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ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning

arXiv:2602.01003v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often

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Research

Estimation of Motor Unit Parameters from Surface Electromyograms using an Informed Autoencoder

DGX agent

arXiv:2605.07458v1 Announce Type: new Abstract: Motor unit parameters such as the innervation zone centre or the conduction velocity of the electrical potential harbour the potential to improve the fi

researcharxiv-cs-lg

11 May 2026

EULER-ADAS: Energy-Efficient & SIMD-Unified Logarithmic-Posit Engine for Precision-Reconfigurable Approximate ADAS Acceleration

arXiv:2605.06875v1 Announce Type: cross Abstract: Advanced driver-assistance systems (ADAS) require neural compute engines that deliver low-latency inference under strict power and area constraints. P

Local Ai

Eulerian Motion Guidance: Robust Image Animation via Bidirectional Geometric Consistency

arXiv:2605.06280v2 Announce Type: replace Abstract: Recent advancements in image animation have utilized diffusion models to breathe life into static images. However, existing controllable frameworks

local-aiarxiv-cs-cv

Evaluating Large Language Models in Scientific Discovery

arXiv:2512.15567v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and

Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs

arXiv:2605.06669v1 Announce Type: cross Abstract: Educational LLM tutors face a core AI alignment challenge: they must follow user intent while preserving pedagogical constraints and safety policies.

Evaluation of an Actuated Spine in Agile Quadruped Locomotion

arXiv:2605.07988v1 Announce Type: new Abstract: The spine plays a crucial role in the dynamic locomotion of quadrupedal animals, improving the stability, speed, and efficiency of their gait, especiall

researcharxiv-cs-ro

Tutorials

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity

arXiv:2605.07097v1 Announce Type: cross Abstract: We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed fin

tutorialsarxiv-cs-lg

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

arXiv:2510.16079v2 Announce Type: replace-cross Abstract: Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from th

Exact Flow Linear Attention: Exact Solution from Continuous-Time Dynamics

arXiv:2512.12602v4 Announce Type: replace Abstract: In this paper, we introduce Exact Flow Linear Attention~(EFLA), an exact-flow formulation of delta-rule linear attention. We show that the delta-rul

Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method

arXiv:2601.22307v2 Announce Type: replace Abstract: We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network

researcharxiv-cs-lg

Exact Is Easier: Credit Assignment for Cooperative LLM Agents

arXiv:2603.06859v2 Announce Type: replace-cross Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts t

Exact Regular-Constrained Variable-Order Markov Generation via Sparse Context-State Belief Propagation

arXiv:2605.07839v1 Announce Type: new Abstract: Variable-order Markov models generate sequences over a finite alphabet by conditioning each symbol on the longest available suffix of the generated hist

Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints

arXiv:2605.07485v1 Announce Type: cross Abstract: Extrapolation to out-of-distribution conditions is a fundamental challenge for physics-constrained deep generative models. Existing methods apply phys

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models

arXiv:2605.07244v1 Announce Type: cross Abstract: We introduce Mutual Reinforcement Learning, a framework for concurrent RL post-training in which heterogeneous LLM policies exchange typed experience

Explainable Part-Based Vehicle Classifier with Spatial Awareness

arXiv:2605.07831v1 Announce Type: new Abstract: In the area of Intelligent Transportation Systems (ITS), fine-grained vehicle classification systems play an essential role. Recently, the authors have

researcharxiv-cs-cv

Applications

Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry

arXiv:2605.07389v1 Announce Type: cross Abstract: The integration of machine learning (ML) into complex software systems has increased challenges in collaboration and communication (CoCo) of the teams

applicationsarxiv-cs-lg

Exploring the non-convexity in machine learning using quantum-inspired optimization

arXiv:2605.07947v1 Announce Type: cross Abstract: The escalating complexity of modern machine learning necessitates solving challenging non-convex optimization problems, particularly in high-dimension

Agents

Exponential Sample Complexity Separation between Flat and Hierarchical Agentic Theorem Provers

arXiv:2602.10512v2 Announce Type: replace Abstract: Agentic theorem provers often introduce intermediate lemmas, proof sketches, or subgoal decompositions before returning to tactic-level search. This

agentsarxiv-cs-lg

Exposing and Mitigating Temporal Attack in Deepfake Video Detection

arXiv:2605.07398v1 Announce Type: cross Abstract: While spatiotemporal deepfake detectors achieve high AUC, our experiments reveal their susceptibility to evasion attacks. These models tend to overfit

ExpThink: Experience-Guided Reinforcement Learning for Adaptive Chain-of-Thought Compression

arXiv:2605.07501v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance via extended chain-of-thought (CoT) reasoning, yet suffer from excessive token consumption an

researcharxiv-cs-cl

Extracting Search Trees from LLM Reasoning Traces Reveals Myopic Planning

arXiv:2605.06840v1 Announce Type: new Abstract: Large language models (LLMs), especially reasoning models, generate extended chain-of-thought (CoT) reasoning that often contains explicit deliberation

EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding

arXiv:2605.07859v1 Announce Type: new Abstract: Driver cognitive distraction is a major cause of road collisions and remains difficult to detect. Unlike manual or visual distraction, cognitive distrac

researcharxiv-cs-cv

f-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses

arXiv:2605.06977v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a cornerstone technique for post-training large language models. While most existing appr

