Bellman equation. Existing minimax, primal-dual, and fitted… 15 arXiv — Machine Learning research 4d ago Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement arXiv:2608.24859v1 Announce Type: new Abstract: Multi-task vehicle routing problem (VRP) solvers seek to handle multiple VRP variants within a single unified model, avoiding the need to train a separate model for every variant. In spite of recent progress, current approaches… 12 arXiv — Machine Learning research 4d ago Symbolic Classification-Enabled LHC Limits Online BSM Global Fits arXiv:2605.22330v1 Announce Type: cross Abstract: Global fits of Beyond the Standard Model (BSM) physics often involve a two-way interplay between theory and experiment. Theoretical models provide guidance for experimental searches, while experimental results, in turn, constrain… 13 arXiv — Machine Learning research 4d ago Finite-Sample Metric Non-Collapse for Geometrically Supervised Latent World Models in Control arXiv:2608.07265v2 Announce Type: cross Abstract: We establish a finite-sample learning-to-control theory for geometrically supervised latent models of nonlinear deterministic systems. Geometric supervision is used only during training: simulator state, proprioception, or state… 15 arXiv — Machine Learning research 4d ago DiD It in 87 Minutes: A Label-Free Softmax-to-Linear Adaptation of Vision Transformers for Object Detection arXiv:2608.22368v1 Announce Type: cross Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the Softmax-attention ViT backbone of a trained detector into a linear-attention… 28 arXiv — Machine Learning research 4d ago InfoDPP-PAC: Principled Patch Selection for Whole Slide Image Analysis arXiv:2608.23574v1 Announce Type: cross Abstract: Each WSI slide contains thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction strategies like Uniform extraction and handcrafted heuristics do not control… 7 arXiv — Machine Learning research 4d ago Transformer Accelerator (TFA): A Macro-Op INT8 Hardware Chip for Transformer Inference and Machine Translation arXiv:2608.23582v1 Announce Type: cross Abstract: We present the Transformer Accelerator (TFA), a synthesizable, parameterizable INT8 memory-to-memory engine for transformer inference. One time-multiplexed datapath handles prompt processing and autoregressive generation. TFA… 38 arXiv — Machine Learning research 4d ago StateTune: Transforming LLM-Assisted EDA Flow Tuning into a Stateful, Closed-Loop Process arXiv:2608.23601v1 Announce Type: cross Abstract: EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-assisted tuners mainly use the LLM as an external… 10 arXiv — Machine Learning research 4d ago When May an Agent Stop? Evidence-Carrying Termination for Tool-Using LLMs arXiv:2608.23623v1 Announce Type: cross Abstract: Tool-using agents must decide when to stop. Existing systems already gate terminal success, certify execution traces, or enforce runtime polici es, but do not test this particular receipt-, scope-, and closed-replay design at the… 8 arXiv — Machine Learning research 4d ago The Blending Ratio Is Not Where the Performance Is: Diagnosing Prototype Blending for Few-Shot Adaptation of Vision-Language Models arXiv:2608.23634v1 Announce Type: cross Abstract: Many few-shot adaptation methods for vision-language models classify with a convex combination of the zero-shot text prototype and the mean of the K labelled image features, with a single blending ratio routinely tuned on… 17 arXiv — Machine Learning research 4d ago Replicable Conformal Prediction arXiv:2608.23638v1 Announce Type: cross Abstract: Two analysts who calibrate the same predictive model on independent samples will deploy different prediction sets every time, because the calibration threshold inherits the randomness of the data. Wherever deployments must be… 20 arXiv — Machine Learning research 4d ago Contextual Embedding Evidence for Main--Light Verb Distinctions in Urdu arXiv:2608.23645v1 Announce Type: cross Abstract: Urdu light verbs contribute schematic event-structural meaning while remaining lexically related to corresponding main verbs. This study tests representational predictions derived from Butt's analysis using contextual embeddings… 32 arXiv — Machine Learning research 4d ago MolEmb: Multimodal Large Language Models Can Be Strong Molecular Embedding Models arXiv:2608.23646v1 Announce Type: cross Abstract: Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most… 6 arXiv — Machine Learning research 4d ago Scaling Reinforcement Learning for Diffusion Models via Velocity Matching arXiv:2608.23664v1 Announce Type: cross Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods largely inherit policy-gradient machinery from large language models. Unlike… 17 arXiv — Machine Learning research 4d ago Automata from Agent Traces: Failure and Next-Step Prediction arXiv:2608.23670v1 Announce Type: cross Abstract: LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtime monitoring that deployment requires. Existing approaches operate per-trace… 5 arXiv — Machine Learning research 4d ago A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems arXiv:2608.23726v1 Announce Type: cross Abstract: Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission fault datasets are frequently imbalanced, and certain fault types produce… 8 arXiv — Machine Learning research 4d ago S-matrix informed neural networks for amplitude analysis arXiv:2608.23750v1 Announce Type: cross Abstract: Reconstructing scattering amplitudes from finite, noisy, and mutually inconsistent measurements is an ill-posed inverse problem common to many reactions relevant to particle physics. We introduce S-matrix informed neural networks… 15 Hacker News — AI on Front Page community 5d ago Black hole singularity is a surface not a point Article URL: https://arxiv.org/abs/2608.21590 Comments URL: https://news.ycombinator.com/item?id=49437210 Points: 257 # Comments: 183 34 r/LocalLLaMA community 5d ago New: Llama.cpp adaptive speculation for faster inference We have been working on some performance optimisations for Qwen3.8 and other models. The main new feature that we introduced is adaptive speculation for Llama.cpp What is it? MTP and DFlash work well to speed up inference work, especially for dense models. However, different… 36 Hugging Face Daily Papers research 5d ago EchoWM: Open and Enterable Omnimodal World Models Abstract EchoWM is an omnimodal world model that generates synchronized high-resolution video, sound, music, and speech while following continuous 6-DoF navigation trajectories across first- and third-person views. Generated by thinkingmachines/Inkling-Small We present EchoWM,… 17 Hugging Face Daily Papers research 5d ago Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection Abstract Task-CoEvolve improves LLM harness optimization by adaptively selecting validation tasks and estimating full-set performance from partial evaluations, cutting evaluation costs by 80%. Generated by thinkingmachines/Inkling-Small We present a novel approach to efficient… 30 arXiv — NLP / Computation & Language research 5d ago Distinguishing Revision and Delayed Elaboration in Incremental Narrative Interpretation arXiv:2608.21364v1 Announce Type: new Abstract: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations must be updated accordingly. Incremental interpretation, therefore, depends not… 20 arXiv — NLP / Computation & Language research 5d ago KSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search arXiv:2608.21365v1 Announce Type: new Abstract: As a low-resource language, Khmer presents several retrieval challenges, including limited annotated data, ambiguous word boundaries, weak support in multilingual embedding models, and frequent mixed Khmer-English usage. This paper… 31 arXiv — NLP / Computation & Language research 5d ago Wazobia Eval: A Benchmark for Nigerian Pidgin Emotion Understanding, Sarcasm Detection, and Cultural Reasoning arXiv:2608.21369v1 Announce Type: new Abstract: Nigerian Pidgin is one of Africa's most widely spoken languages, yet remains severely underrepresented in language model evaluation. Existing benchmarks primarily focus on translation, transcription, or generic sentiment analysis,… 21 arXiv — NLP / Computation & Language research 5d ago On the Role of Citations in Preference Data arXiv:2608.21376v1 Announce Type: new Abstract: Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against model hallucination and as a means for users to verify the credibility of model… 15 arXiv — NLP / Computation & Language research 5d ago Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models arXiv:2608.21377v1 Announce Type: new Abstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied primarily in single-turn settings. This paper investigates a critical question:… 9 arXiv — NLP / Computation & Language research 5d ago Beyond Two Bytes per Letter: Tokenization Overhead in Cyrillic AI Systems arXiv:2608.21384v1 Announce Type: new Abstract: Modern multilingual tokenizers often fragment Ukrainian and other underrepresented Cyrillic-script languages more heavily than English, creating disparities in cost and context capacity. We quantify this overhead across nine… 8 arXiv — NLP / Computation & Language research 5d ago A Social Media Analysis of Discourse on the Israel--Palestine Conflict on Telegram arXiv:2608.21385v1 Announce Type: new Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose broadcast architecture yields an unusually direct record of deliberate political… 11 arXiv — NLP / Computation & Language research 5d ago Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding arXiv:2608.21415v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resulting in biased behavior when processing portraits from… 9 arXiv — NLP / Computation & Language research 5d ago Agentic Security: A Systematization of Tools, Failure Modes, and Design Laws for LLM-Driven Penetration Testing arXiv:2608.21423v1 Announce Type: new Abstract: Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools. As these systems move from demonstrations to deployed products, practitioners repeatedly encounter the same operational… 23 arXiv — NLP / Computation & Language research 5d ago CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance arXiv:2608.21462v1 Announce Type: new Abstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alphabet with large speaker populations, while disadvantaging other language… 27 arXiv — NLP / Computation & Language research 5d ago Forgotten in Weights, Recovered by Tools: Agentic Tool Unlearning for LLM Agents arXiv:2608.21544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents, where responses can depend on tool calls and external observations rather than model parameters alone. This creates an evaluation mismatch for LLM… 26 arXiv — NLP / Computation & Language research 5d ago Automating Multi-Hop RAG Evaluation via TRIAD: From Context Extraction to Validated Dataset Generation arXiv:2608.21558v1 Announce Type: new Abstract: Recent advances in LLMs and the adoption of RAG systems in industry have created a need for domain-specific question-answer datasets that can assess RAG performance on proprietary data. Existing datasets, such as HotpotQA,… 31 Page 10 of 10 · 500 articles ← Newer