Factored Classifier-Free Guidance

arXiv:2506.14399v5 Announce Type: replace-cross Abstract: Counterfactual generation aims to simulate realistic hypothetical outcomes under causal interventions. Diffusion models have emerged as a powe

FactoryBench: Evaluating Industrial Machine Understanding

arXiv:2605.07675v1 Announce Type: new Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs

FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution

arXiv:2605.07208v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to brainstorm and evaluate research ideas, yet assessing such judgments is fundamentally difficult be

FANoS-v2: Feedback-Controlled Momentum with Thermostat Damping for Lightweight Neural Optimization

arXiv:2601.00889v2 Announce Type: replace Abstract: FANOS{} is a PyTorch optimizer that augments RMS-preconditioned momentum with a scalar feedback controller over update energy. The public reference

Applications

Fast and Effective Redistricting Optimization via Composite-Move Tabu Search

arXiv:2605.06682v1 Announce Type: new Abstract: Spatial redistricting is a practical combinatorial optimization problem that demands high-quality solutions, rapid turnaround, and flexibility to accomm

applicationsarxiv-cs-ai

Fast Byte Latent Transformer

arXiv:2605.08044v1 Announce Type: cross Abstract: Recent byte-level language models (LMs) match the performance of token-level models without relying on subword vocabularies, yet their utility is limi

Faster Verified Explanations for Neural Networks

arXiv:2512.00164v2 Announce Type: replace Abstract: Verified explanations are a principled way to explain the decisions taken by neural networks, which are otherwise black-box in nature. However, thes

researcharxiv-cs-lg

FastOmniTMAE: Parallel Clause Learning for Scalable and Hardware-Efficient Tsetlin Embeddings

arXiv:2605.06982v1 Announce Type: new Abstract: Embedding models in natural language processing (NLP) increasingly rely on deep architectures such as BERT, while simpler models such as Word2Vec provid

Local Ai

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

arXiv:2510.02371v2 Announce Type: replace-cross Abstract: Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered

local-aiarxiv-cs-ai

Fidel-TS: A High-Fidelity Multimodal Benchmark for Time Series Forecasting

arXiv:2509.24789v4 Announce Type: replace Abstract: The evaluation of time series forecasting models is hindered by a lack of high-quality benchmarks, leading to overestimated assessments of progress.

Fine-tuning a vision-language model for fracture-surface morphology recognition

model-releasesarxiv-cs-cv

arXiv:2605.07145v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown strong potential for scientific image understanding, but general-purpose models often lack the domain-specifi

Finite-Time Analysis of MCTS in Continuous POMDP Planning

arXiv:2605.07703v1 Announce Type: new Abstract: This paper presents a finite-time analysis for Monte Carlo Tree Search (MCTS) in Partially Observable Markov Decision Processes (POMDPs), with probabili

FinReasoning: A Hierarchical Benchmark for Reliable Financial Research Reporting

model-releasesarxiv-cs-cl

arXiv:2603.19254v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in financial research workflows, where their role is evolving from single-model assistance fo

FiSMiness: A Finite State Machine Based Paradigm for Emotional Support Conversations

arXiv:2504.11837v2 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large l

Local Ai

FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning

arXiv:2605.07962v1 Announce Type: new Abstract: Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (F

local-aiarxiv-cs-lg

FlashMol: High-Quality Molecule Generation in as Few as Four Steps

arXiv:2605.07020v1 Announce Type: cross Abstract: Generating chemically valid 3D molecular conformations is critical for computational drug discovery. Classical diffusion-based models like GeoLDM perf

Flat Channels to Infinity in Neural Loss Landscapes

arXiv:2506.14951v4 Announce Type: replace-cross Abstract: The loss landscapes of neural networks contain minima and saddle points that may be connected in flat regions or appear in isolation. We ident

Flatness and Gradient Alignment Are Both Necessary: Spectral-Aware Gradient-Aligned Exploration for Multi-Distribution Learning

arXiv:2605.07914v1 Announce Type: cross Abstract: Sharpness-aware and gradient-alignment methods have been shown to improve generalization, however each family of methods targets a single geometric pr

safetyarxiv-cs-cv

Flexible Entropy Control in RLVR with a Gradient-Preserving Perspective

arXiv:2602.09782v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a critical method for enhancing the reasoning capabilities of Large Langu

Applications

Flexible Routing via Uncertainty Decomposition

arXiv:2605.07805v1 Announce Type: new Abstract: A key strategy for balancing performance and cost in modern machine learning systems is to dynamically route queries to either a low-cost model or a mor

applicationsarxiv-cs-lg

Agents

FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI

arXiv:2605.07364v1 Announce Type: new Abstract: Flight delays impose cascading operational and financial burdens across the aviation network, costing the U.S. economy billions of dollars annually by d

agentsarxiv-cs-lg

Tutorials

Flock: A Knowledge Graph Foundation Model via Learning on Random Walks

arXiv:2510.01510v3 Announce Type: replace Abstract: We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relatio

tutorialsarxiv-cs-lg

Flow Matching for Count Data

arXiv:2605.07746v1 Announce Type: cross Abstract: High-dimensional count data arise in applications such as single-cell RNA sequencing and neural spike trains, where mapping between distributions acro

researcharxiv-cs-lg

Flow-OPD: On-Policy Distillation for Flow Matching Models

arXiv:2605.08063v1 Announce Type: cross Abstract: Existing Flow Matching (FM) text-to-image models suffer from two critical bottlenecks under multi-task alignment: the reward sparsity induced by scala